Estimation of highly selective channels for OFDM system by complex least squares support vector machines

Charrada, Anis; Samet, Abdelaziz

doi:10.1016/j.aeue.2011.12.011

Cited by 18 publications

(10 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to reduce the approximation errors 1 and 2 , we used the -Huber loss function introduced in [33]. Here, for simplicity reasons, we denote by ( 1 ) the ( , 1) value corresponding to the first reference symbol position at the th line.…”

Section: Tsvr Expressionmentioning

confidence: 99%

Twin Support Vector Regression for complex millimetric wave propagation environment

Charrada

Samet

2020

Heliyon

Self Cite

View full text Add to dashboard Cite

In this article, an effective millimetric wave channel estimation algorithm based on Twin Support Vector Regression (TSVR) is proposed. This algorithm exploits Discrete Wavelet Transform (DWT) in order to denoise samples in learning phase and then enhance fitting performance. An indoor complex conference room environment full of furniture and electronic equipments is adopted for experiments. Through the proposed approach, channel frequency responses are directly estimated using the Orthogonal Frequency Division Multiplexing (OFDM) reference symbol pattern by solving two quadratic programming problems in order to improve generalization aptitude and computational speed. We consider in this work a Channel Impulse Response (CIR) of 60 GHz multipath transmission system generated by the "Wireless InSite" ray tracer by Remcom. The numerical experiments confirm the performance of the proposed approach compared to other conventional algorithms for several configuration scenarios with and without mobility.

show abstract

Section: Tsvr Expressionmentioning

confidence: 99%

Twin Support Vector Regression for complex millimetric wave propagation environment

Charrada

Samet

2020

Heliyon

Self Cite

View full text Add to dashboard Cite

show abstract

“…Then, we perform interpolation in order to determine the overall channel frequency responses for all data symbols. In this paper, we use joint SVR interpolation in frequency and time domains and compared our results with the cubic spline interpolation with LS and decision feedback (DF) estimation techniques (the theory of these well‐known estimators can be found in [7]). The cubic spline interpolation is detailed in [12].…”

Section: Svr Interpolation and Channel Estimationmentioning

confidence: 99%

“…In this packet‐based transmission, each packet consists of a preamble or a header at the beginning of the packet with a known learning sequence to perform channel estimation, followed by some OFDM data symbols. However, block‐type pilot arrangement is not efficient in fast‐fading channels specifically in high mobility environments and hence comb‐type pilot arrangement is suitable, as presented in [7]. However, the authors of [7] only consider the comb‐type pilot patterns in frequency domain.…”

Section: Introductionmentioning

confidence: 99%

“…However, block‐type pilot arrangement is not efficient in fast‐fading channels specifically in high mobility environments and hence comb‐type pilot arrangement is suitable, as presented in [7]. However, the authors of [7] only consider the comb‐type pilot patterns in frequency domain. Furthermore, they consider that every transmitted OFDM symbol contains pilot symbols.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Charrada

Samet²

2016

IET Communications

Self Cite

View full text Add to dashboard Cite

“…A robust cost function is introduced to improve the performance of the estimation algorithm which isHuber robust cost function [11] [13], given by…”

Section: Anis Charrada and Abdelaziz Samet; Support Vector Machine Rementioning

confidence: 99%

Support Vector Machine Regression and Artificial Neural Network for Channel Estimation of LTE Downlink in High-Mobility Environments

Charrada

Aziz²

2016

TMLAI

Self Cite

View full text Add to dashboard Cite

In this paper we apply and assess the performance of support vector machine regression (SVR) and artificial neural network (ANN) channel estimation algorithms to the reference signal structure standardized for LTE Downlink system. SVR and ANN where applied to estimate real channel environment such as vehicular A channel defined by the International Telecommunications Union (ITU) in the presence of nonlinear impulsive noise. The proposed algorithms use the information provided by the received reference symbols to estimate the total frequency response of the time variant multipath fading channel in two phases. In the first phase, each method learns to adapt to the channel variations, and in the second phase it predicts all the channel frequency responses. Finally, in order to evaluate the capabilities of the designed channel estimators, we provide performance of SVR and ANN, which is compared with traditional Least Squares (LS) and Decision Feedback (DF). The simulation results show that SVR has a better accuracy than other estimation techniques.

show abstract

Estimation of highly selective channels for OFDM system by complex least squares support vector machines

Cited by 18 publications

References 2 publications

Twin Support Vector Regression for complex millimetric wave propagation environment

Twin Support Vector Regression for complex millimetric wave propagation environment

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

Support Vector Machine Regression and Artificial Neural Network for Channel Estimation of LTE Downlink in High-Mobility Environments

Contact Info

Product

Resources

About