Generalized message passing detection of SCMA systems based on dynamic factor graph for better and flexible performance-complexity tradeoff

作     者：Fangqi DONG Huijuan ZHENG Lei MA Sheng TONG Fangqi DONG;Huijuan ZHENG;Lei MA;Sheng TONG

作者机构：State Key Laboratory of Integrated Services Networks Xidian University School of Electronic Engineering Xi'an University of Posts and Telecommunications 

出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))

年 卷 期：2022年第65卷第5期

页      面：242-249页

核心收录：

学科分类：0810[工学-信息与通信工程] 080904[工学-电磁场与微波技术] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术] 081001[工学-通信与信息系统] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by National Natural Science Foundation of China (Grant Nos. 61871029, 61771364) Fundamental Research Funds for the Central Universities (Grant No. JB190122.) 

主　　题：sparse code multiple access (SCMA) multiuser detection message passing algorithm (MPA) factor graph computational complexity 

摘      要：Multiuser detection based on the message passing algorithm(MPA) has been considered for sparse code multiple access(SCMA) systems. Recently, some complexity-reduced MPA detectors have been proposed, among which the MPA detector based on dynamic factor graph(DFG-MPA) has been shown to outperform other MPA detectors with comparable complexities. However, all these MPA detectors are somehow not very flexible in terms of performance-complexity tradeoff, i.e., the granularities of computational complexity reduction are relatively large. In this paper, a generalized scheme of DFG-MPA, termed as GDFG-MPA, is proposed to make a better and more flexible performance-complexity tradeoff. The proposed scheme features two aspects:(1) instead of banning a message update forever, a banned message update at some iteration is allowed to be updated at later iterations;(2) different numbers of message updates are banned from updating at different iterations. Optimization of GDFG-MPA can be made by allocating banned message updates among iterations. Numerical results have demonstrated that compared to DFG-MPA the proposed GDFG-MPA can achieve much better performance at the same computational complexity or achieve the same performance with much lower complexity. Moreover, the proposed GDFG-MPA is more flexible in tuning the performance and complexity tradeoff.