A novel spectrum handoff management scheme based on SVM in cognitive radio networks

Guo, Jinghua; Ji, Hong; Li, Yi; Li, Xi

doi:10.1109/chinacom.2011.6158234

Cited by 12 publications

(6 citation statements)

References 10 publications

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“…The results show that Sigma-IF presents a better prediction and reduces the detection time. In [11], SVM is used to predict the moment where the SU must move on to another channel before the channel is busy thereby reducing handoff time. In the article of [12], four different artificial intelligence techniques that involve neural networks and SVM are used where SVM behaves consistently and precisely.…”

Section: Related Workmentioning

confidence: 99%

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Intelligent decision-making model for spectrum in cognitive wireless networks

Bernal¹,

Hernández²

2017

ces

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This document proposes the design of a dynamic decision-making model in cognitive wireless networks that allows secondary users to opportunely harness the spectrum and use channels without affecting the traffic of primary users. The implemented model includes an initial decision-making algorithm with multiple criteria that classifies the best channels according to the spectrum characterization and a final algorithm for spectral occupancy prediction that allows the secondary user to change channels when the current channel is requested by a primary user. This algorithm was chosen after evaluating three prediction techniques. This work manages to integrate two decision methods that contribute to reducing the amount of channel changes that the secondary user must perform. The analysis of the results from prediction techniques indicate that the Grey Rational Analysis (GRA) algorithm in combination with the Support Vector Machine (SVM) algorithm presents the best performance in terms of choosing an available channel, reducing the primary user's interference and diminishing the rate of necessary handoffs.

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Section: Related Workmentioning

confidence: 99%

“…After obtaining the resulting network for each channel, the data conversion process to get the real prediction values. The mathematical formulation is shown in equation (11) where y(t) will be the next value of the previous data and d is the amount of previous delays.…”

Section: Neural Networkmentioning

confidence: 99%

Intelligent decision-making model for spectrum in cognitive wireless networks

Bernal¹,

Hernández²

2017

ces

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“…In Fig. 1 we show the concept of spectrum handoff, which is related to the presence or absence of a primary channel and two secondary users [3], [4]. There are four possible scenarios involving the presence of a primary user in a channel.…”

Section: Introductionmentioning

confidence: 99%

Experimental Evaluation of Spectrum Handoff Management with Machine Learning Algorithms using Software Defined Radio

Babjan

Rajendran

2023

Preprint

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Although the design of spectrum switching has been studied, little is known about how random user movement affects the handoff. This issue can occur when a user moves to a new location. In this paper, the authors present a framework that verifies the necessity of spectrum handoff to improve the performance of the system by implementing machine learning (ML) techniques. Some of these include the Logistic Regression, KNN Algorithm, SVM Algorithm, Naïve Bayes Classifier, Decision Tree Classification and Random Forest Algorithm. The system is implemented on a real-time dataset where all the users are separated in power domain using the concept of non orthogonal multiple access (NOMA) technique. The dataset values are prepared using a software-defined radio experimental setup, which is used to analyze the performance of various ML techniques in terms of confusion matrix, specificity, precision, F1_score, sensitivity and accuracy.

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“…When a PU reappears on a channel, SU will choose to switch to a new channel or stay on the current channel until the release of the PU's connection occupation. There are some researches on spectrum handoff [2][3][4][5]. Guo et al [2] and Roslee et al [3] used the support vector machine and the neural network to select actions for spectrum handoff, respectively.…”

mentioning

confidence: 99%

“…There are some researches on spectrum handoff [2][3][4][5]. Guo et al [2] and Roslee et al [3] used the support vector machine and the neural network to select actions for spectrum handoff, respectively. However, the learning models of these schemes must be trained again as long as the network status varies in CRN.…”

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confidence: 99%

Reinforcement learning‐based spectrum handoff scheme with measured PDR in cognitive radio networks

et al. 2019

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Spectrum handoff plays an important role in cognitive radio networks (CRNs). Secondary users (SUs) use spectrum handoff to hold on the idle channel or to free the channel for primary users (PUs). Spectrum handoff scheme greatly affects the transmission quality and the success rate of SUs connection. In this Letter, a reinforcement learning‐based spectrum handoff scheme with the measured packet drop rate (PDR) for multimedia transmissions over CRNs is proposed. In a system model with multiple PUs and SUs, a new state space description method is designed and an observed state includes not only the status whether PUs arrive on each channel but also several other important factors. Also, the measured PDR, instead of the calculated one, is presented to update the mean opinion score, the Q‐table and the handoff policy. Compared with the existing schemes with the calculated PDR from the Quality‐of‐Experience model, the authors' proposed scheme can converge more rapidly in the dynamic radio environment, and reduce the PDR of SUs more significantly.

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A novel spectrum handoff management scheme based on SVM in cognitive radio networks

Cited by 12 publications

References 10 publications

Intelligent decision-making model for spectrum in cognitive wireless networks

Intelligent decision-making model for spectrum in cognitive wireless networks

Experimental Evaluation of Spectrum Handoff Management with Machine Learning Algorithms using Software Defined Radio

Reinforcement learning‐based spectrum handoff scheme with measured PDR in cognitive radio networks

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