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

arXiv: https://arxiv.org/abs/2607.08272 · HTML: https://mlsanity.com/paper/2607.08272/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 10 of its 39 references that are in the map.
- First posted on arXiv 9 Jul 2026.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 10 of 10)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Multi-Modal Fusion Transformer for End-to-End Autonomous Driving](https://mlsanity.com/paper/2104.09224/) (2021)
- [LQG Control with Minimum Directed Information: Semidefinite Programming Approach](https://mlsanity.com/paper/1510.04214/) (2015)
- [Efficient 3-D Placement of an Aerial Base Station in Next Generation Cellular Networks](https://mlsanity.com/paper/1603.00300/) (2016)
- [Gaussian Approximation of Quantization Error for Estimation from Compressed Data](https://mlsanity.com/paper/2001.03243/) (2020)
- [Near-Field Integrated Sensing and Communications](https://mlsanity.com/paper/2302.01153/) (2023)
- [Edge Information Hub: Orchestrating Satellites, UAVs, MEC, Sensing and Communications for 6G Closed-Loop Controls](https://mlsanity.com/paper/2403.06579/) (2024)
- [Control-Oriented Power Allocation for Integrated Satellite-UAV Networks](https://mlsanity.com/paper/2208.14677/) (2022)
- [Joint Transmit and Receive Beamforming Design in Full-Duplex Integrated Sensing and Communications](https://mlsanity.com/paper/2210.10904/) (2022)
- [Control-Oriented Deep Space Communications For Unmanned Space Exploration](https://mlsanity.com/paper/2308.03658/) (2023)

## Abstract (arXiv)

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.