A Channel Estimation Method Based on Improved Regularized Orthogonal Matching Pursuit for MIMO-OFDM Systems
LIAO Yong1,2, ZHOU Xin1, SHEN Xuan-fan1, HONG Guan3
1. Key Laboratory of Aerocraft TT & C and Communication, Ministry of Education, Chongqing University, Chongqing 400044, China;
2. The State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, Shaanxi 710071, China;
3. College of Communication Engineering, Chongqing University, Chongqing 400044, China
Abstract：The regularized orthogonal matching pursuit (ROMP) has two drawbacks,ie,the sparsity should be known beforehand and once the atom is determined it can not be deleted.According to these two problems,this paper presents an improved ROMP channel estimation method based on compressive sensing by exploiting the sparse structure of channel impulse response.The proposed method combines the advantages of compressive sampling matching pursuit (CoSaMP),sparsity adaptive matching pursuit (SAMP) and variable step size to achieve the reconstruction of sparse signal quickly and accurately.Simulation results demonstrate that compared with the channel estimations respectively based on OMP,ROMP,CoSaMP and SAMP,the proposed method effectively improve the performance of the MIMO-OFDM systems in the normalized mean square error (NMSE) and bit error rate (BER).
[1] CHEEMA S A,NASKOVSKA K,ATTAR M,et al.Performance comparison of space time block codes for different 5G air interface proposals[A].Proceedings of the 20th International ITG Workshop on Smart Antennas (WSA 2016)[C].Germany:VDE VERLAG GmbH,2016.9-11.
[2] GAO Z,DAI L,LU Z,et al.Super-resolution sparse MIMO-OFDM channel estimation based on spatial and temporal correlations[J].IEEE Communications Letters,2015,18(7):1266-1269.
[3] BERGER C R,ZHOU Z,PREISIG J C,et al.Sparse channel estimation for multicarrier underwater acoustic communication:from subspace methods to compressed sensing[J].IEEE Transactions on Signal Processing,2010,58(3):1708-1721.
[4] KHOJASTEPOUR M A,GOMADAM K S.Sparse Channel Estimation for MIMO OFDM Systems[P].US:US8619918,2013.
[5] RANGAN S,RAPPAPORT T S,E Erkip.Millimeter-wave cellular wireless networks:potentials and challenges[J].Proceedings of the IEEE,2014,102(3):366-385.
[6] QI C,HUANG Y,JIN S,et al.Sparse channel estimation based on compressed sensing for massive MIMO systems[A].IEEE International Conference on Communications[C].London UK:IEEE,2015.4558-4563.
[7] REN X,CHEN W,TAO M.Position-based compressed channel estimation and pilot design for high-mobility OFDM systems[J].IEEE Transactions on Vehicular Technology,2015,64(5):1918-1929.
[8] LEE D.MIMO OFDM channel estimation via block stagewise orthogonal matching pursuit[J].IEEE Communications Letters,2016,20(10):2115-2118.
[9] HE Y,SONG R,ZHU W.Pilot allocation for distributed compressed sensing based sparse channel estimation in MIMO-OFDM systems[J].IEEE Transactions on Vehicular Technology,2016,65(5):2990-3004.
[10] 田文飚,康健,张洋,等.基于卡尔曼滤波的压缩感知弱匹配去噪重构[J].电子学报,2014,42(6):1061-1067. TIAN Wen-biao,KANG Jian,ZHANG Yang,et al.Weakly matching pursuit denoising recovery for compressed sensing based on Kalman filtering[J].Acta Electronica Sinica,2014,42(6):1061-1067.(in Chinese)
[11] 石光明,刘丹华,高大化,等.压缩感知理论及其研究进展[J].电子学报,2009,37(5):1070-1081. SHI Guang-ming,LIU Dan-hua,GAO Da-hua,et al.Advances in theory and application of compressed sensing[J].Acta Electronica Sinica,2009,37(5):1070-1081.(in Chinese)
[12] PATI Y C,REZAⅡFAR R,KRISHNAPRASAD P S.Orthogonal matching pursuit:recursive function approximation with applications to wavelet decomposition[A].Conference Record of the Twenty-Seventh Asilomar Conference on Signals,Systems and Computers[C].Pacific Grove CA:IEEE,1993.1-3.
[13] MALLAT S G,ZHANG Z.Matching pursuits with time-frequency dictionaries[J].IEEE Transactions on Signal Processing,1993,41(12):3397-3415.
[14] NEEDELL D,VERSHYNIN R.Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit[J].Foundations of Computational Mathematics,2009,9(3):317-334.
[15] NEEDELL D,TROPP J A.CoSaMP:iterative signal recovery from incomplete and inaccurate samples[J].Applied & Computational Harmonic Analysis,2008,26(12):93-100.
[16] WU H L,WANG S.Adaptive sparsity matching pursuit algorithm for sparse reconstruction[J].IEEE Signal Processing Letters,2012,19(8):471-474.
[17] CANDèS E J.The restricted isometry property and its implications for compressed sensing[J].Comptes Rendus Mathematique,2008,346(9-10):589-592.
2017, Vol. 45 
