Pilot-Assisted Methods for Channel Estimation in MIMO-V-OFDM Systems

Multiple-input multiple-output (MIMO) with Orthogonal Frequency Division Multiplexing (OFDM) technology has both the advantages of MIMO and OFDM. Vector Orthogonal Frequency Division Multiplexing (V-OFDM) is an extension of OFDM, which makes data transmission flexible. In MIMO systems using V-OFDM technology, different novel schemes are proposed to improve channel estimation performance for different channel sparsity. The 2-D Kriging interpolation scheme is proposed for the non-sparse channels, which can significantly improve the performance of conventional Least Square (LS) and Minimum Mean Square Error (MMSE) algorithms. When the channel is sparse, the estimation process can be modeled as a sparse recovery problem using compressed sensing (CS) theory. In this paper, the measurement matrix is determined by pilot locations, and a pilot search algorithm based on random genetic algorithm (RGA) is proposed to minimize the cross-correlation value of the measurement matrix. Besides, a variable threshold sparsity adaptive matching pursuit (VTSAMP) algorithm is designed to obtain more accurate estimates, which achieves better Normalized Mean Square Error (NMSE) performance, higher calculation speed, and lower implementation complexity.

[1]  Ertugrul Basar,et al.  On Multiple-Input Multiple-Output OFDM with Index Modulation for Next Generation Wireless Networks , 2016, IEEE Transactions on Signal Processing.

[2]  Vincent K. N. Lau,et al.  Distributed Compressive CSIT Estimation and Feedback for FDD Multi-User Massive MIMO Systems , 2014, IEEE Transactions on Signal Processing.

[3]  Hem Dutt Joshi,et al.  Performance analysis of comb type pilot aided channel estimation in OFDM with different pilot sequences , 2013, 2013 Tenth International Conference on Wireless and Optical Communications Networks (WOCN).

[4]  Yongming Huang,et al.  Sparse channel estimation based on compressed sensing for massive MIMO systems , 2015, 2015 IEEE International Conference on Communications (ICC).

[5]  Shingchern D. You,et al.  Comparative study of channel estimation methods for LTE downlink transmission , 2015, 2015 IEEE 4th Global Conference on Consumer Electronics (GCCE).

[6]  Chau Yuen,et al.  Super-Resolution Sparse MIMO-OFDM Channel Estimation Based on Spatial and Temporal Correlations , 2014, IEEE Communications Letters.

[7]  Jintao Wang,et al.  Pilot allocation for MIMO-OFDM systems: A structured compressive sensing perspective , 2016, 2016 International Wireless Communications and Mobile Computing Conference (IWCMC).

[8]  Zhanxin Yang,et al.  Deterministic pilot pattern allocation optimization for sparse channel estimation based on CS theory in OFDM system , 2019, EURASIP J. Wirel. Commun. Netw..

[9]  Vincent K. N. Lau,et al.  Joint burst LASSO for sparse channel estimation in multi-user massive MIMO , 2016, 2016 IEEE International Conference on Communications (ICC).

[10]  Bernard H. Fleury,et al.  Interference-aware OFDM receiver for channels with sparse common supports , 2017, 2017 IEEE International Conference on Communications (ICC).

[11]  Wei Zhang,et al.  Performance analysis of V-OFDM for acoustic communication along drill strings , 2017, IET Commun..

[12]  Xiang-Gen Xia,et al.  Analysis and Compensation of Phase Noise in Vector OFDM Systems , 2014, IEEE Transactions on Signal Processing.

[13]  Leila Najjar,et al.  Pilot allocation by Genetic Algorithms for sparse channel estimation in OFDM systems , 2013, 21st European Signal Processing Conference (EUSIPCO 2013).

[14]  Yan Yu,et al.  Performance analysis of different kriging interpolation methods based on air quality index in Wuhan , 2015, 2015 Sixth International Conference on Intelligent Control and Information Processing (ICICIP).

[15]  Thomas Strohmer,et al.  General Deviants: An Analysis of Perturbations in Compressed Sensing , 2009, IEEE Journal of Selected Topics in Signal Processing.

[16]  Aldo G. Orozco-Lugo,et al.  Full-hardware architectures for data-dependent superimposed training channel estimation , 2011, 2011 IEEE Workshop on Signal Processing Systems (SiPS).

[17]  Yong Liao,et al.  M-SAMP: A Low-complexity Modified SAMP Algorithm for Massive MIMO CSI Feedback , 2018, 2018 IEEE/CIC International Conference on Communications in China (ICCC).