Low-complexity signal detection based on deep unfolding adaptive iteration for cell-free massive MIMO systems

SUN Yan; LI Fei; LI Ting; SONG Yunchao

doi:10.11959/j.issn.2096-3750.2025.00400

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Low-complexity signal detection based on deep unfolding adaptive iteration for cell-free massive MIMO systems

Theory and Technology | 更新时间：2026-01-04

- Low-complexity signal detection based on deep unfolding adaptive iteration for cell-free massive MIMO systems
- Chinese Journal on Internet of Things Vol. 9, Issue 4, Pages: 149-158(2025)
- 作者机构：
  
  1.南京邮电大学通信与信息工程学院，江苏南京 210003
  2.南京邮电大学电子与光学工程学院，江苏南京 210003
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62271265)
- DOI：10.11959/j.issn.2096-3750.2025.00400
  CLC：
- Received：26 March 2024，
  
  Revised：2024-07-11，
  
  Published：10 December 2025
- 稿件说明：
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孙焱,李飞,李汀等.基于深度展开自适应迭代的去蜂窝大规模MIMO系统低复杂度信号检测[J].物联网学报,2025,09(04):149-158.

SUN Yan,LI Fei,LI Ting,et al.Low-complexity signal detection based on deep unfolding adaptive iteration for cell-free massive MIMO systems[J].Chinese Journal on Internet of Things,2025,09(04):149-158.
孙焱,李飞,李汀等.基于深度展开自适应迭代的去蜂窝大规模MIMO系统低复杂度信号检测[J].物联网学报,2025,09(04):149-158. DOI： 10.11959/j.issn.2096-3750.2025.00400.

SUN Yan,LI Fei,LI Ting,et al.Low-complexity signal detection based on deep unfolding adaptive iteration for cell-free massive MIMO systems[J].Chinese Journal on Internet of Things,2025,09(04):149-158. DOI： 10.11959/j.issn.2096-3750.2025.00400.

摘要

去蜂窝大规模多输入多输出（CF-MMIMO

cell-free massive multiple-input multiple-output）系统融合了分布式天线系统与大规模MIMO的优势，通过多接入点（AP

access point）协作，极大改善了网络覆盖范围，提高了频谱效率，被认为是未来6G网络的潜在关键架构。然而，现有CF-MMIMO系统检测算法无法实现复杂度与检测性能的良好平衡。为了解决这一问题，提出了一种基于深度展开网络的低复杂度检测算法——理查德森半迭代网络（RSI-Net

Richardson semi-iterative network），用于实现上行CF-MMIMO系统的低复杂度信号检测。该算法引入理查德森半迭代（SI

semi-iterative）理论，并采用深度展开网络（DUN

deep unfolding network）以隐藏层参数训练的方式，取代原有的参数估计方案，以自适应信道统计特性的变化来实现最优的参数估计。同时，引入缩减因子改善迭代矩阵的特征值分布，从而加速收敛。仿真结果表明，在信道硬化特性减弱的CF-MMIMO系统中，无论是用户数量还是AP数量的变化，RSI-Net算法均能够保持较低的计算开销和出色的检测性能。

Abstract

The cell-free massive multiple-input multiple-output (CF-MMIMO) system integrates the advantages of distributed antenna systems and massive MIMO

enabling significant improvements in user coverage and spectrum efficiency through the collaboration of multiple access points (AP). It is a highly promising architecture for future 6G communication networks. However

existing detection algorithms for CF-MMIMO systems fail to achieve a good balance between complexity and detection performance. To address these challenges

a low-complexity optimal signal detection algorithm was proposed for uplink CF-MMIMO systems

called Richardson semi-iterative network (RSI-Net)

based on deep unfolding networks. The Richardson semi-iterative (SI) theory was introduced

and replaced the existing parameter estimation scheme with a deep unfolding network (DUN) trained with hidden layer parameters to achieve adaptive parameter estimation in response to changing channel statistics. Additionally

to accelerate convergence

a scaling factor was introduced to improve the distribution of eigenvalues in the iterative matrix. Simulation results demonstrated that RSI-Net algorithm maintained low computational costs and excellent detection performance in CF-MMIMO systems with weakened channel hardening characteristics

regardless of changes in the number of users or AP.

关键词

Keywords

references

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