基于稀疏贝叶斯学习的大规模多用户检测算法

doi:10.11959/j.issn.1000-436x.2023204

通信学报 ›› 2023, Vol. 44 ›› Issue (10): 186-197.doi: 10.11959/j.issn.1000-436x.2023204

• 学术论文 • 上一篇

基于稀疏贝叶斯学习的大规模多用户检测算法

陈平平¹^,², 王宣达¹^,², 谢肇鹏¹, 方毅³, 陈家辉¹

¹ 福州大学先进制造学院，福建泉州 362251
² 福建省媒体信息智能处理与无线传输重点实验室，福建福州 350108
³ 广东工业大学信息工程学院，广东广州 510006

修回日期:2023-10-13 出版日期:2023-10-01 发布日期:2023-10-01
作者简介:陈平平（1986− ），男，福建泉州人，博士，福州大学教授、博士生导师，主要研究方向为压缩感知、无线通信、信道编码调制、多用户接入
王宣达（1998− ），男，山西吕梁人，福州大学硕士生，主要研究方向为压缩感知、无线通信、多用户接入
谢肇鹏（1995− ），男，福建晋江人，福州大学讲师，主要研究方向为压缩感知、无线通信、多用户接入
方毅（1986− ），男，广东广州人，广东工业大学教授、博士生导师，主要研究方向为信道编码与调制、无线通信、机器学习
陈家辉（1998− ），男，福建龙岩人，福州大学硕士生，主要研究方向为压缩感知、无线通信、多用户接入
基金资助:
国家自然科学基金资助项目(62171135);福建省杰出青年基金资助项目(2022J06010);福建省技术创新重点攻关基金资助项目(2023XQ004)

Sparse Bayesian learning-based massive multi-user detection algorithm

Pingping CHEN¹^,², Xuanda WANG¹^,², Zhaopeng XIE¹, Yi FANG³, Jiahui CHEN¹

¹ School of Advanced Manufacturing, Fuzhou University, Quanzhou 362251, China
² Fujian Provincial Key Laboratory of Intelligent Processing and Wireless Transmission of Media Information, Fuzhou University, Fuzhou 350108, China
³ College of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China

Revised:2023-10-13 Online:2023-10-01 Published:2023-10-01
Supported by:
The National Natural Science Foundation of China(62171135);Fujian Province Distinguished Talent Project(2022J06010);Key Technology Development Project of Fujian Province(2023XQ004)

摘要/Abstract

摘要：

针对现有算法大都基于高斯逆伽马先验模型的稀疏贝叶斯学习（GIG-SBL），忽略了稀疏解所对应的支撑集向量稀疏性的问题，提出一种基于伯努利高斯逆伽马先验模型的稀疏贝叶斯学习（BGIG-SBL）架构，通过引入一个伯努利先验的二元向量，设计了单测量向量（SMV）的BGIG-SBL-SMV算法，结合支撑集向量的稀疏性提高重构性能。进一步将该算法扩展到多测量向量（MMV）方案，通过共享相同控制稀疏解的超参数，利用 MMV 的联合稀疏性提出BGIG-SBL-MMV算法。实验结果表明，所提BGIG-SBL-SMV算法相较于传统GIG-SBL-SMV算法，在 mMTC 用户检测场景可实现 2 dB 的性能增益；同时，所提 BGIG-SBL-MMV 算法相对于单测量向量BGIG-SBL-SMV算法，用户检测率和数据检错率的性能增益可达到4 dB，证明了所提算法的优越性。

关键词: 稀疏贝叶斯学习, 压缩感知, 多用户检测, 海量机器通信

Abstract:

Aiming at the problem that most existing algorithms were based on the Gaussian inverse gamma prior model (GIG-SBL), which ignored the sparsity of the support set vector within the sparse solution, a sparse Bayesian learning framework based on the Bernoulli Gaussian inverse gamma prior model (BGIG-SBL) was proposed.By introducing a binary vector of Bernoulli prior, the BGIG-SBL-SMV algorithm based on single measurement vector (SMV) was designed, which utilized the sparsity of the support set vector to improve reconstruction performance.Then the proposed algorithm was extended to multiple measurement vector (MMV) models by sharing the same hyperparameters.The BGIG-SBL-MMV algorithm was developed based on the joint sparsity of MMV.The experimental results show that the proposed BGIG-SBL-SMV can achieve a performance gain of 2 dB in mMTC over the traditional GIG-SBL-SMV.Moreover, BGIG-SBL-MMV has a performance gain of 4 dB as compared with its SMV counterparts, which demonstrates the advantages of the proposed schemes.

Key words: sparse Bayesian learning, compressive sensing, multi-user detection, mMTC

中图分类号:

TN929.5

陈平平, 王宣达, 谢肇鹏, 方毅, 陈家辉. 基于稀疏贝叶斯学习的大规模多用户检测算法[J]. 通信学报, 2023, 44(10): 186-197.

Pingping CHEN, Xuanda WANG, Zhaopeng XIE, Yi FANG, Jiahui CHEN. Sparse Bayesian learning-based massive multi-user detection algorithm[J]. Journal on Communications, 2023, 44(10): 186-197.

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基于稀疏贝叶斯学习的大规模多用户检测算法

Sparse Bayesian learning-based massive multi-user detection algorithm

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