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

arXiv: https://arxiv.org/abs/2407.16877 · HTML: https://mlsanity.com/paper/2407.16877/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 45 references that are in the map.
- First posted on arXiv 23 Jul 2024.

## Builds on (most cited first, 19 of 19)

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- [Why gradient clipping accelerates training: A theoretical justification for adaptivity](https://mlsanity.com/paper/1905.11881/) (2019)
- [Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks](https://mlsanity.com/paper/1712.00162/) (2017)
- [Diffusion-based Reinforcement Learning for Edge-enabled AI-Generated Content Services](https://mlsanity.com/paper/2303.13052/) (2023)
- [Online Limited Memory Neural-Linear Bandits with Likelihood Matching](https://mlsanity.com/paper/2102.03799/) (2021)
- [TinyML: Tools, Applications, Challenges, and Future Research Directions](https://mlsanity.com/paper/2303.13569/) (2023)
- [Deep Multi-User Reinforcement Learning for Distributed Dynamic Spectrum Access](https://mlsanity.com/paper/1704.02613/) (2017)
- [Goal-Oriented Scheduling in Sensor Networks with Application Timing Awareness](https://mlsanity.com/paper/2306.03750/) (2023)
- [Learning Random Access Schemes for Massive Machine-Type Communication with MARL](https://mlsanity.com/paper/2302.07837/) (2023)
- [Energy-Sustainable IoT Connectivity: Vision, Technological Enablers, Challenges, and Future Directions](https://mlsanity.com/paper/2306.02444/) (2023)
- [Deep Reinforcement Learning for Random Access in Machine-Type Communication](https://mlsanity.com/paper/2201.09841/) (2022)
- [Learning to Speak on Behalf of a Group: Medium Access Control for Sending a Shared Message](https://mlsanity.com/paper/2202.00401/) (2022)
- [Statistical Tools and Methodologies for Ultrareliable Low-Latency Communications -- A Tutorial](https://mlsanity.com/paper/2212.03292/) (2022)
- [Distributed Policy Learning Based Random Access for Diversified QoS Requirements](https://mlsanity.com/paper/1903.02242/) (2019)
- [Coordinated Random Access for Industrial IoT With Correlated Traffic By Reinforcement-Learning](https://mlsanity.com/paper/2109.08389/) (2021)
- [Reinforcement Learning for Improved Random Access in Delay-Constrained Heterogeneous Wireless Networks](https://mlsanity.com/paper/2205.02057/) (2022)
- [The Story of 1/e: ALOHA-based and Reinforcement-Learning-based Random Access for Delay-Constrained Communications](https://mlsanity.com/paper/2206.09779/) (2022)
- [Channel Scheduling for IoT Access with Spatial Correlation](https://mlsanity.com/paper/2312.02176/) (2023)
- [Configuring Transmission Thresholds in IIoT Alarm Scenarios for Energy-Efficient Event Reporting](https://mlsanity.com/paper/2407.03982/) (2024)

## 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.