An Improved Threshold-based Low Complexity Multiuser Detection Scheme for Sparse Code Multiple Access System
-
摘要: 稀疏码多址接入(Sparse Code Multiple Access, SCMA)作为一种基于多维码本的非正交多址技术,能有效满足5G的巨连接、高频谱效率和毫秒级时延需求。针对基于门限的消息传递算法(Message Passing Algorithm, MPA)存在低门限时误比特率(Bit Error Rate, BER)较高的问题,该文提出一种改进的SCMA多用户检测算法。所提出的算法在基于门限MPA的基础上,增加了对用户节点稳定性必要条件的判决,即只有符合门限条件并通过用户节点稳定性必要条件判决的用户才能被提前解码。这提高了提前判决码字的可靠性并减少了因变相硬判的检测机制造成的后验软信息损失。与基于门限的MPA相比,所提出的算法可使消息在低门限时迭代得更加充分,从而在低门限时仍然能够使SCMA用户获得较好的BER性能。仿真结果表明,在低门限时采用该文所提出的算法SCMA用户BER性能明显好于仅采用基于门限的MPA的BER性能。Abstract: Sparse Code Multiple Access (SCMA) is a non-orthogonal multiple access technology based on multi-dimensional codebook, which can effectively address challenges in 5G such as massive connectivity, high spectral efficiency and millisecond delay. For the problem that threshold-based Message Passing Algorithm (MPA) has a high Bit Error Rate (BER) when the threshold is low, an improved SCMA multiuser detection scheme is proposed in this paper. Based on the threshold MPA scheme, the proposed scheme adds a judgment on the necessary conditions of user node stability. The users who not only satisfy the threshold criterion but also pass the judgment on the necessary conditions of user node stability can be decoded in advance. This improves the reliability of codeword which is judged in advance and reduces the loss of posterior soft information caused by the detection mechanism similar to the hard decision. Compared with the threshold-based MPA scheme, the proposed scheme allows the messages to be iterated more fully at low thresholds, which makes SCMA users achieve better BER performance at low thresholds. The simulation results show that better BER performance is achieved with the proposed scheme than that with the threshold-based MPA scheme for SCMA users.
-
DAI Linglong, WANG Bichai, YUAN Yifei, et al. Non- orthogonal multiple access for 5G: solutions, challenges, opportunities, and future research trends[J]. IEEE Communications Magazine, 2015, 53(9): 74-81. doi: 10.1109/ MCOM.2015.7263349. 张平, 陶运铮, 张治. 5G若干关键技术评述[J]. 通信学报, 2016, 37(7): 15-29. doi: 10.11959/j.issn.1000-436x.2016130. ZHANG Ping, TAO Yunzheng, and ZHANG Zhi. Survey of several key technologies for 5G[J]. Journal on Communications, 2016, 37(7): 15-29. doi: 10.11959/j.issn. 1000-436x.2016130. DONG Lei, ZHAO Hongyi, CHEN Yan, et al. Introduction on IMT-2020 5G trials in China[J]. IEEE Journal on Selected Areas in Communications, 2017, 35(8): 1849-1866. doi: 10.1109/JSAC.2017.2710678. CHEN Yan, BAYESTEH Alireza, WU Yiqun, et al. SCMA: A promising non-orthogonal multiple access technology for 5G networks[C]. 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall), Montreal, Canada, 2016: 1-6. doi: 10.1109/VTCFall.2016.7881213. HOSHYAR R, WATHAN F P, and TAFAZOLLI R. Novel low-density signature for synchronous CDMA systems over AWGN channel[J]. IEEE Transactions on Signal Processing, 2008, 56(4): 1616-1626. doi: 10.1109/TSP.2007.909320. BEEK J V D and POPOVIC B M. Multiple access with low-density signatures[C]. IEEE Global Telecommunications Conference, Honolulu, USA, 2009: 1-6. doi: 10.1109/ GLOCOM.2009.5425243. HOSHYAR R, RAZAVI R, and AL-IMARI M. LDS-OFDM an efficient multiple access technique[C]. 2010 IEEE 71st IEEE Vehicular Technology Conference (VTC 2010-Spring), Taipei, 2010: 1-5. doi: 10.1109/VETECS.2010.5493941. NIKOPOUR H and BALIGH H. Sparse code multiple access[C]. 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), London, UK, 2013: 332-336. doi: 10.1109/PIMRC. 