|Table of Contents|

[1] ZHANG Cheng, ZHANG Li, MENG Fan, HUANG Yongming, et al. Deterministic transmission in user-scalable cell-free MIMO-OFDM systems [J]. Journal of Southeast University (English Edition), 2025, 41 (4): 430-436. [doi:10.3969/j.issn.1003-7985.2025.04.004]
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Deterministic transmission in user-scalable cell-free MIMO-OFDM systems()
用户数可扩展无蜂窝MIMO-OFDM确定性传输

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
41
Issue:
2025 4
Page:
430-436
Research Field:
Information and Communication Engineering
Publishing date:
2025-11-30

Info

Title:
Deterministic transmission in user-scalable cell-free MIMO-OFDM systems
用户数可扩展无蜂窝MIMO-OFDM确定性传输
Author(s):
ZHANG Cheng1,2, ZHANG Li1, MENG Fan2, HUANG Yongming1,2
1.National Mobile Communications Research Laboratory, Southeast University, Nanjing 211189, China
2.Purple Mountain Laboratories, Nanjing 211111, China
张铖1,2, 张立1, 孟帆2, 黄永明1,2
1.东南大学移动通信国家重点实验室, 南京 211189
2.紫金山实验室, 南京 211111
Keywords:
delay violation probability constraint;  cell-free;  safety reinforcement learning;  resource scheduling
时延违反率约束; 无蜂窝; 安全强化学习; 资源调度
PACS:
TN929.5
DOI:
10.3969/j.issn.1003-7985.2025.04.004
Abstract:
This paper proposes a data- and model-driven collaborative resource scheduling method to maximize the spectral efficiency (SE) of cell-free (CF) downlink multiuser multiple-input multiple-output (MIMO) systems, subject to delay violation probability and power constraints. The method integrates the weighted minimum mean square error (WMMSE) algorithm within the safety reinforcement learning (Safety-RL) framework. The original optimization problem is decomposed into two coupled subproblems. The Safety-RL algorithm leverages state features to determine user priority weights and allocate bandwidths, while the WMMSE algorithm calculates the precoding matrix and further schedules resources based on user priority weights to obtain the reward and costs of Safety-RL. Considering dynamic user access in CF systems, a distributed algorithm with user scalability is also proposed. Simulation results demonstrate that the proposed approach improves the SE while meeting the different delay violation probability constraints of users. Furthermore, the distributed algorithm offers comparable performance to the fully centralized method while considerably reducing model training overhead, particularly as users dynamically access the system.
针对无蜂窝（CF）下行多用户多输入多输出（MIMO）系统中具有时延违反率约束及功率约束的频谱效率最大化问题，提出了一种基于安全强化学习（Safety‑RL）框架内嵌加权最小均方误差（WMMSE）算法的数模协同资源调度方法。该方法将原始问题转化为2个耦合子问题：Safety‑RL算法通过学习状态特征，输出用户优先级权重并分配带宽；WMMSE算法根据用户优先级权重，计算预编码矩阵并进一步调度资源，得到Safety‑RL的奖励及代价。考虑CF下用户动态接入，提出具备用户可扩展性的分布式调度方案。仿真结果表明，所提方法能够在保证不同用户时延违反率约束的前提下提高系统频谱效率，与完全集中式的算法相比，分布式方案的性能表现相近，但可以有效减少用户动态接入时的额外模型训练开销。

References:

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Memo

Memo:
Received 2025-02-10,Revised 2025-04-08.
Biographies:Zhang Cheng (1988—), male, doctor, associate professor;Huang Yongming (corresponding author), male, doctor, professor, huangym@seu.edu.cn.
Foundation items:The National Natural Science Foundation of China (No. 62271140, 62225107), the Natural Science Foundation of Jiangsu Province (No. BK20240174), the Fundamental Research Funds for the Central Universities (No. 2242022k60002), the Fund of Jiangsu Provincial Scientific Research Center of Applied Mathematics (No. BK20233002).
Citation:ZHANG Cheng,ZHANG Li,MENG Fan,et al.Deterministic transmission in user-scalable cell-free MIMO-OFDM systems[J].Journal of Southeast University (English Edition),2025,41(4):430-436.DOI:10.3969/j.issn.1003-7985.2025.04.004.DOI:10.3969/j.issn.1003-7985.2025.04.004
Last Update: 2025-12-20