Data Compression Using Optimized Compressive Sensing in Cognitive Radio Network

ABSTRACT In cognitive radio networks, cooperative spectrum sensing can greatly improve detection performance and reduce test time because cognitive users work cooperatively to detect spectrum signals. However, it is important to reduce the number of measurements taken from the original signal. To overcome these challenges, this study proposes optimized compressive sensing (CS) to reduce processing time and speed up spectrum sensing. To optimize the performance of CS, the penguin optimization algorithm (POA) is used in this approach. The optimal CS matrix is selected using this algorithm. In this algorithm, mean square error (MSE) is considered an objective function. It selects the CS matrix with the minimum error as the optimal matrix. Step size optimized sparsity adaptive matching pursuit algorithm (SSOSAMP) is used to reconstruct the compressed signal using the optimized sensitivity matrix. MATLAB is used to implement this proposed approach. Simulation results show that the proposed system achieves an SNR of 27.43%, a reconstruction probability of 94.28%, and an MSE of 4.27%.