2013.6666156. TAHERZADEH M, NIKOPOUR H, BAYESTECH A, et al. SCMA codebook design[C]. IEEE Vehicular Technology Conference Fall, Vancouver, Canada, 2014: 14-17. doi: 10.1109/VTCFall.2014.6966170. 廉晋, 周世东, 张秀军, 等. 超蜂窝架构下基于跳码的SCMA控制信道接入设计[J]. 中国科学:信息科学, 2017, 47(6): 789-799. doi: 10.1360/N112017-00038. LIAN Jin, ZHOU Shidong, ZHANG Xiujun, et al. An SCMA control channel accessing scheme based on codebook-hopping under hyper-cellular network architecture[J]. Scientia Sinica (Informationis), 2017, 47(6): 789-799. doi: 10.1360/ N112017-00038. KSCHISCHANG F R, FREY B J, and LOELIGER H. Factor graphs and the sum-product algorithm[J]. IEEE Transactions on Information Theory, 2001, 47(2): 498-519. doi: 10.1109 /18.910572. YANG Lin, LIU Yunyun, and SIU Yunming. Low complexity message passing algorithm for SCMA system[J]. IEEE Communications Letters, 2016, 20(12): 2466-2469. doi: 10.1109/LCOMM.2016.2609382. XIAO Kexin, XIAO Baicen, ZHANG Shutian, et al. Simplified multiuser detection for SCMA with sum-product algorithm[C]. 2015 International Conference on Wireless Communications Signal Processing (WCSP), Nanjing, China, 2015: 1-5. doi: 10.1109/WCSP.2015.7341328. DU Yang, DONG Binhong, CHEN Zhi, et al. A fast convergence multiuser detection scheme for uplink SCMA systems[J]. IEEE Wireless Communications Letters, 2016, 5(4): 388-391. doi: 10.1109/LWC.2016.2565581. 杜洋, 董彬虹, 王显俊, 等. 基于串行策略的SCMA多用户检测算法[J]. 电子与信息学报, 2016, 38(8): 1888-1893. doi: 10.11999/JEIT151259. DU Yang, DONG Binhong, WANG Xianjun, et al. Multiuser detection scheme for SCMA systems based on serial strategy[J]. Journal of Electronics Information Technology, 2016, 38(8): 1888-1893. doi: 10.11999/JEIT151259. 期刊类型引用(21)
1. 李民,郭琳,姚雄. 优化高斯过程回归在太阳能集热效率预测上的应用. 电网与清洁能源. 2023(08): 127-131+138 . 百度学术
2. Han-shan Li. Recognition model and algorithm of projectiles by combining particle swarm optimization support vector and spatial-temporal constrain. Defence Technology. 2023(09): 273-283 . 必应学术
3. 何旭,席佩瑶,辛云宏. 基于代价敏感思想和自适应增强集成的SVM多分类算法. 微型电脑应用. 2023(09): 1-3 . 百度学术
4. 徐红先,张书玮. 基于极限学习机及多姿态信息融合的步态识别. 机械. 2023(11): 72-80 . 百度学术
5. 陈晓禾,曹旭刚,陈健生,胡春华,马羽. 基于三维卷积的帕金森患者拖步识别. 电子与信息学报. 2021(12): 3467-3475 . 本站查看
6. 雷建超,刘栋博,房玉,庄祖江,刘俊豪. 基于表面肌电信号的性别差异性手势识别. 中国医学物理学杂志. 2020(03): 337-341 . 百度学术
7. 金鑫,冯毅,尤雪汐,王佳欣. 基于机器学习的信息安全设备调配保障技术研究. 电子科技. 2020(08): 80-86 . 百度学术
8. 孟明,闫冉,高云园,佘青山. 基于多元变分模态分解的脑电多域特征提取方法. 传感技术学报. 2020(06): 853-860 . 百度学术
9. 王志芳,王书涛,王贵川. 粒子群优化BP神经网络在甲烷检测中的应用. 光子学报. 2019(04): 147-154 . 百度学术
10. 邹倩颖,王小芳. 粒子群优化BP神经网络在步态识别中的研究. 实验技术与管理. 2019(08): 130-133+138 . 百度学术
11. 郭海山,高波涌,陆慧娟. 基于Boruta-PSO-SVM的股票收益率研究. 传感器与微系统. 2018(03): 51-53+57 . 百度学术
12. 周长林,钱志升,王勤民,余道杰,程俊平. 基于PSO-SVM方法的电源线传导泄漏信号识别与还原. 电子与信息学报. 2018(09): 2206-2211 . 本站查看
13. 赵荣建,汤敏芳,陈贤祥,杜利东,曾华林,赵湛,方震. 基于光纤传感的生理参数监测系统研究. 电子与信息学报. 2018(09): 2182-2189 . 本站查看
14. 胡长俊,袁树杰. 煤矿井下WSN中基于自适应粒子群聚类算法的多sink节点部署. 计算机科学. 2018(11): 103-107+123 . 百度学术
15. 王秀娟,相从斌. 基于累积量的DoS攻击检测算法. 北京工业大学学报. 2017(09): 1328-1334 . 百度学术
16. 杜必强,孙立江. 基于PSO-SVM模型的焊接转子环焊缝超声缺陷识别. 动力工程学报. 2017(05): 379-385 . 百度学术
17. 赵湛,韩璐,方震,陈贤祥,杜利东,刘正奎. 基于可穿戴设备的日常压力状态评估研究. 电子与信息学报. 2017(11): 2669-2676 . 本站查看
18. 董广宇. 基于多特征融合的复杂路况步态识别方法. 科学技术与工程. 2017(08): 202-207 . 百度学术
19. 韩笑,佘青山,高云园,罗志增. 基于NA-MEMD和互信息的脑电特征提取方法. 传感技术学报. 2016(08): 1140-1148 . 百度学术
20. 黄成泉,王士同,蒋亦樟,董爱美. v-软间隔罗杰斯特回归分类机. 电子与信息学报. 2016(04): 985-992 . 本站查看
21. 徐超立,林科,杨晨,吴超华,高小榕. 基于小腿表面肌电的智能机器人协同控制方法. 中国生物医学工程学报. 2016(04): 385-393 . 百度学术
其他类型引用(43)
-
计量
- 文章访问数: 1142
- HTML全文浏览量: 120
- PDF下载量: 168
- 被引次数: 64