深度学习辅助上行免调度NOMA多用户检测方法

doi:10.3785/j.issn.1008-973X.2022.04.022

浙江大学学报(工学版)

2022, Vol. 56

Issue (4): 816-822 DOI: 10.3785/j.issn.1008-973X.2022.04.022

计算机技术、信息工程

深度学习辅助上行免调度NOMA多用户检测方法

陈扬钊(

),袁伟娜*(

)

华东理工大学信息科学与工程学院，上海 200237

Deep learning aided multi-user detection for up-link grant-free NOMA

Yang-zhao CHEN(

),Wei-na YUAN*(

)

School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China

全文: PDF(931 KB) HTML

摘要：

针对上行免调度非正交多址接入（NOMA）场景中多用户检测的问题，通过结合传输数据的符号特征，提出基于深度神经网络（DNN）的联合活跃用户检测和数据检测框架. 考虑更一般化的实际场景，即用户在每个时隙中随机活跃. 将DNN求解结果作为改进的正交匹配追踪(OMP)算法先验输入，修正提升活跃用户检测和数据检测性能. 仿真结果表明，提出的多用户检测方案比传统的贪婪追踪及动态压缩感知（DCS）多用户检测算法具有更好的用户活跃性及数据检测性能.

关键词： 大规模机器通信; 免调度传输; 非正交多址接入; 压缩感知; 深度神经网络

Abstract:

A joint active user detection and data detection framework based on deep neural network (DNN) was proposed by combining the symbolic features of transmitted data in order to solve the problem of multi-user detection in uplink grant-free non-orthogonal multiple access (grant-free NOMA). The more general and practical scenario was considered, in which the user was randomly active in each time slot. The DNN solution result was used as a priori input of the modified orthogonal matching pursuit (OMP) algorithm in order to improve the user detection and date detection performance. The simulation results show that the proposed multi-user detection scheme has better user activity and data detection performance than the traditional greedy tracking algorithm and dynamic compressed sensing (DCS) multi-user detection algorithm.

Key words: massive machine type communication grant-free transmission non-orthogonal multiple access compressed sensing deep neural network

收稿日期: 2021-05-20 出版日期: 2022-04-24

CLC:

TN 929

基金资助: 国家自然科学基金资助项目（61501187）

通讯作者: 袁伟娜 E-mail: 15957114952@163.com;wnyuan_ice@163.com

作者简介: 陈扬钊（1997—），男，硕士生，从事免调度非正交多址接入多用户检测的研究. orcid.org/0000-0002-1297-4967. E-mail： 15957114952@163.com

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引用本文:

陈扬钊,袁伟娜. 深度学习辅助上行免调度NOMA多用户检测方法[J]. 浙江大学学报(工学版), 2022, 56(4): 816-822.

Yang-zhao CHEN,Wei-na YUAN. Deep learning aided multi-user detection for up-link grant-free NOMA. Journal of ZheJiang University (Engineering Science), 2022, 56(4): 816-822.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2022.04.022 或 https://www.zjujournals.com/eng/CN/Y2022/V56/I4/816

