基于DQN的广域覆盖信令小区以用户为中心的动态多维资源分配

佟舟; 李娜; 张慧敏; 赵泉; 赵芸; 孙军帅; 刘光毅

doi:10.1631/FITEE.2200220

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基于DQN的广域覆盖信令小区以用户为中心的动态多维资源分配

常规文章 | Updated：2023-01-28

- 基于DQN的广域覆盖信令小区以用户为中心的动态多维资源分配
- Dynamic user-centric multi-dimensional resource allocation for a wide-area coverage signaling cell based on DQN
- 信息与电子工程前沿（英文） 2023年24卷第1期页码：154-163
- Affiliations：
  
  Future Research Lab, China Mobile Research Institute, Beijing 100053, China
- Author bio：
  
  ‡Corresponding authors
  linawx@chinamobile.com;
  zhanghuiminyjy@chinamobile.com
- Funds：
  
  National Key Research and Development Program of China(2020YFB1806800)
- DOI：10.1631/FITEE.2200220
  中图分类号： TN929.5
- 收稿：2022-05-20，
  
  录用：2022-08-30，
  
  纸质出版：2023-01-0
- Accepted：
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佟舟, 李娜, 张慧敏, 等. 基于DQN的广域覆盖信令小区以用户为中心的动态多维资源分配[J]. 信息与电子工程前沿（英文）, 2023,24(1):154-163.

Zhou TONG, Na LI, Huimin ZHANG, et al. Dynamic user-centric multi-dimensional resource allocation for a wide-area coverage signaling cell based on DQN[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(1): 154-163.
佟舟, 李娜, 张慧敏, 等. 基于DQN的广域覆盖信令小区以用户为中心的动态多维资源分配[J]. 信息与电子工程前沿（英文）, 2023,24(1):154-163. DOI： 10.1631/FITEE.2200220.

Zhou TONG, Na LI, Huimin ZHANG, et al. Dynamic user-centric multi-dimensional resource allocation for a wide-area coverage signaling cell based on DQN[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(1): 154-163. DOI： 10.1631/FITEE.2200220.

摘要

通信行业的快速发展催生了更多新业务与新应用。6G网络面临更严苛、更多样的需求。在保证高速率、低时延等性能要求的同时，5G网络中存在的高能耗问题也成为6G网络需要解决的问题之一。广域覆盖信令小区技术顺应未来无线接入网的发展趋势，具有低网络能耗、高资源利用率的优势。在广域覆盖信令小区中，多维资源按需分配是保证用户极致性能需求的重要技术手段，其效果将直接影响网络资源使用效率。本文构建以用户为中心的无线资源动态分配模型，并提出一种基于深度Q网络的资源动态分配算法。该算法可根据用户上报的数据速率和时延等需求，实现动态灵活的接纳控制及多维资源分配。仿真结果表明，所提算法可有效提高长时间尺度下网络平均用户体验，在资源分配过程中保证高速率和低能耗。

Abstract

The rapid development of communications industry has spawned more new services and applications. The sixth-generation wireless communication system (6G) network is faced with more stringent and diverse requirements. While ensuring performance requirements

such as high data rate and low latency

the problem of high energy consumption in the fifth-generation wireless communication system (5G) network has also become one of the problems to be solved in 6G. The wide-area coverage signaling cell technology conforms to the future development trend of radio access networks

and has the advantages of reducing network energy consumption and improving resource utilization. In wide-area coverage signaling cells

on-demand multi-dimensional resource allocation is an important technical means to ensure the ultimate performance requirements of users

and its effect will affect the efficiency of network resource utilization. This paper constructs a user-centric dynamic allocation model of wireless resources

and proposes a deep Q-network based dynamic resource allocation algorithm. The algorithm can realize dynamic and flexible admission control and multi-dimensional resource allocation in wide-area coverage signaling cells according to the data rate and latency demands of users. According to the simulation results

the proposed algorithm can effectively improve the average user experience on a long time scale

and ensure network users a high data rate and low energy consumption.

