Channel Estimation in Long Term Evolution Uplink Using Minimum Mean Square Error-Support Vector Regression

Djouama, Allaeddine; Lim, Myoung-Seob; Ettoumi, Fatiha Youcef

doi:10.1007/s11277-014-1985-7

Cited by 11 publications

(13 citation statements)

References 11 publications

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“…The average of f 1 (τ ) and f 2 (τ ) is the final regression result, which shares the same expression as the linear one (13). The optimization problems can be expressed as (15) and (16) where…”

Section: Mimo-ofdm System Channel Estimationmentioning

confidence: 99%

“…where It is difficult to solve the optimization problems of (15) and (16) directly. However, we can map them into the dual problems by adopting Lagrangian multipliers.…”

Section: Mimo-ofdm System Channel Estimationmentioning

confidence: 99%

“…Support vector regression (SVR) method was also adopted to estimate the parameters of doubly selective channel [16]- [17].…”

mentioning

confidence: 99%

“…A MIMO channel estimation method was proposed in [29], while the channel of which was assumed to be flat in frequency domain. In [30] and [22], basic SVR based OFDM channel estimators were proposed for some application scenarios. Later, many improved SVR algorithms were proposed.…”

mentioning

confidence: 99%

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Weighted TSVR Based Nonlinear Channel Frequency Response Estimation for MIMO-OFDM System

Lei

Zhang

et al. 2020

IEEE Access

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Section: Mimo-ofdm System Channel Estimationmentioning

confidence: 99%

“…where It is difficult to solve the optimization problems of (15) and (16) directly. However, we can map them into the dual problems by adopting Lagrangian multipliers.…”

Section: Mimo-ofdm System Channel Estimationmentioning

confidence: 99%

“…Support vector regression (SVR) method was also adopted to estimate the parameters of doubly selective channel [16]- [17].…”

mentioning

confidence: 99%

mentioning

confidence: 99%

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Weighted TSVR Based Nonlinear Channel Frequency Response Estimation for MIMO-OFDM System

Lei

Zhang

et al. 2020

IEEE Access

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“…Matilde et al [25] developed a multiple-input multipleoutput channel estimation method based on SVR, but the channel was assumed to be non-selective. Djouama et al [26] and Charrada and Samet [19] proposed OFDM channel estimators using SVR for different application scenarios. However, the methods mentioned above are based on the basic SVR, there are still shortcomings in computational complexity and performance.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Channel Estimation for OFDM System by Wavelet Transform Based Weighted TSVR

et al. 2020

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An efficient nonlinear channel estimation method for pilot-aided orthogonal frequency division multiplexing system is proposed in this work. The considered channel is selective in time and frequency domain, that is doubly selective channel. Wavelet transform based weighted twin support vector regression is used for channel frequency response estimation, which is suitable for the regression of nonlinear system. Different from traditional support vector regression algorithm, the proposed algorithm gives samples different weights according to their variance calculated based on wavelet transform. The weights are added into both first and quadratic terms of the objective functions to reduce the impact of outliers, which is likely to appear in the received pilot signal polluted by noise. The proposed channel estimation algorithm has good generalization ability and can reduce the influence of overfitting problem. The results of computational tests show that the proposed algorithm is with better estimating performance compared to the classical pilot-aided channel estimation methods. INDEX TERMS Channel estimation, orthogonal frequency division multiplexing, twin support vector regression, wavelet transform.

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Projection wavelet weighted twin support vector regression for OFDM system channel estimation

Wang

Chang

et al. 2020

Artif Intell Rev

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In this paper, an efficient projection wavelet weighted twin support vector regression (PWWTSVR) based orthogonal frequency division multiplexing system (OFDM) system channel estimation algorithm is proposed. Most Channel estimation algorithms for OFDM systems are based on the linear assumption of channel model. In the proposed algorithm, the OFDM system channel is consumed to be nonlinear and fading in both time and frequency domains. The PWWTSVR utilizes pilot signals to estimate response of nonlinear wireless channel, which is the main work area of SVR. Projection axis in optimal objective function of PWWRSVR is sought to minimize the variance of the projected points due to the utilization of a priori information of training data. Different from traditional support vector regression algorithm, training samples in different positions in the proposed PWWTSVR model are given different penalty weights determined by the wavelet transform. The weights are applied to both the quadratic empirical risk term and the first-degree empirical risk term to reduce the influence of outliers. The final regressor can avoid the overfitting problem to a certain extent and yield great generalization ability for channel estimation. The results of numerical experiments show that the propose algorithm has better performance compared to the conventional pilot-aided channel estimation methods.

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Channel Estimation in Long Term Evolution Uplink Using Minimum Mean Square Error-Support Vector Regression

Cited by 11 publications

References 11 publications

Weighted TSVR Based Nonlinear Channel Frequency Response Estimation for MIMO-OFDM System

Weighted TSVR Based Nonlinear Channel Frequency Response Estimation for MIMO-OFDM System

Nonlinear Channel Estimation for OFDM System by Wavelet Transform Based Weighted TSVR

Projection wavelet weighted twin support vector regression for OFDM system channel estimation

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