A Survey of Spectrum Sensing Algorithms Based on Machine Learning

Liu, Youyao; Li, Juan

doi:10.1007/978-3-031-20738-9_97

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…У звуковому діапазоні така стандартизація неможлива, окрім випадків штучного обмеження потужності генерації звука під час роботи якогось обладнання. Тому після пе-рвинного аналізу звукового спектру ми маємо скористатися одним із методів навчання без контролю (англійською мовою unsupervised learning), найбільш актуальні з яких розглянуто в [3]. Зокрема, специфічні для промислових завдань методи навчання без контролю розглянуті в [4].…”

Section: обробка низькочастотних сигналівunclassified

Low frequency signal classification using clustering methods

Rahozin,

Doroshenko

2024

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The article considers the problem of low frequency signal classification, as sound or vibration pattern footprints may describe types of objects very well. In cases of a priori absence of object signal pattern information, the unsupervised learning methods based on clustering looks good enough for classification, and outperform neural net-based methods in case of limited power envelope. We have used big real-world sound and vibration data set to check several clustering methods (K-Means, OPTICS) for classification without any a priori data and have got good enough results. The article considers data set preparations including primary signal processing and the parameters to select appropriate clustering algorithms, which depends on input data shape. There are several examples of data classification, also cascaded methods for data set improvement are considered. Finally, we provide a good and practical guide for exploring low frequency signals using clustering methods, which can be used for real world observations and analysis for open space and inside buildings.

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Section: обробка низькочастотних сигналівunclassified

Low frequency signal classification using clustering methods

Rahozin,

Doroshenko

2024

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“…One of the key challenges lies in spectrum sensing, which is essential for detecting and identifying unused spectrum bands, mitigating interference, and enabling dynamic spectrum access. Efficient spectrum sensing not only enhances spectrum utilization but also improves the overall performance and reliability of wireless communication systems [2]. This article provides a comprehensive overview of recent advancements in the optimization of energy and spectrum sensing techniques in OFDMA systems.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Energy and Spectrum Sensing Using Orthogonal Frequency Division Multiple Access

Dr. Raghavendra Y M,

Dr. Asha.M,

Dr. Manjula.G

et al. 2024

Int Res J Adv Engg Hub

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Orthogonal Frequency Division Multiple Access (OFDMA) has emerged as a cornerstone technology in modern wireless communication systems due to its ability to efficiently utilize spectrum resources. However, the dynamic nature of wireless environments demands robust spectrum sensing techniques to ensure efficient spectrum utilization and mitigate interference. The fundamentals of OFDMA and its significance in contemporary wireless network is initially discussed by offering high spectral efficiency, flexibility, and support for diverse services, making it a fundamental technology in standards such as LTE (Long-Term Evolution) and Wi-Fi. Its significance is expected to grow further with the deployment of 5G and beyond. This article presents a comprehensive analysis of Optimized energy detection-based spectrum sensing in OFDMA systems. Evaluated the system performance through simulation by considering parameters such as energy consumption, detection accuracy, and spectral efficiency. Finally, we provide insights into future research directions and emerging trends in spectrum sensing optimization for OFDMA systems, including cognitive radio networks, dynamic spectrum access, and fifth-generation (5G) wireless networks.

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A Survey of Spectrum Sensing Algorithms Based on Machine Learning

Cited by 2 publications

References 19 publications

Low frequency signal classification using clustering methods

Low frequency signal classification using clustering methods

Optimization of Energy and Spectrum Sensing Using Orthogonal Frequency Division Multiple Access

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