Design and Optimization of Hybrid Precoders in Massive MIMO Systems: Leveraging Low-Resolution ADCs/DACs, Reconfigurable Intelligent Surfaces, and Deep Learning Algorithms

Abstract

In this research, we propose an advanced hybrid precoding design for massive MIMO systems. Our approach integrates two innovative strategies: employing low-resolution ADCs and DACs with non-uniform quantization (NniQ) and implementing dynamic hybrid relay reconfigurable intelligent surfaces (DHRR-RIS). We utilize an iterative alternating minimization algorithm to improve spectral and energy efficiency in the first strategy. The second approach integrates DHRR-RIS with machine learning techniques, including adaptive back propagation neural network (ABPNN) for channel estimation, deep deterministic policy gradient (DDPG) algorithm for hybrid precoding and combining, fire hawk optimization (FHO) for DHRR-RIS, and enhanced fuzzy C-means (EFCM) for data clustering. These methodologies significantly enhance bit error rate (BER) and weighted sum rate (WSR) compared to traditional uniform quantization (UniQ) methods. Our results show that combining low-resolution ADCs/DACs with NniQ and DHRR-RIS, further optimized by machine learning, effectively reduces hardware complexity and power usage while markedly improving BER and WSR, offering a promising direction for efficient massive MIMO system development.