The DOI matches its Crossref record; 36 of the 37 references it cites were found in a registry or an index.
A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access
Open accessAbstract
To make efficient use of limited spectral resources, we in this work propose a deep actor-critic reinforcement learning based framework for dynamic multichannel access. We consider both a single-user case and a scenario in which multiple users attempt to access channels simultaneously. We employ the proposed framework as a single agent in the single-user case, and extend it to a decentralized multi-agent framework in the multi-user scenario. In both cases, we develop algorithms for the actor-critic deep reinforcement learning and evaluate the proposed learning policies via experiments and numerical results. In the single-user model, in order to evaluate the performance of the proposed channel access policy and the framework's tolerance against uncertainty, we explore different channel switching patterns and different switching probabilities. In the case of multiple users, we analyze the probabilities of each user accessing channels with favorable channel conditions and the probability of collision. We also address a time-varying environment to identify the adaptive ability of the proposed framework. Additionally, we provide comparisons (in terms of both the average reward and time efficiency) between the proposed actor-critic deep reinforcement learning framework, Deep-Q network (DQN) based approach, random access, and the optimal policy when the channel dynamics are known.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The challenge with deep actor criticism is that both actor and critic need stable targets.”
From Deep reinforcement learning-based adaptive fuzzy control for electro-hydraulic servo system (2025) · cites this paper as [16]
In the citing paper
“It requires consistently training the critic without divergence that delays learning and the propagation of value estimations through the target network [16].”
From Deep reinforcement learning-based adaptive fuzzy control for electro-hydraulic servo system (2025) · cites this paper as [16]
Citations
Cites
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,268
Mastering the game of Go without human knowledge
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al. (2017) · Nature · doi:10.1038/nature24270 · Cited by 9,084
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Luong, N. C., Hoang, D. T., Gong, S., Niyato, D. T., Wang, P., Liang, Y., & Kim, D. I. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2916583 · Cited by 2,181
Deep Learning in Mobile and Wireless Networking: A Survey
Zhang, C., Patras, P., & Haddadi, H. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2904897 · Cited by 1,913
Decentralized cognitive MAC for opportunistic spectrum access in ad hoc networks: A POMDP framework
Zhao, Q., Tong, L., Swami, A., & Chen, Y. (2007) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2007.070409 · Cited by 1,587
Monte-Carlo Planning in Large POMDPs
Silver, D., & Veness, J. (2010) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 886
Peters, J., & Schaal, S. (2008) · Neurocomputing · doi:10.1016/j.neucom.2007.11.026 · Cited by 738
Deep Multi-User Reinforcement Learning for Distributed Dynamic Spectrum Access
Naparstek, O., & Cohen, K. (2018) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2018.2879433 · Cited by 504
Flagged first, then by citations
1 of the 37 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Items 101–123 of 123
A cognitive frequency allocation strategy for multi-carrier radar against communication interference
Shan, Z., Wang, L., Liu, P. T., Huang, T., & Liu, Y. (2023) · IET conference proceedings. · doi:10.1049/icp.2022.2297 · Cited by 1
Gholipoor, N., Nouruzi, A., Salarhosseini, S., Javan, M. R., Mokari, N., & Jorswieck, E. A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.05991 · Cited by 1
Sohaib, M., Jeong, J., & Jeon, S.-W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.04077
Power Control Based on DRL Algorithm for D2D-Enabled Networks
