Support Vector Machines Regression for MIMO-OFDM Channel Estimation

Charrada, Anis

doi:10.11591/ij-ai.v1i4.1832

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…The main concept is to find a function that best fits the training data behavior to perform predictions. For example, linear [175][176][177], polynomial [178], 2D nonlinear [99,179,180], and support vector [172,173,[181][182][183][184][185][186][187] regressions have been employed in channel estimation for multicarrier systems. Regression algorithms go under the supervised learning paradigm.…”

Section: Regressionmentioning

confidence: 99%

“…Moreover, a complex LS-SVR channel estimator for pilot-assisted OFDM systems was formulated by observing the signals time-frequency relationship, surpassing the LS estimator [182]. Next, the nonlinear SVR-based algorithm was extended to stand highly selective channels for OFDM systems [184][185][186]. Notably, a method was proposed based on a learning and estimation phase process to get the frequency response of a MIMO-OFDM system.…”

Section: Support Vector Regressionmentioning

confidence: 99%

“…Notably, a method was proposed based on a learning and estimation phase process to get the frequency response of a MIMO-OFDM system. This approach comprises mapping trained data into a high-dimensional space and using the structural risk minimization principle to leverage the regression estimation for the CFR function [186].…”

Section: Support Vector Regressionmentioning

confidence: 99%

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Artificial intelligence for channel estimation in multicarrier systems for B5G/6G communications: a survey

Boas

Silva

Figueiredo

et al. 2022

J Wireless Com Network

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Multicarrier modulation allows for deploying wideband systems resilient to multipath fading channels, impulsive noise, and intersymbol interference compared to single-carrier systems. Despite this, multicarrier signals suffer from different types of distortion, including channel noise sources and long- and short-term fading. Consequently, the receiver must estimate the channel features and compensate it for data recovery based on channel estimation techniques, such as non-blind, blind, and semi-blind approaches. These techniques are model-based and designed with accurate mathematical channel models encompassing their features. Nevertheless, complex environments challenge accurate mathematical channel estimation modeling, which might neither be accurate nor correspond to reality. This impairment decreases the system performance due to the channel estimation accuracy loss. Fortunately, (AI) algorithms can learn the relationship among different system variables using a model-driven or model-free approach. Thereby, AI algorithms are used for channel estimation by exploiting its complexity without unrealistic assumptions, following a better performance than conventional techniques under the same channel. Hence, this paper comprehensively surveys AI-based channel estimation for multicarrier systems. First, we provide essential background on conventional channel estimation techniques in the context of multicarrier systems. Second, the AI-aided channel estimation strategies are investigated using the following approaches: classical learning, neural networks, and reinforcement learning. Lastly, we discuss current challenges and point out future research directions based on recent findings.

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Section: Regressionmentioning

confidence: 99%

Section: Support Vector Regressionmentioning

confidence: 99%

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Artificial intelligence for channel estimation in multicarrier systems for B5G/6G communications: a survey

Boas

Silva

Figueiredo

et al. 2022

J Wireless Com Network

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“…The rise of ML as a potential tool for the future [2], leads to the emerging use of SVM. For example, SVM regression was used in telecommunication to estimate channel in multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) by using the interpolation mechanism [3]. The study in [4] employed SVM, to extract buildings object from very highresolution satellite images of Tetuan, Morocco, by using spatial and spectral radius with 83.76% accuracy.…”

Section: Introductionmentioning

confidence: 99%

Effects of kernels and the proportion of training data on the accuracy of SVM sentiment analysis in lecturer evaluation

Sengkey

Jacobus

Manoppo

2020

IJ-AI

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Support vector machine (SVM) is a known method for supervised learning in sentiment analysis and there are many studies about the use of SVM in classifying the sentiments in lecturer evaluation. SVM has various parameters that can be tuned and kernels that can be chosen to improve the classifier accuracy. However, not all options have been explored. Therefore, in this study we compared the four SVM kernels: radial, linear, polynomial, and sigmoid, to discover how each kernel influences the accuracy of the classifier. To make a proper assessment, we used our labeled dataset of students’ evaluations toward the lecturer. The dataset was split, one for training the classifier, and another one for testing the model. As an addition, we also used several different ratios of the training:testing dataset. The split ratios are 0.5 to 0.95, with the increment factor of 0.05. The dataset was split randomly, hence the splitting-training-testing processes were repeated 1,000 times for each kernel and splitting ratio. Therefore, at the end of the experiment, we got 40,000 accuracy data. Later, we applied statistical methods to see whether the differences are significant. Based on the statistical test, we found that in this particular case, the linear kernel significantly has higher accuracy compared to the other kernels. However, there is a tradeoff, where the results are getting more varied with a higher proportion of data used for training.

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A support vector machine based sub-band CQI feedback compression scheme for 3GPP LTE systems

Cordina

Debono

2017

2017 International Symposium on Wireless Communication Systems (ISWCS)

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Support Vector Machines Regression for MIMO-OFDM Channel Estimation

Cited by 4 publications

References 6 publications

Artificial intelligence for channel estimation in multicarrier systems for B5G/6G communications: a survey

Artificial intelligence for channel estimation in multicarrier systems for B5G/6G communications: a survey

Effects of kernels and the proportion of training data on the accuracy of SVM sentiment analysis in lecturer evaluation

A support vector machine based sub-band CQI feedback compression scheme for 3GPP LTE systems

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