免授权NOMA 系统中基于变步长自适应匹配追踪的抗干扰多用户检测算法

doi:10.11896/jsjkx.220400170

计算机科学 ›› 2023, Vol. 50 ›› Issue (5): 322-328.doi: 10.11896/jsjkx.220400170

免授权NOMA 系统中基于变步长自适应匹配追踪的抗干扰多用户检测算法

李玉阁, 王天荆, 沈航, 罗小康, 白光伟

南京工业大学计算机科学与技术学院南京 211816

收稿日期:2022-04-17 修回日期:2022-09-13 出版日期:2023-05-15 发布日期:2023-05-06
通讯作者: 王天荆(Lyuge@njtech.edu.cn)
作者简介:(lyuge@njtech.edu.cn)
基金资助:
国家自然科学基金(61501224,61502230);江苏省自然科学基金项目(BK20201357);江苏省“六大人才高峰”高层次人才项目(RJFW-020);江苏省大数据安全与智能处理重点实验室项目(南京邮电大学)(BDSIP1910);计算机软件新技术国家重点实验室项目(南京大学)(KFKT2017B21);江苏省研究生科研与实践创新计划(SJCX21_0486)

Anti-interference Multiuser Detection Algorithm Based on Variable Step Size Adaptive Matching Pursuit in Grant-free NOMA System

LI Yuge, WANG Tianjing, SHEN Hang, LUO Xiaokang, BAI Guangwei

School of Computer Science and Technology,Nanjing University of Technology,Nanjing 211816,China

Received:2022-04-17 Revised:2022-09-13 Online:2023-05-15 Published:2023-05-06
About author:LI Yuge,born in 1997,postgraduate.His main research interests include wireless network and machine learning.
WANG Tianjing,born in 1977,Ph.D,associate professor,master supervisor.Her main research interests include wireless network and machine learning.
Supported by:
National Natural Science Foundation of China(61501224,61502230),Natural Science Foundation of Jiangsu Province(BK20201357),Six Talent Peak High-level Talent Project of Jiangsu Province(RJFW-020),Jiangsu Key Laboratory Project of Big Data Security and Intelligent Processing(Nanjing University of Posts and Telecommunications)(BDSIP1910),State Key Laboratory Project of New Computer Software Technology(Nanjing University)(KFKT2017B21) and Jiangsu Graduate Scientific Research and Practice Innovation Plan(SJCX21_0486).

摘要/Abstract

摘要： 第五代移动通信系统(5G)通过非正交多址(NOMA)技术对无线通信资源进行非正交复用,以过载的方式提高了频谱利用效率和系统容量。NOMA系统采用免授权的方式减少了系统流程和信令开销,但是接收端需要进行多用户检测。基站利用活跃用户的稀疏特性,采用压缩感知(CS)重构算法恢复活跃用户的混合稀疏向量,实现了高效的多用户检测。但5G网络中基站密集部署增强了相邻小区间的干扰,因而增加了CS检测难度及降低了检测精度。针对免授权NOMA系统中多用户检测存在干扰的问题,提出了一种基于变步长自适应匹配追踪的抗干扰多用户检测算法。在稀疏度未知的情况下,该算法以大步长快速接近、小步长精确逼近稀疏度的自适应变步长方式,实现抗干扰的活跃用户检测。仿真结果表明,在不同过载率下,所提算法的误比特率均低于传统的基于OMP,gOMP和SAMP的多用户检测算法。

关键词: 免授权NOMA系统, 多用户检测, 抗干扰, 变步长自适应匹配追踪

Abstract: The fifth generation mobile communication system(5G) uses non-orthogonal multiple access(NOMA) technology for non-orthogonal multiplexing of wireless communication resources,which improves the spectrum utilization efficiency and system capacity by the way of overload.The NOMA system uses the grant-free mode to reduce the system flow and signaling overhead,but the receiver needs to perform multi-user detection.Based on the sparse characteristics of active users,the base station uses the compressed sensing(CS) reconstruction algorithm to recover the mixed sparse vectors of active users,and realizes efficient multi-user detection.The dense deployment of base stations in 5G network enhances the interferences among neighboring cells that increases the difficulty of CS-based detection and reduces the accuracy of detection.Aiming at the problem of interference in multi-user detection in the grant-free NOMA system,an anti-interference multiuser detection algorithm based on variable step size adaptive matching pursuit is proposed.Unknowing the sparse degree,the anti-interference active user detection can be realized by the adaptive variable step size way,in which the sparse degree is fast approached with large step size and accurately approximated with small step size.Simulation results show that,under different overload rates,the bit error rates of the proposed algorithm are lower than that of traditional multi-user detection algorithms based on OMP,gOMP and SAMP.

