Dynamic Spectrum Sharing Based on Deep Reinforcement Learning in Mobile Communication Systems
Author:
Liu Sizhuang1, Pan Changyong12, Zhang Chao12, Yang Fang12, Song Jian13
Affiliation:
1. Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China 2. Peng Cheng Laboratory, Shenzhen 518055, China 3. Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
Abstract
The rapid development of mobile communication services in recent years has resulted in a scarcity of spectrum resources. This paper addresses the problem of multi-dimensional resource allocation in cognitive radio systems. Deep reinforcement learning (DRL) combines deep learning and reinforcement learning to enable agents to solve complex problems. In this study, we propose a training approach based on DRL to design a strategy for secondary users in the communication system to share the spectrum and control their transmission power. The neural networks are constructed using the Deep Q-Network and Deep Recurrent Q-Network structures. The results of the conducted simulation experiments demonstrate that the proposed method can effectively improve the user’s reward and reduce collisions. In terms of reward, the proposed method outperforms opportunistic multichannel ALOHA by about 10% and about 30% for the single SU scenario and the multi-SU scenario, respectively. Furthermore, we explore the complexity of the algorithm and the influence of parameters in the DRL algorithm on the training.
Funder
Peng Cheng Laboratory the China National Key R&D Program of China
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference45 articles.
1. Distributive dynamic spectrum access through deep reinforcement learning: A reservoir computing-based approach;Chang;IEEE Internet Things J.,2019 2. Zong, J., Liu, Y., Liu, H., Wang, Q., and Chen, P. (2022, January 27–29). 6G Cell-Free Network Architecture. In Proceedings of the 2022 IEEE 2nd International Conference on Electronic Technology, Communication and Information (ICETCI), Changchun, China. 3. Deep multi-user reinforcement learning for distributed dynamic spectrum access;Naparstek;IEEE Trans. Wirel. Commun.,2013 4. Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M. (2013). Playing atari with deep reinforcement learning. arXiv. 5. Liu, S., Wang, T., Pan, C., Zhang, C., Yang, F., and Song, J. (2021, January 4–6). Deep reinforcement learning for spectrum sharing in future mobile communication system. Proceedings of the 2021 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), Chengdu, China.
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. 5G Spectrum Research;2023 IEEE 10th International Conference on Cyber Security and Cloud Computing (CSCloud)/2023 IEEE 9th International Conference on Edge Computing and Scalable Cloud (EdgeCom);2023-07 2. An Efficient Cooperative Spectrum Sensing for Cognitive Wireless Sensor Networks;IEEE Access;2023
|
|