A Purely Data-Driven Adaptive Impedance Matching Method Robust to Parasitic Effects

Adaptive impedance matching between antennas and radio frequency front-end (RFFE) power modules is essential for mobile communication systems. To address the matching performance degradation caused by parasitic effects in practical tunable matching networks (TMN), this paper proposes a purely data-driven adaptive impedance matching method that avoids trial-and-error physical adjustment. First, we propose the residual enhanced circuit behavior modeling network (RECBM-Net), a deep learning model that maps TMN operating states to their scattering parameters (S-parameters). Then, we formulate the matching process based on the trained surrogate model as a mathematical optimization problem. We employ two classic numerical methods with different online computational overhead, namely simulated annealing particle swarm optimization (SAPSO) and adaptive moment estimation with automatic differentiation (AD-Adam), to search for the matching solution. To further reduce the online inference overhead caused by repeated forward propagation through RECBM-Net, we train an inverse mapping solver network (IMS-Net) to directly predict the optimal solution. Simulation results show that RECBM-Net achieves exceptionally high modeling accuracy. While AD-Adam significantly reduces computational overhead compared to SAPSO, it sacrifices slight accuracy. IMS-Net offers the lowest online overhead while maintaining excellent matching accuracy.