fhss for simultaneous communication and...

FACULDADE DE ENGENHARIA DA UNIVERSIDADE DO PORTO

FHSS for Simultaneous Communicationand Sensing

Hugo Migel Sá da Cruz

FOR JURY EVALUATION

Mestrado Integrado em Engenharia Electrotécnica e de Computadores

Supervisor: Sérgio Reis Cunha

June 25, 2013

Resumo

Uma parceria com o OSG da FEUP e uma iniciativa promovida pela Marinha Portuguesa possi-bilitaram a melhoria do sistema de comunicações implementado na tecnologia AUV, desenvolvidapelo OSG, para transmissão de dados e navegação em meios aquáticos. O conceito no qual o tra-balho foi desenvolvido consiste num sistema de comunicação unidirecional, constituido por duaspartes: um colocado à superfície do meio aquático, possuindo três saídas de áudio síncronas comtransmissão de informação independente, e um em meio subaquático, como o AUV, equipado comum único sistema de captura de som.

Esta tese apresenta um abrangente trabalho realizado ao longo das diferentes partes constitu-intes da arquitetura do sistema. A mesma foca-se assim em três tópicos essenciais: definição deprotocolo do sinal para transmissão de dados e procedimentos de navegação, validação da codi-ficação/descodificação de sinais através do modelo de simulação criado em Matlab/Simulink, e oprojeto dos componentes de hardware necessários em ambos os extremos da comunicação. O pro-tocolo de sinal baseia-se na técnica FHSS com propriedades da modulação OFDM, que viabilizaa transmissão simultânea de dados e navegação com reduzida interferência entre os vários sinaisacústicos. O algoritmo de navegação beneficia da configuração triangular do sistema acústicotransmissor e a fase do sinal para calcular a sua posição realativamente ao Norte. Paralelamente,através de uma contínua transmissão de dados contendo informação sobre o sistema à superfície,é possivel uma consistente e completa navegação aquática.

Os testes realizados à transmissão de dados demonstraram que as propriedades aplicadas aosinal acústico são deveras eficazes. Um teste realizado na câmara anecoica da FEUP com recursoa equipamentos de áudio não evidenciou erros no processo de descodificação.

O produto final deste trabalho providencia modelos de simulação funcionais do sistema, de-senvolvidos em ambiente Simulink, juntamente com abordagens teóricas da navegação aquática,assim como um projeto de hardware para os sistemas de transmissão e receção.

i

Abstract

A partnership with OSG at FEUP and an initiative promoted the Portuguese Marine, opened anopportunity of rework the implemented communication system on the OSG AUV’s technology fordata transmission and underwater navigation. The system concept worked on consist of a singlesurface system with three synchronous but information independent beacons, which provides anunidirectional communication to an underwater system, like AUV, with one single receiver beacon.

This Thesis introduces a comprehensive work over the different parts of the system architec-ture. It is focused in three topics: signal protocol definition for data transmission and sensingprocedures, signal encoding/decoding validation through Matlab/Simulink simulation models andhardware design on both ends of the communication. The signal protocol design is based on theFHSS technique with OFDM modulation properties, which enables simultaneous data transmis-sion and sensing with reduced interference between beacons signal. The sensing algorithm takesadvantage of the beacon’s triangular shape arrangement and the phase signal property to obtain anaccurate heading. Then, a continuous data transmission containing information about the surfacesystem configuration, allows a full underwater navigation. The core approach for hardware design

The tests performed on data transmission showed that the signal properties that lay behindthe acoustic signal are truly effective. An anechoic chamber experiment at FEUP with audioequipments shows no evidence of errors on the signal decoding process.

The output of this work provides full functional simulation models, created on Simulink en-vironment, with theoretical approaches for underwater sensing, as well as, a verified hardwaredesign of the transmitter and receiver systems.

iii

Agradecimentos

Ao meu orientador, Prof. Sérgio Reis Cunha, os meus mais sinceros agradecimentos não só peloapoio durante todo o semestre, conhecimentos transmitidos e disponibilidade, mas também pelaconfiança depositada na minha pessoa.

Agradeço a todos aqueles que de alguma forma ajudaram no desenvolvimento desta disser-tação, nomeadamente, ao Luís Pessoa pelos componentes de hardware fornecidos.

À Mária José Ramos, o meu sentido obrigado pela disponibilidade demonstrada para revereste documento.

Aos meus amigos e colegas de uma etapa, agradeço os momentos vividos e as memóriasguardadas. Ao João Granja pelos longos dias de trabalho. Um apreço especial ao Luís Chéu eDiogo Pernes. A todos, obrigado pela amizade.

À minha namorada, Catarina Abreu, o meu sincero obrigado pela força, coragem e amor. Semti tudo teria sido mais difícil.

Um agradecimento nunca suficiente aos meus Pais por todo o suporte, dedicação e amordemonstrados durante toda a minha vida. À minha Irmã, Tio e Avô pelas pessoas que me são.Este culminar é por vós e para vós.

Hugo Miguel Sá da Cruz

v

“Nobody in life gets exactly what they thought they were going to get. But if you work reallyhard and you’re kind, amazing things will happen.”

Conan O’Brien

vii

Contents

1 Introduction 11.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Thesis structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.4 Support Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2 State of the Art 52.1 Spread Spectrum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.1.1 Purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.1.2 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.1.3 Basis Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.1.4 Processing Gain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.2 Orthogonal Frequency Division Multiplexing . . . . . . . . . . . . . . . . . . . 112.2.1 Modulation Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 122.2.2 PAPR Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152.2.3 Constant Envelope OFDM . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.3 Acoustic Positioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162.3.1 Long Baseline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162.3.2 Short Baseline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3 System Architecture 193.1 The Concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193.2 The Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203.3 Communications Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

4 Signal System Design 254.1 Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254.2 Communication Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

4.2.1 CRC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284.2.2 Synchronization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304.2.3 Cyclic Data Format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

4.3 Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

5 System Implementation 355.1 Simulation Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

5.1.1 Transmitter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355.1.2 Receiver . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

5.2 Hardware Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

ix

x CONTENTS

5.2.1 Core Hardware Unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

6 Tests 456.1 Numerical Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 456.2 Side-by-Side Computers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 466.3 Anechoic Chamber . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

7 Final Remarks 497.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

A Vector Cross-Correlation 51A.1 The Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51A.2 Matlab Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

References 55

List of Figures

2.1 Resulted signal after a Spread Spectrum Technique concerning the environmentalnoise. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.2 Some types of interferences on satellite communications . The blue line representsthe multiple access context, the green line a refracted signal and its alternative pathand the red line a jam signal or an intentional interference. . . . . . . . . . . . . 7

2.3 DSSS system implementation overview. . . . . . . . . . . . . . . . . . . . . . . 82.4 DSSS signal spectrum [1]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.5 FH SS system implementation overview. . . . . . . . . . . . . . . . . . . . . . . 92.6 FHSS signal spectrum [1]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.7 Possible signal dimensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.8 a) Frequency spectrum of N non-overlapping subchannels with empty band guard.

b) Frequency spectrum of N overlaping suchannels without ICI. . . . . . . . . . 122.9 OFDM signal block of N=3 frequencies or subchannels with 2 bits per symbol. . 132.10 OFDM signal spectrum of N=16 orthogonal subchannels [2]. . . . . . . . . . . . 142.11 OFDM signal of N=16 subchannels on time domain, in terms of amplitude (left)

and power (rigth) [2]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152.12 Instantaneous power of OFDM and CE-OFDM signals [2]. . . . . . . . . . . . . 16

3.1 Concept for FHSS based communication system. . . . . . . . . . . . . . . . . . 203.2 Constellation diagram of QPSK and symbol transitions. . . . . . . . . . . . . . . 213.3 System communication architecture overview and information flow. . . . . . . . 23

4.1 FHSS signal spectrum. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264.2 Signal sequence design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274.3 Tukey window sample design. . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

5.1 Simulink simulation model of the transmitter . . . . . . . . . . . . . . . . . . . . 365.2 Typical output sound wave of one beacon. . . . . . . . . . . . . . . . . . . . . . 375.3 Transmitter User Interface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385.4 Synchronism detection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395.5 Receiver simulation model on Simulink. . . . . . . . . . . . . . . . . . . . . . . 405.6 Receiver User Interface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415.7 Hardware architecture and information flow. . . . . . . . . . . . . . . . . . . . . 42

6.1 Transmitter configuration. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 476.2 Receiver configuration. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

A.1 Cross-correlation between 4.1 and 4.2 or 4.1 and 4.3. . . . . . . . . . . . . . . . 52A.2 Cross-correlation between 4.2 and 4.3. . . . . . . . . . . . . . . . . . . . . . . . 52

xi

xii LIST OF FIGURES

List of Tables

4.1 Resume of main characteristics of FHSS communication system. . . . . . . . . . 284.2 Data structure fields. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324.3 Fields distribution for each data page. . . . . . . . . . . . . . . . . . . . . . . . 32

A.1 Cross-correlation for odd vector of three elements (a1 < a2 < a3) . . . . . . . . . 52

xiii

xiv LIST OF TABLES

Abbreviations and Symbols

BPSK Bipolar Phase Shift KeyingBER Bit Error RateCE-OFDM Constant Envelope - Orthogonal Frequency Division MultiplexingCRC Cyclic Redundancy CheckdB DecibelsDS Direct SequenceFDM Frequency Division MultiplexingFEUP Faculdade de Engenharia da Universidade do PortoFH Frequency HoppingGPS Global Positioning SystemGPIO General Purpose Input/OutputICI Inter-Carrier InterferenceI/O Input/OutputIEEE Institute of Electrical and Electronics EngineersISI Inter-Symbol InterferenceLAN Local Area NetworksLBL Long BaselineLNA Low Noise AmplifierOFDM Orthogonal Frequency Division MultiplexingOSG Ocean Systems GroupPAPR Peak-to-Average Power RatioPPS Pulse per SecondPRS Pseudo-Random SequencePSK Phase Shift KeyingQPSK Quadrature Phase Shift KeyingRF Radio FrequencySNR Signal to Noise RatioSS Spread SpectrumUDP User Data ProtocolUI User InterfaceUSB Universal Serial BusUSBL Ultra Short BaselineWiMAX Worldwide Interoperability for Microwave Access

xv

xvi Abbreviations and Symbols

G(x) CRC polynomialfbb Base band frequency referencefch Channel central frequencyfk Frequency block from signal sequenceFD Page Fields referenceN Number of subchannels on a OFDM blockM(x) Binary data wordTblk OFDM block periodTc PRS periodTcp Cyclic prefix periodTs Period of an input signalTsd Symbol data periodTseq OFDM sequence periodTs,seq Signal sequenceR(x) Remainder from CRC computationvecphase Phase vector after the QPSK modulation

Chapter 1

Introduction

In this chapter a brief introduction concerning the Thesis motivations and objectives is presented.

Additionally, both the structure and the most important supporting tools employed are described.

1.1 Motivation

The future of communication can go through many paths, like the optics, but the one more afford-

able for the human being is the wireless. In fact, over the years the systems tend to become smaller

with less physical components [3]. Most of today’s modern domestic systems use some type of

wireless communication within the chain. The GPS and the new 4G networks like WiMAX are

good examples, both using RF signals with advanced modulation technologies to provide reliable

communications. Wireless systems have a tremendous potential in several domestic services and

a huge economic impact on society. A recent field of study about wireless systems is related to

wireless electricity transmission [4].

Another field of study involving wireless communications is oceanography and general under-

water applications. The extremely conditions in deep waters make the oceans an almost unknown

place for Humans. The common approach followed by research teams to discover this deep world

was the development of Autonomous Underwater Vehicles (AUV) and improvement of its related

technologies such as, navigation and autonomy. The AUV is robot system which travellers under-

water without required Human interaction. Recently, the AUV technology has reached maturity

and a larger number of operational systems have emerged. It is employed in several different

fields, such as military, defence applications, industry, oceanographic studies and underwater re-

search [5]. A full example of an AUV system is the MARES, developed at FEUP, design for

shallow water data collection operations [6].

