Corpus ID: 235457990

Cooperative Multi-Agent Reinforcement Learning Based Distributed Dynamic Spectrum Access in Cognitive Radio Networks

@article{Tan2021CooperativeMR,
  title={Cooperative Multi-Agent Reinforcement Learning Based Distributed Dynamic Spectrum Access in Cognitive Radio Networks},
  author={Xiang Tan and Li Zhou and Haijun Wang and Yuli Sun and Haitao Zhao and B. Seet and Jibo Wei and Victor C. M. Leung},
  journal={ArXiv},
  year={2021},
  volume={abs/2106.09274}
}
With the development of the 5G and Internet of Things, amounts of wireless devices need to share the limited spectrum resources. Dynamic spectrum access (DSA) is a promising paradigm to remedy the problem of inef!cient spectrum utilization brought upon by the historical command-and-control approach to spectrum allocation. In this paper, we investigate the distributed DSA problem for multiuser in a typical multi-channel cognitive radio network. The problem is formulated as a decentralized… Expand

References

SHOWING 1-10 OF 34 REFERENCES
Deep Multi-User Reinforcement Learning for Distributed Dynamic Spectrum Access
TLDR
A novel distributed dynamic spectrum access algorithm based on deep multi-user reinforcement leaning is developed for accessing the spectrum that maximizes a certain network utility in a distributed manner without online coordination or message exchanges between users. Expand
Distributive Dynamic Spectrum Access Through Deep Reinforcement Learning: A Reservoir Computing-Based Approach
TLDR
The results suggest that the RC-based spectrum access strategy can help the SU to significantly reduce the chances of collision with PUs and other SUs, and outperforms the myopic method which assumes the knowledge of system statistics, and converges faster than the Q-learning method when the number of channels is large. Expand
The Application of Deep Reinforcement Learning to Distributed Spectrum Access in Dynamic Heterogeneous Environments With Partial Observations
  • Yue Xu, Jianyuan Yu, R. Buehrer
  • Computer Science
  • IEEE Transactions on Wireless Communications
  • 2020
TLDR
This papera1 investigates deep reinforcement learning (DRL) based on a Recurrent Neural Network (RNN) for Dynamic Spectrum Access (DSA) under partial observations, referred to as a Deep Recurrent Q-Network (DRQN), and shows the following benefits of using recurrent neural networks in DSA. Expand
Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks
  • Y. Yu, T. Wang, S. Liew
  • Computer Science
  • 2018 IEEE International Conference on Communications (ICC)
  • 2018
TLDR
The use of neural networks in DRL allows for fast convergence to optimal solutions and robustness against perturbation in hyper- parameter settings, two essential properties for practical deployment of DLMA in real wireless networks. Expand
Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networks
TLDR
This work considers a dynamic multichannel access problem, where multiple correlated channels follow an unknown joint Markov model and users select the channel to transmit data, and proposes an adaptive DQN approach with the capability to adapt its learning in time-varying scenarios. Expand
A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access
TLDR
This work employs the proposed framework as a single agent in the single-user case, and extends it to a decentralized multi-agent framework in the multi-user scenario, and develops algorithms for the actor-critic deep reinforcement learning and evaluates the proposed learning policies via experiments and numerical results. Expand
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
TLDR
This paper presents a comprehensive literature review on applications of deep reinforcement learning (DRL) in communications and networking, and presents applications of DRL for traffic routing, resource sharing, and data collection. Expand
Proactive Resource Management for LTE in Unlicensed Spectrum: A Deep Learning Perspective
TLDR
A novel deep learning approach is proposed for modeling the resource allocation problem of LTE-LAA small base stations (SBSs) and it is shown that the proposed scheme can yield up to 28% and 11% gains over a conventional reactive approach and a proportional fair coexistence mechanism, respectively. Expand
A Comprehensive Survey of Multiagent Reinforcement Learning
TLDR
The benefits and challenges of MARL are described along with some of the problem domains where the MARL techniques have been applied, and an outlook for the field is provided. Expand
Value-Decomposition Networks For Cooperative Multi-Agent Learning
TLDR
This work addresses the problem of cooperative multi-agent reinforcement learning with a single joint reward signal by training individual agents with a novel value decomposition network architecture, which learns to decompose the team value function into agent-wise value functions. Expand
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