scholarly journals Massive-MIMO Sparse Uplink Channel Estimation Using Implicit Training and Compressed Sensing

2017 ◽  
Vol 7 (1) ◽  
pp. 63 ◽  
Author(s):  
Babar Mansoor ◽  
Syed Nawaz ◽  
Sardar Gulfam
Keyword(s):  
Channel Estimation ◽  
Compressed Sensing ◽  
Massive Mimo ◽  
Implicit Training

scholarly journals Structured Turbo Compressed Sensing for Downlink Massive MIMO-OFDM Channel Estimation

2019 ◽  
Vol 18 (8) ◽  
pp. 3813-3826 ◽  
Author(s):  
Xiaoyan Kuai ◽  
Lei Chen ◽  
Xiaojun Yuan ◽  
An Liu
Keyword(s):  
Channel Estimation ◽  
Compressed Sensing ◽  
Massive Mimo ◽  
Mimo Ofdm

scholarly journals OFDM-based Massive MIMO Channel Estimation using Gaussian Mixture Learning and Compressed Sensing Methods

2019 ◽  
Vol 9 (2S3) ◽  
pp. 7-13
Keyword(s):  
Channel Estimation ◽  
Compressed Sensing ◽  
Massive Mimo ◽  
Gaussian Mixture ◽  
Symbol Error Rate ◽  
Mimo Channel ◽  
Sparse Channel Estimation ◽  
High Data ◽  
Data Rates ◽  
Vector Distribution

Massive MIMO-OFDM system is proved to be an effective and most sustainable technology to forthcoming applications of 5G wireless communications. It furnished significant gains that facilitate a higher number of user connections at high data rates with improved latency and reliability. To achieve accurate channel knowledge, lessen pilot overhead is necessary. To resolve this problem, one of the favorite approaches is compressed sensing. Sparse channel estimation develops the essential sparsity between the communicating channels that can be improved by the channel estimation efficacy with lower pilot overhead. To achieve this, non-zero vector distribution can be taking into consideration the Gaussian mixture accordingly, learn their characteristics towards the expectation-maximization procedure. The results of simulation have proved the performance of proposed estimation approach of channel keeping with minimum pilot overhead and developed exceptional symbol error rate (SER) performance of the system.

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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