The source of this Thesis arose from a partnership with Ocean Systems Group (OSG) at FEUP

and from an initiative promoted by the Portuguese Marine. The latter consisted of an activity in

which some projects with studies associated to the oceans were to be performed in real environ-

ment through the Marine infrastructures. The OSG focuses are mainly directed to advanced robotic

systems for automatic collection and processing data in aquatic environments. MARES AUV is an

1

2 Introduction

example of that robotics. Thus, an opportunity to develop another approach of data communication

system connected to the OSG systems have arisen. The author’s interest in telecommunications

systems with a possibility of developing a full and comprehensive system for data transmission

led to the acceptance of this project. The chance of an experiment involving real scenarios and

systems was also a strong complement.

1.2 Objectives

The aquatic mediums are very challenging environments for the most known RF based systems.

The difference between atmosphere and water composition makes the transition from one medium

to another almost impossible for the frequencies range normally employed by this systems. Con-

cerning the underwater navigation, the most useful system, however unavailable, for the modern

aquatic operations is the Global Positioning System (GPS). Another important issue is the data

transmission or system communication with the surface.

The aim of this Thesis is to develop a low rate modular communication system capable of

transmitting information through acoustic signals making use of SS techniques. It is also a goal,

to use this information, in parallel with hardware configuration for sensing meanings, which in-

cludes: distance, positioning and navigation. The underwater context in which the system is

based on, also involves a study of the transmitter and receiver subsystems. Concerning the OSG

technologies, communication is accomplished between a system on the surface and an underwa-

ter vehicle. The foremost supports some type of surface platform which holds the transmitters

hardware. On the other hand, the underwater vehicle should perform an inverse process of the

transmitter. Thus, part of the data flow chain inside the surface system and vehicle was also scope

of this Thesis, namely, the design procedure. The major topics are: the external communication

with the surface platform, the central processing unit on both ends where all the signals transfor-

mations are executed and the connectivity between the previous subsystem and the input/output

ports. Although the system was developed based on a underwater application, its range of usage

can be extended to further systems and environments. The outcome from this project combines a

signal processing background with a full functional simulation models in order to be easily trans-

lated to hardware languages. Furthermore, a brief and theoretical approach for hardware design

implementation is described as well as a navigation algorithm, which takes advantage of the signal

properties and transmitter hardware configuration.

1.3 Thesis structure

This Thesis is organized in seven chapters, each one beginning with a small introduction describing

the chapter’s intention. The current Introduction chapter presents the back-end work motivation

and the main objectives. It also adds to this work structure and the most important supporting

tools.

1.4 Support Tools 3

The second chapter aims to provide a theoretical background and the Start-the-Art about the

main technologies on which the signal system was based, respectively, Spread Spectrum (SS)

and Orthogonal Frequency Division Multiplexing (OFDM). Moreover, the basics about acoustic

positioning is also presented.

The third chapter gives an overview of the system architecture. The general application con-

cept for the communication system, the approach followed to fit the signal requirements and the

communications stages and data flow are the topics covered.

In the fourth chapter the signal system design, namely, its frequency domain scheme, the

communication protocol definition and its critical proprieties for sensing procedures are closely

detailed.

The fifth chapter examines the system implementation. Full simulation models for the trans-

mitter and receiver is described and, afterwards, an approach for a further hardware implementa-

tion are presented.

Followingly, in the sixth, there is a set of numerical and hardware tests based on the simulation

models created. The purpose of this chapter is to validate and evaluate the system performance

about the decoding efficiency.

Finally, in the seventh, the success on the Thesis objectives and also considerations for future

enhancements are inferred.

1.4 Support Tools

During the Thesis development a couple of supporting tools were used to simulate, test and im-

prove the communication system, as well as disclose and describe the work.

The high-level technical language Matlab/Simulink version R2012a and R2013a were used to

create the system models of the transmitter and receiver and evaluate the output signal properties.

A group of sound equipments, such as SingStar microphones, TEAC PowerMax 80/2 speakers and

a external sound card by Creative Sound Blaster were aided to test procedures. Another tool used

was an anechoic chamber to test the performance of the system communication over a minimal

refractive and dispersive channel.

A website was created at paginas.fe.up.pt\~ee08224 to provide the readers an easy

access and quick reading on the Thesis stages during its development. It contains a resumed

description about the motivation, main objectives, final results and conclusions. The "Documenta-

tion" page weekly reports more Thesis details and may be downloaded. The "Team" page personal

information about the author and its team is also available.

As last important tool, stands out the LATEX language used to typeset the weekly reports and

this document.

paginas.fe.up.pt\~ee08224

4 Introduction

Chapter 2

State of the Art

This chapter aims at providing the reader with theoretical knowledge and sensibility for the work

being developed. A general overview concerning the major technologies and their main properties

applied to the communication system is exposed. It starts with SS technology, explaining when it

first appears, the purposes and the basic techniques, and continues with a brief description of the

principles of OFDM, its main problem and a possible solution. Finishes exposing the the basis of

acoustic positioning.

2.1 Spread Spectrum

Starting from the 70s, communications services have experienced a strong evolution mostly due to

the increasing amount of data available and the need to make its transmission more capable, simple

and less expensive. In the other hand, multiple access property on services acquired more impor-

tance. The amount of services offered and its globalization required larger hardware processing

capabilities with support of new technologies. These developments evolved to the point that pre-

vious hardware limitations were suppressed by bandwidth allocation in spectrum [7]. Thus, new

mechanisms for spectrum optimization and services enhancing, became imperative.

SS can be presented as a spectrum optimization technique, that in contrast with the fixed

bandwidth allocation initially used by communications systems, share their spectrum resources. It

also improves the services quality regarding its reliability and interference robustness.

Nikola Tesla might be considered the first person to exhibit the concept of SS through its paper

from 1903 published in United States, where a two channels radio system, able to communicate

on both, without interference from the other is described. However, Hedy Lamarr and George

Antheil, in 1942, with the publication [8], introduced a system to guide torpedoes towards a target.

They described the first system based on SS. Consisted in group of 88 distinguish frequencies,

corresponding to the number of keys on a piano, switched on transmission according to a pseudo-

random pattern so that anyone can interfere or detect.

5

6 State of the Art

Ignored at first but latter on use mainly in military security level, nowadays, this technology is

present on several modern communication systems such as GPS, Global System for Mobile Com-

munications (GSM), Code Division Multiple Access (CDMA), Bluetooth and Wireless LANs [1].

2.1.1 Purpose

Back in 1942, Lamarr’s system aimed to being undetected and secure against other systems. This

property was achieved through constant change of frequency randomly, which spread the signal

spectrum, thus causing a decrease of energy density. The energy of the original narrowband signal

is spread over the spectrum and, for that reason, the power related to each frequency is reduced.

It also improves communication performance with increase of total power transmitted. In some

conditions this feature allows the communication to be extremely well hidden by the environ-

mental noise, as shown on Figure 2.1. Moreover, the undetectable signal drastically reduces the

occurrence of unwanted interferences.

Figure 2.1: Resulted signal after a Spread Spectrum Technique concerning the environmentalnoise.

Since its main focus as military weapon, SS techniques have been proposed to many other ap-

plications. As exposed in [9] some of the key factors of SS are: good resistance to intentional (e.g.

jamming) or unintentional interference (e.g. multipath fading and channels intermodulation on

multiple access), low probability of interception because of its capability to hide, a huge improve-

ment on multiple access communications like currently the CDMA technology, high resolution

ranging and accurate timing, both very useful for today positioning systems. On this thesis, the in-

terest in SS technology lies fundamentally on privacy, security, power improvement, interference

reduction and multiple access communication.

2.1 Spread Spectrum 7

2.1.2 Applications

The acceptance and applicability of SS technologies across different systems was triggered by its

feature of partially suppress many types of interference. Most of the projects developed since its

appearance were for military purposes surrounded by secrecy. Its low probability of interception

combined with anti-jamming capabilities ensured the privacy and security needed to communicate

without being listened. Among these, it includes secure communications channels, radars, satellite

communications and even the GPS. Nowadays, the basic concept of these systems are open and

some of them have an important utility for a number of civil and commercial applications.

Figure 2.2: Some types of interferences on satellite communications . The blue line represents themultiple access context, the green line a refracted signal and its alternative path and the red line ajam signal or an intentional interference.

As already presented, the multiple access capability is a good motivation to use SS. A nowa-

days system that take advantage of it, is the third generation of mobile telecommunications tech-

nology, commonly named 3G, where a CDMA based on a technology works as an underlying

channel method access. The GPS is a further system that employs a SS signal for navigation,

positioning, timing and ranging. Once classified as air-force military project, it is now a tremen-

dous resource for commercial applications, from smartphone applications to aviation support and

terrestrial localizations. For instance, the position calculation is based on signal time arrival [1]

or on pulse delay measure which error are inversely proportional to the signal bandwidth [7].

Therefore, because of the wideband spectrum, SS signals are natural choices. Prior systems are

somehow connected to satellite communications, which are multipath fading systems. As a basis

or support system to others, the forthcoming problems are reflected by them. Beyond all other

interferences, regarding multipath interference it arises from refraction and reflection effects. Fig-

ure 2.2 demonstrates some types of interference, where the green line is representative of multipath

effects. As the communication is performed in atmosphere medium, its different layers work like

sub-channels with their own characteristics. When the signal goes through a medium interface,

the bending of electromagnetic wave occurs, which is the phenomenon of refracting a signal, and

propagates on different paths from the original. However, from SS characteristics this type of

interference becomes highly negligible.

8 State of the Art

2.1.3 Basis Techniques

In a spectrum domain, SS is reflected as an effective increase of signal bandwidth where the

information to transmit are divided by the frequencies available. The basic techniques to create a

SS signal is twofold: Direct Sequence (DS) and Frequency Hopping (FH). In DS, the narrowband

signal is multiplied directly by a Pseudo-Random Sequence (PRS) at much faster rate, whereas in

FH the signal carrier hops across the bandwidth based on PRS. Thus, a SS signal always implies

the use of a PRS.

Other types of SS systems are called hybrid systems. These combine the two basic techniques

in order to take advantage of both. The output spectrum is even more spread than either FH and

DS alone and hence it is suitable for advanced systems, like Smart Grids, that need extremely high

spreading factors for command, control and sensing a huge amount of users and information in

simultaneous [10].

Figure 2.3: DSSS system implementation overview.

Figure 2.4: DSSS signal spectrum [1].

2.1.3.1 Direct Sequence

An implementation overview of a system based on DSSS may be represented as shown on Fig-

ure 2.3. The transmitter, responsible for coding, creating and transmit an SS signal, is basically

a multiplication process between the input data with period Ts and the PRS with period Tc. Next,

depending on the application, a frequency up-conversion may be performed. The resulted signal is

a wideband signal with carrier defined by the up-converter and bandwidth defined by the PRS. In

fact, with the increasing of the PRS rate, the bandwidth becomes wider. A typical DSSS spectrum

is shown on Figure 2.4. At the receiver, the signal is demodulated to baseband and it is multiplied

with the same PRS, which has to be synchronized with the arriving signal. At the end of process

2.1 Spread Spectrum 9

the signal is transformed into its narrow band form again. Note that at receiver arrives a signal

with many types of noise from channels and components

2.1.3.2 Frequency Hopping

FH/SS shows a similar implementation as DSSS system. Its implementation overview is shown

on Figure 2.5. On transmission, follows the same basic procedure of using a PRS to perform the

spread but instead of applying it directly to the incoming signal, a frequency synthesizer takes

constantly it as input to change its frequency output over a predetermined bandwidth. Through

these constant N hops, the effective output spectrum is increased and it is proportional to a factor

of N. An example of FHSS spectrum is shown on Figure 2.6. At reception it is made an inverse

process, again with PRS and signal synchronized, which have similar noise suppression properties.

