Abstract

Modern portable electronic devices demand compact, efficient, and adaptive antennas to support multiple wireless standards and ensure consistent connectivity. Traditional antennas are limited by fixed structural properties and bandwidth constraints. To address this, we propose a novel AI-enabled compact metamaterial antenna integrated with a dynamic reconfiguration mechanism tailored for smart portable electronics. The antenna utilizes a planar metamaterial substrate with tunable unit cells controlled by an artificial intelligence (AI) model—specifically a lightweight reinforcement learning (RL) algorithm—to optimize operational parameters based on environmental feedback. The method enables real-time reconfiguration of frequency, radiation pattern, and gain characteristics. Simulations were conducted using CST Microwave Studio, and a hardware prototype was validated through an anechoic chamber. Results demonstrate that the proposed antenna achieves multiband operation from 2.4 GHz to 6 GHz, 50% size reduction compared to traditional antennas, and adaptive beam steering with <1 µs reconfiguration latency. This intelligent design ensures enhanced signal quality, power efficiency, and seamless interoperability in dynamic mobile environments.