Key words: Grant-free non-orthogonal multiple access system, Multi-user detection, Anti-interference, Variable step size adaptive matching

中图分类号:

TN929.5

李玉阁, 王天荆, 沈航, 罗小康, 白光伟. 免授权NOMA 系统中基于变步长自适应匹配追踪的抗干扰多用户检测算法[J]. 计算机科学, 2023, 50(5): 322-328. https://doi.org/10.11896/jsjkx.220400170

LI Yuge, WANG Tianjing, SHEN Hang, LUO Xiaokang, BAI Guangwei. Anti-interference Multiuser Detection Algorithm Based on Variable Step Size Adaptive Matching Pursuit in Grant-free NOMA System[J]. Computer Science, 2023, 50(5): 322-328. https://doi.org/10.11896/jsjkx.220400170

参考文献

[1]EL A A,SINGH S K,PAN Y,et al.Block5gintell:blockchain for ai-enabled 5G networks[J].IEEE Access,2020,8:145918-145935.
[2]CISCO U.Cisco annual internet report(2018-2023) white paper[EB/OL].https://www.cisco.com/c/en/us/solutions/collateral/executive-perspectives/annual-internet-report/white-paper-c11-741490.html.
[3]GHOSH J,RA I H,SINGH S,et al.On the comparison of optimal NOMA and OMA in a paradigm shift of emerging technologies[J].IEEE Access,2022,10:11616-11632.
[4]HASAN S M,MAHATA K,HYDER M M.Uplink Grant-Free NOMA with Sinusoidal Spreading Sequences[J].IEEE Transactions on Communications,2021,69(6):3757-3770.
[5]LI B,ZHENG J,GAO Y.Compressed Sensing Based Multiuser Detection of Grant-Free NOMA With Dynamic User Activity[J].IEEE Communications Letters,2021,26(1):143-147.
[6]WANG J,JIANG C,KUANG L.Iterative NOMA Detection for Multiple Access in Satellite High-Mobility Communications[J].IEEE Journal on Selected Areas in Communications,2022,40(4):1101-1113.
[7]HUSMANN C,JAYAWARDENA C,MAAREF A,et al.Low-Complexity SCMA detection for unsupervised user access[J].IEEE Communications Letters,2020,25(3):1019-1023.
[8]DING T,YUAN X,LIEW S C.Sparsity learning-based multi-user detection in grant-free massive-device multiple access[J].IEEE Transactions on Wireless Communications,2019,18(7):3569-3582.
[9]LI M,ZHOU Z G,WANG T.Multiuser Detection Scheme forSCMA Systems Based on Stability of Belief Propagation[J].Computer Science,2019,46(1):138-142.
[10]WEI C,LIU H,ZHANG Z,et al.Approximate message passing-based joint user activity and data detection for NOMA[J].IEEE Communications Letters,2016,21(3):640-643.
[11]HARA T,ISHIBASHI K.Low complexity uplink grant-freeNOMA based on boosted approximate message passing[C]//2019 53rd Asilomar Conference on Signals,Systems,and Computers.IEEE,2019:1877-1880.
[12]ZHANG L,MENG X,WANG L,et al.Joint User Activity and Data Detection for NOMA via the Integrated Framework of Expectation Maximization and Expectation Propagation[C]//2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications(SPAWC).IEEE,2019:1-5.
[13]KE M,GAO Z,WU Y,et al.Compressive sensing-based adaptive active user detection and channel estimation:Massive access meets massive MIMO[J].IEEE Transactions on Signal Proces-sing,2020,68:764-779.
[14]ZHANG X,YANG L,DING Z,et al.Sparse vector coding-based multi-carrier NOMA for in-home health networks[J].IEEE Journal on Selected Areas in Communications,2020,39(2):325-337.
[15]KIM J,WANG J,NGUYEN L T,et al.Joint sparse recoveryusing signal space matching pursuit[J].IEEE Transactions on Information Theory,2020,66(8):5072-5096.
[16]SHEN B,WU H B,CUI T P,et al.An optimal number of indices aided gOMP algorithm for multi-user detection in NOMA system[J].Journal of Electronics & Information Technology,2020,42(3):621-628.
[17]QIAO L,ZHANG J,GAO Z,et al.Compressive sensing basedmassive access for IoT relying on media modulation aided machine type communications[J].IEEE Transactions on Vehicular Technology,2020,69(9):10391-10396.
[18]WANG Q Z,FANG D,WU G F.Multi-User detection based on sparsity adaptive matching pursuit compressive sensing for uplink grant-free non-orthogonal multiple access[J].Journal of Electronics and Information Technology,2020,42(9):2216-2222.
[19]MUKHERJEE S,KIM D,LEE J.Base Station CoordinationScheme for Multi-tier Ultra-dense Networks[J].IEEE Transactions on Wireless Communications,2021,20(11):7317-7332.
[20]KIM J,WANG J,SHIM B.Optimal restricted isometry condition of normalized sampling matrices for exact sparse recovery with orthogonal least squares[J].IEEE Transactions on Signal Processing,2021,69:1521-1536.
[21]LI J,CHOW P,PENG Y,et al.FPGA implementation of an improved OMP for compressive sensing reconstruction[J].IEEE Transactions on Very Large Scale Integration(VLSI) Systems,2021,29(2):259-272.
[22]ZHAO C,HWANG W L,LIN C L,et al.Greedy orthogonalmatching pursuit for subspace clustering to improve graph connectivity[J].Information Sciences,2018,459:135-148.
[23]LEE D.MIMO OFDM channel estimation via block stagewise orthogonal matching pursuit[J].IEEE Communications Letters,2016,20(10):2115-2118.
[24]SHOITAN R,NOSSAIR Z,IBRAHIM I I,et al.Improving the reconstruction efficiency of sparsity adaptive matching pursuit based on the Wilkinson matrix[J].Frontiers of Information Technology & Electronic Engineering,2018,19(4):503-512.
[25]XU H,LI C.Massive MIMO Channel Estimation Based on Improved Variable Step Size Regular Backtracking SAMP Algorithms[C]//2019 IEEE 19th International Conference on Communication Technology(ICCT).IEEE,2019:45-49.
[26]WANG X,ZHNAG Y X,HUANG Z Q.Regularized back tra-cking adaptive tracking algorithm based on variable step size[J].Journal of Electronics,2018,46(8):1829-1834.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