Figure 2.5: FH SS system implementation overview.

Figure 2.6: FHSS signal spectrum [1].

As previously explained, the frequency synthesizer generates N frequencies according to the

PRS, which means a hop at every period of PRS (Tc). On the other hand, the incoming signal

arrives with a period of Ts. Unlike in DSSS, Tc and Ts are independent, so the SS bandwidth raises

independently of Tc. The fundamental feature of FHSS by hopping within an N available frequen-

cies with period of Tc, doesn’t allow an instant cover of all SS bandwidth. This occurrence is

explained because of physical limitations on switching of frequency synthesizer. Thus, the period

has to be chosen based on the implementation type and performance required [11]. As present

in [9], an example is the FH CDMA systems, that are defined in two types: Fast Frequency Hop-

ping (FFH) and Slow Frequency Hopping (SFH). The fundamental difference between both is the

period relation where for FFH Ts > Tc and for SFH Ts < Tc. Although both offer very good capabil-

ities again as SS technique, when compared, FFH is the one that is closer to continuous spectrum

10 State of the Art

and also provides a frequency diversity per incoming symbol. However, it is more complex to

implement than SHF due to synchronization and transmits less information per hop. Despite this,

FFH offers benefits regarding anti-jamming features and is less sensitive to interference [12].

2.1.3.3 Comparation

From previously exposed, the first difference between FHSS and DSSS is its implementation.

Despite both use a PRS to perform the spread, on DSSS it is applied directly to the input signal

while on FHSS is used to modulate the frequency synthesizer. When under strong narrowband

interference or on multiple access environment because of near-far interference, FHSS presents

better results than DSSS. Rearmost interference happens when some transmitters are located near

the receiver and others far from it, which lead to considerable power discrepancy over the received

powers. The stronger signal will degrade the performance of all others, specially the weaker

ones [13]. Many studies have been developed to analyse the overall performance of DSSS and

FHSS, however, the results on practical applications, mainly when many is considered metrics, are

hard to apply [14]. Depending on implementation, modulation, coding and system type, different

approaches and solutions may be more reasonable to achieve the requirements. On [14], FHSS

has a better anti-jamming performance than DSSS, respectively, in downlink communication and

on-board processing. However, on [7] is asserted that DSSS efficiency is about double of FHSS

for an alphabet size of 2, since it allows a coherent demodulation.

Concerning its implementation, another important process is how the receptor accomplishes

the demodulation and its influence on complexity and performance. Although DSSS systems

might use coherent demodulation, on FHSS its much more complex to preserve the phase coher-

ence of both [7]. Such difficulty is due to the hops which create phase discontinuities [15]. Thus,

instead of using magnitude and phase for demodulation process, a non-coherent method avoids

these parameters and has as main procedure a statical decision based on correlation between the

received symbol and all possibilities available.

2.1.4 Processing Gain

The inherent advantages of SS based systems is commonly expressed through a measure called

Processing Gain (PG). Its a way of quantify the performance enhancement of SS process. Both

in [11] and [12], PG is generally defined as a quotient between spectrum’s bandwidth,

PG =Wwb

Wnb=

Tc

Ts(2.1)

where Wwb is the bandwidth of the output wideband signal and Wnb is the bandwidth of the input

narrowband signal. For FHSS, PG is just the number of hop frequencies N.

2.2 Orthogonal Frequency Division Multiplexing 11

2.2 Orthogonal Frequency Division Multiplexing

Most of the worldwide services today use some kind of multiplexing mechanisms. This allows

multiple users to communicate over the same channel while maintaining a reasonable level of

privacy. In literature, there are three basic domains on which the user signal could be managed

and distinguished, as shown on Figure 2.7. It can transmit in the same time interval (Time Division

Multiplexing), over a specific frequency (Frequency Division Multiplexing) or with a unique code

(Code Division Multiplexing).

Figure 2.7: Possible signal dimensions

OFDM is a modulation that is worked on frequency domain, and hence, it is created from FDM

technique. The evolution began back in 1870s, with the first FDM system, a "harmonic telegra-

phy", developed by Alexander Graham Bell which adopted multiple communications channels.

Years later, telephone carriers, like AT&T, adopt FDM as the head mechanism for multiplexing

and, for the forthcoming of digital communications this was a huge step further to develop hybrid

communications systems [16]. Like most of communications technologies, FDM was also applied

to military projects, for instance the Kineplex system with 20 frequencies or tones, developed by

Collins Radio Company, for data transmission over a high frequency communication channel with

severe multipath interference [17].

However, FDM had some disadvantages, which may be resumed to: a waste of spectrum

between subchannels and the large computation required by Discrete Fourier Transform (DFT)

for each channel [16]. The first one arises from the need of reduce the channel overlap which

originated ICI and ISI. By introducing an empty frequency guard band within the channels, as

shown on Figure 2.8 a), the overlapping was prevented but considerably decreased the spectral

efficiency. The last drawback resided on the N2 complex computations, where N is the number of

samples used in Fourier transform, needed to perform a spectrum analysis of received signal. With

Cooley and Tukey publication about DFT calculation algorithm developed in 1965, named Fast

Fourier Transform (FFT), digital processing computation and thus FDM, were reinvented. They

12 State of the Art

showed that the previously N2 complex operations could be executed only in Nlog(N) [18]. As a

example, if N was equal to 28, FFT was about 99% more efficient than traditional DFT.

Figure 2.8: a) Frequency spectrum of N non-overlapping subchannels with empty band guard. b)Frequency spectrum of N overlaping suchannels without ICI.

To deal with the spectrum wasteful some overlapping applications were created. The notion

of guard band was replaced by cyclic prefix, in its most effective scheme, a repetition of the

last part of the signal [16]. Although the subchannels were crossed as shown on Figure 2.8 b),

their arrangements were mathematically orthogonal, which led to a higher spectrum efficiency

and decrease of interference. Robert W. Chang with [19], in 1966, was the first person to use to

use signals with orthogonal frequency properties in a communication system. Thus, the principle

of orthogonal frequency multiplexing and the consequent introduction of the OFDM technology,

was followed by a maximization of overall data rate and minimization of ICI and ISI.

On modern digital communications, OFDM is considered as the future technology for wireless

communications. In fact, it is already employed on several systems such as LANs through nor-

malized standard from IEEE 802.11, on mobile communications WiMAX through standard IEEE

802.16 and on European broadcast transmission of digital terrestrial television (DVB-T) [16].

2.2.1 Modulation Overview

OFDM modulation is a multi-carrier technique of parallel transmission which allows high trans-

mission rates over strict channels characterized by a severe multipath interference [20]. Rather

than a serial symbol transmission on a single carrier, OFDM signal combines N subcarriers or

subchannels, which one transmits a symbol, and takes advantage of a band guard, commonly

called cyclic prefx, between each subchannel [2] to avoid ICI and ISI. The parallelism feature

enable a higher data rate transmission and is achieved by the N subchannels of the signal. Thus,

an output OFDM signal might be viewed as a set of blocks, each one composed by N subchannels

and equal number of band guards. On the Figure 2.9 an example, on time domain, of a OFDM

signal sequence for N = 3 can be seen .

A digital modulation, for instance QPSK, takes the input signal data in bits, cluster them in

symbols and then, each symbol is placed on a OFDM block. Considering the incoming signal as


Figure 2.9: OFDM signal block of N=3 frequencies or subchannels with 2 bits per symbol.

the signal after the digital modulation with period Ts, N subchannels and a band guard with period

Tcp, the OFDM signal will have a block period( 2.2) of proportional to N,

Tblk = N(Ts +Tcp). (2.2)

OFDM is characterized by two key features: orthogonality among frequencies and cyclic pre-

fix. Both have a huge importance on system performance and efficiency because of their proper-

ties. The first one means that each frequency has an integer period multiple of the symbol period

Ts. In frequency domain, it is translated into several nulls at frequency multiples of 1Ts

all over the

spectrum. This leads into interference between frequencies, allowing them to be overlapped up

to 50% [17] and, consequently, the bandwidth decreases. Mathematically, as exposed in [2], the

orthogonality between two frequencies over a block period Tblk can be viewed in both frequency

and time domains. A generic OFDM signal without guard band may be expressed as

s(t) = ∑p

[N−1

∑k=0

e j2π fkt

]g(t− pTblk) (2.3)

where fk is the k subchannel frequency centred at kTblk

with 0 ≤ k < N − 1 , N the number of

subchannels and g(t) the rectangular function or a pulse function defined by

g(t) =

1, if −Tblk2 ≤ t < Tblk

2 ,

0, otherwise.(2.4)

It is basically a selection of a part of a complex sinusoidal wave with frequency fk. On fre-

quency domain, for a specific block m and over the period block, the signal is expressed by

s(t) =N−1

∑0

Im,ke j2π fkt . (2.5)

14 State of the Art

Knowing that over the same period

G( f ) = F (g(t)) =∫ Tblk

2

−Tblk2

1e− j2π f tdt = ITblksinc( f Tblk), (2.6)

where sinc(x) is defined

sinc(x) =

1, if x = 0,sin(x)

x , otherwise,(2.7)

the Fourier transform F (.) of signal 2.5 and thus the orthogonality among frequencies is proven

by

S( f ) = F (s(t)) =∫ Tblk

2

−Tblk2

s(t)e− j2π f tdt = Tblke− j2π f Tblk2

N−1

∑0

Im,ksinc(( f − fk)Tblk). (2.8)

Figure 2.10 shows a example of a OFDM block m of N = 16 orthogonal subchannels width wave

form based on equation 2.7 and normalized amplitudes and frequencies. It is easily seen a gap

every block period which indicates the orthogonality within frequencies.

Figure 2.10: OFDM signal spectrum of N=16 orthogonal subchannels [2].

The last main feature might be interpreted as a non-empty band guard between successive

blocks. It is an important part of OFDM concept because it increases the level of robustness

against channel unwanted effects such as ISI and ICI. This is achieved by using a band guard with

information on the last part of the previous signal. Thus, the band may be seen as a symbol length

extension, while maintaining the subchannels orthogonality. However, the band length should be

longer than the impulse response of the channel, so that the induced destructive effect may only be

felt inside. Its main disadvantage is the decrease of data efficiency by reducing the effective data

rate [2, 17]. The OFDM modulation is inherently digital and, therefore, enjoys the advantages of


digital computation. As already presented, the FFT was one of the major steps to make OFDM

become interesting for commercial applications.

2.2.2 PAPR Problem

Peak-to-Average Power Ratio (PAPR) is the most widely and noticed problem about OFDM. It

consists of high amplitude fluctuations which, as consequence, lead to high power oscillations

between the average (E(.)) and the maximum. As a measure, its definition is [21],

PAPR(dB) = 10log10(max|s(t)2|0≤t<Tblk

E|s(t)2|), (2.9)

yet, it does not have practical significance. For better understanding and theoretical analyses, a

statistical approach is normally used [2, 21]. The influence of the oscillation on signal amplitude

and power can be better viewed on 2.11. This example shows that for a OFDM signal with N = 16

subchannels, the PAPR≈ 9.5 dB, meaning a maximum power 9 times higher than the average. The

ideal value is zero so the peak is significantly raised. The greater the PAPR, the more sensible the

OFDM signal to non-linearities will be, mainly derived from the power amplifier on transmission

systems. Thus, a good quality components and an overall integration are essential.

Figure 2.11: OFDM signal of N=16 subchannels on time domain, in terms of amplitude (left) andpower (rigth) [2].

2.2.3 Constant Envelope OFDM

(not finished...)

Many techniques have been developed in order to minimize the PAPR problem on OFDM

signals. The most important ones are described at [22]. These different solutions have distin-

guish efficiencies with respect to the system parameters, like spectral efficiency, complexity and

performance. Another approach of the problem consists of signal transformation. The CE-OFDM

system presented in [20] incorporates a signal transformation on the chain of conventional OFDM.

