一种面向资源高效利用的无蜂窝RAN分层协同资源分配算法

王博业; 夏玮玮; 缪巍巍; 张明轩; 潘裕庆; 燕锋; 沈连丰

doi:10.11959/j.issn.1000-0801.2025243

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一种面向资源高效利用的无蜂窝RAN分层协同资源分配算法

专题：AI赋能通信网络 | 更新时间：2026-01-20

- 一种面向资源高效利用的无蜂窝RAN分层协同资源分配算法
- A layered collaborative resource allocation algorithm for cell-free RAN aimed at efficient resource utilization
- 电信科学 2025年41卷第11期页码：31-46
- 作者机构：
  
  1.东南大学移动通信国家重点实验室，江苏南京 210096
  2.国网江苏省电力有限公司信息通信分公司，江苏南京 210024
  3.国网苏州供电公司，江苏苏州 215031
- 作者简介：
  
  [ "王博业（2001- ），男，东南大学移动通信国家重点实验室硕士生，主要研究方向为无线网络资源管理、6G通信等。" ]
  [ "夏玮玮（1975- ），女，博士，东南大学移动通信国家重点实验室副研究员，主要研究方向为无线网络资源管理、边缘计算、泛在网络与短距离无线通信等。" ]
  [ "缪巍巍（1968- ），男，国网江苏省电力有限公司信息通信分公司正高级工程师，主要从事电力信息通信网络、电力物联网、电力无线专网工作。" ]
  [ "张明轩（1985- ），男，博士，国网江苏省电力有限公司信息通信分公司高级工程师，主要从事电力通信工作。" ]
  [ "潘裕庆（1977- ），男，国网苏州供电公司高级工程师，主要从事电力通信与信息化工作。" ]
  [ "燕锋（1983- ），男，博士，东南大学移动通信国家重点实验室副研究员，主要研究方向为无线传感器网络、异构网络、无人机网络、自组织网络等。" ]
  [ "沈连丰（1952- ），男，东南大学移动通信国家重点实验室教授、博士生导师，主要研究方向为宽带移动通信、短距离无线通信和泛在网络等。" ]
- 基金信息：
  
  国家电网总部科技项目“超五代无线接入理论研究”(5700-202499320A-1-3-ZB)
- DOI：10.11959/j.issn.1000-0801.2025243
  中图分类号： TN92
- 收稿：2025-08-05，
  
  修回：2025-09-17，
  
  录用：2025-09-25，
  
  纸质出版：2025-11-20
- 稿件说明：
移动端阅览
王博业,夏玮玮,缪巍巍等.一种面向资源高效利用的无蜂窝RAN分层协同资源分配算法[J].电信科学,2025,41(11):31-46.

WANG Boye,XIA Weiwei,MIAO Weiwei,et al.A layered collaborative resource allocation algorithm for cell-free RAN aimed at efficient resource utilization[J].Telecommunications Science,2025,41(11):31-46.
王博业,夏玮玮,缪巍巍等.一种面向资源高效利用的无蜂窝RAN分层协同资源分配算法[J].电信科学,2025,41(11):31-46. DOI： 10.11959/j.issn.1000-0801.2025243.

WANG Boye,XIA Weiwei,MIAO Weiwei,et al.A layered collaborative resource allocation algorithm for cell-free RAN aimed at efficient resource utilization[J].Telecommunications Science,2025,41(11):31-46. DOI： 10.11959/j.issn.1000-0801.2025243.

摘要

为实现对6G无蜂窝无线电接入网（radio access network，RAN）切片资源的高效管理，提出了一种基于强化学习分层协同优化的资源分配算法。该算法采用多时间尺度的分层协同架构，在上层大时间尺度，以各切片的资源匹配度作为优化反馈指标，利用双深度Q网络算法动态调整切片的资源配置。在下层小时间尺度，构建了基于近端策略优化的用户接入决策模型，在满足用户服务质量的前提下，通过用户协作簇选择和资源分配机制最小化切片资源消耗量。仿真结果表明，所提算法通过面向用户的动态接入决策显著减少资源消耗量，并通过切片资源的周期性重配置提升资源匹配度，从而实现了对系统资源的高效利用。

