mlsanityData to 7 Oct 2026

Structure-Enhanced DRL for Optimal Transmission Scheduling

arXiv 2212.12704 ↗First posted 24 Dec 2022cs.IT cs.AI cs.LGcited by 6

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Abstract (arXiv)

Remote state estimation of large-scale distributed dynamic processes plays an important role in Industry 4.0 applications. In this paper, we focus on the transmission scheduling problem of a remote estimation system. First, we derive some structural properties of the optimal sensor scheduling policy over fading channels. Then, building on these theoretical guidelines, we develop a structure-enhanced deep reinforcement learning (DRL) framework for optimal scheduling of the system to achieve the minimum overall estimation mean-square error (MSE). In particular, we propose a structure-enhanced action selection method, which tends to select actions that obey the policy structure. This explores the action space more effectively and enhances the learning efficiency of DRL agents. Furthermore, we introduce a structure-enhanced loss function to add penalties to actions that do not follow the policy structure. The new loss function guides the DRL to converge to the optimal policy structure quickly. Our numerical experiments illustrate that the proposed structure-enhanced DRL algorithms can save the training time by 50% and reduce the remote estimation MSE by 10% to 25% when compared to benchmark DRL algorithms. In addition, we show that the derived structural properties exist in a wide range of dynamic scheduling problems that go beyond remote state estimation.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 18 of its 41 references that are in the map. First posted on arXiv 24 Dec 2022.

Builds on

18 of 41 references.

References · 18

  1. 2015Continuous control with deep reinforcement learningcited by 3,279
  2. 2022Semantic Communications for Future Internet: Fundamentals, Applications, and Challengescited by 94
  3. 2014Convolutional Neural Networks at Constrained Time Costcited by 81
  4. 2018Deep Reinforcement Learning for Wireless Sensor Scheduling in Cyber-Physical Systemscited by 21
  5. 2017Wireless Network Design for Control Systems: A Surveycited by 13
  6. 2019Directed Exploration for Reinforcement Learningcited by 9
  7. 2018DeepCAS: A Deep Reinforcement Learning Algorithm for Control-Aware Schedulingcited by 8
  8. 2020Remote State Estimation with Smart Sensors over Markov Fading Channelscited by 7
  9. 2022DRL-based Resource Allocation in Remote State Estimationcited by 7
  10. 2022Deep Learning for Wireless Networked Systems: a joint Estimation-Control-Scheduling Approachcited by 7
  11. 2016Optimal Sensor Scheduling for Multiple Linear Dynamical Systemscited by 5
  12. 2019Optimal Downlink-Uplink Scheduling of Wireless Networked Control for Industrial IoTcited by 5
6 more
  1. 2021Joint Resource Management for MC-NOMA: A Deep Reinforcement Learning Approachcited by 5
  2. 2018Optimal Scheduling of Multiple Sensors over Lossy and Bandwidth Limited Channelscited by 4
  3. 2021Stability Conditions for Remote State Estimation of Multiple Systems over Multiple Markov Fading Channelscited by 4
  4. 2021Deep Reinforcement Learning for Wireless Scheduling in Distributed Networked Controlcited by 4
  5. 2018Learning Optimal Scheduling Policy for Remote State Estimation under Uncertain Channel Conditioncited by 2
  6. 2022Structure-Enhanced Deep Reinforcement Learning for Optimal Transmission Schedulingcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 3; 2026: 1.

6 papers.

  1. 2025Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Modelscited by 4
  2. 2023Semantic-aware Transmission Scheduling: a Monotonicity-driven Deep Reinforcement Learning Approachcited by 1
  3. 2025Belief-Conditioned One-Step Diffusion: Real-Time Trajectory Planning with Just-Enough Sensingcited by 1
  4. 2024Wireless Human-Machine Collaboration in Industry 5.0cited by 0
  5. 2025Goal-oriented Transmission Scheduling: Structure-guided DRL with a Unified Dual On-policy and Off-policy Approachcited by 0
  6. 2026Digital Twin-Driven Adaptive Sim-to-Real Alignment via Reinforcement Learning for Vibration-Based Bearing Health Monitoring Under Data Scarcitycited by 0