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

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 A Novel Signal Detection Algorithm for Underwater MIMO-OFDM Systems Based on Generalized MMSE

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Gaoli Zhao ◽  
Jianping Wang ◽  
Wei Chen ◽  
Junping Song
Keyword(s):  
Computational Complexity ◽  
Signal Detection ◽  
Mean Square Error ◽  
High Performance ◽  
Minimum Mean Square Error ◽  
Multipath Fading ◽  
Detection Algorithm ◽  
Mean Square ◽  
Ofdm System ◽  
Mimo Ofdm

The MIMO-OFDM system fully exploits the advantages of MIMO and OFDM, effectively resisting the channel multipath fading and inter-symbol interference while increasing the data transmission rate. Studies show that it is the principal technical mean for building underwater acoustic networks (UANs) of high performance. As the core, a signal detection algorithm determines the performance and complexity of the MIMO-OFDM system. However, low computational complexity and high performance cannot be achieved simultaneously, especially for UANs with a narrow bandwidth and limited data rate. This paper presents a novel signal detection algorithm based on generalized MMSE. First, we propose a model for the underwater MIMO-OFDM system. Second, we design a signal coding method based on STBC (space-time block coding). Third, we realize the detection algorithm namely GMMSE (generalized minimum mean square error). Finally, we perform a comparison of the algorithm with ZF (Zero Forcing), MMSE (minimum mean square error), and ML (Maximum Likelihood) in terms of the BER (bit error rate) and the CC (computational complexity). The simulation results show that the BER of GMMSE is the lowest one and the CC close to that of ZF, which achieves a tradeoff between the complexity and performance. This work provides essential theoretical and technical support for implementing UANs of high performance.

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 Investigations on wavelet and Fourier transform based channel estimation in MIMO-OFDM system

2018 ◽  
Vol 7 (2.21) ◽  
pp. 228
Author(s):  
B Somasekhar ◽  
Ch Mohana Krishna ◽  
Y Murty
Keyword(s):  
Fourier Transform ◽  
Channel Estimation ◽  
Discrete Fourier Transform ◽  
Fading Channels ◽  
Multipath Fading ◽  
Estimation Methods ◽  
Ofdm System ◽  
Channel Estimator ◽  
Narrow Side ◽  
Mimo Ofdm

In this paper channel estimation methods for MIMO-OFDM system are investigated based on Fourier Transform and Wavelet Transform. The channel estimation algorithm based on Discrete Fourier Transform (DFT) cause energy leakage in multipath channel with non-sample-spaced time delays. Discrete Cosine Transform (DCT) based channel estimator can mitigate the drawback of Discrete Fourier Transform based channel estimator, when the non-sample spaced path delays are available in multipath fading channels. Wavelet based systems provide better spectral efficiency because of no cyclic prefix requirement, with narrow side lobes and also exhibit improved BER performance. Simulation results reveal that the DWT based transform outperforms the conventional DFT and DCT based channel estimator in terms of bit error rate and mean square error.  

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 Precoding Aided Data Correlation Scheme for Channel Estimation Technique in MIMO-OFDM System

2021 ◽  
Vol 16 ◽  
pp. 146-154
Author(s):  
Sidramayya S. Matad ◽  
Ramesha K.
Keyword(s):  
Channel Estimation ◽  
Mean Square Error ◽  
Orthogonal Frequency Division Multiplexing ◽  
Transmission Rate ◽  
Multiple Input Multiple Output ◽  
Blind Channel Estimation ◽  
Mean Square ◽  
Estimation Technique ◽  
Blind Channel ◽  
Mimo Ofdm

Channel estimation is considered as an important phase in Multiple Input Multiple Output – Orthogonal Frequency Division Multiplexing (MIMO-OFDM) networks which can enhances the performance significantly. Channel estimation widely classified as pilot based, blind and semi-blind channel estimation. The pilot-based channel estimation decreases the data transmission rate and spectral efficiency. To overcome these issues of existing schemes, we present a novel blind channel estimation technique. According to proposed scheme, we transmit the data in a block-wise manner. The proposed scheme uses precoding technique to establish the correlation between these blocks. Further, we use channel correlation to solve the diagonal uncertainty of correlation matrix which helps to improve the system performance. We present a comparative analysis study which shows that proposed approach can achieve better performance in terms of Normalized Mean Square Error (NMSE) and Mean Square Error (MSE) when compared with existing techniques.

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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