Low Complexity Hybrid PSO-BB Detection Algorithm for Massive MIMO System

摘要

Objective: A massive Multiple Input and Multiple Output (MIMO) receiver utilizes the proposed detection algorithm to reduce the complexity. Methods: The existing research work namely Noise and Relevancy aware Low Complexity Detection (NRLCD) algorithm for massive MIMO receiver utilizes normalized cross correlation based pruning strategy to viably evacuate uncorrelated signals. However, the existing research work still has more complexity with increasingly number of iterations to find more relevant signal vector. In this research paper, it is proposed to investigate execution of massive MIMO system utilizing Continuous Phase Modulation (CPM) modulation which is used to carry out signal modulation. Then, Hybrid Particle Swarm Optimization-and-Branch-and-Bound (Hybrid PSO-BB) algorithm is proposed for low complexity detection. Findings: CPM demonstrated to give superior performance over Quadrature Amplitude Modulation (QAM) technique with the presence of phase noises. Hybrid PSO-BB is anticipated; in which the best attainable solution were found and renewed using PSO. The performance assessment of the proposed research work and existing methods is done under Adaptive Additive Gaussian Channel (AWGN) using MATLAB Communication tool box. Improvements: From the simulation results, it is inferred that the Hybrid PSO-BB algorithm is superior to the existing methods in-terms of Bit Error Rate (BER) performance and complexity.