distributed spectrum sensing in unlicensed bands using the...

Distributed spectrum sensing in unlicensed bands using the

VESNA platform

Student: Zoltan Padrah

Mentor: doc. dr. Mihael Mohorčič

Agenda

• Motivation

• Theoretical aspects

• Practical aspects

• Stand-alone spectrum sensing

• Distributed spectrum sensing

• Spectrum sensing testbed

• Experimental results

• Conclusions

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MOTIVATION

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• Introduction • Radio spectrum

– Regulation – Usage

• Using the radio spectrum more efficiently – Approach

• Reusing radio frequency bands – Licensed – Unlicensed

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• Motivation







• Conclusions

Motivation

Introduction

• Radio spectrum1

– Many systems use it: AM, FM, TV broadcast, GSM, UMTS, WiFi, GPS, satellite

– Systems need to coexist

– Avoid disturbance (interference)

• Radio spectrum regulation

– Frequency band allocation

– Each system has its own frequency band

1 image credit: Roke Manor reseach, 2004

1

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Frequency band allocation

image credit: Roke Manor reseach, 2004

2

6

Usage of radio spectrum

• Studies about radio spectrum utilization Left: Cabric et al: Implemenation issues

In spectrum sensing

Bottom: Valenta et al: Survey in spectrum

utilization in Europe

3

7



In spectrum sensing



Terminal 1 Terminal 2

Terminal 3

8



In spectrum sensing



Terminal 1 Terminal 2

Terminal 3

Terminal 4

9

10

Get information about radio

spectrum

Take decision on the used

frequency band

4

Approach

11

Get information about radio

spectrum

Take decision on the used

frequency band

Perform database

lookup

Perform sensing with

a radio

Approach

In licensed bands

• Examples: TV VHF, UHF, GSM bands

• Primary user(s)

• Secondary user(s)

• Dynamic spectrum access (DSA)

In unlicensed bands

• Examples: ISM bands (868 MHz; 2.4 GHz)

• Multiple equally threated users

• Spectrum Sharing (SP)

12

5

Reusing radio spectrum

In licensed bands

• Examples: TV VHF, UHF, GSM bands

• Primary user(s)

• Secondary user(s)

• Dynamic spectrum access (DSA)

In unlicensed bands

• Examples: ISM bands (868 MHz; 2.4 GHz)

• Multiple equally threated users

• Spectrum Sharing (SP)

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Reusing radio spectrum

THEORETICAL ASPECTS

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• Problem formulation

• Goals

• Hidden terminal and exposed terminal situations

• Spectrum sensing

• Energy detection

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• Motivation







• Conclusions

Theoretical aspects

Testbed is needed

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For solving the artificial spectrum scarcity problem, it is necessary:

• Experimental-driven research

• Experimental validation and improvement of sensing algorithms

We assume that either:

a) a radio communication experiment is prepared in an

ISM radio frequency band

b) the radio activity in an ISM band is of interest at a given

location

In both cases external interference might be observed.

6

Problem formulation

• Defining the system architecture for a testbed • Developing software that allows performing spectrum

sensing with the VESNA platform • Spectrum sensing:

– Calibration of multiple VESNA devices – Evaluation of their performance – Performing experiments with them

• Implementation of the functionalities needed for – Integrating multiple VESNA devices in a testbed – Communication system of the testbed, supporting

experiments

• Experimental evaluation of the performance of a VESNA-based spectrum sensing testbed.

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7

Goals

Hidden terminal and exposed

terminal situations • Idea: use multiple radios for

observation

– Each radio performs partial

detection

– Results are centralized

• Resolves the problems:

– Hidden transceiver

– Hidden receiver

• Relies on other methods for

partial detection

8

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Spectrum sensing

• Detecting other radios

• Spectrum sensing methods

– Energy detection

– Eigenvalue based detection

– Cyclostationary feature detection

– Matched filter detection

– Collaborative sensing

9

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Energy detection

• Idea: measure the energy in frequency band

and compare it to a threshold

• Simple to implement

• Needs correct threshold value: noise floor

• Does not work well with spread spectrum signals

10

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PRACTICAL ASPECTS

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Practical aspects

• Used devices

• VESNA platform

• Spectrum sensing framework

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• Motivation







• Conclusions

• Sensor network based testbed

• VESNA platform

– Low-cost, low-complexity

• CC1101 radio – 868 MHz ISM band

• CC2500 radio – 2.4 GHz ISM band

• The radios can only provide RSSI values

– Only energy detection is possible

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11

Used devices

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• Developed at Jozef Stefan Institute

• ST ARM Cortex-M3, 64 MHz • JTAG, USB, USART PC interface • I2C, SPI, PWM, ADC, DAC, USART sensor

and actuator interfaces – Code library: C/C++ (GCC)

• 300-900 MHz, 2.4 GHz radio interface (all ISM bands); – TI CC1101, TI CC2500

• Software tools: Open Source

• Eclipse IDE

• Tool-chain: GNU Compiler Collection

• Cygwin, Linux environment for Windows

• JTAG server: OpenOCD

• JTAG hardware interface: Olimex ARM-USB-OCD

12

VESNA platform

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• Developed at Jozef Stefan Institute

• ST ARM Cortex-M3, 64 MHz • JTAG, USB, USART PC interface • I2C, SPI, PWM, ADC, DAC, USART sensor

and actuator interfaces – Code library: C/C++ (GCC)

