Enhanced Spectrum Sensing Algorithm Based on MME Detection and OAS Shrinkage Estimator

Enhanced Spectrum Sensing Algorithm Based on MME Detection and OAS Shrinkage Estimator

Amoon Khalil (HISAT, Damascus, Syria) and Mohiedin Wainakh (HIAST, Damascus, Syria)
DOI: 10.4018/IJERTCS.2019010105

Abstract

Spectrum Sensing is one of the major steps in Cognitive Radio. There are many methods to conduct Spectrum Sensing. Each method has different detection performances. In this article, the authors propose a modification of one of these methods based on MME algorithm and OAS estimator. In MME&OAS method, in each detection window, OAS estimates the covariance matrix of the signal then the MME algorithm detects the signal on the estimated matrix. In the proposed algorithm, authors assumed that there is correlation between two consecutive decisions, so authors suggest the OAS estimator depending on the last detection decision, and then detect the signal using MME algorithm. Simulation results showed enhancement in detection performance (about 2dB when detection probability is 0.9. compared to MME&OAS method).
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2. System Model And Problem Formulation

SUs have to sense the PU's signals, so we have to define some signals and propose a model system. Let the received continuous time signal be IJERTCS.2019010105.m01, where IJERTCS.2019010105.m02 is the possible primary user's signal and IJERTCS.2019010105.m03 is the noise signal. Assume that IJERTCS.2019010105.m04 is a stationary process satisfying IJERTCS.2019010105.m05, IJERTCS.2019010105.m06 and IJERTCS.2019010105.m07 for any IJERTCS.2019010105.m08. We assume that the operating frequency is IJERTCS.2019010105.m09, the bandwidth W, and sampling frequency of the received signal is IJERTCS.2019010105.m10. Define IJERTCS.2019010105.m11, IJERTCS.2019010105.m12 and IJERTCS.2019010105.m13 where IJERTCS.2019010105.m14 the sampling period (Mitola, 2000).

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