免授权NOMA 系统中基于变步长自适应匹配追踪的抗干扰多用户检测算法

Anti-interference Multiuser Detection Algorithm Based on Variable Step Size Adaptive Matching Pursuit in Grant-free NOMA System

PDF (PC)

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 11

Metrics

本文评价

推荐阅读 0

[1]	邱文静, 韩晨, 刘爱军. 干扰环境下无人机群动态频谱决策方法 Dynamic Spectrum Decision-making Method for UAV Swarms in Jamming Environment 计算机科学, 2022, 49(12): 326-331. https://doi.org/10.11896/jsjkx.220400228
[2]	杨莺, 杨武德, 吴华瑞, 缪祎晟. 基于移动sink的农田无线传感器网络数据收集策略 Mobile Sink Based Data Collection Strategy for Farmland WSN 计算机科学, 2019, 46(4): 106-111. https://doi.org/10.11896/j.issn.1002-137X.2019.04.017
[3]	董文华,郭淑霞. 基于优选准则的混合混沌序列的生成 Generation of Mixed Chaotic Sequences Based on Optimization Criterion 计算机科学, 2016, 43(Z11): 268-270. https://doi.org/10.11896/j.issn.1002-137X.2016.11A.062
[4]	郭淑霞,董中要,张宁,刘孟江. 北斗卫星导航接收端抗干扰性能测试系统构建方法研究 Anti-jamming Performance Test System Building Method of Beidou Satellite Navigation Receiver 计算机科学, 2013, 40(7): 28-31.
[5]	崔志富，张杭，张江，辜方林. 基于非高斯度量的多用户DS-CDMA系统多干扰消除与用户检测 External Interference Cancelation and Blind Multi-user Detection of DS-CDMA System Based on Non-gaussianity Measure 计算机科学, 2013, 40(1): 54-58.
[6]	. 数据链抗干扰性能分析计算机科学, 2009, 36(6): 108-111.
[7]	. 基于图像纹理自适应的边缘检测算法计算机科学, 2008, 35(5): 243-245.
[8]	杨力薄煜明田明浩. BOC调制信号的抗干扰性能研究计算机科学, 2008, 35(4): 33-35.
[9]	温万惠刘光远贺一. 基于自适应集中性和多样性搜索策略的多用户检测方法计算机科学, 2005, 32(3): 44-46.
[10]	朱逢华王崇俊陈世福. 电脑刺绣中的字符形状分割算法计算机科学, 2005, 32(10): 197-199.
[11]	刘红卫王新辉刘三阳. 多用户检测问题的半定规划坐标下降算法计算机科学, 2003, 30(5): 134-135.