16 State of the Art

It is based on the phase modulator technique, where the OFDM waveform is used to phase mod-

ulate the carrier. As output, the signal shows a constant amplitude and power, fully reducing the

PAPR to its theoretical minimum of 0dB. The transformation of OFDM into CE-OFDM is better

viewed on Figure 2.12.

Figure 2.12: Instantaneous power of OFDM and CE-OFDM signals [2].

2.3 Acoustic Positioning

(not finished...)

At earth surface, an object position is commonly obtained through a GPS. However, on un-

derwater environments, the signal power is not enough to cross the water surface, and hence, the

positioning is achieved using a GPS reference at the surface which sends its coordinates to the

underwater receiver.

One obstacle immediately emerge due to the sea water characteristics, the GPS radio-frequency

signals are strongly attenuated, hence the submerged module cannot receive directly a signal with

enough power.

The Acoustic positioning basis are related to time of propagation of the acoustic signal ex-

change with the beacon and the underwater vehicle.

The acoustic positioning may be divided in three main methods commonly used today: Long

Baseline (LBL) and Short Baseline (SBL).

2.3.1 Long Baseline

(not finished...)

LBL systems use a sea floor baseline transponder or beacons network, working as reference

points for navigation. This technique provides a high positioning accuracy, generally below one

meter, reliability that is independent of water depths.

2.3 Acoustic Positioning 17

The underwater vehicle triangulates its position from acoustics ranges within the beacons net-

work

Accuracy improvements on LBL may be achieved by the application some type of filtering

techniques like Kalman filter.

2.3.2 Short Baseline

(not finished...)

SBL systems do not require any seafloor mounted transponders or equipment and are thus

suitable for tracking underwater targets from boats or ships that are either anchored or under way.

When operating from larger vessels or a dock, the SBL system can achieve a precision and

position robustness that is similar to that of sea floor mounted LBL

18 State of the Art

Chapter 3

System Architecture

This chapter provides a description about the overall system architecture, beginning with the sys-

tem concept and, then, the approach followed. Finish with the definition of the communications

structure.

3.1 The Concept

The overall concept of the communication system was developed under the perspective of en-

abling data transfer not only on underwater but also from the surface to a central computer. This

data exchange with some signal processing will provide the required information to perform the

navigation and sensing of the underwater vehicle. The existing communication system from OSG

team, implement this concept with a set of Wi-Fi capable buoy systems and a AUV. The buoys are

disposed at the surface in such as way that is possible apply the concepts of acoustic navigation.

The latter one is accomplish with a bidirectional communication between the underwater vehicle

and the buoy.

The previous approach has some disadvantages concerning the underwater vehicle power con-

sumption because of the communication type and the required number of the floating buoys. In

order to suppress the previous problems, another system concept, based on the previous one, was

designed. Additionally, was developed a new communication protocol for simultaneous data trans-

mission and sensing. Figure 3.1 shows a generic illustration about the improved concept on which

the this Thesis was worked on. A supporting system at water surface accessible through Wi-Fi

and with a GPS module, holds a platform which contains three transmission beacons. Each one

of them are essentially a transducer and a hydrophone used as output acoustic port. Beyond both

have the same signal encoding mechanism, they also transmit at the same time using the same

available bandwidth but with minimal interferences. Due to the FHSS technique, this is achieved

using PRS of orthogonal frequencies. Together, the support system and the three beacon represent

the real transmitter model. In the depths, an underwater vehicle works as a receiver. It holds an-

other beacon system, which acts as a input acoustic port to capture the sound signal and, then, an

on-board processing system retrieves the information.

19

20 System Architecture

Figure 3.1: Concept for FHSS based communication system.

The communication was designed unidirectional from the surface system to the underwater

vehicle. Due to this, the sensing feature took a different approach, however with the same basic

concepts of acoustic navigation. The sensing of the underwater vehicle is achieved by disposing

the three beacons in triangle shape and transmitting the GPS coordinates of the buoy.

The beacon system works as three independent channels for data transmission. One of them

is exclusive for navigation purposes, while the others are intended to be as modular as possible to

facilitate future improvements.

3.2 The Approach

The produced signal is a combination of FHSS technique with OFDM properties in which infor-

mation is codified on the signal phase in four states. Such approach was establish concerning

spectral efficiency, data rate, security and, mainly, robustness to environmental attenuations and

reliability on data transmission. The simplicity about implementation and processing was also

another important design factor. Thus, all technologies employed and its major implications are

exploit.

The communication system born from the idea of provide a wireless communication channel,

capable of transmit the required data to fulfil an accurate sensing on a highly attenuator medium,

such as an underwater environment. Although it can be applied to other mediums such as the atmo-

sphere environment, most of them suffer considerable attenuations and interferences. Therefore,

it is necessary strong mechanisms to minimize these problems. The first approach was exactly

this idea. Looking today’s communications systems, a very popular and proven technology for

3.2 The Approach 21

minimize the interferences is the SS techniques. Mostly of the harmful and non-intentional in-

terferences are characterized to be narrowband over a specific frequency band. SS techniques,

through PRS proprieties, allows a efficiently interferences suppression by spreading them over a

large band and reinforce the information signal. The SS, besides being a must have feature on

a communication system, demand a right selection of a type of implementation. The two exist-

ing techniques, DSSS and FHSS, have its inherent pros and cons but its practical performance

depends on the overall system design and requirements. The chosen one for this system was the

FHSS because of three main reasons. First of all, it presents better results under narrowband in-

terferences. Then, its spectrum distribution resembles a set of narrow peaks (Figure 2.6), which

is affordable for FDM modulations. The last reason is related to the way that was pre-designed

the transmitter and receiver models, created on Simulink environment, regarding the simplicity

about interpretation and further improvements. The models itself will be further explained in the

chapter 5.

A pure FHSS wideband signal must experience some type of modulation in order to prevent

common problems during transmission, such as, ICI and ISI interferences. OFDM modulation

presents itself as technique which overcomes the previous problems by splitting the available

bandwidth into N orthogonal frequencies or subchannels. This property also enables a parallel

type transmission and a good spectral efficiency, as well as, becomes the information much more

reliable because of its suitable combination with FHSS. For a accurate and proper sensing of the

underwater vehicle, the data rate of the system could be low. The acceptable threshold for an

delay between sensing computations was defined as one second. Thus, the parallel feature of

OFDM does not provide significant advantages for this system iteration, and hence, was adopted a

simplified version of OFDM that uses only one frequency per sequence. Although simplified, this

approach contains all the inherent advantages of OFDM modulation are available and also have

the PAPR problem strongly reduced.

Figure 3.2: Constellation diagram of QPSK and symbol transitions.

As last stage, it is chosen the codification for the input bits. Taking into account the previous


signal design, the most efficient codification is the Phase Shift Keying (PSK) because, not only

changes the phase characteristic of the signal but also keeps the amplitude constant. Thus, the

PAPR problem of OFDM is mitigated. Several possibilities for the number of PSK states were

available. The trade off choice was on the spectral efficiency and the BER measure [23]. Increasing

the number of PSK states leads to a higher spectral efficiency but at the expense of BER measure.

In other hand, both transmitter and receiver design complexity are directly related with the number

of states. To ensure a reliable communication, the way that was designed the system was for lowest

possible BER. Thus, two solutions were accessible, the BPSK and QPSK, respectively with, two

and four states. However, the choice fell on the QPSK modulation. The higher complexity is

compensated by its double of spectral efficiency. On Figure 3.2 is shown the QPSK constellation

used on the FHSS signal and its possible transitions. Note that the available states obey to Gray

code rules, where only one 1-bit is changed between neighbour states. This binary coding is

affordable to error correction algorithms and allows a lower BER when comparing to others coding

schemes.

3.3 Communications Structure

The communication between both ends of the communication is unidirectional from the surface

system and the underwater vehicle. This improves the power efficiency of the foremost, as well de-

crease its complexity. The information goes through several modulation stages which are followed

described.

The communication chain may be described as a set of generic blocks. Figure 3.3 shows an

overview of the system architecture and its data flow. Generally, the system is fed with three

independent data channels, each one representing each beacon from Figure 3.1, which can be

derived from two distinguish sources. Whereas one might are directly or non-directly dependent

of user interaction (Wi-Fi), which allows a human based control on the system, the other act like

a data base with information coming from other systems. One of the data channels is used to send

cyclic data from a data base or a structured data, which in turn, is connected to a GPS module and

to sensors on the surface system. This means a continuous data defined by set of fields where its

values are updated according to a control signal. The characteristics of the data make this channel

the most important one to achieve the central objective of the system of sensing.

The other two channels were designed to provide flexibility to the system. They might be used

as support channels of the first one, providing capabilities of forward error correction or some type

of user command line to directly control the underwater system. In general, they facilitate a future

system improvement.

The transmitter itself is comprised by a QPSK modulator, a encoder block and a up-converter.

After a phase modulation, where is matched the input data over the available states on the QPSK

constellation, as exposed on Figure 3.2, follows the encoder block, responsible for the major task

on the transmitter. It performs a set of signal transformations regarding the signal frequency and

phase, to incorporate the SS and OFDM features and the data to transmit. The last block executes

3.3 Communications Structure 23

Figure 3.3: System communication architecture overview and information flow.

a frequency conversion, pushing the baseband signal to the channel band. Thus, in the output there

are three independent FHSS/CE-OFDM signals.

After the signal pass through the channel and arrive to the receiver, is performed an inverse

process. Similarly to the transmitter, the receiver is composed by a down converter, a decoder

block and a QPSK demodulator. The down converter make the inverse frequency conversion,

transforming the signal from channel band to base band. Then, the decoder block, which computes

the core process at reception, estimates the data synchronism and decode the signal. At the end,

a QPSK demodulator translate the symbols into useful data before go to a data base, where it is

visible to external systems.

At reception is accomplished an inverse process. A down converter retrieve the signal to

its original frequency band. Next, occurs the decoding process which involves mechanisms of

decimation, spectrum analyses, synchronization and data estimation and processing. It outputs

three data channels, in assent with the same number of beacons on the transmission, to be later

demodulated on a QPSK demodulator. At the end the data is stored and ready to be interpreted by

other systems.

The last characteristic about the communication architecture is how the information flow of

the cyclic data is controlled. As the external systems could adjust its parameters, the exchanged

information along the operation time should also change. To accomplish this, the transmitter uses

the Pulse per Second (PPS) signal from the GPS module (green point) to update the values on the

data base. Thus, for sensing data communication, a internal clock defined by the PPS indicates

when the input data is updated. The other two channels also are synchronized by PPS, although


the possible different input data. Further in this work it will be explained that the reason for such

decision is related to sensing measures.

Chapter 4

Signal System Design

This chapter is split into three subchapters. The first one details the signal design. In the second

subchapter, a communication protocol approach is exposed with reference about error correction

technique Cyclic Redundancy Check (CRC), the synchronization procedure and the cyclic data

format for sensing meanings. Then, the third and last subchapter describes how the navigation is

accomplish and its major characteristics.

4.1 Design

The FHSS output signal has associated two techniques which take advantage of frequency domain.

In fact, the key signal transformations done during the encoding and decoding process are related

to frequency. As a signal based on FHSS technique, a group of 12 frequencies based on fbb =

93.75 Hz was chosen. The multiplicity range goes from −6 to 5 integer numbers, on which is

translated into a baseband frequencies from [−562.5, 468.75]Hz. Therefore the system bandwidth

is around 1.031 kHz. These frequencies are the available hops for the FHSS technique and the

channel frequency for each OFDM sequence. From the rearmost, they also are orthogonal between

each other to avoid ICI and ISI interferences. The hopping propriety is defined by a PRS which

control a frequency synthesizer. The independence of each beacon and also the unique pattern

for each signal, comes exactly from how is defined the PRS. A simple way to visualize a PRS is

through a cyclic vector which elements are the available hop frequencies fk with k = [1,12] ∈ Z,

where f1 =−6 fbb and f12 = 5 fbb. Thus, for each beacon was chosen a vector, respectively 4.1, 4.2

and 4.3, so that the cross-correlation between each one of them was as lower as possible for each

rotation. Thus, the interferences between output beacon signals, related to possible frequency

deviations, are minimized (Appendix A for more details).

