MIMO relay compressed channel estimation using optimized pilot placements

Relay communication channel estimation is one of the recent challenges in this field. One of the crucial point of the proposed channel estimation approach is the bandwidth efficiency. Compressed Sensing (CS) based channel estimation have been emerged as a bandwidth efficient approach. In this paper, we have designed a pilot placement for CS based channel estimation in MIMO-OFDM relays. The proposed approach uses stochastic search algorithm to find the pilot sequences for different transmit antennas which yields the optimal coherence value. Simulation results have shown the superiority of the proposed method to improve the channel estimation performance.

[1]  Xueyun He,et al.  Pilot pattern optimization for compressed sensing based sparse channel estimation in OFDM systems , 2010, 2010 International Conference on Wireless Communications & Signal Processing (WCSP).

[2]  Xiaoyun Hou,et al.  Compressed MIMO chanel estimation and efficient pilot pattern over Doppler sparse environment , 2011, 2011 International Conference on Wireless Communications and Signal Processing (WCSP).

[3]  Holger Rauhut,et al.  Multichannel-compressive estimation of doubly selective channels in MIMO-OFDM systems: Exploiting and enhancing joint sparsity , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[4]  Georgios B. Giannakis,et al.  Error probability minimizing pilots for OFDM with M-PSK modulation over Rayleigh-fading channels , 2004, IEEE Transactions on Vehicular Technology.

[5]  Xiang-Gen Xia,et al.  Optimal Training and Pilot Pattern Design for OFDM Systems in Rayleigh Fading , 2006, IEEE Transactions on Broadcasting.

[6]  Lenan Wu,et al.  A Study of Deterministic Pilot Allocation for Sparse Channel Estimation in OFDM Systems , 2012, IEEE Communications Letters.

[7]  Wei-Ping Zhu,et al.  Pilot Allocation for Distributed-Compressed-Sensing-Based Sparse Channel Estimation in MIMO-OFDM Systems , 2016, IEEE Transactions on Vehicular Technology.

[8]  Michael Elad,et al.  Stable recovery of sparse overcomplete representations in the presence of noise , 2006, IEEE Transactions on Information Theory.

[9]  Marc Moonen,et al.  Optimal training design for MIMO OFDM systems in mobile wireless channels , 2003, IEEE Trans. Signal Process..

[10]  Lenan Wu,et al.  Pilot Design for Sparse Channel Estimation in OFDM-Based Cognitive Radio Systems , 2014, IEEE Transactions on Vehicular Technology.

[11]  Lenan Wu,et al.  Optimized Pilot Placement for Sparse Channel Estimation in OFDM Systems , 2011, IEEE Signal Processing Letters.

[12]  Holger Rauhut,et al.  Compressive Estimation of Doubly Selective Channels in Multicarrier Systems: Leakage Effects and Sparsity-Enhancing Processing , 2009, IEEE Journal of Selected Topics in Signal Processing.

[13]  Feifei Gao,et al.  On channel estimation and optimal training design for amplify and forward relay networks , 2008, IEEE Transactions on Wireless Communications.

[14]  E.J. Candes.  Compressive Sampling , 2022 .

[15]  Meixia Tao,et al.  Channel Estimation for OFDM Systems over Doubly Selective Channels: A Distributed Compressive Sensing Based Approach , 2013, IEEE Transactions on Communications.

[16]  Lenan Wu,et al.  A hybrid compressed sensing algorithm for sparse channel estimation in MIMO OFDM systems , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[17]  Bhaskar D. Rao,et al.  Joint Channel Estimation and Data Detection in MIMO-OFDM Systems: A Sparse Bayesian Learning Approach , 2015, IEEE Transactions on Signal Processing.

[18]  Jong-Soo Seo,et al.  Efficient Pilot Patterns and Channel Estimations for MIMO-OFDM Systems , 2012, IEEE Transactions on Broadcasting.

[19]  Wei-Ping Zhu,et al.  Pilot Allocation for Sparse Channel Estimation in MIMO-OFDM Systems , 2013, IEEE Transactions on Circuits and Systems II: Express Briefs.