莱斯衰落信道下大规模MIMO系统中的信道估计方法

doi:10.11959/j.issn.1000-0801.2017257

Abstract

Abstract:

The high-speed mobile transportations,such as high-speed rails,pilot-less airplanes,are mostly in the open areas.Due to the existence of line of sight (LOS) propagations,the Rayleigh fading model can not describe the channels very well in these environments.The Rice fading channel model is constituted of a LOS component and a multiple-path component,which can characterize the channels more accurately.Based on the Rice model,the improved channel estimation methods and corresponding decoding methods were proposed based on the superimposed training sequences for massive multiple input multiple output (MIMO) system.The improved channel estimation methods were divided into two cases:the LOS component was known to the receiver and the other was unknown.The numerical simulation results show that the superiority of the proposed methods.

Key words: massive MIMO system, Rice fading channel, superimposed training sequence, channel estimation

CLC Number:

TN929

Xueli WANG,Haiquan WANG,Xiao LI,Dakuan YANG. Channel estimation method for massive MIMO system in Rice channel[J]. Telecommunications Science, 2017, 33(9): 10-19.

Figures/Tables 7

References 19

[1]	MARZETTA T L . Noncooperative cellular wireless with unlimited numbers of base station antennas[J]. IEEE Transactions on Wireless Communications, 2010,9(11): 3590-3600.
[2]	阳析, 金石 . 大规模 MIMO 系统传输关键技术研究进展[J]. 电信科学, 2015,31(5): 28-35.
	YANG X , JIN S . Overview of key technologies of transmission in massive MIMO system[J]. Telecommunications Science, 2015,31(5): 28-35.
[3]	PITAROKOILIS A , MOHAMMED S K , LARSSON E G . On the optimality of single-carrier transmission in large-scale antenna systems[J]. IEEE Wireless Communications Letters, 2012,1(4): 276-279.
[4]	HOYDIS J , BRINK S T , DEBBAH M . Massive MIMO in the UL/DL of cellular networks:how many antennas do we need?[J]. IEEE Journal on Selected Areas in Communications, 2013,31(2): 160-171.
[5]	GE X , JING Y , GHARAVI H ,et al. Energy efficiency challenges of 5G small cell networks[J]. IEEE Communications Magazine, 2017,55(5): 184-191.
[6]	GE X , TU S , MAO G ,et al. 5G ultra-dense cellular networks[J]. IEEE Wireless Communications, 2016,23(1): 72-79.
[7]	GE X , ZI R , WANG H ,et al. Multi-user massive MIMO communication systems based on irregular antenna arrays[J]. IEEE Transactions on Wireless Communications, 2016,15(8): 5287-5301.
[8]	AI B , CHENG X , KüRNER T ,et al. Challenges toward wireless communications for high-speed railway[J]. IEEE Transactions on Intelligent Transportation Systems, 2014,15(5): 2143-2158.
[9]	YANG J , AI B . Construction and capacity analysis of high-rank LOS MIMO channels in high speed railway scenarios[C]// International Conference on Wireless Communications,Networking and Mobile Computing,September 21-23,2012,Shanghai,China. New Jersey:IEEE Press, 2014: 1-4.
[10]	YAN L , FANG X , ZHONG S . Quasi-full-duplex wireless communication scheme for high-speed railway[C]// Vehicular Technology Conference,September 14-17,2014,Vancouver,BC,Canada. New Jersey:IEEE Press, 2014: 1-6.
[11]	NOH S , ZOLTOWSKI M D , SUNG Y ,et al. Pilot beam pattern design for channel estimation in massive MIMO systems[J]. IEEE Journal of Selected Topics in Signal Processing, 2014,8(5): 787-801.
[12]	TRUONG K T , LOZANO A , HEATH R W . Optimal training in continuous flat-fading massive MIMO systems[C]// 20th European Wireless,May 14-16,2014,Barcelona,Spain. New Jersey:IEEE Press, 2014: 1-6.
[13]	HE S , TUGNAIT J K , MENG X . On superimposed training for MIMO channel estimation and symbol detection[J]. IEEE Transactions on Signal Processing, 2007,55(6): 3007-3021.
[14]	ZHANG H , GAO S , LI D ,et al. On superimposed pilot for channel estimation in multicell multiuser MIMO uplink:large system analysis[J]. IEEE Transactions on Vehicular Technology, 2016,65(3): 1492-1505.
[15]	TUGNAIT J K , MENG X , HE S . Doubly selective channel estimation using superimposed training and exponential bases models[J]. Eurasip Journal on Advances in Signal Processing, 2006,6(11): 3877-3883.
[16]	UPADHYA K , VOROBYOV S , VEHKAPERA M . Superimposed pilots are superior for mitigating pilot contamination in massive MIMO[J]. IEEE Transactions on Signal Processing, 2016,PP(99):1.
[17]	LI F , WANG H Q , YING M Y ,et al. Channel estimations based on superimposed pilots for massive MIMO uplink system[C]// 8th International Conference on Wireless Communications and Signal Processing (WCSP),October 13-15,2016,Yang Zhou,China. New Jersey:IEEE Press, 2016: 13-15.
[18]	CRAMéR H . Random variables and probability distributions[M]. Cambridge: Cambridge University PressPress, 2004.
[19]	FARROKHI F R , LOZANO A , FOSCHINI G J ,et al. Spectral efficiency of FDMA/TDMA wireless systems with transmit and receive antenna arrays[J]. IEEE Transactions on Wireless Communications, 2002,1(4): 591-599.

