Adaptive Beamforming for Smart Antennas Using Double Deep Q‐Networks With Noisy Exploration and SINR Optimization

ABSTRACT Smart antennas play a crucial role in enhancing wireless communication by improving signal quality, network capacity, and interference management. However, traditional beamforming techniques struggle to adapt to dynamic environments and user mobility. This paper introduces a novel adaptive beamforming approach leveraging double deep Q‐networks (DDQN) with noisy networks and SINR‐based reward optimization. The proposed framework dynamically adjusts beamforming parameters in real time, improving system responsiveness and efficiency. Simulation results illustrate a significant enhancement in the signal‐to‐interference‐plus‐noise ratio (SINR), achieving up to a 30% increase in network capacity and a 25% improvement in signal quality compared to conventional methods. Moreover, the proposed method demonstrates superior adaptability under varying channel conditions, surpassing traditional algorithms in dynamic scenarios. We also tackle challenges such as computational complexity and scalability, offering insights into optimizing reinforcement learning for practical deployment. This study highlights the potential of advanced deep reinforcement learning techniques in transforming smart antenna technologies, setting the stage for more robust and intelligent wireless communication systems.