PRSBeacon1 =[

f1 f2 f3 f4 f5 f6 f7 f8 f9 f10 f11 f12]

(4.1)

PRSBeacon2 =[

f2 f4 f6 f8 f10 f12 f1 f3 f5 f7 f9 f11]

(4.2)

PRSBeacon3 =[

f7 f1 f8 f2 f9 f3 f10 f4 f11 f5 f12 f6]

(4.3)

25

26 Signal System Design

Figure 4.1: FHSS signal spectrum.

The last frequency design is the conversion of the null central base band frequency to a more

convenient one. The up-converter translates the null central frequency on baseband into the chan-

nel frequency fch = 4.8 kHz. Thus, the frequency range was changed to [4.2375,5.2688] kHz

and the channel bandwidth to around 9.506 kHz. On Figure 4.1 is shown, respectively from up

to down, the FHSS signal spectrum in linear and deciles scale. Over the channel bandwidth, its

easily viewed 12 group of samples, representing the hop frequencies.

Also related to every carrier is the information to transmit. The input data is codified on the

phase of each carrier. As previous defined, the chosen modulation was the QPSK and so four

phases (0, π

2 , π and 3π

2 rad) or symbols of two bits (00, 01, 11, and 10) are available (Figure 3.2).

The design of the OFDM technique may be considered the core of all system. The OFDM

structure definition used in the system is the same shown on Figure 2.9. A OFDM sequence is

composed by N = 1 blocks or subchannels, with the same number of cyclic prefix and each block

contains one QPSK symbol. Due to the single OFDM block per sequence, the period of both

are the same, Tseq = Tblk. Globally, the system has 12 available frequencies where a frequency

fk is repeated every 12Tseq seconds and carries one symbol. Thus, for set of 12 frequencies was

defined as a signal sequence Ts,seq = 12Tseq wherein carries equal number of symbols. However,

for robustness purposes the same information is codified on three neighbour frequencies, which

means an total information of four symbols of two bits (one byte) on each signal sequence. The

main disadvantage of this approach is the decrease of the effective data rate transmission. On

Figure 4.2 is shown a general overview on signal sequence design. Relating it with the OFDM

structure definition on Figure 2.9, each sequence is represented by a frequency block fk, with

k ∈ [1,12], and the cyclic prefix and symbol data by, respectively, Tcp and Tsd .

An important feature on OFDM signal structure is the cyclic prefix but even more important

is its integration on the overall design. It terms of positioning, it is placed at the back of each

symbol data. The content is normally a simple but most effective repetition of the last part of the

4.1 Design 27

Figure 4.2: Signal sequence design.

Figure 4.3: Tukey window sample design.

transmitted block. The approach used in the design took the same normal procedure but with some

improvements to better relate the different modulation techniques. During tcp, in addition to the

partial repetition of the last transmitted signal, is combined to some part of the current signal. The

way that is accomplished is through a Tukey window function, with parameter that specifies the

taper section α = 0.3382, applied to both signals. Figure 4.3 shows the Tukey window design its

samples distribution. This approach smooths the transition between each block in amplitude and

phase, and also removes the audible clicks, a characteristic of abrupt phase changes. However,

its main implication is on signal spectrum. The spectral sidelobes are highly attenuated and the

overall signal energy are mostly on the first main lobe. Thus, in addiction to an increase of spectral

efficiency, it is also very useful for the decoding process. A side effect of this is a non-constant

envelope of the signal during Tcp. It have implications on signal PAPR but yet it can be neglected

comparing to the original OFDM technique.

The design over the time domain is also an important part of whole system construction mainly

because to a future hardware implementation. As all others digital systems, a quantization process

is required. The system works with a sample time of 48000 Hz or samples per second and all


others subsystems work based on it. An signal sequence have a period of Ts,seq = 200 ms, which

correspond to a 48000Ts,seq = 9600 samples. For each frequency block is straightforward. The

period is TblkTSeq =Tseq12 ≈ 16.67 ms and so 800 samples. From these, 288 samples is for the

cyclic prefix and the others 512 to the effective symbol data which regarding the time period are,

respectively, Tcp = 6 ms and Tsd ≈ 10.67 ms. A resume of the main features of signal design is

presented on Table 4.1.

Table 4.1: Resume of main characteristics of FHSS communication system.

Parameter Value

Sample time (Hz) 48 kfch (Hz) 4.8 kfbb (Hz) 93.75

Multiplicity of frequencies (∈ Z ) [-6, 5]PG 12

Ts,seq (ms) 200Data rate (bytes/s) 4

4.2 Communication Protocol

The information transmission between the transmitter and the receiver need to obey to certain

rules and procedures so that both systems could understand each other. This is mostly applied

for each input-output ports. Although the communication system was designed to have three

output ports, the pseudo-random capability from FHSS technique allows almost a unequivocal

distinction between channels. Thus, only one input is really needed. This is also translated in to

independence between channels which allows the existence of three "independent" protocols. This

means a set of general procedures for all, like synchronization and measures against errors, but

one the key difference on the format of the data. As already stated, two of the available channels

were developed to provide a future system improvements, and the other one to provide the required

information for sensing computation.

For this first system iteration, the latter channel is the most important one. Through the tri-

angular disposition of the beacons and sending the required information in a cyclic way, from a

surface to the receiver, it can estimate its relative and absolute position.

4.2.1 CRC

In digital communications the errors occurred during the data transmission are reflected as a

change of one or more bits in a data stream. At reception it is translated in BER measure and

the greater it is, more unreliable the system will be. Thus, some techniques derived from infor-

mation and coding theory were developed to become the transport of digital data more reliable

and capable. They consist in algorithms incorporated on the communication protocol that pro-

vides capabilities of error correction and detection at reception. Error correction schemes implies

4.2 Communication Protocol 29

some type of error detection and hence they are more complex to implement on both ends of the

communication. Thus, given the simplicity of implementation in the protocol, was employed the

error detection type technique CRC, a hash function type which through a block of digital data

computes a checksum field. It is appended on the original data before transmission and, although

decrease the effective rate data transmission, allows the receiver, by recomputing the checksum,

verify if the data contains errors.

Mathematically, the CRC algorithm can be described as a simply binary polynomial division

operation over a Galois field of two elements, between a binary data word M(x) of order k and

a CRC polynomial G(x) of order k’ [24]. The obtained remainder R(x)=M(x)modG(x) of order

k’−1 is the checksum field to be used as data integrity check. Binary polynomials are polynomials

where the coefficients powers of x are represented by bits values. In other words, the binary data

M = 11010 can be represented by M(x) = 1x4+1x3+0x2+1x1+0x0 = x4+x3+x. As a full CRC

computation, given the previous binary data M and G(x) = x3+x+1⇔M = 1011, the remainder,

which is R = 010⇔ R(x) = x of order 3, is calculated as follows (more details on [25]):

1 1 0 1 0 0 0 0

1 0 1 1 0 1 1 0 0 0 0 0

1 0 1 1

1 1 1 0 0 0

1 0 1 1

1 0 1 0 0

1 0 1 1

0 1 0

(4.4)

The computation algorithm implement on the communication protocol was exactly the same

as before. However, the binary data was enforced to have "1" as the most significant bit. Basically,

if M(x) start with a "0", the algorithm rearrange the bits by doing a exclusive "OR" operation

between M(x) and a set of k’− 1 bits "1". With this convention implemented on both transmitter

and receiver, all binary data are mapped on CRC checksum and, hence, more robust is the error

detection. The design process about CRC implementation was essentially based on the lengths

of the polynomial M(x) and G(x), its relation with the data page size and the priority distribution

of each page field. The first design step was the choice of the M(x) word length. Note that the

system performance, regarding the effective received data or, in other words, the received blocks

which kept the integrity of the data, are related with the chosen length. In fact, the receiver only

can process the data blocks if they passed through the checksum verification. If not, the block is

simply discarded. As previous exposed, the system is capable of transmit 4 bytes/s. In underwater

environment it is five times faster so 20 bytes/s or 160 bits/s. A possible approach was considering

a M(x) length of 160 bits and cover all data page, however it provides one second of ambiguity and

a risk of loose all information if not properly received. Thus, in other to minimize these problems,


the CRC was computed over a M(x) word length of 24 bits or 3 bytes.

The second design step was the decision of which is the best CRC polynomial for the system.

As described and exposed by the Table 3 on [24], the decision could be made relating the Hamming

Distance (HD), CRC field size and M word length. In order to maintain the coherence within field

sizes, the underwater rate transmission and the size of each data page, the chosen CRC polynomial

was

G(x) = x8 + x5 + x3 + x2 + x+1, (4.5)

where hexadecimal representation is 0x97. It has a length of 1 byte and, for the selected M(x)

length of 3 bytes, enables a detection of errors out of all possible combinations of 1-bit, 2-bit,

3-bit and 5-bit.

4.2.2 Synchronization

One of the key sensitive characteristics on a communication system is the data synchronization

process. The idea is that the transmitted data packets between both sides on communication kept

the coherency along the operation time. Two different synchronization mechanisms are employed,

one for signal sequences and another for its internal information. First of all, the reason for a

double synchronization is connected with a double control layer about data flow. Instead of only

maintain coherency within packets, which in the system is an OFDM sequence, it is also kept

on the information. Note that the coherence among sequences means a time adjustment on the

receiver regarding the expecting arrival time of the next sequence, whereas the coherence among

information is a reference for the receiver about the beginning of each data cyclic transmission.

Thus, is better ensured the control and integrity of the received data, as well as, its information.

From the sequences point of view, the synchronization is achieved through an independent

signal sequence transmitted before a group. Although the normal system operation is modulate

the 8-bit input data into QPSK symbols and the rearrange it with redundancy of three symbols,

when is the turn of the synchronization sequence the input is blocked. The transmitter is reduced to

the encoder and the up-converter. The foremost provides internally a set "unique" QPSK symbols

defined as [0 1 2 0 3 2 0 1 2 0 3 2

]. (4.6)

As the example on section 5.1.1, a typical vector of QPSK values are

[2 2 2 1 1 1 3 3 3 0 0 0

]. (4.7)

Comparing both, it is easily seen a difference in the elements redundancy and its pattern distri-

bution. Whereas in 5.2 a group of three equals elements never repeats, in 4.6 this three elements

are not equal but are repeated twice. This distinction was proven to be enough for the receiver

unequivocally distinguish a data and a synchronism signal sequence.

In similar way, the pages synchronization are accomplished through a signalling mark present

on the first CRC checksum at the beginning of each cyclic transmission. It is implemented with

4.2 Communication Protocol 31

a simple binary inversion after the checksum computation. Taking the example 4.4, the resulted

mark is

[1 1 1

]−[0 1 0

]=[1 0 1

]4.2.3 Cyclic Data Format

The base unit defined for the protocol was the byte. The cyclic data contains all the relevant

information for the receiver compute the sensing parameters. This information can be interpreted

as a data base or a data structure with a fixed number of fields. The transmitter receive this

information on the QPSK modulator, select which use and, then, the encoder modulates the phase

of each frequency. Internally, the transmitter implements a time priority scheme to choose which

byte should use. It can be intended as a pagination of the data structure. On the other hand, the

system data rate is 4 bytes/s on the atmosphere and five times higher on the water so 20 bytes/s.

Thus, each page was designed to have the 20 bytes. On Table 4.2 and Table 4.3 are presented,

respectively, the available fields on data structure and its distribution for each page. Each page

from the latter Table 4.3, take as reference the fields (FD) Table 4.2 using the "ID" and "Bytes"

column. As example, the field FD1 means the first element of the structure data which have 1 byte,

whereas the field FD5/2 means the second byte of the fifth element from structured data. Thus,

each field is represented by its "ID", FDID, and for fields with more than 1 byte, the representation

relatively to each byte is as the last example, FDID/Bytes.

