mlsanityData to 14 Oct 2026

Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networks

arXiv 1802.06958 ↗First posted 20 Feb 2018cs.NIcited by 37

Not on the map. Why?

Abstract (arXiv)

We consider a dynamic multichannel access problem, where multiple correlated channels follow an unknown joint Markov model. A user at each time slot selects a channel to transmit data and receives a reward based on the success or failure of the transmission. The objective is to find a policy that maximizes the expected long-term reward. The problem is formulated as a partially observable Markov decision process (POMDP) with unknown system dynamics. To overcome the challenges of unknown system dynamics as well as prohibitive computation, we apply the concept of reinforcement learning and implement a Deep Q-Network (DQN) that can deal with large state space without any prior knowledge of the system dynamics. We provide an analytical study on the optimal policy for fixed-pattern channel switching with known system dynamics and show through simulations that DQN can achieve the same optimal performance without knowing the system statistics. We compare the performance of DQN with a Myopic policy and a Whittle Index-based heuristic through both simulations as well as real-data trace and show that DQN achieves near-optimal performance in more complex situations. Finally, we propose an adaptive DQN approach with the capability to adapt its learning in time-varying, dynamic scenarios.

Key facts. Cited by 37 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 Feb 2018.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 16; 2022: 4; 2023: 4; 2024: 6; 2025: 2; 2026: 5.

37 papers.

Sort:

By area:

  1. 2021ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platformscited by 35
  2. 2021Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Surveycited by 34
  3. 2021Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Surveycited by 28
  4. 2021NeurWIN: Neural Whittle Index Network For Restless Bandits Via Deep RLcited by 27
  5. 2021Link Scheduling using Graph Neural Networkscited by 26
  6. 2022Programmable and Customized Intelligence for Traffic Steering in 5G Networks Using Open RAN Architecturescited by 12
  7. 2024Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectoriescited by 9
  8. 2021Deep Reinforcement Learning for Simultaneous Sensing and Channel Access in Cognitive Networkscited by 8
  9. 2021Cooperative Multi-Agent Reinforcement Learning Based Distributed Dynamic Spectrum Access in Cognitive Radio Networkscited by 7
  10. 2021Joint Deep Reinforcement Learning and Unfolding: Beam Selection and Precoding for mmWave Multiuser MIMO with Lens Arrayscited by 6
  11. 2023Multi-Flow Transmission in Wireless Interference Networks: A Convergent Graph Learning Approachcited by 6
  12. 2024SINR-Aware Deep Reinforcement Learning for Distributed Dynamic Channel Allocation in Cognitive Interference Networkscited by 5
25 more
  1. 2021Dynamic Multichannel Access via Multi-agent Reinforcement Learning: Throughput and Fairness Guaranteescited by 4
  2. 2021Can You Fix My Neural Network? Real-Time Adaptive Waveform Synthesis for Resilient Wireless Signal Classificationcited by 3
  3. 2022DDPG-Driven Deep-Unfolding with Adaptive Depth for Channel Estimation with Sparse Bayesian Learningcited by 2
  4. 2021Distributed Deep Reinforcement Learning for Collaborative Spectrum Sharingcited by 1
  5. 2021Adversarial Reinforcement Learning in Dynamic Channel Access and Power Controlcited by 1
  6. 2021Ensemble Consensus-based Representation Deep Reinforcement Learning for Hybrid FSO/RF Communication Systemscited by 1
  7. 2022A Machine Learning Based Algorithm for Joint Improvement of Power Control, link adaptation, and Capacity in Beyond 5G Communication systemscited by 1
  8. 2022Using Deep Reinforcement Learning for mmWave Real-Time Schedulingcited by 1
  9. 2023RayNet: A Simulation Platform for Developing Reinforcement Learning-Driven Network Protocolscited by 1
  10. 2023Collaborative Wideband Spectrum Sensing and Scheduling for Networked UAVs in UTM Systemscited by 1
  11. 2024Federated Learning-based Collaborative Wideband Spectrum Sensing and Scheduling for UAVs in UTM Systemscited by 1
  12. 2024GINO-Q: Learning an Asymptotically Optimal Index Policy for Restless Multi-armed Banditscited by 1
  13. 2024Toward Debugging Deep Reinforcement Learning Programs with RLExplorercited by 1
  14. 2025Multi-Agent Actor-Critic with Harmonic Annealing Pruning for Dynamic Spectrum Access Systemscited by 1
  15. 2026Multiagent Reinforcement Learning with Neighbor Action Estimationcited by 1
  16. 2026System One Models for Wireless Decision-Making:Applications and Performance Evaluationcited by 1
  17. 2021Reinforcement Learning for Physical Layer Communicationscited by 0
  18. 2021Learning-based Spectrum Sensing and Access in Cognitive Radios via Approximate POMDPscited by 0
  19. 2021Dynamic Channel Access via Meta-Reinforcement Learningcited by 0
  20. 2023Learning RL-Policies for Joint Beamforming Without Exploration: A Batch Constrained Off-Policy Approachcited by 0
  21. 2024Multi-agent Reinforcement Learning with Deep Networks for Diverse Q-Vectorscited by 0
  22. 2025Deep Reinforcement Learning Approach to QoSAware Load Balancing in 5G Cellular Networks under User Mobility and Observation Uncertaintycited by 0
  23. 2026Agentic AI for Bilevel Long-Term Optimization of Policy-Driven Physical Layer Systemscited by 0
  24. 2026Channel-Token Attention for Reliable Dynamic Spectrum Access under Bursty Primary-User Trafficcited by 0
  25. 2026Multi-Agent Spectrum Sharingcited by 0