Optimizing uplink massive MIMO: a proficient analysis of BER and spectral efficiency with power scaling and interference cancellation using MRC, ZF and RZF techniques

Abstract In Multiple Input Multiple Output (MIMO) wireless communication systems, the number of antennas at both transmitter and receiver significantly influences spatial multiplexing and diversity gain, enhancing communication reliability. Massive MIMO systems, characterized by large antenna arrays, improve spectral efficiency (SE) and reduce power consumption while maintaining high reliability. This study proposes an uplink model to optimize massive MIMO system performance, focusing on three metrics: Signal-to-Interference-plus-Noise Ratio (SINR), Bit Error Rate (BER), and SE. The objectives are to analyze SINR as the base station (BS) antenna count increases, assess the impact of antenna count on BER, and evaluate SE in massive MIMO environments. Three linear detection techniques: Maximum-Ratio Combining (MRC), Zero-Forcing (ZF), and Regularized Zero-Forcing (RZF), are utilized for their simplicity and effectiveness in massive MIMO systems. Simulation results demonstrate that SINR improves with increasing antenna count. At 100 BS antennas, ZF achieves approximately 3.5 dB higher SINR than MRC without power scaling, while MRC surpasses ZF in SINR performance when power scaling is applied. For SE, MRC outperforms ZF and RZF at higher SNRs, achieving 170 b/s/Hz at SNR = 30 dB with 256 BS antennas. This superiority arises from MRC’s effective interference cancellation at high SNR. Further analysis under spatially correlated fading channels reveals that MRC leverages spatial correlation to enhance SE at elevated SNRs, outperforming ZF and RZF despite their better interference mitigation capabilities. This study highlights the robustness of MRC in massive MIMO environments, showcasing its superior SE performance under high SNR and spatially correlated conditions, while also validating the effectiveness of ZF and RZF for interference suppression. The results provide valuable insights for the design and optimization of massive MIMO systems in future wireless networks.