Machine Learning-Based Hybrid Precoding for Next-Generation Wireless Communication Systems: A Comprehensive Review

The demand for high data rates and low latency in modern wireless communication systems has prompted a paradigm shift toward massive multiple-input multiple-output (MIMO) technology, particularly in millimeter wave (mmWave) and beyond-5G systems. Hybrid precoding, a technique that combines digital and analog precoding, has emerged as a critical enabler for achieving the massive antenna arrays required for these systems while maintaining energy efficiency. This review provides a comprehensive analysis of the recent advancements in machine learning-based hybrid precoding techniques. The paper begins by offering a detailed overview of traditional precoding methodologies, highlighting their limitations in handling the complex spatial domain characteristics of massive MIMO channels. Subsequently, it delves into the emergence of machine learning as a powerful tool for enhancing the efficiency and adaptability of precoding strategies. Various machine learning models, including deep neural networks (DNNs), convolutional neural networks (CNNs), reinforcement learning (RL), and support vector machines (SVMs), are discussed in depth, providing insights into their respective strengths and weaknesses in the context of hybrid precoding.