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Learning-based Spectrum Sensing in OFDM Cognitive Radios

Authors:
Muhammad Muzaffar
Mohamed Tarhuni
Khaled Assaleh

Keywords: cognitive radio; OFDM; linear classifier; energy detection; correlation detection

Abstract:
In this paper, spectrum sensing in OFDM-based cognitive radio systems is modeled as a pattern recognition problem. The proposed scheme uses a linear classifier to decide on when the spectrum is busy (class 1) or not busy (class 2). Two types of feature vectors are compared in this work, namely energy estimates and cross-correlation estimates using the cyclic prefix of the OFDM signal. Simulation results indicate that the energy-based linear classifier provides excellent performance in terms of detection probability over AWGN channels but suffers significant degradation if the channel undergoes flat Rayleigh fading conditions. On the other hand, the correlation-based features offer a more robust performance under both AWGN and fading conditions with a detection rate of about 90% at a signal-to-noise ratio of –3 dB.

Pages: 57 to 62

Copyright: Copyright (c) IARIA, 2012

Publication date: April 29, 2012

Published in: conference

ISSN: 2308-4251

ISBN: 978-1-61208-197-7

Location: Chamonix, France

Dates: from April 29, 2012 to May 4, 2012