Joint interpolation for LTE downlink channel estimation in very high‐mobility environments with support vector machine regression

Charrada, Anis; Samet, Abdelaziz

doi:10.1049/iet-com.2016.0132

Cited by 15 publications

(17 citation statements)

References 14 publications

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“…In this section, some simulation results of the proposed channel estimation algorithm based on the novel WTWTSVR will be shown. SVR is effective for channel estimation, and its regression performance has been verified [19], [25], so we compare performance of the proposed algorithm with LS estimation with linear interpolation, TSVR [27] and perfect estimation. Consider an OFDM system with doubly selective channel.…”

Section: Resultsmentioning

confidence: 99%

“…In selective multipath fading channel, the channel response presents complicated nonlinearities caused by some reasons, such as the saturation of components and the dispersion of optical fiber [16]- [18], which may lower the estimation precision if using linear methods [19]. So it is necessary to use the nonlinear method for channel estimation.…”

Section: Introductionmentioning

confidence: 99%

“…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%

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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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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…Researchers resort to ML techniques to solve above mentioned problems. To address the channel estimation in a fast fading time-varying multipath channel, a two dimensional (2D) non-linear complex support vector regression (SVR) based on a RBF kernel was proposed to achieve accurate channel estimate [8]. In [9], a deep learning based channel estimation algorithm was proposed for beamspace mmWave massive MIMO systems.…”

Section: B Channel Estimation Associated With MLmentioning

confidence: 99%

Artificial Intelligence Enabled Wireless Networking for 5G and Beyond: Recent Advances and Future Challenges

Wang

Renzo²,

Stańczak³

et al. 2020

IEEE Wireless Commun.

179

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The fifth generation (5G) wireless communication networks are currently being deployed, and beyond 5G (B5G) networks are expected to be developed over the next decade. Artificial intelligence (AI) technologies and, in particular, machine learning (ML) have the potential to efficiently solve the unstructured and seemingly intractable problems by involving large amounts of data that need to be dealt with in B5G. This article studies how AI and ML can be leveraged for the design and operation of B5G networks. We first provide a comprehensive survey of recent advances and future challenges that result from bringing AI/ML technologies into B5G wireless networks. Our survey touches different aspects of wireless network design and optimization, including channel measurements, modeling, and C.-X. Wang (corresponding author) is with the National

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

mentioning

confidence: 99%

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

Lei

Zhang

et al. 2020

IEEE Access

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Joint interpolation for LTE downlink channel estimation in very high‐mobility environments with support vector machine regression

Cited by 15 publications

References 14 publications

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

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

Artificial Intelligence Enabled Wireless Networking for 5G and Beyond: Recent Advances and Future Challenges

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

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