Abstract

To achieve efficient management of 6G cell-free radio access network (RAN) slice resources

a layered collaborative optimization resource allocation algorithm based on reinforcement learning (RL) was proposed. This algorithm adopted a multi-timescale hierarchical collaborative architecture. At the upper layer with a large timescale

the resource matching degree of each slice was used as the optimization feedback metric

and the dual-depth Q-network (DDQN) algorithm was employed to dynamically adjust the resource configuration of slices. At the lower-level small time scale

a user access decision-making mechanism based on a proximity policy optimization (PPO) algorithm was established. Under the premise of meeting user quality of service （QoS）requirements

the algorithm minimized slice resource consumption through user collaboration cluster selection and resource allocation mechanisms. Simulation results demonstrate that the proposed algorithm significantly reduces resource consumption through user-centric dynamic access decisions and enhances resource matching through periodic reconfiguration of slice resources

thereby achieving efficient utilization of system resources.

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references

ALSABAH M , NASER M A , MAHMMOD B M , et al . 6G wireless communications networks: a comprehensive survey [J ] . IEEE Access , 2021 , 9 : 148191 - 148243 .

NGO H Q , ASHIKHMIN A , YANG H , et al . Cell-free massive MIMO versus small cells [J ] . IEEE Transactions on Wireless Communications , 2017 , 16 ( 3 ): 1834 - 1850 .

BUZZI S , D’ANDREA C , ZAPPONE A , et al . User-centric 5G cellular networks: resource allocation and comparison with the cell-free massive MIMO approach [J ] . IEEE Transactions on Wireless Communications , 2020 , 19 ( 2 ): 1250 - 1264 .

NGO H Q , TRAN L N , DUONG T Q , et al . On the total energy efficiency of cell-free massive MIMO [J ] . IEEE Transactions on Green Communications and Networking , 2018 , 2 ( 1 ): 25 - 39 .

AKYILDIZ I F , KAK A , NIE S . 6G and beyond: the future of wireless communications systems [J ] . IEEE Access , 2020 , 8 : 133995 - 134030 .

WANG Z Y , WEI Y F , YU F R , et al . Utility optimization for resource allocation in edge network slicing using DRL [C ] // Proceedings of the GLOBECOM 2020-2020 IEEE Global Communications Conference . Piscataway : IEEE Press , 2021 : 1 - 6 .

孙君 , 霭振宇 . 基于差异性隔离和复用的网络切片无线资源分配方案 [J ] . 通信学报 , 2025 , 46 ( 3 ): 109 - 121 .

SUN J , AI Z Y . Wireless resource allocation scheme for network slicing based on differentiated isolation and multiplexing [J ] . Journal on Communications , 2025 , 46 ( 3 ): 109 - 121 .

TANG J H , SHIM B , QUEK T Q S . Service multiplexing and revenue maximization in sliced C-RAN incorporated with URLLC and multicast eMBB [J ] . IEEE Journal on Selected Areas in Communications , 2019 , 37 ( 4 ): 881 - 895 .

ALWARAFY A , ABDALLAH M , ÇIFTLER B S , et al . The frontiers of deep reinforcement learning for resource management in future wireless HetNets: techniques, challenges, and research directions [J ] . IEEE Open Journal of the Communications Society , 2022 , 3 : 322 - 365 .

ZAPPONE A , DI RENZO M , DEBBAH M . Wireless networks design in the era of deep learning: model-based, AI-based, or both? [J ] . IEEE Transactions on Communications , 2019 , 67 ( 10 ): 7331 - 7376 .

亓伟敬 , 宋清洋 , 郭磊 . 面向软件定义多模态车联网的双时间尺度RAN切片资源分配 [J ] . 通信学报 , 2022 , 43 ( 4 ): 60 - 70 .

QI W J , SONG Q Y , GUO L . Dual time scale resource allocation for RAN slicing in software-defined oriented polymorphic IoV [J ] . Journal on Communications , 2022 , 43 ( 4 ): 60 - 70 .

CHEN M Z , CHALLITA U , SAAD W , et al . Artificial neural networks-based machine learning for wireless networks: a tutorial [J ] . IEEE Communications Surveys & Tutorials , 2019 , 21 ( 4 ): 3039 - 3071 .

HUA Y X , LI R P , ZHAO Z F , et al . GAN-powered deep distributional reinforcement learning for resource management in network slicing [J ] . IEEE Journal on Selected Areas in Communications , 2020 , 38 ( 2 ): 334 - 349 .

