Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks

Abstract

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).

References in corpus (1)

• Distributive Dynamic Spectrum Access through Deep Reinforcement Learning: A Reservoir Computing Based Approach

Cited by in corpus (35)

• Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks
• Machine Learning for Wireless Communications in the Internet of Things: A Comprehensive Survey
• Applications of deep learning in traffic congestion detection, prediction and alleviation: A survey
• Learn to Schedule (LEASCH): A Deep reinforcement learning approach for radio resource scheduling in the 5G MAC layer
• Towards Joint Learning of Optimal MAC Signaling and Wireless Channel Access
• Multi-Access Point Coordination for Next-Gen Wi-Fi Networks Aided by Deep Reinforcement Learning
• Learning-Based Multi-Channel Access in 5G and Beyond Networks with Fast Time-Varying Channels
• A new hope for network model generalization
• Optimizing Throughput Performance in Distributed MIMO Wi-Fi Networks using Deep Reinforcement Learning
• Contention Window Optimization in IEEE 802.11ax Networks with Deep Reinforcement Learning
• Simultaneous Navigation and Radio Mapping for Cellular-Connected UAV with Deep Reinforcement Learning
• Optimizing Resource Allocation for QoS and Stability in Dynamic VLC-NOMA Networks via MARL
• 20 Years of Evolution from Cognitive to Intelligent Communications
• Adversarial jamming attacks and defense strategies via adaptive deep reinforcement learning
• Topology Aware Deep Learning for Wireless Network Optimization
• Green Deep Reinforcement Learning for Radio Resource Management: Architecture, Algorithm Compression and Challenge
• DRL-Based Dynamic Channel Access and SCLAR Maximization for Networks Under Jamming
• Medium Access Control protocol for Collaborative Spectrum Learning in Wireless Networks
• Deep Reinforcement Learning for Multi-Agent Power Control in Heterogeneous Networks
• Deep Reinforcement Learning Based Massive Access Management for Ultra-Reliable Low-Latency Communications
• Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications
• Multi-Agent Deep Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks with Imperfect Channels
• Scalable Deep Reinforcement Learning for Routing and Spectrum Access in Physical Layer
• PolymoRF: Polymorphic Wireless Receivers Through Physical-Layer Deep Learning
• Neural Network-Based Bandit: A Medium Access Control for the IIoT Alarm Scenario
• Detection of Insider Attacks in Distributed Projected Subgradient Algorithms
• Distributed Deep Reinforcement Learning for Collaborative Spectrum Sharing
• Non-Uniform Time-Step Deep Q-Network for Carrier-Sense Multiple Access in Heterogeneous Wireless Networks
• Dynamic Multichannel Access via Multi-agent Reinforcement Learning: Throughput and Fairness Guarantees
• Robust, Deep, and Reinforcement Learning for Management of Communication and Power Networks
• Fundamental Limits of Reinforcement Learning in Environment with Endogeneous and Exogeneous Uncertainty
• Enhancing WiFi Multiple Access Performance with Federated Deep Reinforcement Learning
• Voting-Based Multi-Agent Reinforcement Learning for Intelligent IoT
• Deep Reinforcement Learning for Dynamic Spectrum Sensing and Aggregation in Multi-Channel Wireless Networks
• Reinforcement Learning Random Access for Delay-Constrained Heterogeneous Wireless Networks: A Two-User Case