基于二维嵌套阵的3D MIMO系统信道估计算法

万应清, 解培中, 李汀, 曾强

万应清, 解培中, 李汀, 曾强. 基于二维嵌套阵的3D MIMO系统信道估计算法[J]. 信号处理, 2020, 36(2): 265-274. DOI: 10.16798/j.issn.1003-0530.2020.02.014
引用本文: 万应清, 解培中, 李汀, 曾强. 基于二维嵌套阵的3D MIMO系统信道估计算法[J]. 信号处理, 2020, 36(2): 265-274. DOI: 10.16798/j.issn.1003-0530.2020.02.014
Wan Yingqing, Xie Peizhong, Li Ting, Zeng Qiang. 2D Nested Arrays Based Channel Estimation Algorithm for 3D MIMO Systems[J]. JOURNAL OF SIGNAL PROCESSING, 2020, 36(2): 265-274. DOI: 10.16798/j.issn.1003-0530.2020.02.014
Citation: Wan Yingqing, Xie Peizhong, Li Ting, Zeng Qiang. 2D Nested Arrays Based Channel Estimation Algorithm for 3D MIMO Systems[J]. JOURNAL OF SIGNAL PROCESSING, 2020, 36(2): 265-274. DOI: 10.16798/j.issn.1003-0530.2020.02.014

