FEARL: AI-Assisted Energy-Aware Real-Time Receiver Adaptation to Dynamic Environments

Internet of Things (IoT) devices need to continuously adapt their wireless receiver based on the varying communication conditions to achieve the right trade-off between system-level performance and energy efficiency. Even though the environmentadaptability functions are developed in the back end of receiver chains, existing front-ends are designed for a fixed communication standard. The only adaptive wireless systems equipped with tunable front-ends use traditional optimization methods to reconfigure their circuit. The first issue is that these methods require a significant time to converge to the optimal configuration. The second limitation is that they cannot generalize to unseen propagation environments and operational conditions. In this paper, we propose Front-End Adaptation with Reinforcement Learning (FEARL) to dynamically optimize front-end circuits considering the ongoing distortion and interference levels with the aim to achieve optimal wireless system-level performance. FEARL characterizes the wireless link quality using the received baseband samples and reconfigures the front-end in real-time to realize an end-to-end optimized energy-efficient receiver. We developed a framework with the circuit simulator-in-the-loop, which is further utilized to train and evaluate the FEARL policy. The results show that FEARL is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$7.8 x$</tex> times faster in responding to variations in the wireless environment, and is able to find a satisfactory circuit configuration even in unseen conditions.