cyclostationary frequency detection 10thfeb

5/13/2018 Cyclostationary Frequency Detection 10thFeb - slidepdf.com

http://slidepdf.com/reader/full/cyclostationary-frequency-detection-10thfeb 1/12

Implementation of Cyclostationary Frequency

Detection Technique.

GROUP MEMBER'S NAME(S)

Muhammad Ali Sahal Khan 2009-TE-001

Muhammad Arsalan Khan 2009-TE-015

Talha Sami Khan 2009-TE-021

Syeda Areeba 2009-TE-063

Final Year Project Proposal

Submitted toDepartment of Telecommunication Engineering



SIR SYED UNIVERSITY OF ENGINEERING AND TECHNOLOGY UNIVERSITY ROAD, GULSHAN-E-IQBAL, KARACHI-75300, PAKISTAN

TEL: (92-21) 34988000-2 FAX: (92-21) 34989527

FINAL YEAR PROJECT REGISTRATION FORM

TELECOMMUNICATION ENGINEERING DEPARTMENT

TITLE OF PROJECT: Implementation of Cyclostationary Frequency Detection Technique.

BATCH: 2009 SUBMISSION DATE: 7th Feb 2012

S# Name Of Student Roll# CGPA Telephone Number &

Email

Signature

1. Muhammad Ali Sahal Khan 2009-TE-001 3.35 0345-3197099

2. Muhammad Arsalan Khan 2009-TE-015 2.9 0321-9090173

3. Talha Sami Khan 2009-TE-21 3.2 0345-3085686

4. Syeda Areeba 2009-TE-63 3.0 0342-2023953



Chapter 1 Motivation

Today, research in cognitive radio is aimed at developing efficient wireless communication

strategies to make use of unused spectrum. The idea is to make smart wireless devices that can

observe their RF environment and detect unused frequency bands in real time. That way, we can

operate more of wireless devices in the same frequency bands that are already in use. It isdesirable to develop devices that can learn from their observations and make their own decisionsabout when and how to transmit without disrupting any existing wireless connections. Based on

observations and past experience the RF cognitive device must determine which possible actionsfrom its current state is optimal and decides on its course of action. This is one of the main

aspects that separate Cyclostationary spectrum from Cognitive radio (CR).

A recent survey has shown another problem of the current spectrum assignment policy, i.e.,

spectrum under utilization. Analysis shows that most of the assigned spectrum is used rarely andsporadically as illustrated in Fig. In fact, studies have shown that at any given time 15%-85% of

the spectrum is unused according to the geographic location. This shows that the under

utilization of the radio spectrum is a bigger problem than its scarcity. To improve the efficiencyof the spectrum utilization, dynamic spectrum access was proposed. With the dynamic spectrumallocation policy, different frequency bands can be assigned to different wireless networks only

when they need it. The concept of secondary or unlicensed users was introduced which transmitin the licensed frequency bands without causing any interference to the users who own the

license.



Chapter 2 Overview

2.1 Significance

Cyclostationary detection is a method for detecting the Primary users (PU) by exploiting thecyclostationary features in the modulated signals. In most cases, the received signals in cognitive

radios are modulated signals, which in general exhibit built-in-periodicity within the trainingsequence or cyclic prefixes. This periodicity is generated by the primary transmitter so that the

primary receiver can use it for parameter estimation, such as channel estimation, and pulsetiming. The cyclic correlation function, also called cyclic spectrum function (CSF), is used for

detecting signals with a particular modulation type in the presence of noise. This is because noiseis usually wide sense stationary (WSS) without correlation, by contrast, modulated signals are

cyclostationary with spectral correlation. Furthermore, since different modulated signals willexhibit different characteristics, cyclostationary detection can be used for distinguishing between

different types of transmitted signals, noise, and interference in low SNR environments.

