Optimization and Performance Evaluation of RIS-Integrated Hybrid Precoding in Millimeter Wave Massive MIMO

Abstract

The evolution toward sixth-generation (6G) wireless systems requires extremely high data rates, massive device connectivity, very low latency, and improved energy efficiency. Millimeter-wave (mmWave) communication is considered a key enabler due to its large available bandwidth and ability to support multi-gigabit transmission. Nevertheless, mmWave signals experience high path loss, vulnerability to blockage, and increased implementation complexity caused by large antenna arrays and multiple RF chains.To overcome these limitations, this thesis explores the integration of Reconfigurable Intelligent Surfaces (RIS) with hybrid analogdigital precoding in mmWave Massive MIMO systems. RIS enables intelligent control of signal propagation by adjusting the phase of reflected waves, while hybrid precoding reduces hardware cost and power consumption without significantly degrading performance. A detailed system model, mathematical analysis, and optimization strategies for precoders and RIS phase shifts are presented. Performance evaluation based on spectral efficiency, energy efficiency, and hardware complexity demonstrates that RIS- assisted hybrid architectures provide notable improvements in coverage, achievable rate, and power utilization.