Performance Optimization of 5G Wireless Networks in Nigeria Using Low-Power Massive MIMO and Intelligent Reflecting Surfaces

Abstract

This paper investigates energy-conscious, performance-optimized strategies for enhancing 5G wireless networks in Nigeria, aiming to address the critical challenges of high operational energy costs and unstable grid infrastructure. The study employed a rigorous methodology using MATLAB simulations to model Massive MIMO systems, Intelligent Reflecting Surfaces (IRS), and adaptive reinforcement learning controllers. The primary objectives were to evaluate power-saving techniques through antenna switching and beam-shaping, assess the trade-offs of low-resolution quantization, and design intelligent resource management systems for hybrid energy environments. The simulation results revealed that energy efficiency in Massive MIMO systems does not scale linearly with hardware size; specifically, a configuration of 20 active antennas was identified as the optimal "sweet spot," achieving a peak efficiency of  before diminishing returns set in. Furthermore, the analysis of Analog-to-Digital Converters (ADCs) demonstrated that power consumption increases by  for every additional bit of resolution, establishing that a mid-range resolution of 4 to 8 bits provides the ideal balance between power economy and data throughput. The study also confirmed that IRS technology could enhance spectral efficiency from  to  using low-precision (3-4 bit) phase shifters, offering a viable solution for extending coverage in shadowed urban and rural areas. Additionally, the reinforcement learning controller successfully minimized reliance on diesel generators by optimizing the usage of grid and battery resources during power fluctuations. The research concludes that sustainable 5G deployment in Nigeria requires a hybrid approach combining dynamic hardware scaling with intelligent, adaptive power control. Consequently, it is recommended that network operators adopt dynamic antenna switching, utilize low-resolution ADCs, and integrate AI-driven power management systems to significantly reduce Operational Expenditure (OPEX) and ensure consistent service quality.