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Distributed Q-Learning-Enabled Multi-Dimensional Spectrum Sharing Security Scheme for 6G Wireless Communication
IEEE Wireless Communications ( IF 12.9 ) Pub Date : 2022-06-20 , DOI: 10.1109/mwc.005.2100395
Pan Ding, Xiaojuan Liu, Zhenguo Wang, Ke Zhang, Zequan Huang, Yong Chen

In 6G wireless communication scenarios, the ultra-dense network (UDN) scenario has received extensive attention, as it is an inevitable case of 6G networks. How to keep users safe while providing universal service to a number of users is becoming a crucial topic of UDN. To battle this issue, we propose a distributed Q-learning-enabled multi-dimensional spectrum sharing security scheme for the millimeter-wave frequency band. With this scheme, a fine-grained spectrum sharing security framework based on spatial multiplexing is established, which balances UDN performance and security. Aiming at the multi-dimensional spectrum sharing security and optimal allocation problem, non-cooperative game theory is used for modeling and analysis. To prove the validity of this scheme, we used numerical simulations to verify the performance of the proposed distributed Q-learning algorithm for solving the spectrum sharing optimization problem in the UDN scenario. The results show that the proposed multi-dimensional spectrum sharing security scheme can significantly reduce the access delay of users, increase the number of unauthorized users that can be accommodated, and improve throughput, while achieving good security performance of the network.

中文翻译:

用于6G无线通信的分布式Q-Learning多维频谱共享安全方案

在6G无线通信场景中,超密集网络(UDN)场景作为6G网络的必然案例而受到广泛关注。如何在为众多用户提供通用服务的同时保证用户安全,成为UDN的一个重要课题。为了解决这个问题,我们提出了一种用于毫米波频段的分布式 Q-learning 多维频谱共享安全方案。通过该方案,建立了基于空间复用的细粒度频谱共享安全框架,平衡了UDN的性能和安全性。针对多维频谱共享安全和最优分配问题,采用非合作博弈论进行建模分析。为了证明该方案的有效性,我们使用数值模拟来验证所提出的分布式 Q 学习算法在 UDN 场景中解决频谱共享优化问题的性能。结果表明,所提出的多维频谱共享安全方案可以显着降低用户的接入时延,增加可容纳的非法用户数量,提高吞吐量,同时实现网络良好的安全性能。
更新日期:2022-06-21
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