基于二维嵌套阵的3D MIMO系统信道估计算法

基金项目: 国家自然科学面上基金(61771254)
详细信息
  • 中图分类号: TN929.5

2D Nested Arrays Based Channel Estimation Algorithm for 3D MIMO Systems

  • 摘要: 信道估计通过对基站接收信号和用户端发送的已知导频序列进行处理获得。二维嵌套阵列可以节约系统的成本并且得到大规模MIMO(multiple-input multiple-output)天线阵列的性能,然而由于二维嵌套阵列的结构不规整,直接对基站接收信号进行处理具有一定的难度。本文提出一种基于2D-DFT(two-dimensional Discrete Fourier Transform)的信道重构算法,首先对接收信号做自相关处理转化为连续差分阵列的接收信号,然后通过2D-DFT估计出用户的每一条路径的初始DOA(the direction of arrival),然后利用角度旋转技术增强DOA估计实现超分辨率估计,再根据精确的DOA估计通过传统的LS(least squares)估计方法估计出信道增益;最后重构出用户的信道。数值仿真验证了算法的有效性。
    Abstract: Channel estimation is obtained by processing the received signal of the base station (BS) and the known pilot sequence sent by the user. Two-dimensional (2D) nested arrays save system cost and enable performance of massive MIMO (multiple-input multiple-output). However, the structure of 2D nested arrays is not a regular planar arrays, so it's difficult to directly process received signal from BS. In this paper, a 2D-DFT-based channel reconstruction algorithm was proposed. Firstly, the received signal was autocorrelation processed and converted into received signal of the continuous differential co-array. Secondly 2D-DFT was used to estimate the initial DOA of each path of the user, and the angle rotation technique was used to enhance the DOA estimation to achieve super-resolution estimation. Then channel gain was estimated by conventional LS estimation method based on accurate DOA estimation. Finally, user's channel was reconstructed. Various numerical results are provided to corroborate the proposed studies.
  • [1] Elijah O, Leow C Y, Rahman T A , et al. A Comprehensive Survey of Pilot Contamination in Massive MIMO—5G System[J]. IEEE Communications Surveys & Tutorials, 2016, 18(2):905-923.
    [2] Wang M, Gao F, Jin S, et al. An Overview of Enhanced Massive MIMO with Array Signal Processing Techniques[J]. IEEE Journal of Selected Topics in Signal Processing, 2019, 13(5):886-901.
    [3] 罗发龙, 张建中. 5G权威指南—信号处理算法及实现, 第12章大规模阵列3D-MIMO:理论、实现和测试[M]. 北京:机械工程出版社, 2018:217-233.
    [4] Luo Fa-Long, Zhang Charlie (Jianzhong). Signal Processing for 5G: Algorithms and Implementations, 12 3D-MIMO with Massive Antennas: Theory, Implementation and Testing[M]. Beijing:China Machine Press, 2018:217-233. (in chinese)
    [5] Yuan S, Liang Q. To achieve Massive MIMO with much less antennas by nested placement[C]// Computer Communications Workshops. IEEE, 2016:668-673.
    [6] Wu L, Qi C. Uplink channel estimation for massive MIMO systems exploring joint channel sparsity[J]. Electronics Letters, 2014, 50(23):1770-1772.
    [7] Dai L, Wang Z, Yang Z. Compressive Sensing Based Time Domain Synchronous OFDM Transmission for Vehicular Communications[J]. IEEE Journal on Selected Areas in Communications, 2013, 31(9):460-469.
    [8] Wen C K, Jin S, Wong K K, et al. Channel Estimation for Massive MIMO Using Gaussian-Mixture Bayesian Learning[J]. IEEE Transactions on Wireless Communications, 2015, 14(3):1356-1368.
    [9] Adhikary A, Nam J, Ahn J Y, et al. Joint Spatial Division and Multiplexing—The Large-Scale Array Regime[J]. IEEE Transactions on Information Theory, 2013, 59(10):6441-6463.
    [10] Sun C, Gao X, Jin S, et al. Beam Division Multiple Access Transmission for Massive MIMO Communications[J]. IEEE Transactions on Communications, 2015, 63(6):2170-2184.
    [11] Xie H, Gao F, Zhang S, et al. A unified transmission strategy for TDD/FDD massive MIMO systems with spatial basis expansion model[J]. IEEE Transactions on Vehicular Technology, 2016, 66(4):3170-3184.
    [12] Xie H, Gao F, Zhang S, et al. UL/DL Channel Estimation for TDD/FDD Massive MIMO Systems Using DFT and Angle Reciprocity[C]// IEEE Vehicular Technology Conference. IEEE, 2016.
    [13] Fan D, Gao F, Yuanwei L, et al. Angle Domain Channel Estimation in Hybrid MmWave Massive MIMO Systems[J]. IEEE Transactions on Wireless Communications, 2018, 17(12):8165-8179.
    [14] Pal P, Vaidyanathan P P. Nested Arrays in Two Dimensions, Part I: Geometrical Considerations[J]. IEEE Transactions on Signal Processing, 2012, 60(9):4694-4705.
    [15] Vaidyanathan P P, Pal P. Sparse Sensing With Co-Prime Samplers and Arrays[J]. IEEE Transactions on Signal Processing, 2011, 59(2):573-586.
    [16] Qin S, Zhang Y D, Amin M G. Generalized Coprime Array Configurations for Direction-of-Arrival Estimation[J]. IEEE Transactions on Signal Processing, 2015, 63(6):1377-1390.
    [17] Liu C L, Vaidyanathan P P. Super Nested Arrays: Linear Sparse Arrays With Reduced Mutual Coupling—Part I: Fundamentals[J]. IEEE Transactions on Signal Processing, 2016, 64(15):3997-4012.
    [18] Liu C L, Vaidyanathan P P. Super Nested Arrays: Linear Sparse Arrays With Reduced Mutual Coupling—Part II: High-Order Extensions[J]. IEEE Transactions on Signal Processing, 2016, 64(16):4203-4217.
    [19] Liu J, Zhang Y, Lu Y, et al. Augmented Nested Arrays with Enhanced DOF and Reduced Mutual Coupling[J]. IEEE Transactions on Signal Processing, 2017, 65(21):5549-5563.
    [20] Raza A, Liu W, Shen Q. Thinned Coprime Arrays for DOA Estimation[C]. //25th European Signal Processing Conference. 2017:395-399.
    [21] Raza A, Liu W, Shen Q. Thinned Coprime Array for Second-Order Difference Co-Array Generation With Reduced Mutual Coupling[J]. IEEE Transactions on Signal Processing, 2019, 67(8):2052-2065.
    [22] Shen Q, Liu W, Cui W, et al. Low-Complexity Direction-of-Arrival Estimation Based on Wideband Co-Prime Arrays[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2015, 23(9):1445-1456.
    [23] Cui W, Shen Q, Liu W, et al. Low Complexity DOA Estimation for Wideband Off-Grid Sources Based on Re-Focused Compressive Sensing With Dynamic Dictionary [J]. IEEE Journal of Selected Topics in Signal Processing, 2019, 13(5):918-930.
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出版历程
  • 收稿日期:  2019-10-23
  • 修回日期:  2020-02-29
  • 发布日期:  2020-02-24

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