The priority algorithm is visible by the number of times that one field is transmitted in all

pages. The field FD1 is always transmitted so it have the maximum priority. On the other hand,

the field FD6 is only transmitted once so it have the minimum priority. On the Table 4.2, all the

fields marked with (*) are the effective paginated fields, meaning the fields with low priority.

The parameters of the structured data was chosen based on the required data to perform the

vehicle sensing and on the decoding algorithm at receiver. Those information used for the sensing

are: GPS coordinates from the surface system (FD9 to FD13) expressed in degrees and decimal

minutes, relative distance between each beacon (FD3 to FD5) in centimetres, heading relatively

to the North (FD7) in degrees and deep of the platform (FD8) in centimetres. In particular, the

field FD9 although seems a repetition from FD10 and FD12, is a maximum priority field used

to resolve the ambiguity in the minutes when is only transmitted the coordinates seconds. The

remaining fields take different meanings. The FD1 represents the number of the page. The FD2 is

a one byte flag used to classify the fields at 4.8 as automatic generated (”0”) or default (”1”),[FD7 FD8 FD3 FD4 FD5 FD8 Latitude Longitude �

], (4.8)

where Latitude and Longitude refers the all the fields connected to its respective coordinate and

the symbol ”�” means that this bit is empty or free to use. The source name at FD6 is the surface

system identification. The FD14 is for error detection mechanisms and, finally, the FD15 is a

default byte used, for example, to complete a page.


ID Field Bytes

1 Page number 12 Flag 13 Beacon 1 (*) 34 Beacon 2 (*) 35 Beacon 3 (*) 36 Source name(*) 47 Heading 28 Deep 29 Coordinates minutes 110 Latitude degree/minutes (*) 211 Latitude seconds 212 Longitude degree/minutes (*) 213 Longitude seconds 214 CRC Checksum 115 Empty 1

Table 4.2: Data structurefields.

Page 1 Page 2 Page 3 Page 4 Page 5

FD1 FD1 FD1 FD1 FD1

FD9 FD9 FD9 FD9 FD9

FD2 FD2 FD2 FD2 FD2

FD14 FD14 FD14 FD14 FD14

FD7/1 FD7/1 FD7/1 FD7/1 FD7/1

FD7/2 FD7/2 FD7/2 FD7/2 FD7/2

FD8/1 FD8/1 FD8/1 FD8/1 FD8/1


FD8/2 FD8/2 FD8/2 FD8/2 FD8/2

FD10/1 FD3/1 FD4/1 FD5/1 FD6/1

FD10/2 FD3/2 FD4/2 FD5/2 FD6/2


FD12/1 FD3/3 FD4/3 FD5/3 FD6/3

FD12/2 FD15 FD15 FD15 FD6/4

FD11/1 FD11/1 FD11/1 FD11/1 FD11/1


FD11/2 FD11/2 FD11/2 FD11/2 FD11/2

FD13/1 FD13/1 FD13/1 FD13/1 FD13/1

FD13/2 FD13/2 FD13/2 FD13/2 FD13/2


Table 4.3: Fields distributionfor each data page.

4.3 Sensing 33

4.3 Sensing

(not finished...) The sensing capabilities are achieved through a combination of signal phase prop-

erty, the transmitter beacons arrangement and the cyclic data.

The fields from cyclic data are threefold: GPS coordinates from the surface system, relative

distance between each beacon, heading relatively to the North and the deep of the platform that

holds the beacons.

Chapter 5

System Implementation

This chapter will be focused in the implementation and design of the system for validation tests.

Begins with an description about the simulation models of the transmitter and receiver, created

on Matlab/Simulink environment and, then, follows with a hardware design approach for future

implementation on both surface system and underwater vehicle.

5.1 Simulation Models

Concluded the signal design procedures, follows the creation of the transmitter and receiver soft-

ware models and its simulation. This was a very important stage of the Thesis development be-

cause it defined how possible, simple and reasonable was the its translation to a real hardware.

Thus, the system models were developed taking in mind three key features: simplicity, easy to

understand and, mostly, smooth transition between software model and hardware implementation.

In order to fill the previous requirements, the models were created in the high-level techni-

cal numerical language and block based design Matlab/Simulink version R2012a. The models

were also tested on Matlab version R2013a, from which the Figures 5.1 and 5.5 was take off.

Besides the high numerical processing capabilities and dedicated toolboxes for signal process-

ing, the main reason for its choice was the huge collection of hardware supporting packages for

Simulink. Generically, the packages transforms the input/output (I/O) ports of a supported hard-

ware in a set a abstract blocks but there are some packages, like the Xilinx System Generator for

Xilinx Field-programmable gate arrays (FPGA), which provides blocks for control internal hard-

ware components, such as, registers and memories. Thus, more effort is spent in the overall system

details and less in the low-level hardware languages.

5.1.1 Transmitter

The transmitter model aims to represent the existing stages within the surface system through

Matlab numerical abstractions. These stages are: capture the information from Wi-Fi and GPS,

modulate the signal with the pre-defined techniques and output the acoustic sound. The interface

between the external systems and the model is done by the global Matlab workspace. The model

35

36 System Implementation

Figure 5.1: Simulink simulation model of the transmitter .

simulates the information coming from the three inputs channels by accessing a variables placed

on Matlab global workspace. For instance, the cyclic data abstraction is represented by a Matlab

structured data with the same fields in the Table 4.2. After retrieve the information from the

workspace, the model creates five data pages and makes it available to the QPSK modulator.

Figure 5.1 shows the Simulink diagram block of the transmitter. At the center, the blue blocks

represents the three output beacons. As already exposed, the independence of each beacon is

achieved by two features: a PRS and independent inputs data channels. They are represented by,

respectively, the leftmost input blocks of each blue block ( 4.1, 4.2 and 4.3) and each block beneath

the same ones. At the top of the model, a time control system emulates the PPS signal from the

GPS and sends a set of control signals to each beacon. Those are used to constantly trigger the

different structures of the signal, for instance, the signal sequences at 1Ts,seq

= 5 Hz and the pages

fields at 1Tseq

= 60 Hz. On the model right side, the data outputs are stored on Matlab workspace

and can also be listened through an audio device.

5.1 Simulation Models 37

Figure 5.2: Typical output sound wave of one beacon.

The developed algorithm to compute the output signal was employed on all beacons, hence,

its internal blocks are the same. Taking as reference the beacon for cyclic data, the first blue block

on top, after all data pages were created, the QPSK modulator transforms the input data byte into

a vector of 12 elements compatible with the PRS cyclic vector. As full example of this stage, if

the input byte or 8-bit vector is the character "9",

M =[1 0 0 1 1 1 0 0

]⇔M(x) = x7 + x4 + x3 + x2,

the QPSK symbols results from cluster every two bits with the most significant bit on the right,

which outcomes a phase vector

vecphase =[10 01 11 00

]=[2 1 3 0

]. (5.1)

In terms of signal phase rotation in radians, by inspecting the QPSK constellation diagram on

Figure 3.2, the previous vector is the equivalent to

vecphase,rad =[

π

2 π3π

2 0].

As last step, the modulator changes the phase vector 5.1 to be coherent with the number of fre-

quencies and to the pre-defined redundancy of three symbols, which results in

vecphase =[2 2 2 1 1 1 3 3 3 0 0 0

]. (5.2)

Then, both PRS vector and vecphase are coherently combined to select the correspondent signal

fraction. This is made with a table lookup which contains all possible combinations regarding each

combination of frequency and QPSK symbol. For instance, selecting the third element of both,

PRSBeacon1(3) = 3 and vecphase(3) = 2, corresponds the a chosen signal of with a Tukey window

wave form 4.3 where the middle 512 samples define a sine wave with frequency of f3 = 4.425


Figure 5.3: Transmitter User Interface.

kHz and phase of −π rad. With this approach the simulation is able to run smoothly enough

to reproduce the sound in real time and the hardware implementation is improved in terms of

computation speed.

Appended to transmitter model is also a User Interface (UI). It was created to, in parallel

with the Matlab workspace, simulate the GPS and Wi-Fi communication to the surface system

and also give a more user friendly approach to the model. It was done using the Matlab UI

program GUIDE. Figure 5.3 shows the transmitter UI design with some experimental values. The

windows is organized in several sections, each one representing a different part of the model.

On the right side the model controls provides information about what model is being simulated,

the useful buttons to start, stop and pause the simulation, its simulation time and an option to

mute the output sound. On the left side a set of windows shows the the major current parameters

of the data base or sensing parameters, which is a structured data stored on Matlab workspace.

Here is possible to update manually the parameters, hence, it a way to simulate a real behaviour

of the external systems. On the bottom, a small window displays the data pages information,

respectively, the polynomial employed on CRC computation, the page number being transmitted

and the flag field of the data base. The last one updates its bits according to the changing of the

UI left side parameters. Finally, in the middle of the UI, three windows shows the current data

being transmitted during the simulation. The top one is for the cyclic sensing data. The next two

windows are for the other two channels. They were designed to allow the user write directly in the

UI the desirable information or, programmatically, through a Matlab workspace variable.

5.1.2 Receiver

Taking a similar approach, the receiver model aims to represent all decoding stages within the

underwater vehicle. The receiver model the stages are: retrieve the base band signal, decimation,


Figure 5.4: Synchronism detection.

frequency analysis by FFT computation, signal synchronization and data decoding. Figure 5.5

shows the Simulink block diagram of the receiver. The interface between the model and external

system, besides the Matlab workspace, can be also be through the computer’s microphone and a

audio sound file. This inputs are defined by the yellow blocks on the upper left conner, where the

audio sound file comes from the topmost block.

After the combination of three beacon’s sound arrive to the input, it is transformed back to the

base band form and suffers a decimation process (magenta block) to reduce the data rate in sixteen

times. The input sample time is 48 kHz so, from the decimation results a signal with 48k16 = 3 kHz,

where each signal sequence have 48k.Ts,seq16 = 600 samples with 512

16 = 32 effective data samples.

Then, a sliding FFT of 32 samples applied along the signal sequence (red block) transforms the

signal from time domain to frequency domain, in other words, for each sample arrived is computed

the FFT of the 31 previous samples plus the new one. The output result is a group of 12 complex

numbers representative of each frequency block fk.

In the next stage, the complex data is analysed in order to synchronize the data flow. This is

accomplish by finding the signals maximums or peaks and examine if near them exits a phase peak

related with the sync sequence (blue blocks). The maximums are searched over the energy of the

input complex data and its correlation with the unique sequences of the channels ( 4.1, 4.2 and 4.3)

and the sync sequence (4.6). On Figure 5.4 is shown a typical synchronization computation in

graphical view. The data was gathered by the green blocks placed during the synchronization

chain. From the blue line, which represents the correlation between the total energy and each

channel, it is easily seen the peaks of the signal and its signalling with the red lines. In the other

hand, from the green line, which represents the signal phase, the model marks the phase peaks

with a cyan line but only indicates a sync sequence if it is near the read line. When it happens,

a impulse on black line arises. The end of the process results a trigger signal which indicates the

synchronization frequency.


Figure 5.5: Receiver simulation model on Simulink.


If no errors affect the signal, the synchronization is updated at every 600 ms, otherwise, the

model automatically generates a sync trigger up to five times, until it loses the synchronization.

In the last stage, the signal decoding and QPSK demodulation is performed. The computation is

represented by the orange block and runs according to the sync frequency. Thus, if the synchro-

nization is not acquired, the decoding process is aborted until be found again.

As already exposed, the models were created taking into account, mainly, its easy translation to

hardware language. Some of the implementations on the receiver went further and were developed

to improve the hardware performance. The receiver model is far more complex than the transmit-

ter in both software and hardware implementation. So, in order to decrease this complexity, some

of the blocks were "hardware" designed. The decimation block provides an increasing of perfor-

mance by decreasing the sample time of the signal which, consequently, decrease the number of

computations. At the same time, the FFT computation is strongly mitigated. Another back end

approach was the decoding process and the QPSK demodulation stages. The core computations

were developed using the Matlab function block, a progamatically Matlab language based block.

