scholarly journals Massive MIMO Channel Estimation Considering Pilot Contamination and Spatially Correlated Channels

2019 ◽  
Author(s):  
Felipe Augusto Pereira de Figueiredo ◽  
Dimas A. M. Lemes ◽  
Claudio Ferreira Dias ◽  
Gustavo Fraidenraich
Keyword(s):  
Channel Estimation ◽  
Mean Square Error ◽  
Massive Mimo ◽  
Mimo Channel ◽  
Mean Square ◽  
Pilot Contamination ◽  
Spatially Correlated ◽  
Correlated Channels ◽  
Spatially Correlated Channels ◽  
The Impact

In this letter, we present a study on linear channel estimators and their respective mean square error (MSE) expressions acknowledging spatially correlated channels and pilot contamination. We also investigate the impact of imperfect channel covariance matrix knowledge.

scholarly journals Massive MIMO Channel Estimation Considering Pilot Contamination and Spatially Correlated Channels

2019 ◽  
Author(s):  
Felipe Augusto Pereira de Figueiredo ◽  
Dimas A. M. Lemes ◽  
Claudio Ferreira Dias ◽  
Gustavo Fraidenraich
Keyword(s):  
Channel Estimation ◽  
Mean Square Error ◽  
Massive Mimo ◽  
Mimo Channel ◽  
Mean Square ◽  
Pilot Contamination ◽  
Spatially Correlated ◽  
Correlated Channels ◽  
Spatially Correlated Channels ◽  
The Impact

In this letter, we present a study on linear channel estimators and their respective mean square error (MSE) expressions acknowledging spatially correlated channels and pilot contamination. We also investigate the impact of imperfect channel covariance matrix knowledge.

scholarly journals Massive MIMO channel estimation considering pilot contamination and spatially correlated channels

2020 ◽  
Vol 56 (8) ◽  
pp. 410-413
Author(s):  
F.A. Pereira de Figueiredo ◽  
D.A. Mendes Lemes ◽  
C. Ferreira Dias ◽  
G. Fraidenraich
Keyword(s):  
Channel Estimation ◽  
Massive Mimo ◽  
Mimo Channel ◽  
Pilot Contamination ◽  
Spatially Correlated ◽  
Correlated Channels ◽  
Spatially Correlated Channels

scholarly journals Efficient Channel Estimation in Massive MIMO Partially Centralized Cloud-Radio Access Network Systems

2021 ◽  
Vol 12 (1) ◽  
pp. 64-86
Author(s):  
Emmanuel Mukubwa ◽  
Oludare Sokoya
Keyword(s):  
Channel Estimation ◽  
Mean Square Error ◽  
Projection Method ◽  
Massive Mimo ◽  
Minimum Mean Square Error ◽  
Access Network ◽  
Mimo Channel ◽  
Mean Square ◽  
Network Systems ◽  
Compressed Data

This article investigates channel estimation problem in massive MIMO partially centralized cloud-RAN (MPC-RAN). The channel estimation was realized through compressed data method to minimize the huge pilot overhead, then combined with parallel Givens data projection method (PGDPM) to form a semi-blind estimator. Comparison and analysis of improved minimum mean square error (MMSE), fast data projection method (FDPM), compressed data, and PGDPM techniques was evaluated for achievable normalized mean square error (NMSE) in MPC-RAN. The PGDPM-based estimator had the lowest normalized mean square error. The FDPM and PGDPM based methods are comparable in performance with PGDPM based estimator having a slight edge over FDPM-based estimator. This vindicates PGDPM-based estimator as a method to be utilized in channel estimation since it compresses the massive MIMO channel information, hence mitigating the fronthaul finite capacity problem, and at the same time, it is geared towards efficient parallelization for optimal BBU resource utilization.

scholarly journals A Joint Approach for Low-Complexity Channel Estimation in 5G Massive MIMO Systems

Electronics ◽  
2018 ◽  
Vol 7 (10) ◽  
pp. 218 ◽  
Author(s):  
Kifayatullah Bangash ◽  
Imran Khan ◽  
Jaime Lloret ◽  
Antonio Leon
Keyword(s):  
Computational Complexity ◽  
Channel Estimation ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Minimum Mean Square Error ◽  
Low Complexity ◽  
Least Square ◽  
Mimo Channel ◽  
Finite Sample ◽  
The Impact

Traditional Minimum Mean Square Error (MMSE) detection is widely used in wireless communications, however, it introduces matrix inversion and has a higher computational complexity. For massive Multiple-input Multiple-output (MIMO) systems, this detection complexity is very high due to its huge channel matrix dimension. Therefore, low-complexity detection technology has become a hot topic in the industry. Aiming at the problem of high computational complexity of the massive MIMO channel estimation, this paper presents a low-complexity algorithm for efficient channel estimation. The proposed algorithm is based on joint Singular Value Decomposition (SVD) and Iterative Least Square with Projection (SVD-ILSP) which overcomes the drawback of finite sample data assumption of the covariance matrix in the existing SVD-based semi-blind channel estimation scheme. Simulation results show that the proposed scheme can effectively reduce the deviation, improve the channel estimation accuracy, mitigate the impact of pilot contamination and obtain accurate CSI with low overhead and computational complexity.

Research of Improved Algorithm for MIMO Channel Estimation

2013 ◽  
Vol 475-476 ◽  
pp. 893-899
Author(s):  
Miao Miao Chang ◽  
Jin He Zhou ◽  
Ju Rong Wang
Keyword(s):  
Singular Value Decomposition ◽  
Channel Estimation ◽  
Mean Square Error ◽  
Minimum Mean Square Error ◽  
Multiple Input Multiple Output ◽  
Estimation Algorithm ◽  
Singular Value ◽  
Mimo Channel ◽  
Mean Square ◽  
Value Decomposition

We introduced an improved singular value decomposition (SVD) channel estimation algorithm for multiple-input multiple-output (MIMO) wireless communication system. The algorithm is supposed to solve the issue that the channel estimation result is not accurate when the training sequences have some 0 elements. The improvement is also applicable in the other channel estimation algorithms. We made some comparisons between the linear least squares (LS) and the linear minimum mean square error (LMMSE) channel estimation, the traditional singular value decomposition and the improved SVD algorithm to demonstrate the efficiency. Results show that the proposed improved SVD algorithm has better performance in mean square error (MSE) and bit error rate (BER) of channel estimation and the estimated values approach the actual channel state.

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