Smart Antenna Array Design with Reinforcement Learning-Based Beam Steering

Smart antenna arrays have emerged as a cornerstone technology for fifth-generation (5G) and beyond wireless communication systems, enabling adaptive beamforming, spatial multiplexing, and interference suppression. Conventional beam steering methods relying on exhaustive codebook search or model-based optimization suffer from high computational overhead and limited adaptability to dynamic channel conditions. This paper presents a comprehensive survey and a novel framework integrating Reinforcement Learning (RL)—specifically Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO)—with phased antenna array architectures for intelligent, real-time beam steering. The proposed approach models the beam selection problem as a Markov Decision Process (MDP) and trains an RL agent to maximize long-term spectral efficiency while minimizing beam misalignment and tracking latency. Simulation results demonstrate that the RL-based approach achieves up to 97.3% beam accuracy and 23% throughput gain over conventional codebook-based methods in highly mobile multipath environments.

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