一种大规模MIMO系统的联合用户活跃性和信号检测方法

doi:10.11959/j.issn.1000-0801.2021002

电信科学 ›› 2021, Vol. 37 ›› Issue (5): 113-123.doi: 10.11959/j.issn.1000-0801.2021002

一种大规模MIMO系统的联合用户活跃性和信号检测方法

宋晓群, 金明, 贾忠杰

宁波大学信息科学与工程学院，浙江宁波 315211

修回日期:2020-12-22 出版日期:2021-05-20 发布日期:2021-05-01
作者简介:宋晓群（1995− ），女，宁波大学信息科学与工程学院硕士生，主要研究方向为认知无线电、机器学习等
金明（1981− ），男，博士，宁波大学信息科学与工程学院教授，主要研究方向为认知无线电技术、优化算法、机器学习等
贾忠杰（1996− ），男，宁波大学信息科学与工程学院硕士生，主要研究方向为认知无线电中的频谱感知技术等
基金资助:
国家自然科学基金资助项目(61871246);浙江省杰出青年科学基金资助项目(LR21F010001);宁波市自然科学基金重点项目(202003N4013)

Joint user activity and signal detection for massive multiple-input multiple-output

Xiaoqun SONG, Ming JIN, Zhongjie JIA

Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China

Revised:2020-12-22 Online:2021-05-20 Published:2021-05-01
Supported by:
The National Natural Science Foundation of China(61871246);Zhejiang Provincial Natural Science Funds for Distinguished Young Scholars(LR21F010001);Ningbo Key Program of Natural Science Funds(202003N4013)

摘要/Abstract

摘要：

上行免调度大规模多输入多输出（mMIMO）系统中，当接收天线相关性增加或活跃设备数增大时，传统的联合用户活跃性和信号检测方法性能急剧下降。另外，传统方法需要知道噪声功率，而实际中往往难以获得准确信息。针对上述问题，提出了一种基于酉变换近似消息传递和期望最大化的联合用户活跃性和信号检测方法。与传统的近似消息传递算法不同，所提的酉变换近似消息传递算法假设噪声功率未知。首先，利用酉变换近似消息传递算法将mMIMO基站侧接收信号进行解耦处理，同时估计噪声功率。然后，利用解耦信号，基于期望最大化算法实现用户活跃性检测。最后，通过计算解耦信号所属星座点的后验概率得到信号检测结果。仿真结果表明，所提方法在联合用户活跃性和信号检测方面优于传统方法。

关键词: 大规模多输入多输出, 用户活跃性检测, 信号检测, 酉变换近似消息传递

Abstract:

In uplink grant-free massive multiple-input multiple-output (mMIMO) systems, the performance of available methods for joint user activity and signal detection deteriorates when the correlation of receiving antennas or the number of active devices increases.Moreover, the available methods require the knowledge of noise power, which is often practically unknown.To address the above issues, combining approximate message passing with unitary transformation and expectation maximization algorithm to jointly implement user activity and signal detection was proposed.Different from the conventional approximate message passing algorithm, the proposed one assumes that the noise power was unknown.Firstly, by exploiting the approximate message passing algorithm with unitary transform, the distribution of transmitted symbols together with the distribution of noise power was obtained.Secondly, expectation maximization algorithm was applied to estimate the user activity.Finally, the signal detection was implemented by deriving the posterior distribution of the decoupled signal belongs.Simulation results show that the proposed method is better than the traditional method in joint user activity and signal detection.

Key words: massive multiple-input multiple-output, user activity detection, signal detection, approximate message passing with unitary transformation

中图分类号:

TP929.5

宋晓群, 金明, 贾忠杰. 一种大规模MIMO系统的联合用户活跃性和信号检测方法[J]. 电信科学, 2021, 37(5): 113-123.

Xiaoqun SONG, Ming JIN, Zhongjie JIA. Joint user activity and signal detection for massive multiple-input multiple-output[J]. Telecommunications Science, 2021, 37(5): 113-123.

