Channel Estimation via Orthogonal Matching Pursuit for Hybrid MIMO Systems in Millimeter Wave Communications

被引:483
作者
Lee, Junho [1 ]
Gil, Gye-Tae [2 ]
Lee, Yong H. [1 ]
机构
[1] Korea Adv Inst Sci & Technol, Dept Elect Engn, Taejon 305701, South Korea
[2] Korea Adv Inst Sci & Technol, Inst Informat Technol Convergence, Taejon 305701, South Korea
关键词
Channel estimation; hybrid RF/baseband processing; millimeter wave communication; orthogonal matching pursuit; sparsity; PERFORMANCE GUARANTEES; NETWORKS; CAPACITY; 5G;
D O I
10.1109/TCOMM.2016.2557791
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
We propose an efficient open-loop channel estimator for a millimeter-wave (mm-wave) hybrid multiple-input multiple-output (MIMO) system consisting of radio-frequency (RF) beamformers with large antenna arrays followed by a baseband MIMO processor. A sparse signal recovery problem exploiting the sparse nature of mm-wave channels is formulated for channel estimation based on the parametric channel model with quantized angles of departures/arrivals (AoDs/AoAs), called the angle grids. The problem is solved by the orthogonal matching pursuit (OMP) algorithm employing a redundant dictionary consisting of array response vectors with finely quantized angle grids. We suggest the use of non-uniformly quantized angle grids and show that such grids reduce the coherence of the redundant dictionary. The lower and upper bounds of the sum-of-squared errors of the proposed OMP-based estimator are derived analytically: the lower bound is derived by considering the oracle estimator that assumes the knowledge of AoDs/AoAs, and the upper bound is derived based on the results of the OMP performance guarantees. The design of training vectors (or sensing matrix) is particularly important in hybrid MIMO systems, because the RF beamformer prevents the use of independent and identically distributed random training vectors, which are popular in compressed sensing. We design training vectors so that the total coherence of the equivalent sensing matrix is minimized for a given RF beamforming matrix, which is assumed to be unitary. It is observed that the estimation accuracy can be improved significantly by randomly permuting the columns of the RF beamforming matrix. The simulation results demonstrate the advantage of the proposed OMP with a redundant dictionary over the existing methods such as the least squares method and the OMP based on the virtual channel model.
引用
收藏
页码:2370 / 2386
页数:17
相关论文
共 41 条
[1]   Millimeter Wave Channel Modeling and Cellular Capacity Evaluation [J].
Akdeniz, Mustafa Riza ;
Liu, Yuanpeng ;
Samimi, Mathew K. ;
Sun, Shu ;
Rangan, Sundeep ;
Rappaport, Theodore S. ;
Erkip, Elza .
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 2014, 32 (06) :1164-1179
[2]   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
[3]  
[Anonymous], 2013, P80211AD IEEE
[4]  
[Anonymous], 802153C IEEE
[5]  
[Anonymous], 1988, Matrix Analysis
[6]   Coverage and Capacity of Millimeter-Wave Cellular Networks [J].
Bai, Tianyang ;
Alkhateeb, Ahmed ;
Heath, Robert W., Jr. .
IEEE COMMUNICATIONS MAGAZINE, 2014, 52 (09) :70-77
[7]   Compressed Channel Sensing: A New Approach to Estimating Sparse Multipath Channels [J].
Bajwa, Waheed U. ;
Haupt, Jarvis ;
Sayeed, Akbar M. ;
Nowak, Robert .
PROCEEDINGS OF THE IEEE, 2010, 98 (06) :1058-1076
[8]   Coherence-Based Performance Guarantees for Estimating a Sparse Vector Under Random Noise [J].
Ben-Haim, Zvika ;
Eldar, Yonina C. ;
Elad, Michael .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2010, 58 (10) :5030-5043
[9]   Sparse Channel Estimation for Multicarrier Underwater Acoustic Communication: From Subspace Methods to Compressed Sensing [J].
Berger, Christian R. ;
Zhou, Shengli ;
Preisig, James C. ;
Willett, Peter .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2010, 58 (03) :1708-1721
[10]  
Bullen P.S., 2003, Handbook of Means and Their Inequalities