Exploring AI-Driven Antenna Optimization Techniques

Abstract

The growing need for effective, flexible, and high-performance wireless communication systems has brought attention to investigating AI-driven antenna optimization methods. Conventional approaches for antenna optimization can depend on heuristic algorithms or manual corrections, which struggle with complicated settings and fail to dynamically adapt to changing circumstances, therefore producing less than ideal performance. It present a framework using Antenna Optimization using Reinforcement Learning (AO-RL) to handle these problems. This method uses intelligent agents to repeatedly maximize antenna characteristics including frequency management, beamforming angles, and power distribution. The AO-RL framework reaches adaptive optimization by interacting with the surroundings and getting incentives as feedback. The suggested approach works for situations including interference reduction and dynamic spectrum control. Experimental results reveal that AO-RL greatly enhances parameters including signal quality, throughput, and energy economy, thereby demonstrating its potential to surpass more traditional methods and satisfy current wireless communication needs.