Performance analysis of precoding schemes for massive multiple-input multiple-output (MIMO) systems

Abstract

The transformative impact of Multiple-Input Multiple-Output (MIMO) technology on terrestrial wireless networks is indisputable, revolutionizing applications ranging from social media (WhatsApp, Twitter, Instagram, Facebook) to video streaming and online gaming. Massive MIMO (M-MIMO), an advanced system scaling up MIMO with hundreds of antennas, has emerged to meet escalating demands for capacity and data throughput. This technology serves multiple user equipment (UEs) concurrently, encompassing mobile phones, tablets, smart cars, smartwatches, smart homes, and intelligent industries, utilising the same timefrequency resources. Despite its superior performance, M-MIMO faces challenges, notably pilot contamination (PiC). PiC occurs when identical pilot sequences interfere across home and adjacent cells, leading to channel estimation errors and degraded system performance. This study investigates the performance of linear and non-linear precoding schemes for mitigation of pilot contamination. The main focus is assessing the efficacy of various precoding strategies in the presence of channel interference errors and explore their impact on critical performance metrics. The recommendation in terms of performance comparison of M-MIMO by this research may be used to further optimise the performance of the M-MIMO systems. Theoretical analyses and simulations are employed to unravel the complexities of precoding schemes, providing a nuanced understanding of their strengths, limitations, and practical implications in wireless communication scenarios. The research confronts challenges encompassing computational complexity, realistic channel modelling, imperfect channel state information (CSI), trade-offs, practical validations, interference management, and standardisation. These challenges necessitate interdisciplinary collaboration, advanced modelling techniques, and realistic simulations in the context of analysing the performance of M-MIMO precoding schemes. The study employs precoding structures such as Zero-Forcing (ZF), Minimum Mean Square Error (MMSE), Neumann Series Approximation (NSA), Lattice Reduction-Lenstra-LenstraLovász (LR-LLL), and Tomlinson-Harashima Precoding (THP) for simulation, comparison, and analysis in the context of M-MIMO. The chosen metrics for evaluation are the bit-error rate (BER), spectral efficiency (SE), signal-to-noise ratio (SNR) and Achievable Sum Rate (ASR). The outcomes of this research unequivocally demonstrate the superior performance of nonlinear precoding over linear precoding in the context of M-MIMO systems. The analysis, conducted using MATLAB® simulations, reveals that nonlinear precoding strategies outperform their linear counterparts in mitigating pilot contamination, addressing channel estimation errors, and enhancing overall system performance.