2.2 Description of project

Cyclostationary feature detection is a much optimized technique that can easily isolate the noisefrom the user signal. In cyclostationary feature detection technique, CR can distinguish between

noise and user signal by analyzing its periodicity. In Cyclostationary feature detection,modulated signals (transmitted signal) which carry information are usually sine waves; pulse

trains i.e. have some periodicity in it. These signals are named as Cyclostationary since the meanand the autocorrelation functions are periodic. Spectral correlation function (SCF) is used for

analyzing the features of signals i.e.; whether exhibit periodicity or not. SCF can clearly

distinguish between the noise energy and the modulated signal energy because the noise is wide-sense stationary which has no periodicity. At the first stage, BPF is used to measure the energyaround the related band, and then FFT is computed of the signal received from band pass filter.

Correlation block will correlate the signal and feature detection block will detect features likemodulation type, number of signals and symbol rates. Cyclostationary detection can perform

better than energy detection and matched filter detection because it has the ability to distinguish between noise and signal. The major advantage of cyclostationary feature detection over other

detection techniques is that it performs very well for larger noise on channels. The major limitations of this method are that it requires long processing time and is complex in

computation, so difficult to implement. Further, it cannot detect the type of communication, so itreduces the flexibility of cognitive radio.



2.3 Background

In 2002 the FCC Spectrum Policy Task Force published a report which clearly stated the case for

spectrum policy reform on the basis of three principle arguments. First, it was argued that currentspectrum policies are outdated and under strain from the dramatic increase in demand for

spectrum-based services and devices. Second, it was acknowledged that technological advancesare enabling alternative approaches to spectrum policy. As communications systems become

increasingly sophisticated and the manner in which they are capable of using spectrum becomesmore flexible, the policies which govern their operation may also become more flexible. Third, it

was stated that the lack of availability of spectrum for new services or for expansion of existingones was not a result of heavy utilization but rather extremely inefficient usage due to inflexible

spectrum policies. This argument has been strengthened by recent studies illustrating the lowlevels of utilization of much of the allocated spectrum. The potential benefits of overcoming the

issue of spectrum scarcity are considerable. By developing wireless systems capable of usingspectrum more efficiently and introducing more flexible approaches to spectrum management,

spectrum access for new and existing systems and services may be made readily available.

Increased availability of spectrum would reduce entry and overhead costs for systems andservices and further encourage the type of innovation already seen in unlicensed spectrum bands.In addition, increased exposure to competition and market forces may be used to ensure that the

maximum benefit is derived from the spectrum resources available. It is important to note that asolution to the problem of spectrum scarcity may not be achieved by either spectrum policy

reform or technological advances alone. Rather, approaches in which the two are developed side- by-side are required. Thus, the term Dynamic Spectrum Access (DSA) Network is used to

describe wireless system designed to efficiently utilize radio spectrum through the exploitation of increasingly flexible spectrum management regimes.

2.4 Problem Statement

Spectrum awareness is currently one of the most challenging problems in cognitive radio (CR)design. Detection and classification of very low SNR signals with relaxed information on the

signal parameters being detected is critical for proper CR functionality as it enables the CR toreact and adapt to the changes in its radio environment. Here we will use Cyclostationary

Frequency Detection technique for detecting and analyzing the signal.

2.5 Assumptions and Limitations

The noise is assumed to be white noise also we are assuming 128 point FFT to be used in this

method. The major limitations of this method are that it requires long processing time and iscomplex in computation, hence difficult to implement.



Chapter 3 Methodology

The section addresses the optimization of major communication parameters, considering terrain

profile for an experimental scenario. The scenario is formulated so that a complete infrastructurefor drive testing facilities can be examined and optimized.

3.1 Design phase

In this phase, individual parts of the system are designed and the project is broken down intoindividual sections:

y Implementation on Simulator

y Implementation on FPGA

y Comparison With Energy detection

y Designing of Card

3.2 Implementation phase:

After the testing of individual sections of the system, we will integrate all the units together to

give the system a single platform to give its actual shape and to work as a single body. This is themost important step because compatibility will be a major issue in this step. After all the sections

are integrated together we will proceed to the testing phase.

3.3 Testing phase:

In this phase the performance of the system is analyzed. In case some errors do occur in the

system they are rectified to make the system more efficient and reliable.



