scholarly journals Optimization of Training Signal Transmission for Estimating MIMO Channel under Antenna Mutual Coupling Conditions

2010 ◽  
Vol 2010 ◽  
pp. 1-10 ◽  
Author(s):  
Xia Liu ◽  
Marek E. Bialkowski
Keyword(s):  
Channel Estimation ◽  
Mutual Coupling ◽  
Minimum Mean Square Error ◽  
Signal Transmission ◽  
Multiple Input Multiple Output ◽  
Least Square ◽  
Mimo Channel ◽  
Estimation Methods ◽  
Estimation Errors ◽  
Training Signal

This paper reports investigations on the effect of antenna mutual coupling on performance of training-based Multiple-Input Multiple-Output (MIMO) channel estimation. The influence of mutual coupling is assessed for two training-based channel estimation methods, Scaled Least Square (SLS) and Minimum Mean Square Error (MMSE). It is shown that the accuracy of MIMO channel estimation is governed by the sum of eigenvalues of channel correlation matrix which in turn is influenced by the mutual coupling in transmitting and receiving array antennas. A water-filling-based procedure is proposed to optimize the training signal transmission to minimize the MIMO channel estimation errors.

scholarly journals Effect of Antenna Mutual Coupling on MIMO Channel Estimation and Capacity

2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
Xia Liu ◽  
Marek E. Bialkowski
Keyword(s):  
Channel Estimation ◽  
Mutual Coupling ◽  
Multiple Input Multiple Output ◽  
Mimo Channel ◽  
Channel State ◽  
Estimation Errors ◽  
Channel Estimation Errors ◽  
Perfect Channel State Information ◽  
Multiple Input ◽  
Input Multiple Output

This paper describes investigations into the antenna mutual coupling (MC) effect on channel estimation and capacity of a multiple-input multiple-output (MIMO) wireless communication system. The presented investigations close the gap existing in the previous works which assessed the effect of mutual coupling on MIMO capacity under the assumption of availability of perfect channel state information (CSI) at the receiver. The new approach assumes that the perfect CSI is not available due to channel estimation errors. The investigations are carried out for different spacing between array antenna elements producing a varying effect of mutual coupling on the channel estimation and the resulting MIMO channel capacity.

scholarly journals A Comprehensive Review on Channel Estimation in OFDM System

2019 ◽  
Vol 5 (3) ◽  
pp. 6 ◽  
Author(s):  
Neha Dubey ◽  
Ankit Pandit
Keyword(s):  
Channel Estimation ◽  
Mean Square Error ◽  
Orthogonal Frequency Division Multiplexing ◽  
Transmission Rate ◽  
Minimum Mean Square Error ◽  
Estimation Method ◽  
Least Square ◽  
Estimation Methods ◽  
Mean Square ◽  
Mimo Ofdm

In wireless communication, orthogonal frequency division multiplexing (OFDM) plays a major role because of its high transmission rate. Channel estimation and tracking have many different techniques available in OFDM systems. Among them, the most important techniques are least square (LS) and minimum mean square error (MMSE). In least square channel estimation method, the process is simple but the major drawback is it has very high mean square error. Whereas, the performance of MMSE is superior to LS in low SNR, its main problem is it has high computational complexity. If the error is reduced to a very low value, then an exact signal will be received. In this paper an extensive review on different channel estimation methods used in MIMO-OFDM like pilot based, least square (LS) and minimum mean square error method (MMSE) and least minimum mean square error (LMMSE) methods and also other channel estimation methods used in MIMO-OFDM are discussed.

scholarly journals Channel Estimation Based on Statistical Frames and Confidence Level in OFDM Systems

2018 ◽  
Vol 8 (9) ◽  
pp. 1607 ◽  
Author(s):  
Xiao Zhou ◽  
Chengyou Wang ◽  
Ruiguang Tang ◽  
Mingtong Zhang
Keyword(s):  
Channel Estimation ◽  
Mean Square Error ◽  
Confidence Level ◽  
Orthogonal Frequency Division Multiplexing ◽  
Minimum Mean Square Error ◽  
Estimation Method ◽  
Least Square ◽  
Estimation Methods ◽  
Mean Square ◽  
Inverse Operation

