Detection of the Primary User’s Behavior for the Intervention of the Secondary User Using Machine Learning

Soto, Deisy Dayana Zambrano; Parra, Octavio José Salcedo; Sarmiento, Danilo Alfonso López

doi:10.1007/978-3-030-03192-3_15

Cited by 5 publications

(9 citation statements)

References 12 publications

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“…Unlike other methods, decision trees can process categorical and numerical data even without data normalization. Decision trees have been applied for spectrum sensing [84], and modulation recognition [61], [62]. Support vector machines (SVMs) use training data to come up with a hyperplane that separates classes with the largest margin.…”

Section: Machine Learning Methods For Context Awarenessmentioning

confidence: 99%

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Artificial Intelligence for Radio Communication Context-Awareness

et al. 2021

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This paper surveys Artificial Intelligence (AI) methods for acquiring and managing contextof-operation awareness of radio communication nodes, links, and networks. The meaning and significance of context information and suitability of Machine Learning (ML) methods for the enrichment of context information is discussed. A number of context features are considered in this regard and thorough analysis on which ML methods are suitable to which part of context learning is provided. The added value of the paper is the presentation of a synthesized framework of context-information processing, sharing, and management in a radio communication network by delineating a network-embedded subsystem for this management. Recommendations for a future AI/ML-based radio communication system architectures are also provided.

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Section: Machine Learning Methods For Context Awarenessmentioning

confidence: 99%

“…In [84] the authors applied several ML methods for spectrum sensing to data from the GSM 850 MHz band on one day of March 2016. Records of the power of a radio channel have been obtained in 290 ms intervals throughout 15 h per day.…”

Section: ) Application Of Classifiersmentioning

confidence: 99%

Artificial Intelligence for Radio Communication Context-Awareness

et al. 2021

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“…For instance, in [185] four ML techniques are examined k-NN, SVM, DT and logistic regression (LR) in order to predict the presence or absence of a PU in CR applications. The authors in [188] go a step further and design a spectrum sensing framework based on CNNs to facilitate a SU to achieve higher sensing accuracy compared with conventional approaches.…”

Section: Referencementioning

confidence: 99%

“…), non-adaptive modulation scheme, static non-application cognizant MAC, etc. • Wireless interference identification [128,169,[177][178][179][180][181][182][183][184][185][186][187][188][189][190][191][192][193] MAC analysis…”

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confidence: 99%

A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer

et al. 2021

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This paper presents a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack: PHY, MAC and network. First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning to help non-machine learning experts understand all discussed techniques. Then, a comprehensive review is presented on works employing ML-based approaches to optimize the wireless communication parameters settings to achieve improved network quality-of-service (QoS) and quality-of-experience (QoE). We first categorize these works into: radio analysis, MAC analysis and network prediction approaches, followed by subcategories within each. Finally, open challenges and broader perspectives are discussed.

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“…For instance, in [182] four ML techniques are examined k-NN, SVM, DT and logistic regression (LR) in order to predict the presence or absence of a PU in CR applications. The authors in [185] go a step further and design a spectrum sensing framework based on CNNs to facilitate a SU to achieve higher sensing accuracy compared with conventional approaches.…”

Section: Activity Recognitionmentioning

confidence: 99%

A survey on Machine Learning-based Performance Improvement of Wireless Networks: PHY, MAC and Network layer

Kulin¹,

Kazaz²,

Moerman³

et al. 2020

Preprint

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This paper provides a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack (PHY, MAC and network). First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning for non-machine learning experts to understand all discussed techniques. Then, a comprehensive review is presented on works employing ML-based approaches to optimize the wireless communication parameters settings to achieve improved network quality-of-service (QoS) and quality-of-experience (QoE). We first categorize these works into: radio analysis, MAC analysis and network prediction approaches, followed by subcategories within each. Finally, open challenges and broader perspectives are discussed.

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Detection of the Primary User’s Behavior for the Intervention of the Secondary User Using Machine Learning

Cited by 5 publications

References 12 publications

Artificial Intelligence for Radio Communication Context-Awareness

Artificial Intelligence for Radio Communication Context-Awareness

A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer

A survey on Machine Learning-based Performance Improvement of Wireless Networks: PHY, MAC and Network layer

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