图 1 提出的基于DNN-MUD方法系统框图

表 1 传输符号特征映射示意表

表 2 Grant-free NOMA系统的仿真参数表

图 2 不同信噪比下的各算法误码率性能比较

图 3 用户随机活跃情况下不同信噪比下不同算法的误码率性能比较

图 4 300%负载条件下各算法误码率性能比较

图 5 不同稀疏度等级条件下各算法误码率性能

图 6 用户随机活跃情况下不同信噪比下各算法的重构成功率比较

表 3 不同多用户检测算法的仿真运行时间

1	BOCKELMANN C, PRATAS N, NIKOPOUR H, et al Massive machine-type communications in 5G: physical and MAC-layer solutions[J]. IEEE Communications Magazine, 2016, 54 (9): 59- 65 doi: 10.1109/MCOM.2016.7565189
2	CIRIK A C, BALASUBRAMANYA N M, LAMPE L Multi-user detection using ADMM-based compressive sensing for uplink grant-free NOMA[J]. IEEE Wireless Communications Letters, 2018, 7 (1): 46- 49 doi: 10.1109/LWC.2017.2752165
3	HONG J, CHOI W, RAO B D Sparsity controlled random multiple access with compressed sensing[J]. IEEE Transactions on Wireless Communications, 2015, 14 (2): 998- 1010 doi: 10.1109/TWC.2014.2363165
4	TROPP J A, GILBERT A C Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 2007, 53 (12): 4655- 4666 doi: 10.1109/TIT.2007.909108
5	DAI W, MILENKOVIC O Subspace pursuit for compressive sensing signal reconstruction[J]. IEEE Transactions on Information Theory, 2009, 55 (5): 2230- 2249 doi: 10.1109/TIT.2009.2016006
6	NEEDELL D, TROPP J A Cosamp: iterative signal recovery from incomplete and inaccurate samples[J]. Applied and Computational Harmonic Analysis, 2009, 26 (3): 301- 321 doi: 10.1016/j.acha.2008.07.002
7	WEI C, LIU H, ZHANG Z, et al Approximate message passing-based joint user activity and data detection for NOMA[J]. IEEE Communications Letters, 2017, 21 (3): 640- 643 doi: 10.1109/LCOMM.2016.2624297
8	WANG B, DAI L, MIR T, et al Joint user activity and data detection based on structured compressive sensing for NOMA[J]. IEEE Communications Letters, 2016, 20 (7): 1473- 1476
9	ABEBE A T, KANG C G Iterative order recursive least square estimation for exploiting frame-wise sparsity in compressive sensing-based MTC[J]. IEEE Communications Letters, 2016, 20 (5): 1018- 1021 doi: 10.1109/LCOMM.2016.2539255
10	CUI Y, XU W, WANG Y, et al Side-information aided compressed multi-user detection for up-link grant-free NOMA[J]. IEEE Transactions on Wireless Communications, 2020, 19 (11): 7720- 7731 doi: 10.1109/TWC.2020.3015760
11	DU Y, CHENG C, DONG B, et al Block-sparsity-based multiuser detection for uplink grant-free NOMA[J]. IEEE Transactions on Wireless Communications, 2018, 17 (12): 7894- 7909 doi: 10.1109/TWC.2018.2872594
12	WANG B, DAI L, ZHANG Y, et al Dynamic compressive sensing-based multi-user detection for uplink grant-free NOMA[J]. IEEE Communications Letters, 2016, 20 (11): 2320- 2323 doi: 10.1109/LCOMM.2016.2602264
13	DU Y, DONG B, CHEN Z, et al Efficient multi-user detection for uplink grant-free NOMA: prior-information aided adaptive compressive sensing perspective[J]. IEEE Journal on Selected Areas in Communications, 2017, 35 (99): 2812- 2828
14	YE H, LI G Y, JUANG B Power of deep learning for channel estimation and signal detection in OFDM systems[J]. IEEE Wireless Communication Letters, 2017, 7 (1): 114- 117
15	GUAN G, HUANG H, SONG Y, et al Deep learning for an effective nonorthogonal multiple access scheme[J]. IEEE Transactions on Vehicular Technology, 2018, 67 (9): 8440- 8450 doi: 10.1109/TVT.2018.2848294
16	KIM W, AHN Y, SHIM B Deep neural network based active user detection for grant-free NOMA systems[J]. IEEE Transactions on Communications, 2019, 68 (4): 2143- 2155
17	MIAO X, GUO D, LI X Grant-free NOMA with device activity learning using long short-term memory[J]. IEEE Wireless Communication Letters, 2020, 9 (7): 981- 984
18	HOCHREITER S, SCHMIDHUBER J Long short-term memory[J]. Neural Computation, 1997, 9 (8): 1735- 1780 doi: 10.1162/neco.1997.9.8.1735
19	WANG J, KWON S, SHIM S Generalized orthogonal matching pursuit[J]. IEEE Transactions on Signal Processing, 2012, 60 (12): 6202- 6216 doi: 10.1109/TSP.2012.2218810
20	OYERINDE O O. Multiuser detector for uplink grant free NOMA systems based on modified subspace pursuit algorithm [C]// 2018 12th International Conference on Signal Processing and Communication Systems. [S. l. ]: IEEE, 2018: 1-6.
21	袁伟娜, 严秋基于压缩感知的FBMC/OQAM系统信道估计方法[J]. 通信学报, 2019, 40 (12): 98- 104 YUAN Wei-na, YAN Qiu Channel estimation method based on compressive sensing for FBMC/OQAM system[J]. Journal on Communications, 2019, 40 (12): 98- 104 doi: 10.11959/j.issn.1000-436x.2019239

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