Development of a Hybrid Algorithm for an Improved Channel Estimation in Massive Mimo Communication Network

This research seeks to develop a hybrid algorithm for an improved channel estimation through the application of statistical methods, such as maximum likelihood estimation (MLE), in conjunction with computational techniques, specifically Particle Swarm Optimization. For this purpose, several techniques, such as Discrete Fourier Transform (DFT) and Least Square Estimation (LSE), have been proposed or adopted for channel estimation with varying performances for different QoS indices. The limitations of these techniques include constraints in the time domain for DFT and susceptibility to noise or interference due to the inherent large mean square errors for LSE, reducing the accuracy and overall efficiency. Therefore, this paper proposes and explores a mixture of hybrid Particle Swarm Optimization and Maximum Likelihood Estimation (PSO+MLE) for channel estimation to minimize and eliminate pilot contamination and related problems associated with noise and interference. To gauge and evaluate the effectiveness of this hybrid mix method, comparisons were made with conventional existing techniques, such as DFT only and LSE combined with DFT (i.e., LSE+DFT). The results were explicit; the PSO + MLE method demonstrated a significant and overwhelming advantage over conventional techniques. The metrics of the evaluations or benchmarks were the Mean Square Error (MSE) and Bit Error Rate (BER). The results show significant improvements in the MSE and BER values.