Channel estimation is an important module for improving the performance of the orthogonal frequency division multiplexing (OFDM) system. The pilot-based least square (LS) algorithm can improve the channel estimation accuracy and the symbol error rate (SER) performance of the communication system. In pilot-based channel estimation, a certain number of pilots are inserted at fixed intervals between OFDM symbols to estimate the initial channel information, and channel estimation results can be obtained by one-dimensional linear interpolation. The minimum mean square error (MMSE) and linear minimum mean square error (LMMSE) algorithms involve the inverse operation of the channel matrix. If the number of subcarriers increases, the dimension of the matrix becomes large. Therefore, the inverse operation is more complex. To overcome the disadvantages of the conventional channel estimation methods, this paper proposes a novel OFDM channel estimation method based on statistical frames and the confidence level. The noise variance in the estimated channel impulse response (CIR) can be largely reduced under statistical frames and the confidence level; therefore, it reduces the computational complexity and improves the accuracy of channel estimation. Simulation results verify the effectiveness of the proposed channel estimation method based on the confidence level in time-varying dynamic wireless channels.

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.

scholarly journals Frequency Domain Preamble-Based Channel Estimation and Equalization in LoRa

2021 ◽  
Author(s):  
Vincent Savaux ◽  
Patrick Savelli
Keyword(s):  
Channel Estimation ◽  
Frequency Domain ◽  
Minimum Mean Square Error ◽  
Least Square ◽  
Estimation Methods ◽  
Multicarrier Systems ◽  
Cyclic Property ◽  
Selective Channel ◽  
Channel Estimation And Equalization ◽  
Phase Equalizer

This paper deals with multipath channel estimation and equalization in LoRa. It is suggested to take advantage of the cyclic property of the symbols in the LoRa frame preamble to obtain an interference-free version of the symbols in the frequency domain. Then, estimation methods used in multicarrier systems can be applied, such as the least square (LS), and the minimum mean square error (MMSE) estimators. It is shown that the cyclic property in LoRa is inherently independent of the length of the channel, making these estimation techniques robust to any frequency-selective channel. In addition the frequency domain zero-forcing (ZF) equalizer is used, and an original phase equalizer is introduced, taking advantage of the constant modulus property of LoRa symbols in the frequency domain. The performance of the investigated estimators and equalizers is shown through simulations, and applications to the presented results are further discussed.

ESTIMATION METHODS OF A CHANNEL WITH SPATIAL MODULATION BASED ON CORRELATION

2021 ◽  
pp. 106-110
Author(s):  
О.Н. Чирков
Keyword(s):  
Channel Estimation ◽  
Communication Channel ◽  
Energy Savings ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Spatial Modulation ◽  
Mimo Channel ◽  
Estimation Methods ◽  
Signal Ratio ◽  
Single Stream

Рассматриваются методы оценки канала связи с пространственной модуляцией. Данный вид модуляции представляет собой методику однопотоковой передачи данных с несколькими входами и несколькими выходами (MIMO), при которой одновременно активируется только одна передающая антенна. Пространственная модуляция позволяет добиться полного исключения межканальных помех, а также демонстрирует большую экономию энергии в радиочастотной цепи. Однако, в отличие от многопоточных систем MIMO, оценка канала для пространственной модуляции становится проблемой, поскольку канал MIMO не может быть оценен на одном этапе передачи по единственному потоку. На основании этого факта была предложена новая схема оценки канала, которая использует корреляцию каналов и совместно оценивает каналы для разных передающих антенн. Предложенный метод обеспечивает тот же период оценки, что и многопоточные схемы MIMO. Исследовано изменение количества передаваемых пилот-сигналов при пространственной модуляции как для традиционных, так и для новых методов оценки канала связи. Уравновешивая точность и объем данных, можно достичь оптимального отношения пилот-сигналов для максимальной пропускной способности канала. Результаты моделирования показывают, что новый подход оценки превосходит традиционный метод с гораздо более низким оптимальным коэффициентом количества пилотов The article considers methods for estimating a communication channel with spatial modulation. This type of modulation is a single-stream multiple-input multiple-output (MIMO) technique in which only one transmit antenna is activated at a time. Spatial modulation allows for complete elimination of inter-channel interference, and also demonstrates great energy savings in the RF circuit. However, unlike multi-stream MIMO systems, channel estimation for spatial modulation becomes a problem because a MIMO channel cannot be estimated in a single transmission step on a single stream. Based on this fact, I proposed a new channel estimation scheme that uses channel correlation and jointly estimates channels for different transmit antennas. The proposed method provides the same evaluation period as multithreaded MIMO schemes. The change in the number of transmitted pilot-signals with spatial modulation is investigated for both traditional and new methods of estimation of the communication channel. By balancing accuracy and data volume, an optimal pilot signal ratio can be achieved for maximum channel throughput. Simulation results show that the new scoring approach outperforms the traditional method with a much lower optimal pilot count ratio

