大規(guī)模MIMO系統(tǒng)上行鏈路時間-空間結(jié)構(gòu)信道估計算法

路新華; MANCHóNCarles Navarro; 王忠勇; 張傳宗

doi:10.11999/JEIT180676

大規(guī)模MIMO系統(tǒng)上行鏈路時間-空間結(jié)構(gòu)信道估計算法

doi: 10.11999/JEIT180676

1.
鄭州大學(xué)信息工程學(xué)院 ??鄭州 ??450001
2.
南陽理工學(xué)院通信信號處理工程技術(shù)研究中心南陽 473004
3.
Department of Electronic Systems, Aalborg University, Aalborg 9220

基金項目: 國家自然科學(xué)基金(61571402, 61501404, 61640003)

詳細(xì)信息

作者簡介:
路新華：男，1980年生，講師，博士生，研究方向為大規(guī)模MIMO、信道估計、變分貝葉斯推理和狄利克雷過程

MANCHóNCarles Navarro：男，副教授，研究方向為無線通信中的統(tǒng)計信號處理，包括聯(lián)合信道估計和檢測、稀疏信號估計和重構(gòu)、多天線信號處理技術(shù)等

王忠勇：男，1965年生，教授，研究方向為通信系統(tǒng)及其信號處理、嵌入式系統(tǒng)等

張傳宗：男，1982年生，副教授，研究方向為移動通信系統(tǒng)和接收機(jī)的設(shè)計、變分推理、因子圖與消息傳遞算法

通訊作者:
王忠勇　zywangzzu@gmail.com

中圖分類號: TN92
計量
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- PDF下載量: 107
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出版歷程
- 收稿日期: 2018-07-06
- 修回日期: 2019-02-02
- 網(wǎng)絡(luò)出版日期: 2019-05-21
- 刊出日期: 2020-02-19

Channel Estimation Algorithm Using Temporal-spatial Structure for Up-link of Massive MIMO Systems

1.
School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China
2.
Communication and Signal Processing RC, Nanyang Institute of Technology, Nanyang 473004, China
3.
Department of Electronic Systems, Aalborg University, Aalborg 9220, Denmark

Funds: The National Natural Science Foundation of China (61571402, 61501404, 61640003)

摘要

摘要:
針對大規(guī)模多入多出(MIMO)系統(tǒng)上行鏈路非平穩(wěn)空間相關(guān)信道的估計問題，該文利用信道的時間-空間2維稀疏結(jié)構(gòu)信息，應(yīng)用狄利克雷過程(DP)和變分貝葉斯推理(VBI)，設(shè)計了一種低導(dǎo)頻開銷和計算復(fù)雜度的信道估計迭代算法，提高了信道估計精度。由于平穩(wěn)空間相關(guān)信道難以適用于大規(guī)模MIMO系統(tǒng)，該文借助于狄利克雷過程構(gòu)建了非平穩(wěn)空間相關(guān)信道先驗?zāi)Ｐ停蓪⒕哂锌臻g關(guān)聯(lián)的多個物理信道映射為具有相同時延結(jié)構(gòu)的概率信道，并應(yīng)用變分貝葉斯推理設(shè)計了低導(dǎo)頻開銷和計算復(fù)雜度的信道估計迭代算法。實驗結(jié)果驗證了所提算法的有效性，且具有對系統(tǒng)關(guān)鍵參數(shù)魯棒性的優(yōu)點。
- 大規(guī)模 MIMO /
- 非平穩(wěn)信道 /
- 時間-空間 /
- 狄利克雷過程 /
- 變分貝葉斯推理
Abstract:
To deal with the estimation problem of non-stationary channel in massive Multiple-Input Multiple-Output (MIMO) up-link, the 2D channels’ sparse structure information in temporal-spatial domain is used, to design an iterative channel estimation algorithm based on Dirichlet Process (DP) and Variational Bayesian Inference (VBI), which can improve the accuracy under a lower pilot overhead and computation complexity. On account of that the stationary channel models is not suitable for massive MIMO systems anymore, a non-stationary channel prior model utilizing Dirichlet Process is constructed, which can map the physical spatial correlation channels to a probabilistic channel with the same sparse temporal vector. By applying VBI technology, a channel estimation iteration algorithm with low pilot overhead and complexity is designed. Experiment results show the proposed channel method has a better performance on the estimation accuracy than the state-of-art method, meanwhile it works robustly against the dynamic system key parameters.
- Massive Multi-Input Multi-Output (MIMO) /
- Non-stationary channel /
- Temporal-spatial /
- Dirichlet Process (DP) /
- Variational Bayesian Inference (VBI)

HTML全文

圖 1 大規(guī)模MIMO系統(tǒng)上行鏈路信道分層貝葉斯圖模型

下載: 全尺寸圖片幻燈片

圖 2 信道估計均方誤差隨信噪比變化曲線圖

下載: 全尺寸圖片幻燈片

圖 3 信道估計均方誤差隨帶寬變化曲線圖

下載: 全尺寸圖片幻燈片

表 1 信道估計算法的計算復(fù)雜度

算法	復(fù)雜度
本文方法	$\cal{O}\left( {R{L^{\rm{2}}}} \right)$
FSBL	$\cal{O}\left( {R{N_{\rm{p}}}L} \right)$
BSBL	$\cal{O}\left( {{{\left( {RL} \right)}^3}} \right)$
SABMP	$\cal{O}\left( {R{N_{\rm{p}}}{L^2}} \right)$

