Machine Learning to Data Fusion Approach for Cooperative Spectrum Sensing

Mikaeil, Ahmed Mohammed; Guo, Bin; Wang, Zhijun

doi:10.1109/cyberc.2014.80

Cited by 35 publications

(31 citation statements)

References 8 publications

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“…Authors in [20][21][22] have considered KNN for spectrum sensing. In [20] the authors have considered a binary hypothesis testing and have proposed to optimize the distance between the two classes.…”

Section: Introductionmentioning

confidence: 99%

Reliable Machine Learning Based Spectrum Sensing in Cognitive Radio Networks

Shah

Koo

2018

Wireless Communications and Mobile Computing

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Spectrum sensing is of crucial importance in cognitive radio (CR) networks. In this paper, a reliable spectrum sensing scheme is proposed, which uses K-nearest neighbor, a machine learning algorithm. In the training phase, each CR user produces a sensing report under varying conditions and, based on a global decision, either transmits or stays silent. In the training phase the local decisions of CR users are combined through a majority voting at the fusion center and a global decision is returned to each CR user. A CR user transmits or stays silent according to the global decision and at each CR user the global decision is compared to the actual primary user activity, which is ascertained through an acknowledgment signal. In the training phase enough information about the surrounding environment, i.e., the activity of PU and the behavior of each CR to that activity, is gathered and sensing classes formed. In the classification phase, each CR user compares its current sensing report to existing sensing classes and distance vectors are calculated. Based on quantitative variables, the posterior probability of each sensing class is calculated and the sensing report is classified into either representing presence or absence of PU. The quantitative variables used for calculating the posterior probability are calculated through K-nearest neighbor algorithm. These local decisions are then combined at the fusion center using a novel decision combination scheme, which takes into account the reliability of each CR user. The CR users then transmit or stay silent according to the global decision. Simulation results show that our proposed scheme outperforms conventional spectrum sensing schemes, both in fading and in nonfading environments, where performance is evaluated using metrics such as the probability of detection, total probability of error, and the ability to exploit data transmission opportunities.

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Section: Introductionmentioning

confidence: 99%

Reliable Machine Learning Based Spectrum Sensing in Cognitive Radio Networks

Shah

Koo

2018

Wireless Communications and Mobile Computing

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“…In particular, we train a machine-learning classifier (i.e., K-nearest neighbor (KNN), support vector machine (SVM), Naive Bayes (NB), and Decision tree (DT)) over a set containing energy test statistics of PU channel frames along with their corresponding decisions about the presence or absence of PU transmission in the channel. Then, we use the trained classifier to predict the decisions for newly unseen PU channel frames [3]. The second part focuses on estimating the near future of PU channel state.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for Spectrum Management in Cognitive Radio Networks

Mikaeil¹

2018

Machine Learning - Advanced Techniques and Emerging Applications

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Cognitive radio (CR) provides a better way for utilization of spectrum resource by introducing an opportunistic usage of the frequency bands that are not heavily occupied by a licensed spectrum user or a primary user (PU). In cognitive radio, the detection and estimation of PU channel availability (unoccupied spectrum) are the key challenges that need to be overcome in order to prevent the interference with licensed spectrum user and improve spectrum resource utilization efficiency. This chapter focuses on developing new ways for detecting and estimating primary user channel availability based on machinelearning (ML) techniques.

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“…In [15], the authors construct subjective classification systems to predict sensation of reality from multimedia experiences based on EEG and peripheral physiological signals such as heart rate and respiration. In [16], the authors propose a machine learning based data fusion algorithm that can provide real time per frame training and decision based cooperative spectrum sensing. For the labelled data imbalance, the authors in [17] propose a framework based on the correlations generated between concepts.…”

Section: Related Workmentioning

confidence: 99%

Quality Assessment of Training Data with Uncertain Labels for Classification of Subjective Domains

Dai¹

2017

JCC

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In order to improve the performance of classifiers in subjective domains, this paper defines a metric to measure the quality of the subjectively labelled training data (QoSTD) by means of K-means clustering. Then, the QoSTD is used as a weight of the predicted class scores to adjust the likelihoods of instances. Moreover, two measurements are defined to assess the performance of the classifiers trained by the subjective labelled data. The binary classifiers of Traditional Chinese Medicine (TCM) Zhengs are trained and retrained by the real-world data set, utilizing the support vector machine (SVM) and the discrimination analysis (DA) models, so as to verify the effectiveness of the proposed method. The experimental results show that the consistency of likelihoods of instances with the corresponding observations is increased notable for the classes, especially in the cases with the relatively low QoSTD training data set. The experimental results also indicate the solution how to eliminate the miss-labelled instances from the training data set to re-train the classifiers in the subjective domains.

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Machine Learning to Data Fusion Approach for Cooperative Spectrum Sensing

Cited by 35 publications

References 8 publications

Reliable Machine Learning Based Spectrum Sensing in Cognitive Radio Networks

Reliable Machine Learning Based Spectrum Sensing in Cognitive Radio Networks

Machine Learning Approaches for Spectrum Management in Cognitive Radio Networks

Quality Assessment of Training Data with Uncertain Labels for Classification of Subjective Domains

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