Frame-theoretic Precoding and Beamforming Design for Robust mmWave Channel Estimation

被引:0
作者
Stoica, Razvan-Andrei [1 ]
de Abreu, Giuseppe Thadeu Freitas [1 ]
机构
[1] Jacobs Univ Bremen, Focus Area Mobil, Campus Ring 1, D-28759 Bremen, Germany
来源
2019 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC) | 2019年
关键词
Frame Theory; Compressed Sensing; mmWave precoding / beamforming; mmWave channel estimation; MIMO; SPARSITY;
D O I
暂无
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
We propose a new method for the design of TX-precoders and RX-beamformers to improve the robustness of millimeter wave (mmWave) channel estimation, focusing on a frame-theoretic approach and a sparse formulation of the mmWave channel estimation problem. Concretely, the high-level design criteria of the transmit precoders and receive beamformers are modeled through a single frame, while their practical realization is achieved by distributed frames, related to the latter by the Kronecker product. We prove that the desirable frame properties are invariant to the derived Kronecker decomposition allowing for the same strategy to be applicable both in the design of a joint measurement matrix - leading to optimal but theoretical-only performance - as well as in the design of practical TX/RX precoding/beamforming matrices. Simulations outline both the improvement achieved by the proposed scheme against the state-of-the-art, as well as its trend towards optimality for systems with large number of antennas and/or subjected to high noise variances.
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页数:6
相关论文
共 30 条
[1]   Channel Estimation and Hybrid Precoding for Millimeter Wave Cellular Systems [J].
Alkhateeb, Ahmed ;
El Ayach, Omar ;
Leus, Geert ;
Heath, Robert W., Jr. .
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, 2014, 8 (05) :831-846
[2]   Enhancing Sparsity by Reweighted l1 Minimization [J].
Candes, Emmanuel J. ;
Wakin, Michael B. ;
Boyd, Stephen P. .
JOURNAL OF FOURIER ANALYSIS AND APPLICATIONS, 2008, 14 (5-6) :877-905
[3]  
Casazza P.G., 2012, Finite frames: Theory and applications
[4]  
CHEN SB, 1994, CONF REC ASILOMAR C, P41, DOI 10.1109/ACSSC.1994.471413
[5]   Projection Design for Statistical Compressive Sensing: A Tight Frame Based Approach [J].
Chen, Wei ;
Rodrigues, Miguel R. D. ;
Wassell, Ian J. .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2013, 61 (08) :2016-2029
[6]   Compressed Sensing for Wireless Communications: Useful Tips and Tricks [J].
Choi, Jun Won ;
Shim, Byonghyo ;
Ding, Yacong ;
Rao, Bhaskar ;
Kim, Dong In .
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2017, 19 (03) :1527-1550
[7]   Compressed sensing [J].
Donoho, DL .
IEEE TRANSACTIONS ON INFORMATION THEORY, 2006, 52 (04) :1289-1306
[8]   Energy-Efficient Hybrid Analog and Digital Precoding for MmWave MIMO Systems With Large Antenna Arrays [J].
Gao, Xinyu ;
Dai, Linglong ;
Han, Shuangfeng ;
I, Chih-Lin ;
Heath, Robert W., Jr. .
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 2016, 34 (04) :998-1009
[9]  
Ghauch H, 2015, IEEE INT WORK SIGN P, P395, DOI 10.1109/SPAWC.2015.7227067
[10]   An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems [J].
Heath, Robert W., Jr. ;
Gonzalez-Prelcic, Nuria ;
Rangan, Sundeep ;
Roh, Wonil ;
Sayeed, Akbar M. .
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, 2016, 10 (03) :436-453