# Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networks

arXiv: https://arxiv.org/abs/1802.06958 · HTML: https://mlsanity.com/paper/1802.06958/

## Key facts

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

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

- [ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platforms](https://mlsanity.com/paper/2112.09559/) (2021)
- [Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey](https://mlsanity.com/paper/2110.13484/) (2021)
- [Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey](https://mlsanity.com/paper/2110.01411/) (2021)
- [NeurWIN: Neural Whittle Index Network For Restless Bandits Via Deep RL](https://mlsanity.com/paper/2110.02128/) (2021)
- [Link Scheduling using Graph Neural Networks](https://mlsanity.com/paper/2109.05536/) (2021)
- [Programmable and Customized Intelligence for Traffic Steering in 5G Networks Using Open RAN Architectures](https://mlsanity.com/paper/2209.14171/) (2022)
- [Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectories](https://mlsanity.com/paper/2404.14238/) (2024)
- [Deep Reinforcement Learning for Simultaneous Sensing and Channel Access in Cognitive Networks](https://mlsanity.com/paper/2110.14541/) (2021)
- [Cooperative Multi-Agent Reinforcement Learning Based Distributed Dynamic Spectrum Access in Cognitive Radio Networks](https://mlsanity.com/paper/2106.09274/) (2021)
- [Joint Deep Reinforcement Learning and Unfolding: Beam Selection and Precoding for mmWave Multiuser MIMO with Lens Arrays](https://mlsanity.com/paper/2101.01336/) (2021)
- [Multi-Flow Transmission in Wireless Interference Networks: A Convergent Graph Learning Approach](https://mlsanity.com/paper/2303.15544/) (2023)
- [SINR-Aware Deep Reinforcement Learning for Distributed Dynamic Channel Allocation in Cognitive Interference Networks](https://mlsanity.com/paper/2402.17773/) (2024)
- [Dynamic Multichannel Access via Multi-agent Reinforcement Learning: Throughput and Fairness Guarantees](https://mlsanity.com/paper/2105.04077/) (2021)
- [Can You Fix My Neural Network? Real-Time Adaptive Waveform Synthesis for Resilient Wireless Signal Classification](https://mlsanity.com/paper/2103.03745/) (2021)
- [DDPG-Driven Deep-Unfolding with Adaptive Depth for Channel Estimation with Sparse Bayesian Learning](https://mlsanity.com/paper/2201.08477/) (2022)
- [Distributed Deep Reinforcement Learning for Collaborative Spectrum Sharing](https://mlsanity.com/paper/2104.02059/) (2021)
- [Adversarial Reinforcement Learning in Dynamic Channel Access and Power Control](https://mlsanity.com/paper/2105.05817/) (2021)
- [Ensemble Consensus-based Representation Deep Reinforcement Learning for Hybrid FSO/RF Communication Systems](https://mlsanity.com/paper/2108.02551/) (2021)
- [A Machine Learning Based Algorithm for Joint Improvement of Power Control, link adaptation, and Capacity in Beyond 5G Communication systems](https://mlsanity.com/paper/2201.07090/) (2022)
- [Using Deep Reinforcement Learning for mmWave Real-Time Scheduling](https://mlsanity.com/paper/2210.01423/) (2022)
- [RayNet: A Simulation Platform for Developing Reinforcement Learning-Driven Network Protocols](https://mlsanity.com/paper/2302.04519/) (2023)
- [Collaborative Wideband Spectrum Sensing and Scheduling for Networked UAVs in UTM Systems](https://mlsanity.com/paper/2308.05036/) (2023)
- [Federated Learning-based Collaborative Wideband Spectrum Sensing and Scheduling for UAVs in UTM Systems](https://mlsanity.com/paper/2406.01727/) (2024)
- [GINO-Q: Learning an Asymptotically Optimal Index Policy for Restless Multi-armed Bandits](https://mlsanity.com/paper/2408.09882/) (2024)
- [Toward Debugging Deep Reinforcement Learning Programs with RLExplorer](https://mlsanity.com/paper/2410.04322/) (2024)
- [Multi-Agent Actor-Critic with Harmonic Annealing Pruning for Dynamic Spectrum Access Systems](https://mlsanity.com/paper/2503.15172/) (2025)
- [Multiagent Reinforcement Learning with Neighbor Action Estimation](https://mlsanity.com/paper/2601.04511/) (2026)
- [System One Models for Wireless Decision-Making:Applications and Performance Evaluation](https://mlsanity.com/paper/2610.04345/) (2026)
- [Reinforcement Learning for Physical Layer Communications](https://mlsanity.com/paper/2106.11595/) (2021)
- [Learning-based Spectrum Sensing and Access in Cognitive Radios via Approximate POMDPs](https://mlsanity.com/paper/2107.07049/) (2021)

## 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.