电子学报2025，Vol.53Issue(4)：1142-1152,11.DOI:10.12263/DZXB.20240798
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
Model-Driven Deep Learning-Enhanced Markov Chain Monte Carlo MIMO Detector:Design,Simulation and Prototyping
摘要
Abstract
The scale of multiple-input multiple-output(MIMO)systems is growing rapidly,leading to a dramatic in-crease 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(MC-MC)-based detection algorithms can achieve near-optimal detection performance with polynomial complexity,but their per-formance 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 trans-mission environments.关键词
MIMO检测/马尔可夫链蒙特卡罗/模型驱动/深度学习/原型验证平台Key words
MIMO detection/Markov chain Monte Carlo/model-driven/deep learning/prototyping platform分类
信息技术与安全科学引用本文复制引用
曹益枭,周星宇,张静,梁乐,李勇,金石..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(4):1142-1152,11.基金项目
国家自然科学基金(No.62261160576,No.62301154,No.623B2019,No.62231019) （No.62261160576,No.62301154,No.623B2019,No.62231019）
先进通信网全国重点实验室基金课题(No.SCX23641X011) National Natural Science Foundation of China(No.62261160576,No.62301154,No.623B2019,No.62231019) （No.SCX23641X011）
National Key Laboratory of Advanced Communication Networks Foundation Project(No.SCX23641X011) （No.SCX23641X011）
