An Adaptive Antenna Impedance Matching Method via Deep Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning approach for adaptive antenna impedance matching, improving accuracy, efficiency, and stability over traditional methods in mobile communication systems.

Contribution

It models impedance tuning as an optimal control problem and designs a tailored DRL framework with a novel reward function and exploration mechanism.

Findings

Achieves higher tuning accuracy than conventional methods.

Demonstrates improved efficiency and stability in impedance matching.

Reduces local optimal trapping and high-frequency tuning variance.

Abstract

Adaptive impedance matching between antennas and radio frequency front-end modules is critical for maximizing power transmission efficiency in mobile communication systems. Conventional numerical and analytical methods struggle with a trade-off between accuracy and efficiency, while deep neural network (DNN)-based supervised learning approaches rely heavily on large labeled datasets and lack flexibility for dynamic environments. To address these limitations, this paper proposes a deep reinforcement learning (DRL)-based approach for adaptive impedance matching. First, we model the impedance tuning problem as an optimal control problem, proving the feasibility of solving the optimal control law via reinforcement learning. Then, we design a tailored DRL framework for impedance tuning, which employs a compact state representation that integrates key frequency characteristics and matching quality metrics. Additionally, this framework incorporates a piecewise reward function that accounts for both matching accuracy and tuning speed. Furthermore, a test-phase exploration mechanism is introduced to enhance tuning stability, which effectively reduces local optimal trapping and high-frequency tuning variance. Experimental results demonstrate that the proposed method achieves superior performance in terms of tuning accuracy, efficiency, and stability compared with conventional heuristic and gradient-based methods, making it promising for practical impedance tuning systems.