A layered collaborative resource allocation algorithm for cell-free RAN aimed at efficient resource utilization

WANG Boye; XIA Weiwei; MIAO Weiwei; ZHANG Mingxuan; PAN Yuqing; YAN Feng; SHEN Lianfeng

doi:10.11959/j.issn.1000-0801.2025243

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A layered collaborative resource allocation algorithm for cell-free RAN aimed at efficient resource utilization

Topic: AI-Empowered Communication Networks | 更新时间：2026-01-20

- A layered collaborative resource allocation algorithm for cell-free RAN aimed at efficient resource utilization
- Telecommunications Science Vol. 41, Issue 11, Pages: 31-46(2025)
- 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：
- 基金信息：
  
  Science and Technology Project of State Grid Corporation of China “Research on Beyond-5G Wireless Access Theory”(5700-202499320A-1-3-ZB)
- DOI：10.11959/j.issn.1000-0801.2025243
  CLC： TN92
- Received：05 August 2025，
  
  Revised：2025-09-17，
  
  Accepted：25 September 2025，
  
  Published：20 November 2025
- 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.

关键词

Keywords

references

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