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

This research seeks to develop a hybrid algorithm to improve channel estimation through the application of statistical methods, such as maximum likelihood estimation (MLE), in conjunction with computational techniques, specifically Particle Swarm Optimization. The goal is to address the inherent constraints and complexities of massive Multiple-Input Multiple-Output (mMIMO) communication networks. For this purpose, several techniques, such as Discrete Fourier Transform (DFT) and Least Square Estimation (LSE), have been proposed and 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 inherent large mean square errors for LSE, reducing accuracy and overall efficiency. Therefore, this paper proposes and explores a mixture of hybrid Particle Swarm Optimization and Maximum Likelihood Estimation (i.e., 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 explicitly clear; the PSO + MLE method demonstrated a significant and overwhelming advantage over conventional techniques. The metrics of the evaluations or benchmarks were Mean Square Error (MSE) and Bit Error Rate (BER). The results show significant improvements in the MSE and BER. The proposed hybrid estimation technique fulfills the criteria of good and accurate performance compared to conventional techniques. In terms of performance efficiency, the results outperform those of the LSE + DFT technique, which is better than the DFT in that order, leading to a more efficient and accurate interpretation of channel estimation in massive MIMO communication networks.