Automatic Spectrum Sensing via Energy Detection for Cognitive

Radios

Source : MS Thesis by Jyh-Chyuan Sun, California State University, North-ridge

Vaibhav KumarY13PG052The LNM IIT Jaipur

Problem Statement

• To determine the availability of WHITE SPACES/ SPECTRAL HOLES in a underutilized licensed spectrum.

Need/Motivation for research

• According to the data made available by Cisco based on analysis, “Annual global IP traffic will pass the zettabyte threshold by the end of 2015, and will reach 1.4 zettabytes by 2017”

• 1 ZB = 10007 bytes = 10 21 bytes = 1000 exabytes

• It would take an individual more than 5 million years to watch the amount of video that will cross global IP networks each month in 2017

• Traffic from wireless and mobile devices will exceed traffic from wired devices by 2017

Reviewing concept and theories

• The concept of measuring the energy of a spectrum comes for the Radar Engineering, where the basis of evaluation was the ROC (Receiver operating characteristic) curve.

• Measuring the energy of a particular spectral band is a well established task for Signal detection & Estimation theory.

Previous research findings :-

Alexander M. Wyglinski showed the model for an energy detector by taking the fft (Fast Fourier transform) of the received signal.

Researchers showed that the fluctuations in the spectrum due to the presence of white noise can be smoothened by taking the Periodogram instead of fft

Hypothesis formulation

The spectrum sensor essentially performs a binary hypothesis test on whether or not there are primary signals in a particular channel

The channel is idle under the null hypothesis and busy under the alternate:

y(k) = w(k) : H0 (Idle)

y(k)

= s(k) + w(k) : H

1 (Busy)

s(k) = signal energy

w(k) = noise

Research Design

Constraints for the study :- Time Geographical Location Results may vary according to the

environmental conditions Results may vary according to the infrastructure

of surroundings

Sampling Design / Method of selecting items :-

Figure : Universal Software Radio Peripheral (USRP) by Ettus Research

Figure : Complete Transceiver setup for a SDR (Software Defined Radio)

Alternate method to collect data with the help of SIMULINK

Observational Design

The channel between the Transmitter and Receiver is assumed to be AWGN channel

The extension can be made with Rayleigh, Rician, Nakagami – m or generalized k-μ fading channels

So far we are not considering Multipath effect for simulation purpose

Statistical Design

For the present work we will analyze the model only for Amplitude modulation (AM), Frequency Shift Keying (FSK) and QPSK (Quadrature Shift Keying)

Operational Design

Figure : The flow chart for the algorithm

Figure : The overall operational design

Analysis of data

Figure : Default test conditions for Amplitude Modulation

Figure : Default test conditions for Frequency Shift Keying

Figure : GUI output for Amplitude Modulation

Figure: GUI output for Frequency Shift Keying with resolution segment of 64

Interpretation of Results

Results show that the parts of the spectrum which are above the red line (threshold), are busy at particular test locality and the adjacent bands are “spectrum opportunity.”

There is always a trade-off between the probability of false alarm & probability of miss detection

In case of poor SNR conditions of channel or deep fade, we need to deploy MAS (Multi Antenna System) or Co-operative sensing.

Contents for Report

Signature page Dedication Acknowledgements List of Tables List of figures Abstract Chapter 1 : Introduction Chapter 2 : Technical Overview Chapter 3 : Hardware Chapter 4 : Software

Contents for Report (contd...)

Chapter 6 : Discussion of test results Chapter 7 : Conclusion Bibliography

References:

[1] FCC, “Spectrum policy task force report,” in Proceedings of the Federal Communications Commission (FCC ’02), Washington,DC, USA, November 2002.

[2] Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2013–2018

[3] IEEE 802.22 Working Group on Wireless Regional Area Networks

[4] Chicago Spectrum Occupancy Measurements & Analysis and a Long-term Studies Proposal

[5] http://www.arrl.org/software-defined-radio [6] General Survey of Radio Frequency Bands – 30 MHz to 3 GHz,

Shared Spectrum Company,Vienna, VA [7] Stensby, John. “Chapter 8 – Power Density Spectrum.”

Retrieved 9 April 2012 http://www.ece.uah.edu/courses/ee420-500/500ch8.pdf