Abstract
Closed-Loop Antenna Impedance Tuning (CL-AIT) is a technique to adaptively reduce the antenna impedance mismatch by monitoring changes in the antenna impedance and adjusting a tunable matching network (or tuner). This work presents a data-driven CL-AIT solution to find the optimal code for configuring the tuner in order to maximize the power transferred to the antenna; it also minimizes the need for extensive antenna characterization required normally for tuners. A key idea is to learn a transfer function representing the reflection coefficient from data by utilizing the tuner topology. Once the data-driven cost function becomes available, the optimal tuner code is efficiently found with the help of hill-climbing (HC) search. Experimental results demonstrate that the proposed solution sufficiently reduces the impedance mismatch under various antenna conditions and maximum power is delivered to antenna.
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