scholarly journals Improvement of Complexity and Performance of Least Square Based Channel Estimation in MIMO System

2016 ◽  
Vol 1 ◽  
pp. 11
Author(s):  
Anand Kumar Sah ◽  
Arun Kumar Timalsina
Keyword(s):  
Channel Estimation ◽  
Mimo Systems ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Estimation Method ◽  
Vital Role ◽  
Least Square ◽  
Mimo Channel ◽  
Fourth Generation ◽  
Simulation Results

<p>Multiple-input multiple-output (MIMO) systems play a vital role in fourth generation wireless systems to provide advanced data rate. In this paper, a better performance and reduced complexity channel estimation method is proposed for MIMO systems based on matrix factorization. This technique is applied on training based least squares (LS) channel estimation using STBC for performance improvement. Simulation results indicate that the proposed method not only alleviates the performance of MIMO channel estimation but also significantly reduces the complexity caused by matrix inversion. The performance evaluations are validated through computer simulations using MATLAB in terms of bit error rate (BER) for modified LS with LS and MMSE channel estimation techniques. Simulation results show that the BER performance and complexity of the proposed method clearly outperforms the conventional LS channel estimation method.</p><p><em>Journal of Advanced College of Engineering and Management, Vol. 1, 2015</em>, pp. 11-24</p>

PILOT-AIDED MULTIUSER CHANNEL ESTIMATION AND TRACKING IN MIMO-OFDM SYSTEMS

2007 ◽  
Vol 16 (03) ◽  
pp. 319-335 ◽  
Author(s):  
QINGHAI YANG ◽  
KYUNG SUP KWAK
Keyword(s):  
Channel Estimation ◽  
Orthogonal Frequency Division Multiplexing ◽  
Multiple Input Multiple Output ◽  
Least Square ◽  
Estimation Methods ◽  
Ofdm Systems ◽  
Estimation Algorithms ◽  
Channel Estimate ◽  
Multiuser Channel ◽  
Input Multiple Output

This paper addresses the pilot-aided multiuser least square (LS) channel estimation for the uplink of multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. The systems under consideration allow all users use all available subcarriers independently and thus involve multiuser interference in the frequency domain. Direct application of the known pilot-aided single-user channel estimation methods to these systems is prohibited, requiring much more new investigations. The decentralized and centralized channel estimation algorithms are developed according to different multiuser scenarios. Optimal multiuser pilots are proposed, especially for centralized estimation methods with respect to the mean square error (MSE) of LS channel estimate. In addition, channel tracking algorithms are represented in terms of individual user's channels.

An Improved SVD-LMMSE Channel Estimation Algorithm of LTE System

2013 ◽  
Vol 756-759 ◽  
pp. 862-866
Author(s):  
Yang Zhou ◽  
Hong Cheng Dong ◽  
Xiao Wen Li
Keyword(s):  
Channel Estimation ◽  
Orthogonal Frequency Division Multiplexing ◽  
Minimum Mean Square Error ◽  
Multiple Input Multiple Output ◽  
Estimation Algorithm ◽  
Least Square ◽  
Superior Performance ◽  
Estimation Algorithms ◽  
Dct Transform ◽  
Value Decomposition

This paper first introduces the MIMO-OFDM (Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing) system model, and then studies several classical channel estimation algorithms: LS (Least Square) algorithm, LMMSE (Liner Minimum Mean-Square Error) algorithms and SVD-LMMSE (Singular value decomposition) algorithm. Finally, based on the DCT transform domain channel estimation, the original SVD-LMMSE algorithm has been improved. Theoretical analysis and simulation results show that: The improved SVD-LMMSE algorithm not only can reduce the complexity of the implementation, but also has more superior performance.

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