Adaptive Channel Sensing And Precoding In Relay-Based Massive MIMO: A Deep Reinforcement Learning Approach

This study presents an innovative approach for the design and optimization of hybrid precoding and relay-based Massive Multiple-Input, Multiple-Output (MIMO) models. The core novelty of our methodology lies in the integration of reinforcement learning algorithms into a hybrid deep learning framework, enabling dynamic adaptation and continuous performance enhancement through feedback-driven learning. Our approach harnesses a hybrid deep learning model that synergizes ResNet50 and AlexNet, effectively mapping received pilot signals into analog and digital precoders. This flexible model caters to both frequency-flat and frequency-selective propagation environments. Moreover, to further fine-tune and optimize system performance, we seamlessly incorporate reinforcement learning algorithms. These algorithms engage with the dynamic wireless environment, constantly adapting system parameters based on real-time feedback, fostering continuous learning and evolution. The synergy of deep learning and reinforcement learning provides a comprehensive and adaptive solution for channel sensing and hybrid precoding. This adaptability proves particularly advantageous in ever-changing wireless environments where channel conditions are subject to fluctuations. By perpetually learning from interactions with the environment and dynamically adjusting system parameters, our proposed methodology holds the promise of significantly enhancing system performance in relay-based Massive MIMO systems.