Therefore, the process translation to hardware language is somehow facilitated.

The last characteristic added to the receiver model is the UI. Developed under the same basic

ideas of the transmitter UI, the main role is provide to the user de decoded data in real time.

Figure 5.6 shows the receiver UI design with experimental values. The overall window scheme is

very similar to the transmitter. The sensing parameters and the model control buttons are in the

same position, respectively, on the left and right side of the window. At the bottom, the pages

information present the same information. The main difference is on how the received data is

displayed. The decoded information from the cyclic channel is automatically updated in their

specific boxes on the right and the information from the other two channels are displayed in the

sub-windows on the center of the window.

Figure 5.6: Receiver User Interface.


5.2 Hardware Design

The hardware design, was the last stage to be develop under this Thesis. Although the overall

design was not tested, the concept was studied and carefully evaluated regarding its integration

with the existing hardware technology from OSG and available components at FEUP. Thus, the

outlined hardware policy are liable to implement.

Figure 5.7 presents the projected hardware architecture of both transmitter and receiver sys-

tems. Both ends of communication, as independent but complementary systems, must be designed

in a integrated configuration. This means an identical core hardware unit especially detached for

signal processing in both systems. Therefore, as initial procedure, the systems were planned to

have the same core hardware unit.

The surface system hardware is represented by the diagram blocks on the left side. The hard-

ware unit are the base of whole transmitter connections. To provide the Wi-Fi I/O and GPS input

data, a router and a GPS module are connected via, respectively, Ethernet network and GPIO

or RS232C serial data, depending upon the available ports. Then, by GPIO outputs, three bea-

cons systems composed by an amplifier/transducer and a hydrophone, are connected to produce a

acoustic sound.

At the receiver, defined by the diagram blocks on the right side, the acoustic sound is captured

by a complementary beacon system, composed by low noise amplifier (LNA), a transducer and a

hydrophone. The signal is then conducted to the core hardware unit where all the signal decoding

process will occur. All the retrieve information is passed to the main central on-board computer to

be further processing.

Figure 5.7: Hardware architecture and information flow.

5.2 Hardware Design 43

5.2.1 Core Hardware Unit

At this point, two possibilities were engaged with different Matlab/Simulink supporting tools. The

first unit and maybe the most logical one, was through a FPGA system. The maximum configura-

bility, reduced power, strong processing capabilities and relatively affordable programming makes

it widely employed for digital signal processing. The FPGA studied was the Atlys system, based

on Xilinx Spartan 6 LX45 architecture. This specific system from Xilinx has a very capable in-

tegration with Simulink environment by the Xilinx System Generator support tool. Consists in

a set of Xilinx libraries containing blocks for communication, control logic, signal processing,

mathematics and memory, which enable algorithms implementation and automatic code gener-

ation using high-level abstractions. Thus, as the models were developed using a block based

methodology, this approach is a very suitable way to implement the simulation models.

The other hardware unit examined was the credit-card sized single-board computer raspberry

pi. The main reasons for a trade-off against the Xilinx board are the component cost and a recent

support for Simulink version R2013a. The raspberry pi does not allow a full hardware config-

urability as an FPGA but offers a Linux operating system layer and all its controlling benefits for

I/O ports. The supporting mechanism for Simulink takes advantage of the Linux layer to let a

full Simulink based model run as standalone application and use the available I/O ports. This is

accomplished with a full I/O library support for audio, video and GPIO pins.

As a sound based system, the hardware units should offer I/O ports for sound management.

Although both hardware units present a general sound port from 3.5mm jack, it is a unreliable port

because of the bandwidth limitation to Human ear frequencies around 20 Hz to 20 kHz. In fact, the

hardware approach is intended to aggregate the signal baseband at the central frequency greater

than 20 kHz because of the existing components. The same is applied for microphone input, which

only FPGA provides. Thus, the I/O sound design was projected to use a non restrictive Human

frequency range port as the GPIO pins. Regarding the available hardware ports, both presents

GPIO pins, however, it was experimental verified that raspberry pi does not provide the frequency

response requires unlike the FPGA.

The wave form of sound signal was another design method examined. In order to simplify

the transmitter hardware approach for sound output and so use the GPIO pins, the wave form was

designed to be a constant amplitude square wave form instead of a sinusoidal. Thus, the digital

signal can be easy translated to output hardware power signals. Although simplified, the system

performance will practically remain the same. The main implication about on the signal spectrum.

As the square wave may be represented as an infinite summation of sinusoidal waves, the spectrum

will evidence more powerful sidelobes and so more interferences. However, with a bandpass filter

over the desire band, the interferences are mitigated.

An high-level implementation does not necessary means the best approach for the system

implementation. Although less effort is required, the abstractions imposed by the libraries limits

the system customization. However, due to initial block based methodology employed in the

simulation models, the hardware implementation should follow the same path, to guarantee the


system success and also avoid a huge time effort. Both hardware approaches and its Simulink

supporting tools were tested under some block parts of the system simulation models and was

validated its further implementation.

The FPGA provides a more reliable system with increased performance and a deep control on

all hardware components. It is capable of suppress many external systems required for raspberry

pi, such as: a mandatory high frequency carrier if the output signal was obtained through 3.5mm

jack port, and an external sound card, connected via USB, to capture the sound. However, the

raspberry pi presents a more affordable cost, easiest and quick hardware model implementation.

Chapter 6

Tests

This chapter covers a set of tests conducted to validate the simulation models. All of them take

advantage of the transmitter and receiver Simulink models. Begins with a numerical simulation

only on Matlab and Simulink environments and then goes to a set of two hardware based tests.

The first one was performed with two computers side-by-side. Then, again with the same previous

configuration but inside of an anechoic chamber.

6.1 Numerical Simulation

Before practical tests under non-ideal environments, a numerical simulation should be performed

in order to evaluate the signal details. This type of simulations ensures high reliability on system

overall performance because enables a full control in every stage of the communication. Thus, it is

possible evaluate the signal and detect some bugs if exists. The main objective of this simulation

was evaluate the critical stages during the communication process, in particular, the output of each

beacon block on transmitter and the synchronism detection and data decoding on receiver. The

transmitter and receiver models were created on Simulink environment and so this simulation was

run on it. The Matlab versions tested were R2012a and R2013a.

Several possibilities to stablish a communication channel between the two models were tested.

The first two was based on both models running at the same time. Begins by a User Data Protocol

(UDP) block which through a local communication loop, it forwards the data from the transmitter

to the receiver. The other one was by the global workspace of Matlab, which is accessible by

both models. The transmitter to store the data on a global variable and the receiver read from it.

However, both approaches showed a clear lack of computation speed and, regarding the second

one, the synchronization of write and read on the same global variable was not guaranteed. Thus,

although the simulation could run, it would take too much time to be reasonable.

The approach that was followed to really test the whole system was based on the first one but

with difference of each was model run separately. So, still using the global Matlab workspace, the

transmitter starts the execution, writes the output in a variable and then stops. The receiver do the

45

46 Tests

same procedure as before but instead of write in the variable, it reads from it. This methodology

do not allow a real time execution, however, it is not a critical point for the main objectives.

From the results perspective, the simulation data was very satisfactory. The output data showed

a spectrum as theoretical expected (Figure 4.1), with 12 frequencies centred at 4.8 kHz. On re-

ceiver model, the synchronization sequence was nicely found like the data sequences, where the

spectrum peaks was much higher than the average power. The decoding data was successfully re-

trieved without errors. A video footage of this test is available in the Thesis webpage on "Gallery"

section.

6.2 Side-by-Side Computers

After a set of only numerical tests, some hardware components were appended into communica-

tion chain. Its propose were to validate the system signal when enrolled on real hardware and

the system performance with a non-numerical environmental channel. As a acoustic signal, the

hardware components were basically a speakers to output the sound and a microphone to capture

it.

The transmitter and the receiver model were simulated in two computers using the same Mat-

lab version R2012a. In first test, the hardware components were those available on each computer.

In regular computers, the hardware sound components are stereo, hence, the models were arranged

to meet that requirement. Due to the three output channels of the transmitter, an agreement should

be made about what channels are transmitted. However, as these tests were only for validate data

transmission, all the channels could be combined into one and transmitted in two speakers chan-

nels. Thus, the test was executed inside a room with one computer and its speakers working as

transmitter and the other with the microphone working as receiver. From the results point of view,

by examining the received data, the procedure works as expected. The signal was decoded yet,

its success was strongly dependent in the distance between the computers. Even increasing the

volume with the distance, the decoding processing was not appreciable. In this context, the pri-

mary effect for that behaviour was assigned the sound reflections all over the room, which were

behaving as interferences. Thus, for an effective decoding process the distance should be small

and the sound volume considerably high. As main conclusion, the system performance showed an

inverse relation with the computers distance, mainly due to sound reflections.

In the second test, other hardware components were used to better simulate the real com-

munication system and its unwanted effects. A set of three external speakers PowerMax 80/2

from TEAC, an external USB Sound Blaster 5.1 sound card from Creative and two external USB

SingStar microphones from Sony, have replaced the internal computers hardware. The three

speakers were intended to simulate the three underwater output beacons on transmitter. How-

ever, as the computer used did not provide three output sound ports, a external sound sound card

was required to enable more than two output sound channels. In other hand, the sound capture

were better accomplish by a more sensitive and capable pair of stereo microphones.

6.3 Anechoic Chamber 47

Figure 6.1: Transmitter configuration. Figure 6.2: Receiver configuration.

The first two components were connected to the transmitter computer and the other to the receiver

computer. On Figure 6.1 and 6.2 can be viewed both configurations.

After a set of tests where several hardware distributions were evaluated regarding its relative dis-

tance, the results showed to be more satisfactory than the previous test. Although the reflections

interferences still present, more degrees of freedom were available to find the best position for the

hardware. It was concluded that a triangular distribution of the speakers in line of sight with the

microphones obtained the best communication.

For the scenario on which the system was based on, the major conclusions obtained from these

tests were satisfactory. The majority of the underwater reflections happens mostly on seafloor. Due

to the receiver beacon design which was placed at the top of the underwater vehicle, it is somehow

protected against above reflections. Thus, in underwater mediums is expected less interferences

by sound reflections.

6.3 Anechoic Chamber

With the same configuration of the previous test, the next one was performed inside an anechoic

chamber. It is a room specially designed to performed tests over electromagnetic waves, such

as acoustic signals, without reflections or external noise. It also might be compared to a infinite

space or open space. The experiment aimed to evaluate the signal over almost ideal conditions

with hardware components, in terms of, decoding performance and angle precision. The test

were conducted on the anechoic chamber present in the department of electronic and computers at

FEUP. The system configuration was the same as the second side-by-side computers test, however

the distance between the computers was bigger, about 5 meters, and they were placed in line of

sight at the ends of the chamber. About the obtained results, were not observed any errors in terms

of information retrieved. This was somehow predictable due to the previous well accomplish

tests in less suitable environments. For the analysis of angle precision, the chamber contained a

movable platform which allowed smooth angular movements of 180 degrees controlled externally.

Whenever the simulation starts on both computers, a set angular movements were tested and then

they were confirmed on Matlab environment by the gathered data.

48 Tests

Chapter 7

Final Remarks

This chapter is intended to infer on the proposed objectives. It will retrieve the main conclusions

and also propose further developments.

7.1 Conclusions

In this Thesis a communication system for simultaneous data transmission and sensing, based

on the FHSS technique and OFDM proprieties, is presented. Three main objectives were pro-

posed: create and define an acoustic communication system based on the FHSS technique for data

transmission, which includes a communication protocol, a system capable of providing sensing

parameters through the transmitted data and a specific transmitter hardware configuration, and a

brief study concerning the hardware implementation of both transmitter and receiver. All of them

were successfully fulfilled, even the data transmission was the only objective to be tested.

