Cooperative spectrum sensing for cognitive radios using jointly adaptive FRESH filters

Abstract

In this paper, the problem of cooperative spectrum sensing of cyclostationary signals for cognitive radios is considered. It has been shown that the MMSE optimal filters for cyclostationary signals involve one or more frequency shift branches and are called FRESH (FREquency SHift) filters. Adaptive FRESH filters have been used in the past to enhance cyclostationary signals so as to ease their detection in AWGN channels. It has been observed that space time FRESH filtering leads to gains of up to 10 dB over a standard energy detector in a single user case. In this paper, it is proposed to extend this approach to a cooperative setting under fading channels resulting in gains of up to 3 dB over the standard energy detectors. Following this, the problem of joint adaptation of weights at different sensors is considered. It is seen via simulation that joint adaptation leads to additional gains of nearly 2 dB for a flat fading channel and 1 dB for a dispersive channel, in comparison to local adaptation schemes.

Single channel steepest descent algorithm for the correction of cycle frequency error

“…Most man‐made signals prefer a cyclostationary (CS) model rather than a stationary one, or equivalently, they exhibit spectral correlation , that is correlation between time‐shifted and frequency‐shifted versions of themselves, with the frequency shifts related to their hidden periodicities [1]. In the last decade, beneficial characteristics of signal cyclostationarity have received much attention, and have been intensively exploited in varied domains including signal parameter estimation [2–5], interference suppression or signal separation[1, 6–8], array beamforming [9–12] and recently, spectrum sensing [13–16].…”