Compressive sensing based on novel chaotic matrix for cognitive radio. (5th January 2022) - Abed, Hadeel S.; Abdullah, Hikmat N.
Summary: Cognitive radio (CR) is an emerging technology for optimum spectrum utilization. Spectrum sensing (SS) is the first step of CR. Important challenge in the next generations is wideband SS. Compressive sensing (CS) can be used as an SS in CR to solve this challenge. Sensing matrix (SM) plays an important role in CS. For better performance, SM must have low mutual coherence. The existing matrices in the literature are random and chaotic. All random matrices have a good performance, but they have problems of large memory storage, complexity, sharing bandwidth, and low security. Chaotic matrices (CMs) are the best but still have some problems including the use of sample distance in chaotic sequence that consumes high storage resources, low performance and compression under low signal‐to‐noise ratio (SNR), and low security. In this paper, we propose a new 1‐D chaotic map for constructing a novel SM. The proposed map has simple structure, wide range chaotic behavior, and high security. Its chaotic behavior is confirmed using Lyapunov exponent, bifurcation, and trajectory. The CS performance based on novel CM is measured using absolute error ( Abserr ), mutual coherence, memory cost, computational complexity, and MSE (mean square error), while evaluated through comparisons with matrices in the literature. The simulation and mathematical results show that the proposed map is a hyperchaotic with efficient chaotic behavior in wide range of parameters and low memory storage and complexity. The results also show that it has low Abserr, MSE, and mutual coherence at low SNR with high compression. Abstract : The proposed chaotic map has a simple structure, wide range chaotic behavior, high security, hyper chaotic with efficient chaotic behavior in wide range of parameters, and low memory storage and complexity. The sensing matrix‐based proposed map improves the performance of compressive spectrum sensing in cognitive radio. The simulation results show that the compressive sensing‐based proposed chaotic matrix has low Abserr, MSE, and mutual coherence at low SNR with high compression.