scholarly journals Exploiting the Massive MIMO Channel Structural Properties for Minimization of Channel Estimation Error and Training Overhead

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 32434-32452 ◽  
Author(s):  
Samer Bazzi ◽  
Stelios Stefanatos ◽  
Luc Le Magoarou ◽  
Salah Eddine Hajri ◽  
Mohamad Assaad ◽  
...  
Keyword(s):  
Channel Estimation ◽  
Structural Properties ◽  
Massive Mimo ◽  
Estimation Error ◽  
Mimo Channel ◽  
Channel Estimation Error ◽  
And Training

scholarly journals Iterative Mean Removal Superimposed Training for SISO and MIMO Channel Estimation

2008 ◽  
Vol 2008 ◽  
pp. 1-9 ◽  
Author(s):  
O. Longoria-Gandara ◽  
R. Parra-Michel ◽  
M. Bazdresch ◽  
A. G. Orozco-Lugo
Keyword(s):  
Channel Estimation ◽  
Radio Channel ◽  
Estimation Error ◽  
Mimo Channel ◽  
Channel Estimation Error ◽  
Superimposed Training ◽  
Iterative Techniques ◽  
Alamouti Coding ◽  
The Impact ◽  
Ber Performance

This contribution describes a novel iterative radio channel estimation algorithm based on superimposed training (ST) estimation technique. The proposed algorithm draws an analogy with the data dependent ST (DDST) algorithm, that is, extracts the cycling mean of the data, but in this case at the receiver's end. We first demonstrate that this mean removal ST (MRST) applied to estimate a single-input single-output (SISO) wideband channel results in similar bit error rate (BER) performance in comparison with other iterative techniques, but with less complexity. Subsequently, we jointly use the MRST and Alamouti coding to obtain an estimate of the multiple-input multiple-output (MIMO) narrowband radio channel. The impact of imperfect channel on the BER performance is evidenced by a comparison between the MRST method and the best iterative techniques found in the literature. The proposed algorithm shows a good tradeoff performance between complexity, channel estimation error, and noise immunity.

scholarly journals Massive MIMO Performance With Imperfect Channel Reciprocity and Channel Estimation Error

2017 ◽  
Vol 65 (9) ◽  
pp. 3734-3749 ◽  
Author(s):  
De Mi ◽  
Mehrdad Dianati ◽  
Lei Zhang ◽  
Sami Muhaidat ◽  
Rahim Tafazolli
Keyword(s):  
Channel Estimation ◽  
Massive Mimo ◽  
Estimation Error ◽  
Channel Estimation Error ◽  
Channel Reciprocity

scholarly journals Channel Rectification and Signal Estimation Based on EIV Model in Massive MIMO System

2017 ◽  
Vol 2017 ◽  
pp. 1-7
Author(s):  
Liang Zhong ◽  
Li Huang ◽  
Zhengmin Kong
Keyword(s):  
Channel Estimation ◽  
Massive Mimo ◽  
Channel Model ◽  
Least Squares Method ◽  
Mimo System ◽  
Estimation Error ◽  
Estimation Algorithm ◽  
Channel Estimation Error ◽  
Signal Estimation ◽  
Pilot Contamination

The performance of Massive MIMO is severely limited by channel estimation error, which is caused by pilot contamination and channel aging. In this paper, we propose an estimation algorithm based on the weighted total least-squares method with errors-in-variables (EIV) model to alleviate the influence of pilot contamination and channel aging. Then, a channel rectification method has been investigated to diminish the inaccuracy of channel estimation. Comparing with the traditional methods, it not only helps to make the signal estimation more accurate, but also provides opportunities to correct the channel model with estimation error and update the aged channel statement information. Simulations are provided to verify the efficacy of this method.

Influence of Channel Estimation Error on MIMO Multiplexing Using Precoding in Downlink OFDM Radio Access

2009 ◽  
Vol E92-B (5) ◽  
pp. 1553-1562
Author(s):  
Takashi ISOGAI ◽  
Mamoru SAWAHASHI ◽  
Hidekazu TAOKA ◽  
Kenichi HIGUCHI
Keyword(s):  
Channel Estimation ◽  
Estimation Error ◽  
Channel Estimation Error ◽  
Radio Access
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