Metrics

Recommended 0

No Suggested Reading articles found!

Channel estimation method for massive MIMO system in Rice channel

RichHTML

PDF下载

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 7

References 19

Related Articles 15

Metrics

Recommended 0

[1]	Min LU, Zehao QIN, Zhihui CHEN, Min ZHANG, Guangxue YUE. 1D-Concatenate based channel estimation DNN model optimization method [J]. Telecommunications Science, 2023, 39(4): 71-86.
[2]	Zilu GAO, Shaohui SUN, Li LI. Overview of reconfigurable intelligent surface for new-generation mobile communication [J]. Telecommunications Science, 2022, 38(10): 20-35.
[3]	Yuxiu HUA, Rongpeng LI, Zhifeng ZHAO, Jianjun WU, Honggang ZHANG. GAN-based channel estimation for massive MIMO system [J]. Telecommunications Science, 2021, 37(6): 14-22.
[4]	Xuan YU, Xuan GENG. A reduced-complexity compressed sensing channel estimation for underwater acoustic channel [J]. Telecommunications Science, 2021, 37(3): 114-124.
[5]	Lu CHENG,Lihua YANG,Zenghao WANG,Jie ZHANG,Yan LIANG. Historical information based iterative soft Kalman time-varying channel estimation method [J]. Telecommunications Science, 2020, 36(9): 23-31.
[6]	Jiafeng QIU. Optimized BM3D for mmWave massive MIMO channel estimation [J]. Telecommunications Science, 2020, 36(9): 44-50.
[7]	Jiafeng QIU. Low-complexity beamspace channel estimation for wideband millimeter-wave massive MIMO system [J]. Telecommunications Science, 2020, 36(8): 122-129.
[8]	Jiafeng QIU. Low-complexity massive MIMO channel estimation for mmWave systems via matrix completion [J]. Telecommunications Science, 2020, 36(4): 99-106.
[9]	Kun LI,Jing ZHANG,Xiao LI,Shi JIN. An overview of artificial intelligence assisted channel estimation [J]. Telecommunications Science, 2020, 36(10): 46-55.
[10]	Rui ZHANG,Guchen SHI,Banteng LIU,Yourong CHEN. Low-complexity variable step-size sign algorithm based on Weibull distribution function [J]. Telecommunications Science, 2018, 34(9): 87-96.
[11]	Xiang YU,Yanni GAO,Sirui DUAN. Channel estimation of UFMC system in multipath channel [J]. Telecommunications Science, 2018, 34(4): 22-30.
[12]	Pan MAO,Xiaoguang HUANG,Wei WANG,Jian SONG. Massive MIMO channel estimation and feedback based on compressed sensing [J]. Telecommunications Science, 2018, 34(12): 46-52.
[13]	Xinrong LV,Youming LI,Mingchen YU. Design of transceivers based on sparse Bayesian learning for power line carrier communications [J]. Telecommunications Science, 2017, 33(9): 92-99.
[14]	Mingfu LI,Yong LIAO,Xuanfan SHEN. MIMO iterative channel estimation based on extended Kalman filter [J]. Telecommunications Science, 2017, 33(9): 100-107.
[15]	Dan QIAO,Chaokai WEN,Feifei GAO,Shi JIN. An overview of low resolution quantization in wireless communication system [J]. Telecommunications Science, 2017, 33(6): 21-33.