下載: 導(dǎo)出CSV

表 2 大規(guī)模MIMO-OFDM系統(tǒng)參數(shù)

參數(shù)名	參數(shù)意義	數(shù)值
R	基站側(cè)天線數(shù)	128
f_c	載波中心頻率	2.6 GHz
N	OFDM總子載波數(shù)	1024
N_p	信道估計占用子載波數(shù)	64
B_W	用戶帶寬	10～100 MHz
Q	QAM調(diào)制階數(shù)	4
L	信道抽頭個數(shù)	64
I_p	多徑總徑數(shù)	20

下載: 導(dǎo)出CSV

參考文獻(xiàn)(20)

MARZETTA T L. Noncooperative cellular wireless with unlimited numbers of base station antennas[J]. IEEE Transactions on Wireless Communications, 2010, 9(11): 3590–3600. doi: 10.1109/TWC.2010.092810.091092

RUSEK F, PERSSON D, LAU B K, et al. Scaling up MIMO: opportunities and challenges with very large array[J]. IEEE Signal Processing Magazine, 2013, 30(1): 40–60. doi: 10.1109/MSP.2011.2178495

PAYAMI S and TUFVESSON F. Channel measurements and analysis for very large array systems at 2.6 GHz[C]. The 6th European Conference on Antennas and Propagation, Prague, Czech Republic, 2012: 433–437.

GAO Xiang, TUFVESSON F, and EDFORS O. Massive MIMO channels - Measurements and models[C]. Asilomar Conference on Signals, Systems and Computers, Pacific Grove, USA, 2013: 280–284.

WU Shangbin, WANG Chengxiang, HAAS H, et al. A Non-stationary wideband channel model for massive MIMO communication systems[J]. IEEE Transactions on Wireless Communications, 2015, 14(3): 1434–1446. doi: 10.1109/TWC.2014.2366153

NGUYEN S. Compressive sensing for multi-channel and large-scale MIMO networks[D]. [Ph.D. dissertation], Concordia University, 2013.

QI Chenhao, HUANG Yongming, JIN Shi, et al. Sparse channel estimation based on compressed sensing for massive MIMO systems[C]. 2015 IEEE International Conference on Communications, London, UK, 2015: 4558–4563.

CHEN Lei, LIU An, and YUAN Xiaojun. Structured turbo compressed sensing for massive MIMO channel estimation using a Markov prior[J]. IEEE Transactions on Vehicular Technology, 2018, 67(5): 4635–4639. doi: 10.1109/TVT.2017.2787708

HOU Weikun and LIM C W. Structured compressive channel estimation for large-scale MISO-OFDM systems[J]. IEEE Communications Letters, 2014, 18(5): 765–768. doi: 10.1109/LCOMM.2014.030714.132630.

NAN Yang, ZHANG Li, and SUN Xin. Weighted compressive sensing based uplink channel estimation for time division duplex massive Multi-Input Multi-Output systems[J]. IET Communications, 2017, 11(3): 355–361. doi: 10.1049/iet-com.2016.0625

MASOOD M, AFIFY L H, and AL-NAFFOURI T Y. Efficient coordinated recovery of sparse channels in massive MIMO[J]. IEEE Transactions on Signal Processing, 2015, 63(1): 104–118. doi: 10.1109/TSP.2014.2369005

PEDERSEN N L, MANCHóN C N, and FLEURY B H. A fast iterative Bayesian inference algorithm for sparse channel estimation[C]. 2013 IEEE International Conference on Communications, Budapest, Hungary, 2013: 4591–4596.

MA Jianpeng, LI Hongyan, ZHANG Shun, et al. Sparse Bayesian learning for the channel statistics of the massive MIMO systems[C]. 2017 IEEE Global Communications Conference, Singapore, 2017: 1–6.

GUI Guan, XU Li, and SHAN Lin. Block Bayesian sparse learning algorithms with application to estimating channels in OFDM systems[C]. 2014 International Symposium on Wireless Personal Multimedia Communications, Sydney, Australia, 2014: 238–242.

CHENG Xiantao, SUN Jingjing, and LI Shaoqian. Channel estimation for FDD multi-user massive MIMO: a variational Bayesian inference-based approach[J]. IEEE Transactions on Wireless Communications, 2017, 16(11): 7590–7602. doi: 10.1109/TWC.2017.2751046

TIPPING M. Sparse Bayesian learning and the relevance vector machine[J]. The Journal of Machine Learning Research, 2001, 1: 211–244. doi: 10.1162/15324430152748236

BLEI D M and JORDAN M I. Variational inference for dirichlet process mixtures[J]. Bayesian Analysis, 2006, 1(1): 121–143. doi: 10.1214/06-BA104

梅素玉, 王飛, 周水庚. 狄利克雷過程混合模型、擴(kuò)展模型及應(yīng)用[J]. 科學(xué)通報, 2012, 57(34): 3243–3257.

MEI Suyu, WANG Fei, and ZHOU Shuigeng. Dirichlet process mixture model, extensions and applications[J]. Chinese Science Bulletin (Chinese Version), 2012, 57(34): 3243–3257.

WANG Lu, ZHAO Lifan, BI Guoan, et al. Novel wideband DOA estimation based on sparse bayesian learning with dirichlet process priors[J]. IEEE Transactions on Signal Processing, 2016, 64(2): 275–289. doi: 10.1109/TSP.2015.2481790

TSE D and VISWANATH P. Fundamentals of Wireless Communication[M]. Cambridge: Cambridge University, 2004: 32–50, 348–352.

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