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Neural Network-Based Bandit: A Medium Access Control for the IIoT Alarm Scenario

arXiv 2407.16877 ↗First posted 23 Jul 2024math.OC cs.LGcited by 0

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Abstract (arXiv)

Efficient Random Access (RA) is critical for enabling reliable communication in Industrial Internet of Things (IIoT) networks. Herein, we propose a deep reinforcement learning based distributed RA scheme, entitled Neural Network-Based Bandit (NNBB), for the IIoT alarm scenario. In such a scenario, the devices may detect a common critical event, and the goal is to ensure the alarm information is delivered successfully from at least one device. The proposed NNBB scheme is implemented at each device, where it trains itself online and establishes implicit inter-device coordination to achieve the common goal. Devices can transmit simultaneously on multiple orthogonal channels and each possible transmission pattern constitutes a possible action for the NNBB, which uses a deep neural network to determine the action. Our simulation results show that as the number of devices in the network increases, so does the performance gain of the NNBB compared to the Multi-Armed Bandit (MAB) RA benchmark. For instance, NNBB experiences a 7% success rate drop when there are four channels and the number of devices increases from 10 to 60, while MAB faces a 25% drop.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 45 references that are in the map. First posted on arXiv 23 Jul 2024.

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19 of 45 references.

References · 19

  1. 2022Diffusion Models in Vision: A Surveycited by 1,008
  2. 2019Why gradient clipping accelerates training: A theoretical justification for adaptivitycited by 335
  3. 2017Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networkscited by 37
  4. 2023Diffusion-based Reinforcement Learning for Edge-enabled AI-Generated Content Servicescited by 33
  5. 2021Online Limited Memory Neural-Linear Bandits with Likelihood Matchingcited by 14
  6. 2023TinyML: Tools, Applications, Challenges, and Future Research Directionscited by 14
  7. 2017Deep Multi-User Reinforcement Learning for Distributed Dynamic Spectrum Accesscited by 5
  8. 2023Learning Random Access Schemes for Massive Machine-Type Communication with MARLcited by 3
  9. 2023Energy-Sustainable IoT Connectivity: Vision, Technological Enablers, Challenges, and Future Directionscited by 3
  10. 2023Goal-Oriented Scheduling in Sensor Networks with Application Timing Awarenesscited by 3
  11. 2022Deep Reinforcement Learning for Random Access in Machine-Type Communicationcited by 2
  12. 2022Learning to Speak on Behalf of a Group: Medium Access Control for Sending a Shared Messagecited by 2
7 more
  1. 2022Statistical Tools and Methodologies for Ultrareliable Low-Latency Communications -- A Tutorialcited by 2
  2. 2019Distributed Policy Learning Based Random Access for Diversified QoS Requirementscited by 1
  3. 2021Coordinated Random Access for Industrial IoT With Correlated Traffic By Reinforcement-Learningcited by 1
  4. 2022Reinforcement Learning for Improved Random Access in Delay-Constrained Heterogeneous Wireless Networkscited by 1
  5. 2022The Story of 1/e: ALOHA-based and Reinforcement-Learning-based Random Access for Delay-Constrained Communicationscited by 1
  6. 2023Channel Scheduling for IoT Access with Spatial Correlationcited by 1
  7. 2024Configuring Transmission Thresholds in IIoT Alarm Scenarios for Energy-Efficient Event Reportingcited by 1

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