AI-Driven Beamforming for MIMO Systems: A Deep Reinforcement Learning Approach for Energy-Efficient and Low-Latency Wireless Networks
TL;DR
A Deep Deterministic Policy Gradient-based beamforming framework that formulates beamforming optimization as a continuous-action deep reinforcement learning problem and results validate the effectiveness of the proposed framework for energy-efficient, low-latency beamforming in next-generation massive MIMO wireless networks.
Abstract
: Massive multiple-input multiple-output (MIMO) technology is a key enabler for 5G and beyond wireless networks, offering significant improvements in spectral efficiency and link reliability. However, conventional beamforming techniques such as Zero Forcing (ZF) and Minimum Mean Square Error (MMSE) require complex matrix computations and fail to adapt efficiently to dynamic channel variations. To address these challenges, this paper proposes a Deep Deterministic Policy Gradient (DDPG)-based beamforming framework that formulates beamforming optimization as a continuous-action deep reinforcement learning problem. The proposed model directly generates complex-valued beamforming weight vectors to jointly maximize spectral efficiency (SE) and energy efficiency (EE) while minimizing the bit error rate (BER) and decision latency. An adaptive state representation incorporating channel state information (CSI), previous beamforming vectors, and performance metrics enables real-time policy learning under time-varying channel conditions. Simulation results demonstrate that the proposed method outperforms conventional and heuristic beamforming schemes, achieving up to 25% higher SE, 45% lower BER, 20% improvement in EE, and 30% latency reduction. The results validate the effectiveness of the proposed framework for energy-efficient, low-latency beamforming in next-generation massive MIMO wireless networks.