郝保明1,
邵凯2
1.宿州学院机械与电子工程学院 宿州 234000
2.重庆邮电大学移动通信重点实验室 重庆 400065
基金项目:安徽省科技重大专项(18030901023)，宿州学院重点科研项目(2016yzd02)
详细信息
作者简介:武汉：男，1991年生，助理教授，研究方向为新型多载波调制技术、新型多址接入技术
郝保明：男，1980年生，讲师，研究方向为数字图像处理和语音信号处理
邵凯：男，1977年生，副教授，研究方向为新型多载波调制技术、新型多址接入技术
通讯作者:武汉 wuhan05023@163.com
中图分类号:TN911计量
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被引次数:0
出版历程
收稿日期:2020-06-30
修回日期:2020-12-13
网络出版日期:2020-12-30
刊出日期:2021-08-10
An Improved Detection Algorithm for Sparse Code Multiple Access System
Han WU1,,,Baoming HAO1,
Kai SHAO2
1. College of Mechanical and Electronic Engineering, Suzhou University, Suzhou 234000, China
2. Chongqing Key Laboratory of Mobile Communication Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Funds:Anhui Science and Technology Major Project (18030901023), The Key Scientific Research Project of Suzhou University (2016yzd02)
摘要
摘要:稀疏码分多址(SCMA)系统中基于球形译码算法(SD)由于具有优良的性能受到越来越多的关注，然而现有基于SD的算法只能用于某些特定星座结构的检测，导致其应用受限。该文提出一种适用于任意星座且性能达到最大似然(ML)算法性能的改进球形译码(ISD)算法。该算法将用户星座图拆分，并将用户星座图转换为多层树结构，利用对树结构的搜索完成译码操作，并且对树的搜索是从高层向低层进行的。因此，可以将SCMA检测转换成最小化树结构部分度量问题；同时，所提出的改进算法对星座图的结构无任何限制，所以该算法适用于任意类型的星座图。此外，由于SCMA的稀疏性，每一层的部分度量均与分配给每个资源元素的用户无关，从而进一步降低了计算复杂度。
关键词:稀疏码分多址/
球形译码/
星座图/
最大似然/
信道矩阵
Abstract:Sphere Decoding (SD) based detection algorithms for Sparse Code Multiple Access (SCMA) system receive more and more attention due to excellent performance. However, the existing SD-based detection algorithms can only be applied to some certain constellation structures for SCMA system, which limit their application. An Improved SD (ISD) detection scheme is proposed in this paper, which achieves ML (Maximum Likelihood) performance for any constellation. The improved algorithm splits user constellations and converts them into a multi-layer tree structure, which also uses the research of the tree carried out from the high-layer to the low-layer to achieve the decoding operation. Therefore, the SCMA detection can be converted into minimizing the metrics of the tree structure. In the meanwhile, the improved algorithm does not have any restrictions on the structure of the constellation, so it is suitable for any structure of constellation. In addition, due to the sparse characteristics of SCMA structure, the partial metric at each layer is independent of users assigned to each Resource Element (RE), which further reduces the computational complexity.
Key words:Sparse Code Multiple Access (SCMA)/
Sphere Decoding (SD)/
Constellation/
Maximum Likelihood (ML)/
Channel matrix
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