图/表 8

图1

图2

图3

图4

图5

图6

图7

图8

参考文献 20

[1]	BOCCARDI F , HEATH R W , LOZANO A ,et al. Five disruptive technology directions for 5G[J]. IEEE Communications Magazine, 2014,52(2): 74-80.
[2]	KHAN I . Notice of violation of IEEE publication principles: a robust signal detection scheme for 5G massive multiuser MIMO systems[J]. IEEE Transactions on Vehicular Technology, 2018,67(10): 9597-9604.
[3]	NGO H Q , LARSSON E G , MARZETTA T L . Energy and spectral efficiency of very large multiuser MIMO Systems[J]. IEEE Transactions on Communications, 2013,61(4): 1436-1449.
[4]	MARZETTA T L . Noncooperative cellular wireless with unlimited numbers of base station antennas[J]. IEEE Transactions on Wireless Communications, 2010,9(11): 3590-3600.
[5]	DAI L , WANG B , YUAN Y ,et al. Non-orthogonal multiple access for 5G: solutions,challenges,opportunities and future research trends[J]. IEEE Communications Magazine, 2015,53(9): 74-81.
[6]	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.
[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.
[8]	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.
[9]	WANG T , LIU S , YANG F ,et al. Generalized spatial modulation-based multi-user and signal detection scheme for terrestrial return channel with NOMA[J]. IEEE Transactions on Broadcasting, 2018,64(2): 211-219.
[10]	MA X , KIM J , YUAN D ,et al. Two-level sparse structure-based compressive sensing detector for uplink spatial modulation with massive connectivity[J]. IEEE Communications Letters, 2019,23(9): 1594-1597.
[11]	QIAO L , GAO Z . Joint active device and data detection for massive MTC relying on spatial modulation[C]// Proceedings of IEEE Wireless Communications and Networking Conference Workshops (WCNCW). Piscataway: IEEE Press, 2020: 1-6.
[12]	LUO M , GUO Q , HUANG D ,et al. Sparse Bayesian learning based on approximate message passing with unitary transformation[C]// Proceedings of IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS). Piscataway: IEEE Press, 2019: 1-5.
[13]	韩锞 . Massive MIMO中矩阵SVD分解算法研究[D]. 成都:电子科技大学, 2016.
	HAN K . Research on matrix SVD in massive MIMO system[D]. Chengdu:University of Electronic Science and Technology of China, 2016.
[14]	CHEN J , LAU V K N . Multi-stream iterative SVD for massive MIMO communication systems under time varying channels[C]// Proceedings of IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP). Piscataway:IEEE Press, 2014: 3152-3156.
[15]	PEDERSEN N L , MANCHON C N , FLEURY B H . Low complexity sparse Bayesian learning for channel estimation using generalized mean field[C]// Proceedings of 20th European Wireless Conference.[S.l.:s.n.], 2014: 1-6.
[16]	SHIU DA-SHAN , FOSCHINI G J , GANS M J ,et al. Fading correlation and its effect on the capacity of multielement antenna systems[J]. IEEE Transactions on Communications, 2000,48(3): 502-513.
[17]	MA X , GAO Z . Data-driven deep learning to design pilot and channel estimator for massive MIMO[J]. IEEE Transactions on Vehicular Technology, 2020,69(5): 5677-5682.
[18]	ZOU Q , ZHANG H , CAI D ,et al. A low-complexity joint user activity,channel and data estimation for grant-free massive MIMO systems[J]. IEEE Signal Processing Letters, 2020,27(99): 1290-1294.
[19]	FIGUEIREDO F P D , LEMES D A M , DIAS C F ,et al. Massive MIMO channel estimation considering pilot contamination and spatially correlated channels[J]. Electronics Letters, 2020,56(8): 410-413.
[20]	路新华， NAVARRO M，王忠勇，等. 大规模MIMO系统上行链路时间-空间结构信道估计算法[J]. 电子与信息学报, 2020,42(2): 519-525.
	LU X H , NAVARRO M , WANG Z Y ,et al. Channel estimation algorithm using temporal-spatial structure for up-link of massive MIMO systems[J]. Journal of Electronics ＆ Information Technology, 2020,42(2): 519-525.

一种大规模MIMO系统的联合用户活跃性和信号检测方法

Joint user activity and signal detection for massive multiple-input multiple-output

在线阅读

PDF下载

可视化

摘要/Abstract

引用本文

使用本文

图/表 8

参考文献 20

相关文章 9

Metrics

推荐阅读 0

[1]	王越, 许方敏, 曹海燕. TDD模式下无小区大规模MIMO系统的导频干扰抑制[J]. 电信科学, 2022, 38(5): 87-94.
[2]	陈浩椅, 李光球, 李辉. 基于IAFS算法的毫米波大规模MIMO混合预编码方法[J]. 电信科学, 2021, 37(8): 77-84.
[3]	毛攀,黄小光,汪伟,宋建. 基于压缩感知的大规模MIMO信道估计与反馈[J]. 电信科学, 2018, 34(12): 46-52.
[4]	王雪丽,王海泉,李肖,杨大款. 莱斯衰落信道下大规模MIMO系统中的信道估计方法[J]. 电信科学, 2017, 33(9): 10-19.
[5]	杨丽花,梁彦. 信道老化时高速移动大规模MIMO上行系统的性能分析[J]. 电信科学, 2017, 33(7): 19-27.
[6]	何世文,黄永明,王海明,洪伟. 毫米波无线通信发展趋势及技术挑战[J]. 电信科学, 2017, 33(6): 11-20.
[7]	秦闯,郑紫微,娄达平,富显祖. 基于近似信息传递算法的大规模MIMO信号检测[J]. 电信科学, 2016, 32(9): 16-21.
[8]	曲桦,梁静,赵季红,王伟华. 基于预处理共轭梯度法的低复杂度信号检测算法[J]. 电信科学, 2016, 32(4): 30-35.
[9]	李小文,任旭. 一种基于LTE的改进型V-BLAST算法研究[J]. 电信科学, 2012, 28(2): 59-62.