Spectrum sensing technique of OFDM signal under noise uncertainty based on Mean Ambiguity Function for Cognitive Radio

Abstract In this paper, we consider the Spectrum Sensing ( SS ) problem in Cognitive Radio ( CR ). SS is an important task and one of the main functions of CR , as it has the ability to detect the presence/absence of a Primary User ( i.e. , PU , licensed user) signal in order to allow the Secondary User ( i.e. , SU , unlicensed user) to access PU frequency band using CR . The common purpose of all SS techniques is performing an efficient utilization of the available radio spectrum, thus reducing the spectrum scarcity problem. In this paper, we propose a new SS technique of OFDM (Orthogonal Frequency Division Multiplexing) signal based on Mean Ambiguity Function ( MAF ). Furthermore, we exploited some specific algebraic properties of OFDM signal to propose a test statistics for the detector. The theoretical expressions of the OFDM signal MAF and threshold are also derived. The calculation of threshold expression is based on the Probability Distribution Function ( PDF ) of the proposed test statistics. In addition, we studied the effect of different parameters, e.g. , low SNR (Signal to Noise) values, noise uncertainty and number of OFDM symbol on the detection performance of the proposed detector. Moreover, the obtained simulations results are compared to the Energy Detector (Urkowitz, 1967) and a recent spectrum sensing technique of OFDM signal; DC − OFDM detector (Shi et al., 2015). Furthermore, the proposed detector is also implemented and tested using Universal Software Radio Peripheral ( USRP ) devices.