The system validation was accomplished by two Simulink simulation models representing the

transmitter and the receiver. A set of tests proved that the designed signal properties that lay

behind the acoustic sound are truly effective on data transmission. The successful anechoic cham-

ber experiment was the key trigger since it showed no evidence of errors on the signal decoding

process.

The hardware implementation of the Simulink models may be simplified if the chosen core

hardware unit for signal processing were supported the Matlab program. Two boards were studied

and briefly tested: an FPGA from Xilinx and a low cost credit-sized raspberry pi. The GPIO pins

were the most reliable solution to I/O a square wave acoustic sound, however, only the FPGA

was able to manage the sound on the desired frequency range. Thus, the FPGA provides a self

contained solution for the overall design, whereas an implementation with the raspberry pi can

only be achieved by external systems.

Despite not exhaustively tested, the outcomes of this Thesis, namely, the full simulation mod-

els, the sensing algorithm and hardware approach, are very promising materials for further im-

provements.

49

50 Final Remarks

7.2 Future Work

This Thesis presents a first iteration of a very capable and robust communication system for un-

derwater applications. The modulation techniques QPSK, FHSS and OFDM were the approach

followed on signal processing, as well as, the Simulink based model was the chosen path to vali-

date the whole system and a start for hardware implementation.

In a nearby future, the logical next step is a complete validation of the achieved data transmis-

sion success. This could be accomplished through a set of hardware experiments in real environ-

ments, involving a full transmitter and receiver implementations. It will allow outwit possible bugs

and obtain practical data, which enables an extensive data analysis and estimation of the system

efficiency parameters like BER and signal to noise ratio (SNR). The overall stages of the proposed

future work could be:

• A deep study about the core hardware unit chosen to perform the signal processing, for

instance, the Xilinx FPGA or a raspberry pi. It should be supported by the Matlab/Simulink

program.

• Translation of the transmitter and receiver simulation models to equivalent hardware

blocks.

• Validation of the overall hardware design on both ends of the communication and perfor-

mance of individual tests.

• Execution of a set of experiments in aquatic mediums with different system configura-

tions.

• Extensive analysis of the gathered data.

Additionally, since the only feature tested was the data transmission, several individual tests

concerning the navigation algorithm are a very important future work. In this context, all the hard-

ware implementation and design must be completed. Thus, this future work will focus essentially

on the navigation algorithm evaluation under distinct scenarios.

Appendix A

Vector Cross-Correlation

The reason for the choices about the PRS vectors is explained in this appendix. A cross-correlation

Matlab sample code is also provided.

A.1 The Approach

Mathematically, cross-correlation is a measure about similarity between two signals where one of

them is time shifted and slides along the other. This is applied for both continuous and discrete

time domains.

Regarding the specific application of this Thesis, the signals are finite vectors but which may

be seen as cyclic vectors because of the context, or as a infinite periodic discrete signal. Thus, the

cross-correlation between two cyclic vectors may be intended as a computation between one vector

and the other with the elements being rotated. For the Thesis case only matters cyclic vectors with

same length and with the same elements. Therefore, the maximum number of rotations is the

vector length. Also, the meaning of vectors similarity is intended as the number of equal elements

after each rotation, in others words, when the signals subtraction give null elements. Its important

to note that the cross-correlation computation also gives a cyclic vector.

The primordial objective about the PRS vectors choices is to minimize and homogenize the

similarity for each rotation. Considering two vectors a and b with elements [a1 a2 ... al−1 al]

and [b1 b2 ... bl−1 bl] where l is the length of the vectors, and if b=a, the total and unique cross-

correlation achieved is equal to l. This is applied for both even and odd number of elements. The

main difference is how the similarity is distributed along the rotations. For instance, if l = 2, the

cyclic distribution is [0 1], i.e. in the first rotation there is no coincident elements and for the last

there is one. In the other hand, for l = 3 the distribution could be [1 1 1] or [0 0 3], depending how

the elements are permuted. On table A.1 is shown the cross-correlation possibilities for the last

case. From these two basic cases, it is possible to conclude that the best measure homogeneity

along all rotations is only possible for odd vectors.

51

52 Vector Cross-Correlation

Table A.1: Cross-correlation for odd vector of three elements (a1 < a2 < a3)

a = [a1 a2 a3] a = [a1 a2 a3]

b = [a1 a2 a3] b = [a1 a3 a2]

[a1 a2 a3]a - [a1 a2 a3]b = [0 0 0] [a1 a2 a3]a - [a1 a3 a1]b = [0 −c3 c2]

[a1 a2 a3]a - [a3 a1 a2]b = [−c2 c1 c3] [a1 a2 a3]a - [a2 a1 a3]b = [−c1 c1 0][a1 a2 a3]a - [a2 a3 a1]b = [−c1 −c3 c2] [a1 a2 a3]a - [a3 a2 a1]b = [−c2 0 c2]

Extrapolating the previous computations for longer vectors, the conclusion remain the same.

For the Thesis case of 12 elements, as even vectors, the best homogeneity is not achieved. How-

ever, choosing the right combination of elements is possible to obtain the lower similarity possible

between each one of the PRS with the best homogeneity. It was verified that the vectors 4.1, 4.2

and 4.3 were the best on both requirements. This is illustrated on Figure A.1 and A.2, created

with the help of Matlab.

Figure A.1: Cross-correlation between 4.1and 4.2 or 4.1 and 4.3.

Figure A.2: Cross-correlation between 4.2and 4.3.

A.2 Matlab Code

Below is the Matlab code used to obtain the results of the previous Figures A.1 and A.2.

1 function c=vecc_ccorr(a,b)

2 %% Cross−Correlation between two vectors (cyclic rotation)

3 %

4 % vecc_ccorr(a,b)

5 %

6 % 'a' and 'b' column or line vectors with same length

7 % 'b' cyclic rotates by 1 element over 'a'

8 %

9 % Hugo Cruz 2013_MS.c Thesis_"FHSS for Simultaneous Communication and ...

Sensing"_FEUP

A.2 Matlab Code 53

10 %% Input errors treatment

11 if(nargin==0 || nargin>3 || nargin==1)

12 error('Number of inputs must be 2 vectors');

13 end

14

15 if(length(a) 6=length(b))

16 error('Vector "a" and "b" must have the same length');

17 end

18

19 if(¬isvector(a) && ¬isvector(b))20 error('Elements "a" and "b" must be a vector');

21 end

22

23 %% Cross−Correlation process

24 c=zeros(1,length(a));

25 siz=length(a);

26 d=b;

27

28 for i=1:1:siz

29 aux=a−b;30 c(i)=siz−length(find(aux));31

32 b(i+1:1:siz)=d(1:1:siz−i);33 b(1:1:i)=d((siz−i+1):1:siz);34 end

35

36 %% Plot output

37 stem(0:1:siz−1,c,'r','Marker','o','MarkerEdgeColor','r',...38 'MarkerFaceColor','auto');

39 grid;

40 axis([0 siz−1 0 max(c)]);

41 xlabel('Sliding index');

42 ylabel('Number of matches');

43

44 end

54 Vector Cross-Correlation

References

[1] R Malik. Spread spectrum - secret military technology to 3g. IEEE Conference on theHistory of Telecommunications, 2001.

[2] Steve C. Thompson. Constant Envelope OFDM Phase Modulation. PhD thesis, University ofCalifornia, San Diego, 2005. Electrical Engineering (Communications Theory and Systems).

[3] Ali Khatzibadeh Michael L. McMahan and Pradeep Shah. Wireless systems and technologyoverview. Texas Instruments, 2005. Dallas, Texas.

[4] Z. Chen, S. Kawasaki, and N.B. Carvalho. Wireless power transmission - the last cut ofwires... [from the guest editors’ desk]. Microwave Magazine, IEEE, 14(2):22–24, 2013.

[5] Gwyn Griffiths. Technology and Applications of Autonomous Underwater Vehicles. CRCPress, 2002.

[6] N.A. Cruz and A.C. Matos. The mares auv, a modular autonomous robot for environmentsampling. In OCEANS 2008, pages 1–6, 2008.

[7] Andrew J. Viterbi. Spread spectrum communications: Myths and realities. IEEE Comunica-tions Magazine, pages 34–41, May 2002.

[8] H. Kiesler Markey. Secret communication system. US Patent 2,292,387, August 1942.

[9] R.L. Pickholtz, D.L. Schilling, and L.B. Milstein. Theory of spread-spectrumcommunications–a tutorial. Communications, IEEE Transactions on, 30(5):855–884, 1982.

[10] M. Olama, S. Smith, T. Kuruganti, and Xiao Ma. Performance study of hybrid ds/ffh spread-spectrum systems in the presence of frequency-selective fading and multiple-access interfer-ence. In Communications Quality and Reliability (CQR), 2012 IEEE International WorkshopTechnical Committee on, pages 1–5, 2012.

[11] R.M. Buehrer. Spread spectum communications, Spring 2008. The Mobile and PortableRadio Research Group - Virginia Tech, available on Dr. Buehrer’s Homepage at http://www.mprg.org/people/buehrer/5660/ece_5660.htm. Accessed on May 2013.

[12] C. Cook and H. Marsh. An introduction to spread spectrum. IEEE Communications Maga-zine, pages 8–16, 1983.

[13] B.G. Evans. Satellite Communication Systems. The Institution of Engineering and Technol-ogy, thirdh edition, 1999.

[14] Ruimin Lu, Ye GanHua, Ma JinLing, Li YongChao, and Huang Wei. A numerical compari-son between fhss and dsss in satellite communication systems with on-board processing. In

55

http://www.mprg.org/people/buehrer/5660/ece_5660.htm

http://www.mprg.org/people/buehrer/5660/ece_5660.htm

56 REFERENCES

Image and Signal Processing, 2009. CISP ’09. 2nd International Congress on, pages 1–4,2009.

[15] J. Meel. Spread spectum (ss) - introduction, November 1999. Developed at the Polytechnic’DE NAYER instituut’between Nov. 97 and Nov. 99 under the project ’Spread Spectrum’.

[16] S.B. Weinstein. The history of orthogonal frequency-division multiplexing [history of com-munications]. Communications Magazine, IEEE, 47(11):26–35, 2009.

[17] Nick LaSorte, W. Justin Barnes, and Hazem H. Refai. The history of orthogonal frequencydivision multiplexing. Usenix Association, March 2008.

[18] MichaelT. Heideman, DonH. Johnson, and C.Sidney Burrus. Gauss and the history of thefast fourier transform. Archive for History of Exact Sciences, 34(3):265–277, 1985.

[19] R. W. Chang. High-speed multichannel data transmission with bandlimited orthogonal sig-nals. Bell Sys. Tech. J., 45:1775–1796, December 1966.

[20] Steve C. Thompson, Ahsen U. Ahmed, John G. Proakis, James R. Zeidler, and Michael J.Geile. Constant envelope ofdm. 56(3):1300–1312, August 2008.

[21] Si Wang, Yulong Gao, and Xiuzhi Guan. The performance analyses of papr reductionschemes for ofdm and improvement. In Consumer Electronics, Communications and Net-works (CECNet), 2012 2nd International Conference on, pages 1442–1446, 2012.

[22] Seung Hee Han and Jae Hong Lee. An overview of peak-to-average power ratio reduc-tion techniques for multicarrier transmission. Wireless Communications, IEEE, 12(2):56–65,2005.

[23] Ian A.Glover and Peter M.Grant. Digital Communications. Pearson Education Limited,thirdh edition, 2010.

[24] P. Koopman and T. Chakravarty. Cyclic redundancy code (crc) polynomial selection forembedded networks. In Dependable Systems and Networks, 2004 International Conferenceon, pages 145–154, 2004.

[25] T.V. Ramabadran and S.S. Gaitonde. A tutorial on crc computations. Micro, IEEE, 8(4):62–75, 1988.

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