Deep Reinforcement Learning-Empowered Wireless Sensor Networking for 6G Closed-Loop Controls

Jul 16, 2026

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Robots increasingly perform mission-critical control in remote or hazardous settings but rely on field sensors and an edge information hub (EIH) to exchange information, analyze sensing data, and generate control commands within a sensing-communication-computing-control (SC3) closed loop. To improve overall closed-loop performance, the work minimizes linear quadratic regulator (LQR) control cost by optimizing sensor-to-EIH bandwidth allocation. Distortion from limited communication rate is modeled via mutual information, then the control process is cast as a POMDP and learned with a PPO-trained DRL agent, using a Kalman-filter plus LQR control policy. Simulations validate the proposed scheme’s superiority.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

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Deep Reinforcement Learning-Empowered Wireless Sensor Networking for 6G Closed-Loop Controls Chengleyang Lei, Wei Feng, Senior Member, IEEE, Yunfei Chen, Fellow, IEEE, Yongxu Zhu, Senior Member, IEEE, Ning Ge, Member, IEEE, and Shi Jin, Fellow, IEEE arXiv :2607 .08272v 1 [ cs .IT] 9 Jul 2026 Abstract—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-computingcontrol (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. Index Terms—Bandwidth allocation, edge information hub (EIH), linear quadratic regulator (LQR), sensingcommunication-computing-control (SC3) closed loop. I. INTRODUCTION THANKS to the advances in robotics and artificial in telligence, autonomous robots are increasingly deployed in remote areas to perform mission-critical tasks, such as emergency rescue [1], scientific exploration, and oil extraction [2] . These robots usually rely on sensors to obtain global environmental information and communication networks to enable information exchange [3] . However, the terrestrial infrastructures are usually unavailable to serve robots in remote areas due to the harsh geographical conditions. In such scenarios, autonomous aerial vehicles (AAVs, also known as UAVs) can be equipped with devices integrating communication and computing modules that serve as edge information hubs (EIHs) to provide edge intelligence for the robots [4] . In addition, a digital twin can be deployed on the EIH, as a virtual representation of the physical control system [5], [6] . During the control process, the sensors collect environmental information and transmit it to the EIH. Subsequently, the C. Lei, W. Feng, and N. Ge are with the Department of Electronic Engineering, State Key Laboratory of Space Network and Communications, Tsinghua University, Beijing 100084, China (email: [lcly21@mails.tsinghua.edu.cn](lcly21@mails.tsinghua.edu.cn), [fengwei@tsinghua.edu.cn](fengwei@tsinghua.edu.cn), [gening@tsinghua.edu.cn](gening@tsinghua.edu.cn)). Y. Chen is with the Department of Engineering, University of Durham, DH1 3LE Durham, U.K. (e-mail: [yunfei.chen@durham.ac.uk](yunfei.chen@durham.ac.uk)). Y. Zhu and S. Jin are with the National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China (email: [yongxu.zhu@seu.edu.cn](yongxu.zhu@seu.edu.cn), [jinshi@seu.edu.cn](jinshi@seu.edu.cn)). EIH processes this data to analyze the current state of the target, update the target digital twin model, and generate corresponding control commands. These control commands are then transmitted to the robots to guide their actions. The above process is performed periodically, forming a sensingcommunication-computing-control (SC3 ) closed loop [7] . The overall performance and reliability of the robotic tasks depend critically on the efficiency and integrity of the SC3 closed loop. Due to the inherent limitation on the payload capacity of UAVs, the communication

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Which learning and control components are used to realize the approach?

The control process is modeled as a POMDP and solved using deep reinforcement learning with a PPO-trained agent, while the underlying control policy uses a Kalman filter combined with an LQR controller.

Jul 16, 2026

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