Chapter 4 Project Planning

4.1 Time Analysis

4.1.1 Time period WorkingMarch, April & May ««««««««.. Structure Designing, Preparation of Mid-PresentationJune.««««««««««««««««««..«««««««««Designing a schematic

July..««««.................................................«««««Software Programming & ControllingAugust ««««««««««««««««««««««««««...««. Implementation

September & October ...««««««««««««««««««««. Testing & Analyzing November«««««..«««««««..... Report Writing & Preparation of Final Presentation

4.2 Cost Analysis

As we have to buy FPGA kit and design a model for this technique this will cost us about

Rs. 30,000 to Rs. 40,000



Chapter 5 Software and Hardware

5.1 NI Multisim

NI Multisim is an electronic schematic capture and simulation program which is part of a suite of circuit design programs, along with NI Ultiboard. Multisim is one of the few circuit design programs to employ the original Berkeley SPICE based software simulation. Multisim wasoriginally created by a company named Electronics Workbench, which is now a division of

National Instruments. Multisim includes microcontroller simulation (formerly known asMultiMCU), as well as integrated import and export features to the Printed Circuit Board layout

software in the suite, NI Ultiboard.

5.2 Matlab\Simulink

MATLAB (matrix laboratory) is a numerical computing environment and fourth-generation

programming language. Developed by MathWorks, MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, andinterfacing with programs written in other languages, including C, C++, Java, and Fortran.

5.3 Verilog

In the semiconductor and electronic design industry, Verilog is a hardware description language

(HDL) used to model electronic systems. Verilog HDL, not to be confused with VHDL (a

competing language), is most commonly used in the design, verification, and implementation of digital logic chips at the register-transfer level of abstraction. It is also used in the verification of

analog and mixed-signal circuits.

5.4 FPGAs

The field-programmable gate array (FPGA) is a semiconductor device that can be programmed

after manufacturing. Instead of being restricted to any predetermined hardware function, an

FPGA allows you to program product features and functions, adapt to new standards, andreconfigure hardware for specific applications even after the product has been installed in the

field²hence the name "field-programmable". You can use an FPGA to implement any logicalfunction that an application-specific integrated circuit (ASIC) could perform, but the ability to

update the functionality after shipping offers advantages for many applications.



Chapter 6 Diagrammatical Representation of System

6.1 Block diagram of overall system



Chapter 7 Application of the Project

Cyclostationary signals appear in various applications, but here we will deal with problems

where Cyclostationarity is exploited for signal extraction, modeling, and system identification.The tools common to all applications are cyclic (cross-)correlations, cyclic (cross-)spectra, or multivariate stationary correlations and spectra which result from the multichannel equivalent

stationary processes. Because these tools are time-invariant, the resulting approaches follow thelines of similar methods developed for applications involving stationary signals. As a general

rule for problems entailing CS signals, one can either map the scalar CS signal model to

a multichannel stationary process, or work in the time-invariant domain of cyclic statistics andfollow techniques similar to those developed for stationary signals and time-invariant systems.

CS signal analysis exploits two extra features not available with scalar stationary signal processing, namely:

y ability to separate signals on the basis of their cycles and

y diversity offered by means of cycle.



Chapter 8 Conclusion

In this project, we will present Cyclostationary detection for spectrum sensing. Furthermore,

estimation and spectral autocorrelation function techniques will also be a target to analyze. Atheoretical and simulation analysis suggests that cyclostationary spectrum detection is optimalfor signal detection having low signal-to-noise (SNR) values. For 10% false alarm probability,

90% detection probability of BPSK signals with SNR of -8dB or greater has been achieved.



Chapter 9 References

[1] ³Spectrum sensing methods in cognitive radio´ by Siddharth Jain and Ravi Baid Jain,

National Institute Of Technology, 2011

[2] ³Cyclostationary spectrum detection in Cognitive radios´ by Jian Chen, Andrew Gibson andJunaid Zafar, Xidian University, China

[3] ³Cognitive wireless networks: concepts, methodologies and visions inspiring´ by Frank H. P.

Fitzek, Marcos D. Katz

cyclostationary frequency detection 10thfeb

Documents