Model-Driven Deep Learning-Enhanced Markov Chain Monte Carlo MIMO Detector: Design, Simulation and Prototyping

CAO Yi-xiao; ZHOU Xing-yu; ZHANG Jing; LIANG Le; LI Yong; JIN Shi

doi:10.12263/DZXB.20240798

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Model-Driven Deep Learning-Enhanced Markov Chain Monte Carlo MIMO Detector: Design, Simulation and Prototyping

Special Issue: Recipients of CIE Science and Technology Awards | 更新时间：2025-07-24

- Model-Driven Deep Learning-Enhanced Markov Chain Monte Carlo MIMO Detector: Design, Simulation and Prototyping
- ACTA ELECTRONICA SINICA Vol. 53, Issue 4, Pages: 1142-1152(2025)
- 作者机构：
  
  1.东南大学信息科学与工程学院，江苏南京 211189
  2.先进通信网全国重点实验室，河北石家庄 050081
  3.中国电科网络通信研究院，河北石家庄 050081
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62261160576;62301154;623B2019;62231019);National Key Laboratory of Advanced Communication Networks Foundation Project(SCX23641X011)
- DOI：10.12263/DZXB.20240798
  CLC： TN929.5;
- Received：03 September 2024，
  
  Revised：2025-04-23，
  
  Published：25 April 2025
- 稿件说明：
移动端阅览
曹益枭, 周星宇, 张静, 等. 模型驱动深度学习增强的马尔可夫链蒙特卡罗MIMO检测器：设计、仿真与原型验证[J]. 电子学报, 2025, 53(04): 1142-1152.

CAO Yi-xiao, ZHOU Xing-yu, ZHANG Jing, et al. Model-Driven Deep Learning-Enhanced Markov Chain Monte Carlo MIMO Detector: Design, Simulation and Prototyping[J]. Acta Electronica Sinica, 2025, 53(04): 1142-1152.
曹益枭, 周星宇, 张静, 等. 模型驱动深度学习增强的马尔可夫链蒙特卡罗MIMO检测器：设计、仿真与原型验证[J]. 电子学报, 2025, 53(04): 1142-1152. DOI：10.12263/DZXB.20240798

CAO Yi-xiao, ZHOU Xing-yu, ZHANG Jing, et al. Model-Driven Deep Learning-Enhanced Markov Chain Monte Carlo MIMO Detector: Design, Simulation and Prototyping[J]. Acta Electronica Sinica, 2025, 53(04): 1142-1152. DOI：10.12263/DZXB.20240798

摘要

多输入多输出（Multiple-Input Multiple-Output，MIMO）系统规模日益增长，导致接收机信号检测计算复杂度急剧上升，传统检测算法难以在误码性能和复杂度之间取得良好平衡.基于马尔可夫链蒙特卡罗（Markov Chain Monte Carlo，MCMC）的检测算法能以多项式量级的复杂度实现近最优的检测性能，然而该方法在低采样数下性能损失严重.因此，本文引入了基于模型驱动的深度学习技术，将MCMC迭代过程展开为级联网络结构，向网络中引入可训练参数，通过深度学习方法优化参数设置.根据复杂度分析与仿真验证，所提方案在编码场景下的误码性能优于原始算法约1 dB，同时计算复杂度显著低于原始算法.为验证模型驱动深度学习方案在实际传输中的性能，搭建2 × 2 MIMO智能通信原型验证平台，并进行端到端空口传输测试.测试结果表明，模型驱动深度学习增强的MCMC检测算法可以更低的计算复杂度实现误码性能优势，从而证实了所提方案在实际传输环境中的有效性和鲁棒性.

Abstract

The scale of multiple-input multiple-output (MIMO) systems is growing rapidly

leading to a dramatic increase in the computational complexity of receiver signal detection. Traditional detection algorithms struggle to achieve a good balance between bit error rate (BER) performance and computational complexity. Markov chain Monte Carlo (MCMC)-based detection algorithms can achieve near-optimal detection performance with polynomial complexity

but their performance deteriorates significantly with low sampling rates. To address this issue

this paper introduces a model-driven deep learning approach

which transforms the MCMC iterative process into a cascade network structure. Trainable parameters are incorporated into the network

and deep learning techniques are employed to optimize their settings. Based on complexity analysis and simulation results

the proposed method outperforms the original algorithm in terms of BER by approximately 1 dB in coding scenarios

while significantly reducing computational complexity. To validate the performance of the model-driven deep learning approach in real-world transmission

a 2×2 MIMO smart communication prototype is developed

and end-to-end air interface transmission tests are conducted. The test results demonstrate that the MCMC detection algorithm enhanced by the model-driven deep learning approach still achieves a significant BER performance advantage with lower computational complexity

thereby confirming the effectiveness and robustness of the proposed solution in practical transmission environments.

关键词

Keywords

references

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