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.

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.

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.

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.

Performance of Pilot-Aided 3D- OFDM Channel Estimation using Different Antenna Configurations

2017 ◽  
Vol 8 (1) ◽  
pp. 77
Author(s):  
Dinesh N. Bhange ◽  
Chandrashekhar G. Dethe
Keyword(s):  
Channel Estimation ◽  
Orthogonal Frequency Division Multiplexing ◽  
Rayleigh Fading ◽  
Minimum Mean Square Error ◽  
Vertical Direction ◽  
Performance Comparison ◽  
Least Square ◽  
Digital Video Broadcasting ◽  
Video Broadcasting ◽  
Mimo Ofdm

<p>This paper aims, a 3D-Pilot Aided Multi-Input Multi-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) Channel Estimation (CE) for Digital Video Broadcasting -T2 (DVB-T2)for the 5 different proposed block and comb pilot patterns model and performed on different antenna configuration. The effects of multi-transceiver antenna on channel estimation are addressed with different pilot position in frequency, time and the vertical direction of spatial domain framing. This paper first focus on designing of 5- different proposed spatial correlated pilot pattern model with optimization of pilot overhead. Then it demonstrates the performance comparison of Least Square (LS) &amp;Linear Minimum Mean Square Error (LMMSE), two linear channel estimators for 3D-Pilot Aided patterns on different antenna configurations in terms of Bit Error Rate. The simulation results are shown for Rayleigh fading noise channel environments. Also, 3x4 MIMO configuration is recommended as the most suitable configuration in this noise channel environments.</p>

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 An Improved Channel Estimation Algorithm Based on WD-DDA in OFDM System

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Dan Wang ◽  
Zhiqiang Mei ◽  
Jiamin Liang ◽  
Jinzhi Liu
Keyword(s):  
Channel Estimation ◽  
Orthogonal Frequency Division Multiplexing ◽  
Signal To Noise Ratio ◽  
Estimation Algorithm ◽  
Transform Domain ◽  
Ofdm System ◽  
Low Snr ◽  
Estimation Algorithms ◽  
Residual Noise ◽  
First Time

Channel estimation is the key technology to ensure reliable transmission in orthogonal frequency division multiplexing (OFDM) system. In order to improve the accuracy of the channel estimation algorithm in a low signal-to-noise ratio (SNR) channel environment, in this paper, we proposed an improved channel estimation algorithm based on the transform domain. The improved algorithm with wavelet denoising (WD) and distance decision analysis (DDA) to perform secondary denoising on the channel estimation algorithm based on the transform domain is proposed. First, after the least-squares (LS) algorithm, WD is used to denoise for the first time, then the DDA is used to further suppress the residual noise in the transform domain, and the important channel taps are screened out. Simulation results show that the proposed algorithm can improve the detection performance of existing channel estimation algorithms based on transform domain in low SNR.

scholarly journals Application of a Channel Estimation Algorithm to Spectrum Sensing in a Cognitive Radio Context

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Vincent Savaux ◽  
Moïse Djoko-Kouam ◽  
Yves Louët ◽  
Alexandre Skrzypczak
Keyword(s):  
Channel Estimation ◽  
Spectrum Sensing ◽  
Orthogonal Frequency Division Multiplexing ◽  
Variance Estimation ◽  
Minimum Mean Square Error ◽  
Estimation Algorithm ◽  
Joint Estimation ◽  
Noise Variance ◽  
Low Snr ◽  
Mean Square Error Criterion

This paper deals with spectrum sensing in an orthogonal frequency division multiplexing (OFDM) context, allowing an opportunistic user to detect a vacant spectrum resource in a licensed band. The proposed method is based on an iterative algorithm used for the joint estimation of noise variance and frequency selective channel. It can be seen as a second-order detector, since it is performed by means of the minimum mean square error criterion. The main advantage of the proposed algorithm is its capability to perform spectrum sensing, noise variance estimation, and channel estimation in the presence of a signal. Furthermore, the sensing duration is limited to only one OFDM symbol. We theoretically show the convergence of the algorithm, and we derive its analytical detection and false alarm probabilities. Furthermore, we show that the detector is very efficient, even for low SNR values, and is robust against a channel uncertainty.

SUPERIMPOSED TRAINING-BASED CHANNEL ESTIMATION FOR MIMO-MB-OFDM UWB SYSTEMS

2008 ◽  
Vol 17 (05) ◽  
pp. 865-870 ◽  
Author(s):  
QINGHAI YANG ◽  
HUAMIN ZHU ◽  
KYUNG SUP KWAK
Keyword(s):  
Channel Estimation ◽  
Orthogonal Frequency Division Multiplexing ◽  
Multiple Input Multiple Output ◽  
Least Square ◽  
Ultra Wideband ◽  
Superimposed Training ◽  
Square Channel ◽  
Channel Estimate ◽  
Input Multiple Output ◽  
Estimation Scheme

A novel channel estimation scheme based on superimposed training is proposed for multiple-input multiple-output multi-band orthogonal frequency division multiplexing ultra-wideband systems. The optimal training symbols are derived with respect to the least-square channel estimate mean square error. Simulation shows that the proposed scheme benefits much higher effective data throughput over the conventional method.

A Two-Way Relay Channel Estimation Method Based on Sparsity Adaptive Matching Pursuit Algorithm

2012 ◽  
Vol 263-266 ◽  
pp. 1037-1042
Author(s):  
Ju Rong Wang ◽  
Jin He Zhou
Keyword(s):  
Channel Estimation ◽  
Orthogonal Frequency Division Multiplexing ◽  
Prior Information ◽  
Matching Pursuit ◽  
Minimum Mean Square Error ◽  
Estimation Method ◽  
Reconstruction Algorithm ◽  
Least Square ◽  
Estimation Methods ◽  
Relay Channel

To solve the problem that many existing two-way relay channel (TWRC) estimation methods require the sparse degree of the channel as prior information, we introduced a novel iterative greedy reconstruction algorithm based on compressed sensing (CS), called the sparisty adaptive matching pursuit (SAMP) to reconstruct the channel impulse response under orthogonal frequency division multiplexing (OFDM) system. The most innovative feature of SAMP is its capability of channel reconstruction without prior information of the sparse degree. Under the same condition we compared the algorithm with the other channel estimation methods including conventional least square (LS) algorithm, minimum mean square error (MMSE) algorithm and a orthogonal matching pursuit (OMP) algorithm based on CS. Simulation results show that the proposed algorithm has a better estimation performance and the algorithm improves the utilization of communication resources such as spectrum and energy. Thus it is suitable for real application.

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