# Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks

arXiv: https://arxiv.org/abs/1712.00162 · HTML: https://mlsanity.com/paper/1712.00162/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2017.

## Built on by (most cited first, 30 of 37)

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- [3D UAV Trajectory and Data Collection Optimisation via Deep Reinforcement Learning](https://mlsanity.com/paper/2106.03129/) (2021)
- [Cooperative Multi-Agent Reinforcement Learning Based Distributed Dynamic Spectrum Access in Cognitive Radio Networks](https://mlsanity.com/paper/2106.09274/) (2021)
- [Multi-Flow Transmission in Wireless Interference Networks: A Convergent Graph Learning Approach](https://mlsanity.com/paper/2303.15544/) (2023)
- [Towards Multi-agent Reinforcement Learning for Wireless Network Protocol Synthesis](https://mlsanity.com/paper/2102.01611/) (2021)
- [Dynamic Multichannel Access via Multi-agent Reinforcement Learning: Throughput and Fairness Guarantees](https://mlsanity.com/paper/2105.04077/) (2021)
- [Multiple Access in the Era of Distributed Computing and Edge Intelligence](https://mlsanity.com/paper/2403.07903/) (2024)
- [Can You Fix My Neural Network? Real-Time Adaptive Waveform Synthesis for Resilient Wireless Signal Classification](https://mlsanity.com/paper/2103.03745/) (2021)
- [Consensus-based Decentralized Multi-agent Reinforcement Learning for Random Access Network Optimization](https://mlsanity.com/paper/2508.07001/) (2025)
- [Self-Sustaining Multiple Access with Continual Deep Reinforcement Learning for Dynamic Metaverse Applications](https://mlsanity.com/paper/2309.10177/) (2023)
- [Distributed Deep Reinforcement Learning for Collaborative Spectrum Sharing](https://mlsanity.com/paper/2104.02059/) (2021)
- [Medium Access using Distributed Reinforcement Learning for IoTs with Low-Complexity Wireless Transceivers](https://mlsanity.com/paper/2104.14549/) (2021)
- [Medium Access Control protocol for Collaborative Spectrum Learning in Wireless Networks](https://mlsanity.com/paper/2111.12581/) (2021)
- [Distributed Transmission Control for Wireless Networks using Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2205.06800/) (2022)
- [Learning-based Autonomous Channel Access in the Presence of Hidden Terminals](https://mlsanity.com/paper/2207.03605/) (2022)
- [Deep Reinforcement Learning for Power Control in Next-Generation WiFi Network Systems](https://mlsanity.com/paper/2211.01107/) (2022)
- [Generalization of Deep Reinforcement Learning for Jammer-Resilient Frequency and Power Allocation](https://mlsanity.com/paper/2302.02250/) (2023)
- [FAIRO: Fairness-aware Adaptation in Sequential-Decision Making for Human-in-the-Loop Systems](https://mlsanity.com/paper/2307.05857/) (2023)
- [Dynamic Spectrum Access for Ambient Backscatter Communication-assisted D2D Systems with Quantum Reinforcement Learning](https://mlsanity.com/paper/2410.17971/) (2024)
- [Detection of Insider Attacks in Distributed Projected Subgradient Algorithms](https://mlsanity.com/paper/2101.06917/) (2021)
- [Fundamental Limits of Reinforcement Learning in Environment with Endogeneous and Exogeneous Uncertainty](https://mlsanity.com/paper/2106.08477/) (2021)
- [AI-aided Traffic Control Scheme for M2M Communications in the Internet of Vehicles](https://mlsanity.com/paper/2204.03504/) (2022)
- [Reinforcement Learning for Protocol Synthesis in Resource-Constrained Wireless Sensor and IoT Networks](https://mlsanity.com/paper/2302.05300/) (2023)
- [Evolution of MAC Protocols in the Machine Learning Decade: A Comprehensive Survey](https://mlsanity.com/paper/2302.13876/) (2023)
- [Sparsity-Aware Intelligent Massive Random Access Control in Open RAN: A Reinforcement Learning Based Approach](https://mlsanity.com/paper/2303.02657/) (2023)
- [FinA: Fairness of Adverse Effects in Decision-Making of Human-Cyber-Physical-System](https://mlsanity.com/paper/2311.03468/) (2023)
- [Prioritized League Reinforcement Learning for Large-Scale Heterogeneous Multiagent Systems](https://mlsanity.com/paper/2403.18057/) (2024)

## Abstract (arXiv)

This paper investigates the use of deep reinforcement learning (DRL) in a MAC protocol for heterogeneous wireless networking referred to as Deep-reinforcement Learning Multiple Access (DLMA). The thrust of this work is partially inspired by the vision of DARPA SC2, a 3-year competition whereby competitors are to come up with a clean-slate design that "best share spectrum with any network(s), in any environment, without prior knowledge, leveraging on machine-learning technique". Specifically, this paper considers the problem of sharing time slots among a multiple of time-slotted networks that adopt different MAC protocols. One of the MAC protocols is DLMA. The other two are TDMA and ALOHA. The nodes operating DLMA do not know that the other two MAC protocols are TDMA and ALOHA. Yet, by a series of observations of the environment, its own actions, and the resulting rewards, a DLMA node can learn an optimal MAC strategy to coexist harmoniously with the TDMA and ALOHA nodes according to a specified objective (e.g., the objective could be the sum throughput of all networks, or a general alpha-fairness objective).