scholarly journals Iterative Joint Channel Estimation, User Activity Tracking, and Data Detection for FTN-NOMA Systems Supporting Random Access

2020 ◽  
Vol 68 (5) ◽  
pp. 2963-2977 ◽  
Author(s):  
Weijie Yuan ◽  
Nan Wu ◽  
Qinghua Guo ◽  
Derrick Wing Kwan Ng ◽  
Jinhong Yuan ◽  
...  
Keyword(s):  
Channel Estimation ◽  
Random Access ◽  
Data Detection ◽  
Activity Tracking ◽  
User Activity ◽  
Joint Channel

scholarly journals Deep-Learning-Aided Joint Channel Estimation and Data Detection for Spatial Modulation

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 191910-191919 ◽  
Author(s):  
Luping Xiang ◽  
Yusha Liu ◽  
Thien Van Luong ◽  
Robert G. Maunder ◽  
Lie-Liang Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Channel Estimation ◽  
Spatial Modulation ◽  
Data Detection ◽  
Joint Channel

Joint Channel Estimation and Data Detection for STBC MIMO-OFDM Wireless Communication System

2015 ◽  
Vol 24 (04) ◽  
pp. 1550059 ◽  
Author(s):  
Gajanan R. Patil ◽  
Vishwanath K. Kokate
Keyword(s):  
Channel Estimation ◽  
Orthogonal Frequency Division Multiplexing ◽  
Multiple Input Multiple Output ◽  
Data Detection ◽  
Channel Estimate ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Mimo Ofdm ◽  
The Cost ◽  
Joint Channel

This paper presents a joint channel estimation and data detection technique for multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) system. Initial estimate of the channel is obtained using semi-blind channel estimation (SBCE). The whitening rotation (WR)-based orthogonal pilot maximum likelihood (OPML) method is used to obtain the channel estimate. The estimate is further enhanced by extracting information through the received data symbols. The performance of the proposed estimator is studied under various channel models. The simulation study shows that this approach gives better performance over training-based channel estimation (TBCE) and OPML SBCE methods but at the cost of higher computational complexity.

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