K-Nearest Neighbour (KNN) Algorithm based Cooperative Spectrum Sensing in Cognitive Radio Networks

Somula, Lakshmikantha Reddy; Meena, M.

doi:10.1109/icccmla56841.2022.9988996

Cited by 8 publications

(3 citation statements)

References 19 publications

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“…It typically uses the assumption of the data feature similarity that the data points can be found near one to another. The new data can be assigned a value based on how similarly the data match the points trained in the training set [ 91 , 92 ]. The advantages of k-NN algorithm can be summarized: Firstly, it is easy to implement and achieve high-accuracy results.…”

Section: Detection In Uwb Positioning Algorithmsmentioning

confidence: 99%

Indoor Positioning System (IPS) Using Ultra-Wide Bandwidth (UWB)—For Industrial Internet of Things (IIoT)

Che

Ahmed

Lazaridis

et al. 2023

Sensors

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The integration of the physical and digital world has become increasingly important, and location-based services have become the most sought-after application in the field of the Internet of Things (IoT). This paper delves into the current research on ultra-wideband (UWB) indoor positioning systems (IPS). It begins by examining the most common wireless communication-based technologies for IPSs followed by a detailed explanation of UWB. Then, it presents an overview of the unique characteristics of UWB technology and the challenges still faced by the IPS implementation. Finally, the paper evaluates the advantages and limitations of using machine learning algorithms for UWB IPS.

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Section: Detection In Uwb Positioning Algorithmsmentioning

confidence: 99%

Indoor Positioning System (IPS) Using Ultra-Wide Bandwidth (UWB)—For Industrial Internet of Things (IIoT)

Che

Ahmed

Lazaridis

et al. 2023

Sensors

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show abstract

“…A noticeable example about this is its application in radar detection for coherent targets amidst non-Gaussian noise [5], where it has been proven to surpass traditional methods in such demanding noise conditions. In the area of cognitive radio networks, the implementation of the KNN method in cooperative spectrum sensing is specially noteworthy [6]. Tis adaptation aims to refne dynamic spectrum access with a focus on the precision and fexibility of the algorithm in practical settings.…”

Section: Introductionmentioning

confidence: 99%

Bayes-Decisive Linear KNN with Adaptive Nearest Neighbors

Zhang,

Bian,

Wang

2024

International Journal of Intelligent Systems

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While the classical KNN (k nearest neighbor) shares its avoidance of the consistent distribution assumption between training and testing samples to achieve fast prediction, it still faces two challenges: (a) its generalization ability heavily depends on an appropriate number k of nearest neighbors; (b) its prediction behavior lacks interpretability. In order to address the two challenges, a novel Bayes-decisive linear KNN with adaptive nearest neighbors (i.e., BLA-KNN) is proposed to obtain the following three merits: (a) a diagonal matrix is introduced to adaptively select the nearest neighbors and simultaneously improve the generalization capability of the proposed BLA-KNN method; (b) the proposed BLA-KNN method owns the group effect, which inherits and extends the group property of the sum of squares for total deviations by reflecting the training sample class-aware information in the group effect regularization term; (c) the prediction behavior of the proposed BLA-KNN method can be interpreted from the Bayes-decision-rule perspective. In order to do so, we first use a diagonal matrix to weigh each training sample so as to obtain the importance of the sample, while constraining the importance weights to ensure that the adaptive k value is carried out efficiently. Second, we introduce a class-aware information regularization term in the objective function to obtain the nearest neighbor group effect of the samples. Finally, we introduce linear expression weights related to the distance measure between the testing and training samples in the regularization term to ensure that the interpretation of Bayes-decision-rule can be performed smoothly. We also optimize the proposed objective function using an alternating optimization strategy. We experimentally demonstrate the effectiveness of the proposed BLA-KNN method by comparing it with 7 comparative methods on 15 benchmark datasets.

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“…CR is also an effective technique to mitigate co-sharing spectrum interference [9]. It can sense the available frequency band in the spectrum for use, which can improve spectrum efficiency and reduce the mutual interference between systems [10][11][12]. Zhang et al have proposed a spectrum strategy to distinguish the GEO signal from the interfering NGEO and noise [13], and the GEO satellite can use the appropriate power to protect its normal communication.…”

Section: Introductionmentioning

confidence: 99%

Interference Situational Aware Beam Pointing Optimization for Dense LEO Satellite Communication System

He,

Cui,

Wang

et al. 2024

Electronics

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Recently, the low earth orbit (LEO) mega-constellation faces serious time-varying interferences due to spectrum sharing, dense deployment, and high mobility. Therefore, it is important to study the interference avoidance techniques for the dense LEO satellite system. In this paper, the interference situational aware beam pointing optimization technique is proposed. Firstly, the angle of departure (AoD) and angle of arrival (AoA) of the interfering links are obtained to represent the time-varying interference. Then, the interference avoidance problem for dense LEO satellite systems is modeled as a non-convex optimization problem, and a particle swarm optimization (PSO) based method is proposed to obtain the optimal beam pointing of the user terminal (UT). Simulations show that the relative error of the mean signal-to-interference plus noise ratio (SINR) obtained by the proposed method is 0.51%, so the co-channel interference can be effectively mitigated for the dense LEO satellite communication system.

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K-Nearest Neighbour (KNN) Algorithm based Cooperative Spectrum Sensing in Cognitive Radio Networks

Cited by 8 publications

References 19 publications

Indoor Positioning System (IPS) Using Ultra-Wide Bandwidth (UWB)—For Industrial Internet of Things (IIoT)

Indoor Positioning System (IPS) Using Ultra-Wide Bandwidth (UWB)—For Industrial Internet of Things (IIoT)

Bayes-Decisive Linear KNN with Adaptive Nearest Neighbors

Interference Situational Aware Beam Pointing Optimization for Dense LEO Satellite Communication System

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