Detection and estimation of FHSS signals using Enhanced Orthogonal Matching Pursuit

Abstract

Cognitive Radio (CR) can solve the spectrum scarcity problem; also Compressive Sensing (CS) minimizes the required sampling rate lower than the ordinary Nyquist rate. In this work, a combined technique for detection and frequency estimation for Slow Frequency Hopping Spread Spectrum (SFHSS) based on CS and FFT Averaging Ratio (FAR) spectrum sensing algorithm is introduced. The proposed algorithm is merging FAR algorithm and Orthogonal Matching Pursuit (OMP) algorithm in order to enhance the signal detection capability. Using OMP is preferred in the proposed algorithm because it has the minimum complexity and sufficient performance among the most of CS reconstruction algorithms. Since it is composed of OMP and FAR, the proposed algorithm is called Enhanced Orthogonal Matching Pursuit (EOMP). EOMP operation is compared with both Incoherent Detection and Estimation Algorithm (IDEA) and Cosine Similarity (COSSIM) techniques for detection and frequency estimation for single primary user. EOMP has a good signal detection and frequency estimation performance compared with IDEA and COSSIM techniques but with increasing in sensing time, which can be compensated using parallel processing.