Channel estimation for multi-panel millimeter wave MIMO based on joint compressed sensing

doi:10.19682/j.cnki.1005-8885.2020.0041

中国邮电高校学报(英文版) ›› 2020, Vol. 27 ›› Issue (6): 1-7.doi: 10.19682/j.cnki.1005-8885.2020.0041

• Wireless • 下一篇

Channel estimation for multi-panel millimeter wave MIMO based on joint compressed sensing

刘旭,谢洋

江苏省无线通信重点实验室，南京邮电大学

收稿日期:2019-06-14 修回日期:2020-12-08 出版日期:2020-12-31 发布日期:2020-12-31
通讯作者: 刘旭 E-mail:liuxu@njupt.edu.cn
基金资助:
中国博士后科学基金

Channel estimation for multi-panel millimeter wave MIMO based on joint compressed sensing

Received:2019-06-14 Revised:2020-12-08 Online:2020-12-31 Published:2020-12-31
Supported by:
China Post-doctoral Science Foundation

摘要/Abstract

摘要：

Channel state information (CSI) is essential for downlink transmission in millimeter wave( mmWave) multiple input multiple output (MIMO) systems. Multi-panel antenna array is exploited in mmWave MIMO system due to its superior performance. Two channel estimation algorithms are proposed in this paper, named as generalized joint orthogonal matching pursuit (G-JOMP) and optimized joint orthogonal matching pursuit (O-JOMP) for multi-panel mmWave MIMO system based on the compressed sensing (CS) theory. G-JOMP exploits common sparsity structure among channel response between antenna panels of base station ( BS) and users to reduce the computational complexity in channel estimation. O-JOMP algorithm is then developed to further improve the accuracy of channel estimation by optimal panel selection based on the power of the received signal. Simulation results show that the performance of the proposed algorithms is better than that of the conventional orthogonal matching pursuit (OMP) based algorithm in multi-panel mmWave MIMO system.

关键词: channel estimation, multi-panel MIMO, millimeter wave, joint orthogonal matching pursuit, common sparsity

Abstract: Channel state information (CSI) is essential for downlink transmission in millimeter wave( mmWave) multiple input multiple output (MIMO) systems. Multi-panel antenna array is exploited in mmWave MIMO system due to its superior performance. Two channel estimation algorithms are proposed in this paper, named as generalized joint orthogonal matching pursuit (G-JOMP) and optimized joint orthogonal matching pursuit (O-JOMP) for multi-panel mmWave MIMO system based on the compressed sensing (CS) theory. G-JOMP exploits common sparsity structure among channel response between antenna panels of base station ( BS) and users to reduce the computational complexity in channel estimation. O-JOMP algorithm is then developed to further improve the accuracy of channel estimation by optimal panel selection based on the power of the received signal. Simulation results show that the performance of the proposed algorithms is better than that of the conventional orthogonal matching pursuit (OMP) based algorithm in multi-panel mmWave MIMO system.

Key words: channel estimation, multi-panel MIMO, millimeter wave, joint orthogonal matching pursuit, common sparsity

中图分类号:

TN929.5

Liu Xu, Xie Yang. Channel estimation for multi-panel millimeter wave MIMO based on joint compressed sensing[J]. The Journal of China Universities of Posts and Telecommunications, 2020, 27(6): 1-7.

参考文献

[1] Marzetta T L. Noncooperative cellular wireless with unlimited numbers of base station antennas. IEEE Trans on Wireless Communications, 2010, 9(11): 3590-3600.

[2] Rusek F, Persson D, Lau B K, et al. Scaling up MIMO: Opportunities and challenges with very large arrays. IEEE Signal Processing Magazine, 2012, 30(1): 40-60.

[3] Rangan S, Rappaport T S, Erkip E. Millimeter-wave cellular wireless networks: Potentials and challenges. Proceedings of the IEEE, 2014, 102(3): 366-385.

[4] Bai T, Alkhateeb A, Heath R. Coverage and capacity of millimeter-wave cellular networks. IEEE Communications Magazine, 2014, 52(9): 70-77.

[5] Rappaport T S, Sun S, Mayzus R, et al. Millimeter wave mobile communications for 5G cellular: It will work!. IEEE Access, 2013, 1: 335-349.

[6] Samimi M K, Rappaport T S. 3-D millimeter-wave statistical channel model for 5G wireless system design. IEEE Trans on Microwave Theory and Techniques, 2016, 64(7): 2207-2225.

[7] Alkhateeb A, El Ayach O, Leus G, et al. Channel estimation and hybrid precoding for millimeter wave cellular systems. IEEE Journal of Selected Topics in Signal Processing, 2014, 8(5): 831-846.

[8] Manoj A，Kannu A P. Multi-user millimeter wave channel estimation using generalized block OMP algorithm. Proceedings of the 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC'17), 2017, Jul 3-6, Sapporo, Japan. Piscataway, NJ, USA: IEEE, 2017: 5p.

[9] Lee J, Gil G T, Lee Y H. Channel estimation via orthogonal matching pursuit for hybrid MIMO Systems in millimeter wave communications. IEEE Trans on Communications, 2016, 64(6): 2370-2386.

[10] Sun S, Rappaport T S. Millimeter wave MIMO channel estimation based on adaptive compressed sensing. Proceedings of the 2017 IEEE International Conference on Communications Workshops (ICC Workshops'17), 2017, May 21-25, Paris, France. Piscataway, NJ, USA: IEEE, 2017: 47-53.

[11] Gurbuz A C, Yapici Y, Guvenc I. Sparse channel estimation in millimeter-wave communications via parameter perturbed OMP. Proceedings of the 2018 IEEE International Conference on Communications Workshops (ICC Workshops'18), 2018, May 20-24, Kansas City, MO, USA. Piscataway, NJ, USA: IEEE, 2018: 6p.

[12] Huawei, Hisilicon. Considerations on multi-panel based uplink transmission. 3GPP TSG RAN WG1 Meeting. Jan 16-20, Reno, NV, USA. 2017: R1-1700065.

[13] Huawei, Hisilicon. DL codebook design for multi-panel structured MIMO in NR. 3GPP TSG RAN WG1 Meeting. Jan 16-20, Reno, NV, USA. 2017: R1-1700066.

[14] Huawei, Hisilicon. Codebook design for multi-panel structured MIMO in NR. 3GPP TSG RAN WG1 Meeting #87, 2016, Nov 14-18, Reno, NV, USA. 2016: R1-1611666

[15] Wang W, Zhang W, Li Y J, et al, Channel estimation and hybrid precoding for multi-panel millimeter wave MIMO. Proceedings of the 2018 IEEE International Conference on Communications (ICC'18), 2018, May 20-23, Kansas City, MO, USA. Piscataway, NJ, USA: IEEE, 2018: 6p.

Channel estimation for multi-panel millimeter wave MIMO based on joint compressed sensing

Channel estimation for multi-panel millimeter wave MIMO based on joint compressed sensing

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