Support Vector Machine-Based Transmit Antenna Allocation for Multiuser Communication Systems

Lin, Huifa; Shin, Won-Yong; Joung, Jingon

doi:10.3390/e21050471

Cited by 15 publications

(12 citation statements)

References 56 publications

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“…Moreover, in multi-antenna systems, the optimal antenna subset selection (e.g., see [259], [260]) problem can be interpreted as a multi-class classification and/or decision-making task, which is readily solvable via machine learning. Multi-class classifier algorithms are the k-nearest neighbours (k-NN) and support vector machine (SVM), Naive-Bayes (NB) and others [261], [262]. These may improve the design and optimization of complex and dynamic wireless communication systems.…”

Section: Artificial Intelligence Based Approachesmentioning

confidence: 99%

Large-Scale Wireless-Powered Networks With Backscatter Communications—A Comprehensive Survey

Rezaei

Tellambura

Herath

2020

IEEE Open J. Commun. Soc.

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Massive and ubiquitous deployment of devices in networks of fifth generation (5G) and beyond wireless has necessitated the development of ultra-low-power wireless communication paradigms. Recently, wireless-powered networks with backscatter communications (WPN-BCs) has been emerged as a most prominent technology for enabling large-scale selfsustainable wireless networks with the capabilities of RF energy harvesting (EH) and of extreme low power consumption. Therefore, we provide a comprehensive literature review on the fundamentals, challenges and the on-going research efforts in the domain of WPN-BCs. Our emphasis is on large-scale networks. In particular, we discuss signal processing aspects, network design issues and efficient communication techniques. Moreover, we review emerging technologies for WPN-BCs to bring about the best use of resources. Some applications of this innovative technology are also highlighted. Finally, we address some open research problems and future research directions.

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Section: Artificial Intelligence Based Approachesmentioning

confidence: 99%

Large-Scale Wireless-Powered Networks With Backscatter Communications—A Comprehensive Survey

Rezaei

Tellambura

Herath

2020

IEEE Open J. Commun. Soc.

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“…Recently, SVM has been used in various tasks of communications. In [33], SVM has been used with multiple antennas in multiuser communication systems. The authors proposed an antenna allocation system based on SVM.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna

Shahbazi¹,

Byun²

2022

Computers, Materials &Amp; Continua

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Metamaterial Antenna is a special class of antennas that uses metamaterial to enhance their performance. Antenna size affects the quality factor and the radiation loss of the antenna. Metamaterial antennas can overcome the limitation of bandwidth for small antennas. Machine learning (ML) model is recently applied to predict antenna parameters. ML can be used as an alternative approach to the trial-and-error process of finding proper parameters of the simulated antenna. The accuracy of the prediction depends mainly on the selected model. Ensemble models combine two or more base models to produce a better-enhanced model. In this paper, a weighted average ensemble model is proposed to predict the bandwidth of the Metamaterial Antenna. Two base models are used namely: Multilayer Perceptron (MLP) and Support Vector Machines (SVM). To calculate the weights for each model, an optimization algorithm is used to find the optimal weights of the ensemble. Dynamic Group-Based Cooperative Optimizer (DGCO) is employed to search for optimal weight for the base models. The proposed model is compared with three based models and the average ensemble model. The results show that the proposed model is better than other models and can predict antenna bandwidth efficiently.

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“…However, for the problems where the dimension of vector x is high, the classifier problem (2) may become complicated to be solved. Toward this issue, we can introduce the dual problem of ALGORITHM 1 Picker algorithm (2) in problem (3), and by applying the kernel trick, the original feature space can be mapped into to a higher-dimensional feature space where the training set could become more separable [30].…”

Section: Svm-based Learning Model and Its Solutionmentioning

confidence: 99%

“…However, for the problems where the dimension of vector

x

is high, the classifier problem () may become complicated to be solved. Toward this issue, we can introduce the dual problem of () in problem (), and by applying the kernel trick, the original feature space can be mapped into to a higher‐dimensional feature space where the training set could become more separable [30].

\begin{matrix} true \\ left \max_{α} (() \sum_{i = 1}^{l} α_{i} - \frac{1}{2} \sum_{i, j = 1}^{l} α_{i} α_{j} y_{i}^{t} y_{j}^{t} 〈ϕ ({bold-italicx}_{i}^{t}), ϕ ({bold-italicx}_{j}^{t})〉) \\ left s . t . 0 \leq α_{i} \leq scriptC, i = 1, \dots, l, \sum_{i = 1}^{l} α_{i} y_{i}^{t} = 0 . \end{matrix}

where

{bold-italicα}_{i}, i \in false{1, \dots, l false}

are the Lagrange multipliers.…”

Section: Adaptively Svm‐based Multi‐cell Interference Classifiermentioning

confidence: 99%

SVM‐based online learning for interference‐aware multi‐cell mmWave vehicular communications

Zhu

Zhao

et al. 2021

IET Communications

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This paper proposes a data-driven method of mmWave beam selection in multi-cell systems to achieve a near-optimal fast beam allocation with low complexity. In particular, an online learning algorithm based on support vector machine (SVM) equipped with the radial basis function kernel, namely SVM-based online beam selection (SBOS) algorithm is proposed. The proposed algorithm starts with an adaptive beam selection process for certain traffic pattern that uses an SVM learning model to adaptively refine the beam selection strategy. Specifically, SVM-based model labels the feedback (the average information rate) from the cellular system, then learns from samples, and makes the scheme space smaller by maximising samples' minimum distances to all labelled samples in the sample space constrained by newly learned boundaries. Then, according to the aggregated data about the traffic patterns and the performance of corresponding beam selection strategy, SBOS algorithm exploits beam selection schemes recorded in the database or explores new schemes for unknown situations, respectively, and how to tune the hyperparameters for the SBOS algorithm is discussed. Furthermore, the extensive simulation results show that the proposed algorithm achieves a better performance versus upper confidence bound and Random methods. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Support Vector Machine-Based Transmit Antenna Allocation for Multiuser Communication Systems

Cited by 15 publications

References 56 publications

Large-Scale Wireless-Powered Networks With Backscatter Communications—A Comprehensive Survey

Large-Scale Wireless-Powered Networks With Backscatter Communications—A Comprehensive Survey

An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna

SVM‐based online learning for interference‐aware multi‐cell mmWave vehicular communications

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