Channel estimation for sparse channel OFDM systems using least square and minimum mean square error techniques

2017 ◽  
Author(s):  
Ali Farzamnia ◽  
Ngu War Hlaing ◽  
Manas Kumar Haldar ◽  
Javad Rahebi
Keyword(s):  
Channel Estimation ◽  
Mean Square Error ◽  
Minimum Mean Square Error ◽  
Least Square ◽  
Mean Square ◽  
Ofdm Systems ◽  
Sparse Channel

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 Minimum mean‐square‐error expression of LMMSE channel estimation in SISO OFDM systems

2013 ◽  
Vol 49 (18) ◽  
pp. 1152-1154 ◽  
Author(s):  
V. Savaux ◽  
Y. Louët ◽  
M. Djoko‐Kouam ◽  
A. Skrzypczak
Keyword(s):  
Channel Estimation ◽  
Mean Square Error ◽  
Minimum Mean Square Error ◽  
Mean Square ◽  
Ofdm Systems

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 Channel Estimation in MIMO-OFDM System

2014 ◽  
Vol 14 (2) ◽  
pp. 97-102
Author(s):  
SR Aryal ◽  
H Dhungana
Keyword(s):  
Channel Estimation ◽  
Mean Square Error ◽  
Minimum Mean Square Error ◽  
Multipath Fading ◽  
Spatial Multiplexing ◽  
Diversity Gain ◽  
Estimation Methods ◽  
Mean Square ◽  
Ofdm System ◽  
Mimo Ofdm

There are no limit of human desire, so day by day we need much higher data speed to facilitate our need but every physical resource like frequency band, transmit signal strength are finite. Within the given limited resource, higher data speed is accomplished by new proficiency called Multiple Input Multiple Output (MIMO), Orthogonal Frequency Division Multiplexing (OFDM) system. MIMO-OFDM fulfills the high data rate requirement through spatial multiplexing gain and improved link reliability due to antenna diversity gain. With this technique, both interference reduction and maximum diversity gain are achieved by increasing number of antennae on either side. Received signal in MIMO-OFDM system is usually distorted by multipath fading. In order to recover the transmitted signal correctly, channel effect must be estimated and repaired at receiver. In this paper the performance evaluating parameter mean square error and symbol error rate of least square error, minimum mean square error and DFT based channel estimation methods are estimated and appropriate solution is recommended. Furthermore, comparison among their characteristics is simulated in MATLAB and useful conclusion is delineated. DOI: http://dx.doi.org/10.3126/njst.v14i2.10421   Nepal Journal of Science and Technology Vol. 14, No. 2 (2013) 97-102

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 تقدير دالة الأنحدار اللامعلمي باستخدام بعض الطرائق اللامعلمية الرتيبة

2008 ◽  
Vol 14 (50) ◽  
pp. 304
Author(s):  
ياسمين عبد الرحمن محمد ◽  
دجلة ابراهيم مهدي
Keyword(s):  
Mean Square Error ◽  
Monotone Function ◽  
Minimum Mean Square Error ◽  
Kernel Estimator ◽  
Regression Function ◽  
Least Square ◽  
Maximum Efficiency ◽  
Mean Square ◽  
Statistical Measures ◽  
Monotone Methods

This research was concerning to study monotone nonparametric methods for estimating the nonparametric regression function (i.e treatment outlier) to achieve a monotone function (increasing or decreasing). So we will use the monotone methods to treatment outlier but after estimate the regression function with use kernel estimator (Nadarya - Watson) these methods are:- 1- Mukerjee method takes averages of maximums and minimum of subsets of the data was used to adjust the initial kernel regression estimates and use the researcher special case when . 2- Algorithm least square isotonic regression. In the experimental aspect comparison was done of which is the best methods through the simulation procedure using Mote Carlo method using five models. While in the application aspect practical application was done on data represent the measurements for blood pressure patients. In both aspects we use two of the important statistical measures which are Mean square error (MSE) and efficiency. We find through the application that the best method is Mukerjee method for general case as it has minimum Mean square error and maximum efficiency.  

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