ZHANG H , PAN G J , XU S G , et al . A hard and soft hybrid slicing framework for service level agreement guarantee via deep reinforcement learning [C ] // Proceedings of the 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring) . Piscataway : IEEE Press , 2022 : 1 - 5 .

SUN G L , GEBREKIDAN Z T , BOATENG G O , et al . Dynamic reservation and deep reinforcement learning based autonomous resource slicing for virtualized radio access networks [J ] . IEEE Access , 2019 , 7 : 45758 - 45772 .

YAN M , FENG G , ZHOU J H , et al . Intelligent resource scheduling for 5G radio access network slicing [J ] . IEEE Transactions on Vehicular Technology , 2019 , 68 ( 8 ): 7691 - 7703 .

MEI J , WANG X B , ZHENG K , et al . Intelligent radio access network slicing for service provisioning in 6G: a hierarchical deep reinforcement learning approach [J ] . IEEE Transactions on Communications , 2021 , 69 ( 9 ): 6063 - 6078 .

YE F , WANG J , LI J M , et al . Intelligent hierarchical network slicing based on dynamic multi-connectivity in cell-free distributed massive MIMO systems [J ] . IEEE Transactions on Vehicular Technology , 2023 , 72 ( 9 ): 11855 - 11870 .

夏玮玮 , 王博业 , 夏雅星 , 等 . 基于多时间尺度协同的无蜂窝RAN切片资源分配算法 [J ] . 通信学报 , 2025 , 46 ( 7 ): 60 - 77 .

XIA W W , WANG B Y , XIA Y X , et al . Cellular-free RAN slicing resource allocation algorithm based on multi-timescale collaboration [J ] . Journal on Communications , 2025 , 46 ( 7 ): 60 - 77 .

WANG J Y , DAI L , YANG L , et al . Clustered cell-free networking: a graph partitioning approach [J ] . IEEE Transactions on Wireless Communications , 2023 , 22 ( 8 ): 5349 - 5364 .

BJORNSON E , JALDEN N , BENGTSSON M , et al . Optimality properties, distributed strategies, and measurement-based evaluation of coordinated multicell OFDMA transmission [J ] . IEEE Transactions on Signal Processing , 2011 , 59 ( 12 ): 6086 - 6101 .

JIANG J , WANG J C , CHU H Y , et al . Whale swarm reinforcement learning based dynamic cooperation clustering method for cell-free massive MIMO systems [J ] . IEEE Transactions on Vehicular Technology , 2023 , 72 ( 3 ): 4114 - 4118 .

HOSSAIN A R , ANSARI N . Priority-based downlink wireless resource provisioning for radio access network slicing [J ] . IEEE Transactions on Vehicular Technology , 2021 , 70 ( 9 ): 9273 - 9281 .

CAO J , WANG D M , LI J M , et al . Uplink spectral efficiency analysis of multi-cell multi-user massive MIMO over correlated Ricean channel [J ] . Science China Information Sciences , 2018 , 61 ( 8 ): 082305 .

BJÖRNSON E , SANGUINETTI L . Scalable cell-free massive MIMO systems [J ] . IEEE Transactions on Communications , 2020 , 68 ( 7 ): 4247 - 4261 .

YANG H J , ZHENG K , ZHANG K , et al . Ultra-reliable and low-latency communications for connected vehicles: challenges and solutions [J ] . IEEE Network , 2020 , 34 ( 3 ): 92 - 100 .

SONG F , LI J , MA C , et al . Dynamic virtual resource allocation for 5G and beyond network slicing [J ] . IEEE Open Journal of Vehicular Technology , 2020 , 1 : 215 - 226 .

NEELY M J . Stochastic network optimization with application to communication and queueing systems [M ] . Cham : Springer International Publishing , 2010 .

BERTSEKAS D P , GALLAGER R G . Data networks: 2nd edition [M ] . Englewood Cliffs, New Jersey : Prentice-Hall , 1992 .

ZENG W B , HE Y G , LI B , et al . Pilot assignment for cell free massive MIMO systems using a weighted graphic framework [J ] . IEEE Transactions on Vehicular Technology , 2021 , 70 ( 6 ): 6190 - 6194 .

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