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Auto-Correlation based Spectrum Sensing at Low SNR for Cognitive Radio

Published on June 2013 by Abhirup Das Barman
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International Conference on Communication, Circuits and Systems 2012
Foundation of Computer Science USA
IC3S - Number 3
June 2013
Authors: Abhirup Das Barman
69d2c550-c68f-4d01-b41d-bf5e512b536b

Abhirup Das Barman . Auto-Correlation based Spectrum Sensing at Low SNR for Cognitive Radio. International Conference on Communication, Circuits and Systems 2012. IC3S, 3 (June 2013), 4-7.

@article{
author = { Abhirup Das Barman },
title = { Auto-Correlation based Spectrum Sensing at Low SNR for Cognitive Radio },
journal = { International Conference on Communication, Circuits and Systems 2012 },
issue_date = { June 2013 },
volume = { IC3S },
number = { 3 },
month = { June },
year = { 2013 },
issn = 0975-8887,
pages = { 4-7 },
numpages = 4,
url = { /proceedings/ic3s/number3/12296-1327/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Communication, Circuits and Systems 2012
%A Abhirup Das Barman
%T Auto-Correlation based Spectrum Sensing at Low SNR for Cognitive Radio
%J International Conference on Communication, Circuits and Systems 2012
%@ 0975-8887
%V IC3S
%N 3
%P 4-7
%D 2013
%I International Journal of Computer Applications
Abstract

A challenging problem in Cognitive radio is that the secondary users in cognitive radios must be able to detect primary users under low signal-to-noise ratio (SNR) and dispersive channel. Spectrum sensing based on auto-correlation of the received signal samples being more prone to correlate under dispersive condition, has been investigated. Simplified theoretical expressions for probability of false alarm and probability of detection of the auto-correlation based algorithm are derived in the presence of multi-path fading channel. Spectrum of an unknown primary signal has been obtained through auto-regressive parametric signal modeling. By the proposed auto-correlation technique the detection probability near unity can be achieved at finite input samples and at a very low SNR for OFDM DVB-T signal and a wireless FM microphone signal in VHF band. It is found that dimension of auto-correlation matrix and signal sample duration is inversely proportional to achieve unity detection probability.

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Index Terms

Computer Science
Information Sciences

Keywords

Spectrum Sensing Autoregressive Analysis Ofdm Multi-path Fading