Analyzing performance of joint SVR interpolation for LTE system with 64-QAM modulation under 500 Km/h mobile velocity

Charrada, Anis; Samet, Abdelaziz

doi:10.1109/comnet.2017.8285585

Cited by 4 publications

(2 citation statements)

References 4 publications

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“…The system was composed from N classifiers, one per bit, or 2 N classifiers in the one-vs.-one or one-vs.-rest techniques. SVM regression (SVR) was used in [34,35] for channel estimation.…”

Section: Other Related Workmentioning

confidence: 99%

Learning Maximum Margin Channel Decoders

Tsvieli¹,

Weinberger²

2022

Preprint

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The problem of learning a channel decoder is considered for two channel models. The first model is an additive noise channel whose noise distribution is unknown and nonparametric. The learner is provided with a fixed codebook and a dataset comprised of independent samples of the noise, and is required to select a precision matrix for a nearest neighbor decoder in terms of the Mahalanobis distance. The second model is a non-linear channel with additive white Gaussian noise and unknown channel transformation. The learner is provided with a fixed codebook and a dataset comprised of independent input-output samples of the channel, and is required to select a matrix for a nearest neighbor decoder with a linear kernel. For both models, the objective of maximizing the margin of the decoder is addressed. Accordingly, for each channel model, a regularized loss minimization problem with a codebook-related regularization term and hinge-like loss function is developed, which is inspired by the support vector machine paradigm for classification problems. Expected generalization error bounds for the error probability loss function are provided for both models, under optimal choice of the regularization parameter. For the additive noise channel, a theoretical guidance for choosing the training signal-to-noise ratio is proposed based on this bound. In addition, for the non-linear channel, a high probability uniform generalization error bound is provided for the hypothesis class.For each channel, a stochastic sub-gradient descent algorithm for solving the regularized loss minimization problem is proposed, and an optimization error bound is stated. The performance of the proposed algorithms is demonstrated through several examples.

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“…The system was composed from N classifiers, one per bit, or 2 N classifiers in the one-vs.-one or one-vs.-rest techniques. SVM regression (SVR) was used in [34,35] for channel estimation.…”

Section: Other Related Workmentioning

confidence: 99%

Learning Maximum Margin Channel Decoders

Tsvieli¹,

Weinberger²

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…They showed that both of the estimators perform the same in high signal-to-noise regimes but the SVM-based estimator outperforms the other in low signal-to-noise ratio regimes. Likewise, the authors in [12] compared an SVM-based channel estimator with least-squares and neural-network based estimators in Long-Term Evolution systems considering non-linear regression and showed that the SVM-based estimator performs better than the other estimators. Herein, we further refer interested readers to the other SVM-based channel estimation techniques used in MIMO channels [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Joint Channel Estimation and Data Decoding using SVM-based Receivers

Akın¹,

Penner²,

Peissig³

2020

Preprint

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Modern communication systems organize receivers in blocks in order to simplify their analysis and design. However, an approach that considers the receiver design from a wider perspective rather than treating it block-by-block may take advantage of the impacts of these blocks on each other and provide better performance. Herein, we can benefit from machine learning and compose a receiver model implementing supervised learning techniques. With this motivation, we consider a one-toone transmission system over a flat fast fading wireless channel and propose a support vector machine (SVM)-based receiver that combines the pilot-based channel estimation, data demodulation and decoding processes in one joint operation. We follow two techniques in the receiver design. We first design one SVM-based classifier that outputs the class of the encoding codeword that enters the encoder at the transmitter side. Then, we put forward a model with one SVM-based classifier per one bit in the encoding codeword, where each classifier assigns the value of the corresponding bit in the encoding vector. With the second technique, we simplify the receiver design especially for longer encoding codewords. We show that the SVM-based receiver performs very closely to the maximum likelihood decoder, which is known to be the optimal decoding strategy when the encoding vectors at the transmitter are equally likely. We further show that the SVM-based receiver outperforms the conventional receivers that perform channel estimation, data demodulation and decoding in blocks. Finally, we show that we can train the SVM-based receiver with 1-bit analog-to-digital converter (ADC) outputs and the SVM-based receiver can perform very closely to the conventional receivers that take 32-bit ADC outputs as inputs.

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Deep Learning for Joint Channel Estimation and Signal Detection in OFDM Systems

Zhong

2020

IEEE Commun. Lett.

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Analyzing performance of joint SVR interpolation for LTE system with 64-QAM modulation under 500 Km/h mobile velocity

Cited by 4 publications

References 4 publications

Learning Maximum Margin Channel Decoders

Learning Maximum Margin Channel Decoders

Joint Channel Estimation and Data Decoding using SVM-based Receivers

Deep Learning for Joint Channel Estimation and Signal Detection in OFDM Systems

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