Pilot allocation for MIMO-OFDM systems: A structured compressive sensing perspective

Channel estimation for MIMO orthogonal frequency division multiplexing (MIMO-OFDM) systems has been improved observably based on compressive sensing (CS) techniques. Recently, some block-structured CS methods have been utilized in MIMO-OFDM systems. However, the block-coherence which characterizes the performance of a block-structured measurement matrix is still not optimized. In this paper, we proposed a two-step scheme to optimize the pilot locations and power of different transmitting antennas, in order to minimize the block-coherence of the aggregate system matrix. The selection of pilot locations and power can be obtained by two genetic algorithms. Then we introduce a block version of the orthogonal matching pursuit (OMP) algorithm, termed BOMP, to obtain the channel estimation consistent with the block-sparse model. Simulation results demonstrate that the proposed pilot allocation scheme with superimposed pilots design and block-structured CS method performs very well in MIMO-OFDM systems.

[1]  Yao Xie,et al.  On Block Coherence of Frames , 2013, 1307.7544.

[2]  Leandro de Haro-Ariet,et al.  A 2 $\times$ 2 MIMO DVB-T2 System: Design, New Channel Estimation Scheme and Measurements With Polarization Diversity , 2010, IEEE Transactions on Broadcasting.

[3]  Yonina C. Eldar,et al.  Coherence-Based Performance Guarantees for Estimating a Sparse Vector Under Random Noise , 2009, IEEE Transactions on Signal Processing.

[4]  Jintao Wang,et al.  Optimal pilot pattern for sparse channel estimation in TFT-OFDM systems , 2015, 2015 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting.

[5]  Mona Z. Saleh,et al.  Modified MIMO-OFDM Channel Estimation Technique for DVB-T2 Systems , 2014, 2014 International Conference on Computational Intelligence and Communication Networks.

[6]  Georgios B. Giannakis,et al.  Wireless multicarrier communications , 2000, IEEE Signal Process. Mag..

[7]  Lenan Wu,et al.  Uplink channel estimation for massive MIMO systems exploring joint channel sparsity , 2014 .

[8]  Dong-Ho Kim,et al.  Cooperative MIMO transmission scheme for the DVB-T2 system , 2010, 2010 International Conference on Information and Communication Technology Convergence (ICTC).

[9]  E. Candès,et al.  Stable signal recovery from incomplete and inaccurate measurements , 2005, math/0503066.

[10]  Sundeep Rangan,et al.  Estimation of Sparse MIMO Channels with Common Support , 2011, IEEE Transactions on Communications.

[11]  Linglong Dai,et al.  Structured Compressive Sensing Based Superimposed Pilot Design in Downlink Large-Scale MIMO Systems , 2014, ArXiv.

[12]  Hyoung-Nam Kim,et al.  A MIMO DVB-T2 system with a newly designed bit mapper for UHDTV broadcasting , 2015, 2015 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting.

[13]  Yuhei Nagao,et al.  Pilot Aided Channel Estimation for a 2×2 MIMO DVB-T2 System in High Speed Mobile Environment , 2012, 2012 IEEE Vehicular Technology Conference (VTC Fall).

[14]  Chia Wei Lim,et al.  Structured Compressive Channel Estimation for Large-Scale MISO-OFDM Systems , 2014, IEEE Communications Letters.

[15]  Jian Song,et al.  Time–Frequency Joint Sparse Channel Estimation for MIMO-OFDM Systems , 2015, IEEE Communications Letters.

[16]  Farrokh Marvasti,et al.  OFDM pilot allocation for sparse channel estimation , 2011, EURASIP J. Adv. Signal Process..

[17]  Yonina C. Eldar,et al.  Block-Sparse Signals: Uncertainty Relations and Efficient Recovery , 2009, IEEE Transactions on Signal Processing.