6G閉ループ制御のための深層強化学習を用いたワイヤレスセンサネットワーク
Deep Reinforcement Learning-Empowered Wireless Sensor Networking for 6G Closed-Loop Controls
遠隔地や危険な環境で動作するロボットの制御性能を最適化するため、センサからエッジ情報ハブへの帯域割り当てを深層強化学習（PPO）で学習する手法を提案した。
著者: Chengleyang Lei, Wei Feng, Yunfei Chen, Yongxu Zhu, Ning Ge, Shi Jin
分類: cs.IT, eess.SY
原文アブストラクト
Robots are increasingly deployed in remote or hazardous areas for mission-critical control tasks. Due to their limited individual capabilities, they have to rely on other field sensors to obtain the state information of targets, and also a dedicated edge information hub (EIH) to enable information exchange, sensing data analysis and control command generation. Such configuration follows a sensing-communication-computing-control (SC3) closed loop. To optimize the whole closed-loop performance, this paper minimizes the linear quadratic regulator (LQR) control cost by designing the sensor-to-EIH bandwidth allocation. Specifically, we first model the distortion noise caused by limited communication data rate based on the mutual information theory. Next, under the control policy based on the Kalman filter and LQR controller, we formulate the control process as a partially observable Markov decision process (POMDP), and develop a deep reinforcement learning (DRL)-based sensor-to-EIH bandwidth allocation scheme. The proximal policy optimization (PPO) algorithm is utilized to train the DRL agent. Simulation results are provided to show the superiority of the proposed DRL-based scheme.