• 300-900 MHz, 2.4 GHz radio interface (all ISM bands); – TI CC1101, TI CC2500

• Software tools: Open Source

• Eclipse IDE

• Tool-chain: GNU Compiler Collection

• Cygwin, Linux environment for Windows

• JTAG server: OpenOCD

• JTAG hardware interface: Olimex ARM-USB-OCD

Performance:

- Comparable to other sensor node platforms,

like TelosB or Sensinode

- Lot less processing power than a PC

VESNA platform

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Radio VESNA Communication

and control

Communication

interface Data

storage

On-line

processing

Off-line

processing

Control system

13

Spectrum sensing framework

STANDALONE SPECTRUM SENSING

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• Goals

• Experimental setup

• Calibration results

– CC2500

– CC1101

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• Motivation







• Conclusions

Standalone spectrum sensing

• Implementation of spectrum sensing functionality

• Calibration of the prototype

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14

VESNA

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Signal generator

Coaxial Cable

VESNA

Measured signal level

Offset value

Generated signal level

15

Experimental setup

• Absolute error: < 6 dB

• Nonlinearity: < 2 dB

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16

Calibration CC2500

• Absolute error: < 8 dB

• Nonlinearity: < 0.5 dB

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17

Calibration CC1101

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Malfunction

18

Calibration CC1101

DISTRIBUTED SPECTRUM SENSING

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• Goals

• Demonstration – Devices

– Environment

– Representative results

• Device comparison – Introduction

– Environment

– Results

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• Motivation







• Conclusions

Distributed spectrum sensing

• Demonstrate the functioning of heterogeneous sensing system

• Benchmark

– Devices

– Combinations of devices

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19

Goals

• eZ430-RF2500 • Texas Instruments wireless

development tool • MSP430 CPU • CC2500 radio

• USRP2 • Universal Software Radio Peripheral • SBX daugthterboard • Software defined radio device • GNU radio software

• VESNA • CC2500 radio

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20

Demonstration - devices

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21

Demonstration - environment

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22

Representative results

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Path loss model

with parameters

Measurement

results from

devices

Fitting

Parameter

values

Error relative

to the model

For each

device

Comparison

23

Device comparison

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Path loss model

with parameters

Measurement

results from

devices

Fitting

Parameter

values

Error relative

to the model

For each

device

Comparison

Device comparison

Device comparison

07.12.2012

Seminar II

43

TODO intro

More text,

because work

has been done

Path loss model

with parameters

Measurement

results from

devices

Fitting

Parameter

values

Error relative

to the model

For each

device

Comparison

• One static

continuous

transmission

• Multiple

measurement

locations

Device comparison

07.12.2012

Seminar II

44

Path loss model

with parameters

Measurement

results from

devices

Fitting

Parameter

values

Error relative

to the model

For each

device

Comparison

• One static

continuous

transmission

• Multiple

measurement

locations

Mean Squared Error (MSE): average of squared error values for each data point

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24

Environment

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25

Results - plotted

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26

Results - numerical

SPECTRUM SENSING TESTBED

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• Architecture

• Goals

• Requirements

• Constraints

• Measurements

– Setup

– Representative results

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• Motivation







• Conclusions

Spectrum sensing testbed

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27

Architecture

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• Functionality abstracted

in resources

• RESTful design: GET

and POST requests

• All nodes addressable

• Requests initiated by

management and

control part

Architecture

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• Custom application layer

protocol

• Similar to HTTP

Architecture

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• Management and control

part

• Access control

• HTTP interface

• Scriptable

Architecture

• Everything configurable remotely

– No physical access

• Unified control interface

– Simple design and usage

• Centralized control and data collection

– Simplicity, reliability

• Possibility of easily adding functionality in the future

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28

Goals

• Spectrum sensing data collection

– Performance level

– Nodes Control system

• Reprogramming functionality

– firmware image transmission performance level

– Control system Nodes

• Reliability

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29

Requirements

• Availability of Internet access

– for the gateway node

• Location of light poles

• Power connections to the light poles

• Radio connectivity

• Possibilities for experiments

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30

Constraints

• Goal: measuring radio propagation

– For the control network

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31

Measurements - setup

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32 Measurements –

representative results

EXPERIMENTAL RESULTS

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• Scenario

• Radio wave propagation in the testbed – Link quality

categories

• Experiment scenario

• Results

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• Motivation







• Conclusions

Experimental results

• In the industrial zone

• 2.4 GHz ISM band

• Emulated behavior

– Scripted

• Observed by multiple nodes

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33

Scenario

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34

Radiowave propagation

1) Good link quality 2) Medium link quality 3) Bad link quality

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1) 2)

3)

35

Link quality categories

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36

Experimental scenario

• Node 17: terminal with cognitive radio capabilities (c)

• Node 2: terminal without cognitive radio capabilities (n)

• Rest of the nodes: observers

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(c)

(n)

37

Node roles in the experiment

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38

Results – Node 25

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39

Results – Node 6

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40

Results – Node 13

CONCLUSIONS

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• Spectrum sensing: energy detection is suitable for low-complexity platform

• Stand-alone spectrum sensing prototype

– Developed

– Calibrated

– Integrated in a heterogeneous system

– Accuracy has been determined

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41

Conclusions (1)


– Architecture defined

– Network planning performed

– Developed, set up

• Including HTTP like protocol

• Spectrum sensing experiment

– Prepared

– Performed

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42

Conclusions (2)

THANK YOU FOR YOUR ATTENTION!

Questions?

07.12.2012 72

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