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references

Gang J , Friderikos V , 2019 . Inter-tenant resource sharing and power allocation in 5G virtual networks . IEEE Trans Veh Technol , 68 ( 8 ): 7931 - 7943 . doi: 10.1109/TVT.2019.2917426 http://doi.org/10.1109/TVT.2019.2917426

Ji CY , Bi MH , Zhou Z , et al. , 2021 . Online bandwidth resources allocation algorithm for multi-tenancy PON based on deep reinforcement learning . Opt Commun Technol , 45 ( 9 ): 36 - 39 ( in Chinese ). doi: 10.13921/j.cnki.issn1002-5561.2021.09.009 http://doi.org/10.13921/j.cnki.issn1002-5561.2021.09.009

Kalil M , Al-Dweik A , Sharkh MFA , et al. , 2017 . A framework for joint wireless network virtualization and cloud radio access networks for next generation wireless networks . IEEE Access , 5 ( 1 ): 20814 - 20827 . doi: 10.1109/access.2017.2746666 http://doi.org/10.1109/access.2017.2746666

Lin MT , Zhao YP , 2020 . Artificial intelligence-empowered resource management for future wireless communications: a survey . China Commun , 17 ( 3 ): 58 - 77 . doi: 10.23919/JCC.2020.03.006 http://doi.org/10.23919/JCC.2020.03.006

Liu GY , Deng J , Zheng QB , et al. , 2022a . Native intelligence for 6G mobile network: technical challenges, architecture and key features . J China Univ Posts Telecommun , 29 ( 1 ): 27 - 40 . doi: 10.19682/j.cnki.1005-8885.2022.2004 http://doi.org/10.19682/j.cnki.1005-8885.2022.2004

Liu GY , Li N , Deng J , et al. , 2022b . The SOLIDS 6G mobile network architecture: driving forces, features, and functional topology . Engineering , 8 ( 1 ): 42 - 59 . doi: 10.1016/j.eng.2021.07.013 http://doi.org/10.1016/j.eng.2021.07.013

Luo Y , Shi ZP , Zhou X , et al. , 2014 . Dynamic resource allocations based on Q-learning for D2D communication in cellular networks . 11 th Int Computer Conf on Wavelet Active Media Technology and Information Processing , p. 385 - 388 . doi: 10.1109/ICCWAMTIP.2014.7073432 http://doi.org/10.1109/ICCWAMTIP.2014.7073432

Lü YP , Jia XD , Lu Y , et al. , 2021 . A deep Q-learning based resource allocation algorithm in indoor wireless networks . Comput Eng Sci , 43 ( 7 ): 1250 - 1255 ( in Chinese ).

Ren YJ , Sun YH , Peng MG , 2021 . Deep reinforcement learning based computation offloading in fog enabled Industrial Internet of Things . IEEE Trans Ind Inform , 17 ( 7 ): 4978 - 4987 . doi: 10.1109/TII.2020.3021024 http://doi.org/10.1109/TII.2020.3021024

Xu H , Tong Z , Shen H , et al. , 2021 . Dynamic communication and computation resource allocation algorithm for end-to-end slicing in mobile networks . 3 rd Int Conf on Artificial Intelligence for Communications and Networks , p. 251 - 267 . doi: 10.1007/978-3-030-90196-7_22 http://doi.org/10.1007/978-3-030-90196-7_22

Zhang TK , Wang XF , Yang LW , et al. , 2021 . A SFC deployment and computation resource allocation joint algorithm in mobile networks . J Beijing Univ Posts Telecommun , 44 ( 1 ): 7 - 13 ( in Chinese ). doi: 10.13190/j.jbupt.2020-035 http://doi.org/10.13190/j.jbupt.2020-035

Zhao QY , Grace D , Vilhar A , et al. , 2015 . Using K-means clustering with transfer and Q learning for spectrum, load and energy optimization in opportunistic mobile broadband networks . Int Symp on Wireless Communication Systems , p. 116 - 120 . doi: 10.1109/ISWCS.2015.7454310 http://doi.org/10.1109/ISWCS.2015.7454310

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