Learning-based Autonomous Channel Access in the Presence of Hidden Terminals
Not on the map. Why?
Abstract (arXiv)
We consider the problem of autonomous channel access (AutoCA), where a group of terminals tries to discover a communication strategy with an access point (AP) via a common wireless channel in a distributed fashion. Due to the irregular topology and the limited communication range of terminals, a practical challenge for AutoCA is the hidden terminal problem, which is notorious in wireless networks for deteriorating the throughput and delay performances. To meet the challenge, this paper presents a new multi-agent deep reinforcement learning paradigm, dubbed MADRL-HT, tailored for AutoCA in the presence of hidden terminals. MADRL-HT exploits topological insights and transforms the observation space of each terminal into a scalable form independent of the number of terminals. To compensate for the partial observability, we put forth a look-back mechanism such that the terminals can infer behaviors of their hidden terminals from the carrier sensed channel states as well as feedback from the AP. A window-based global reward function is proposed, whereby the terminals are instructed to maximize the system throughput while balancing the terminals' transmission opportunities over the course of learning. Extensive numerical experiments verified the superior performance of our solution benchmarked against the legacy carrier-sense multiple access with collision avoidance (CSMA/CA) protocol.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 16 of its 42 references that are in the map. First posted on arXiv 7 Jul 2022.
Builds on
16 of 42 references.
References · 16
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
- 2017Counterfactual Multi-Agent Policy Gradientscited by 751
- 2018QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 708
- 2019Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithmscited by 567
- 2015Deep Recurrent Q-Learning for Partially Observable MDPscited by 465
- 2017Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networkscited by 37
- 2020Towards Joint Learning of Optimal MAC Signaling and Wireless Channel Accesscited by 18
- 2021The Emergence of Wireless MAC Protocols with Multi-Agent Reinforcement Learningcited by 15
- 2020Adaptive Contention Window Design using Deep Q-learningcited by 13
- 2019A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Accesscited by 8
- 2020Contention Window Optimization in IEEE 802.11ax Networks with Deep Reinforcement Learningcited by 6
4 more
- 2020Multi-Agent Deep Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks with Imperfect Channelscited by 5
- 2020Partially Observable Minimum-Age Scheduling: The Greedy Policycited by 5
- 2019Non-Uniform Time-Step Deep Q-Network for Carrier-Sense Multiple Access in Heterogeneous Wireless Networkscited by 3
- 2020Sporadic Ultra-Time-Critical Crowd Messaging in V2Xcited by 2
Built on by
Citing papers per year: 2022: 1.
1 paper.