Low-Complexity Compressed Sensing-Based Channel Estimation With Virtual Oversampling for Digital Terrestrial Television Broadcasting

2017 ◽  
Vol 63 (1) ◽  
pp. 82-91 ◽  
Author(s):  
Ryan Paderna ◽  
Duong Quang Thang ◽  
Yafei Hou ◽  
Takeshi Higashino ◽  
Minoru Okada
Keyword(s):  
Channel Estimation ◽  
Compressed Sensing ◽  
Low Complexity ◽  
Television Broadcasting ◽  
Digital Terrestrial Television

scholarly journals Low-Complexity Channel Estimation in 5G Massive MIMO-OFDM Systems

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 713 ◽  
Author(s):  
Omar A. Saraereh ◽  
Imran Khan ◽  
Qais Alsafasfeh ◽  
Salem Alemaishat ◽  
Sunghwan Kim
Keyword(s):  
Channel Estimation ◽  
Compressed Sensing ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Matching Pursuit ◽  
Low Complexity ◽  
Mimo Channel ◽  
Ofdm Systems ◽  
Mimo Channels ◽  
Low Snr

Pilot contamination is the reuse of pilot signals, which is a bottleneck in massive multi-input multi-output (MIMO) systems as it varies directly with the numerous antennas, which are utilized by massive MIMO. This adversely impacts the channel state information (CSI) due to too large pilot overhead outdated feedback CSI. To solve this problem, a compressed sensing scheme is used. The existing algorithms based on compressed sensing require that the channel sparsity should be known, which in the real channel environment is not the case. To deal with the unknown channel sparsity of the massive MIMO channel, this paper proposes a structured sparse adaptive coding sampling matching pursuit (SSA-CoSaMP) algorithm that utilizes the space–time common sparsity specific to massive MIMO channels and improves the CoSaMP algorithm from the perspective of dynamic sparsity adaptive and structural sparsity aspects. It has a unique feature of threshold-based iteration control, which in turn depends on the SNR level. This approach enables us to determine the sparsity in an indirect manner. The proposed algorithm not only optimizes the channel estimation performance but also reduces the pilot overhead, which saves the spectrum and energy resources. Simulation results show that the proposed algorithm has improved channel performance compared with the existing algorithm, in both low SNR and low pilot overhead.

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