Wang, X., Zhang, H., & Long, K. (2021) · Globecom · doi:10.1109/globecom46510.2021.9686009
2019 Index IEEE Transactions on Cognitive Communications and Networking Vol. 5
(2019) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2019.2960672
Distributed Learning for 6G–IoT Networks: A Comprehensive SurveyPreprint
Das, S. K., Mudi, R., Rahman, M. S., & Fapojuwo, A. O. (2022) · doi:10.36227/techrxiv.20069051
Model-Based Reinforcement Learning for Wireless Channel Access
Park, J. I., & Choi, K. W. (2022) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc55196.2022.9952476
Deep Reinforcement Learning for Optimizing Agri-Food Supply ChainPreprint
Shukla, A., Kakde, S. T., Mitra, R., Mandal, J., & Tiwari, M. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4868017
Generatice Adversarial Imitation Learning Algorithm Based on Improved Curiosity Module
Fu, Y., Liu, Q., Zhang, X., Li, H., & Wu, L. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-8490-5_31
Dynamic Multi-Channel Access in Heterogeneous Industrial Wireless Sensor Networks: A MADRL Approach
Wang, P., Zheng, C., Pu, C., & Tian, Z. (2024) · China Automation Congress (CAC) · doi:10.1109/cac63892.2024.10865069
Dai, L. (2025) · IEEE Transactions on Communications · doi:10.1109/tcomm.2025.3548025
AI-Driven Multiple Access for IoT
Shao, Y. (2025) · Wireless networks · doi:10.1007/978-3-031-98816-5_4
Delay-Optimal Random Access: A Learning Framework
Zhang, H., Zhao, X., & Dai, L. (2026) · IEEE Transactions on Communications · doi:10.1109/tcomm.2026.3652495
Vishwanath, V. K., Rajendra, A. B., & Gururaj, H. L. (2026) · Discover Applied Sciences · doi:10.1007/s42452-026-08386-7
That, V., Muy, S., & Lee, J.-R. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-00493-0
PPO-GPR: A Custom Proximal Policy Optimization Tool for Active Reinforcement Learning
Osaro, E., & Colón, Y. J. (2026) · ACS Engineering Au · doi:10.1021/acsengineeringau.5c00122
Reinforcement Learning for Uncoordinated Multiple Access
Robaglia, B.-M. (2024) · theses.fr (ABES) · doi:10.70675/08ed61cdzb5a5z4c2az9f76z431c4c92727d
Duraimurugan, J., & Indra Gandhi, K. (2026) · International Journal of Communication Systems · doi:10.1002/dac.70489
Shashank, R., Aswin Kumer, S., V, Sennan, S., Thankaraj, A., Britto Raj, S., & Mohan, E. M. (2026) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2026.102417
Xue, D., Lin, Y., Liu, J., Qi, W., Li, K., Li, B., Sun, K., Zhang, Z., Su, X., Xu, K., et al. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.116423
Jiang, H. Q., Bian, X., & Li, M. (2026) · Sensors · doi:10.3390/s26154737
A Survey of AI-Enabled Architectures for Next-Generation Wireless Communication Networks
Javed, A., Ahmed, I., Basit, A., Akhtar, F., Ali, H., & Khalid, M. (2026) · Wireless Personal Communications · doi:10.1007/s11277-026-12228-5
Nandhakumar, T., Bathmanaban, G., Venkatachalam, R., Sivalingam, R., Muthusamy, S., & Priyanka, T. P. (2026) · Information Security Journal A Global Perspective · doi:10.1080/19393555.2026.2743549
Metadata
- Authors
- Zhong, C., Lu, Z., Gursoy, M. C., & Velipasalar, S.
- Affiliations
- Syracuse University (US)
- Venue
- IEEE Transactions on Cognitive Communications and Networking
- Published
- 2019-12
- Volume
- 5
- Issue
- 4
- Pages
- 1125-1139
- ISSN
- 2332-7731 (ISSN-L)
- 2372-2045
- OpenAlex
- W2983432303
- Cited by
- 123
- References
- 37
- Citations by year
- 2026: 11 · 2025: 15 · 2024: 20 · 2023: 20 · 2022: 21 · 2021: 26 · 2020: 9 · 2019: 1
- FWCI
- 7.80
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Cognitive Communications and Networking (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Networks and Protocols
- Physical Sciences › Computer Science › Computer Networks and Communications › Cognitive Radio Networks and Spectrum Sensing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Network Optimization
- Keywords
- channel switching, multi-agent reinforcement learning, collision probability, deep Q-network comparison, random access, cognitive radio
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
