Performance Analysis of massive MIMO based CR using Maximum-Minimum subband energy detection and receiver diversity

Abstract

Cognitive radio is an emerging solution to the spectrum shortage problem .In CR, spectrum sensing is one of the important tasks.However, it faces many challenges, such as hardware implementations, synchronization, complexity, a low SNR envi ronment, noise uncertainties, and hidden problems. In this thesis, we proposed Maximum-Minimum subband energy detection for a CR-based Massive MIMO system using antenna combining techniques that over come noise uncertainty problems, and perform adequately even in low SNR condi tions. We have seen two spectrum sensing techniques in which antenna combining techniques such as maximal ratio combing and selection combining were applied to Maximum-Minimum Subband Energy detection. The first technique is increas ing the probability of detection by an increase in SNR, followed by the number of antennas. The second technique is selection combining, which focuses on reduc ing the probability of false alarm and the probability of missed detection. So, we use antenna combining techniques to combine antennas and Maximum-Minimum Subband Energy detection to decide whether the primary user is present or not. The MATLAB software is used to verify the theoretical analysis. The simulation results showed that the proposed techniques improve the probabil ity of detection by 8% because of the use of massive MIMO antennas. The sensing performance depends on the number of antennas. As the number of antennas increases, the detection performance improves.