Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163822
Title: Dynamic spectrum access for Internet-of-Things based on federated deep reinforcement learning
Authors: Li, Feng
Shen, Bowen
Guo, Jiale
Lam, Kwok-Yan
Wei, Guiyi
Wang, Li
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: Li, F., Shen, B., Guo, J., Lam, K., Wei, G. & Wang, L. (2022). Dynamic spectrum access for Internet-of-Things based on federated deep reinforcement learning. IEEE Transactions On Vehicular Technology, 71(7), 7952-7956. https://dx.doi.org/10.1109/TVT.2022.3166535
Journal: IEEE Transactions on Vehicular Technology 
Abstract: The explosive growth of Internet-of-Things (IoT) applications such as smart cities and Industry 4.0 have led to drastic increase in demand for wireless bandwidth, hence motivating the rapid development of new techniques for enhancing spectrum utilization needed by new generation wireless communication technologies. Among others, dynamic spectrum access (DSA) is one of the most widely accepted approaches. In this paper, as an enhancement of existing works, we take into consideration of inter-node collaborations in a dynamic spectrum environment. Typically, in such distributed circumstances, intelligent dynamic spectrum access almost invariably relies on self-learning to achieve dynamic spectrum access improvement. Whereas, this paper proposes a DSA scheme based on deep reinforcement learning to enhance spectrum and access efficiency. Unlike traditional Q-learning-based DSA, we introduce the following to enhance the spectrum efficiency in dynamic IoT spectrum environments. First, deep double Q-learning is adopted to perform local self-spectrum-learning for IoT terminals in order to achieve better dynamic access accuracy. Second, to accelerate learning convergence, federated learning (FL) in edge nodes is used to improve the self-learning. Third, multiple secondary users, who do not interfere with each other and have similar operation condition, are clustered for federated learning to enhance the efficiency of deep reinforcement learning. Comparing with the traditional distributed DSA with deep learning, the proposed scheme has faster access convergence speed due to the characteristic of global optimization for federated learning. Based on this, a framework of federated deep reinforcement learning (FDRL) for DSA is proposed. Furthermore, this scheme preserves privacy of IoT users in that FDRL only requires model parameters to be uploaded to edge servers. Simulations are performed to show the effectiveness of theproposed FDRL-based DSA framework.
URI: https://hdl.handle.net/10356/163822
ISSN: 0018-9545
DOI: 10.1109/TVT.2022.3166535
Schools: School of Computer Science and Engineering 
Rights: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TVT.2022.3166535.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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