Learning and Reasoning in Cognitive Radio Networks

Gavrilovska, Liljana; Atanasovski, Vladimir; Macaluso, Irene; DaSilva, Luiz A.

doi:10.1109/surv.2013.030713.00113

Cited by 86 publications

(51 citation statements)

References 59 publications

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“…For example, neural network genetic algorithms can be used for maximum likelihood estimation of HMM parameters [51]. Neural networks-based techniques are presented extensively in cognitive radio networks [18,[51][52][53][54][55], with application on spectrum prediction presented in [56,57]. Support vector machines [47], pattern mining [48,49], and dictionary-based prediction [9] were suggested for spectrum prediction and user activity modelling.…”

Section: Machine Learning-based Techniquesmentioning

confidence: 99%

Statistical spectrum occupancy prediction for dynamic spectrum access: a classification

Eltom

Kandeepan

Evans

et al. 2018

J Wireless Com Network

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Spectrum scarcity due to inefficient utilisation has ignited a plethora of dynamic spectrum access solutions to accommodate the expanding demand for future wireless networks. Dynamic spectrum access systems allow secondary users to utilise spectrum bands owned by primary users if the resulting interference is kept below a pre-designated threshold. Primary and secondary user spectrum occupancy patterns determine if minimum interference and seamless communications can be guaranteed. Thus, spectrum occupancy prediction is a key component of an optimised dynamic spectrum access system. Spectrum occupancy prediction recently received significant attention in the wireless communications literature. Nevertheless, a single consolidated literature source on statistical spectrum occupancy prediction is not yet available in the open literature. Our main contribution in this paper is to provide a statistical prediction classification framework to categorise and assess current spectrum occupancy models. An overview of statistical sequential prediction is presented first. This statistical background is used to analyse current techniques for spectrum occupancy prediction. This review also extends spectrum occupancy prediction to include cooperative prediction. Finally, theoretical and implementation challenges are discussed.

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Section: Machine Learning-based Techniquesmentioning

confidence: 99%

Statistical spectrum occupancy prediction for dynamic spectrum access: a classification

Eltom

Kandeepan

Evans

et al. 2018

J Wireless Com Network

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“…The main reconfigurable parameters listed in Section 1.2 include for instance (1) power control, (2) frequency band allocation, (3) time slot allocation, (4) adaptive modulation and coding, (5) frame size, (6) symbol rate, (5) rate control, (6) antenna selection and parameters, (7) scheduling, (8) handover, (9) admission control, (10) congestion control, (11) load control, (12) routing plan, and (13) base station deployment. The reconfigurable capability is based on decision-making, which can be based on optimization algorithms.…”

Section: Cognitive Radio Tasks and Corresponding Challengesmentioning

confidence: 99%

“…The variables in fuzzy logic are not limited to only two values (True or False) as it is defined in classical and crisp sets [8]. A fuzzy element has a degree of membership or compatibility with the set and its negation.…”

Section: Fuzzy Logicmentioning

confidence: 99%

“…• Making decisions for different decision variables: In order to achieve the objectives mentioned before, the CR network needs to make decisions concerning the following important variables: (1) power control, (2) frequency band allocation, (3) time slot allocation, (4) adaptive modulation and coding, (5) frame size, (6) symbol rate, (5) rate control, (6) antenna selection and parameters, (7) scheduling, (8) handover, (9) admission control, (10) congestion control, (11) load control, (12) routing plan, and (13) base station deployment [12]. The decision-making can be based on optimization algorithms; however, in order to reduce the complexity and achieve efficient real-time resource allocation, cognitive radio networks use machine learning and artificial intelligence.…”

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confidence: 99%

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Recent advances on artificial intelligence and learning techniques in cognitive radio networks

Abbas

Nasser

Ahmad

2015

J Wireless Com Network

124

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Cognitive radios are expected to play a major role towards meeting the exploding traffic demand over wireless systems. A cognitive radio node senses the environment, analyzes the outdoor parameters, and then makes decisions for dynamic time-frequency-space resource allocation and management to improve the utilization of the radio spectrum. For efficient real-time process, the cognitive radio is usually combined with artificial intelligence and machine-learning techniques so that an adaptive and intelligent allocation is achieved. This paper firstly presents the cognitive radio networks, resources, objectives, constraints, and challenges. Then, it introduces artificial intelligence and machine-learning techniques and emphasizes the role of learning in cognitive radios. Then, a survey on the state-of-the-art of machine-learning techniques in cognitive radios is presented. The literature survey is organized based on different artificial intelligence techniques such as fuzzy logic, genetic algorithms, neural networks, game theory, reinforcement learning, support vector machine, case-based reasoning, entropy, Bayesian, Markov model, multi-agent systems, and artificial bee colony algorithm. This paper also discusses the cognitive radio implementation and the learning challenges foreseen in cognitive radio applications. IntroductionAccording to Cisco Visual Networking Index, the global IP traffic will reach 168 exabytes per month by 2019 [1], and the number of devices will be three times the global population. In addition, the resources in terms of power and bandwidth are scarce. Therefore, novel solutions are needed to minimize energy consumption and optimize resource allocation. Cognitive radio (CR) was introduced by Joseph Mitola III and Gerald Q. Maguire in 1999 for a flexible spectrum access [2]. Basically, they defined cognitive radio as the integration of model-based reasoning with software radio technologies [3]. In 2005, Simon Haykin had given a review of the cognitive radio concept and had treated it as brain-empowered wireless communications [4]. Cognitive radio is a radio or system that senses the environment, analyzes its transmission parameters, *Correspondence: nfa23@aub.edu.lb Department of Electrical and Computer Engineering, American University of Beirut, Beirut, Lebanon and then makes decisions for dynamic time-frequencyspace resource allocation and management to improve the utilization of the radio electromagnetic spectrum.Generally, radio resource management aims at optimizing the utilization of various radio resources such that the performance of the radio system is improved. For instance, the authors in [5] proposed an optimal resource (power and bandwidth) allocation in cognitive radio networks (CRNs), specifically in the scenario of spectrum underlay, while taking into consideration the limitations of interference temperature limits. The optimization formulations provide optimal solutions for resources allocation at, sometimes, the detriment of global convergence, computation time, and...

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“…Sin embargo, las conclusiones de la técnica están respaldas por mé-tricas iniciales que describen el conjunto de valores admisibles de una muestra. De tal manera, la lógica difusa -aunque con una tasa de estudio aleatoria-permite obtener valores diferentes a los supuestos de verdadero o falso (Gavrilovska, Atanasovski, Macaluso y Da Silva, 2013).…”

Section: Lógica Difusaunclassified

Tecnicas inteligentes en la asignacion de espectro dinamica para redes inalambricas cognitivas

Salgado

Marquez

Gómez³

2016

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RESUMENObjetivo: El objetivo de este trabajo es estudiar las aplicaciones de las diferentes técnicas de inteligencia artificial y aprendizaje autónomo en la asignación dinámica de espectro para redes inalámbricas cognitivas, es especial las distribuidas. Método: El desarrollo del presente trabajo se realizó a través del estudio y análisis de algunas de las publicaciones más relevantes en la literatura actual por medio de la búsqueda en revistas internacionales indexadas en ISI y Scopus. Resultados: Se determinaron las técnicas de inteligencia artificial y aprendizaje autónomo más relevantes y con mayor aplicación en la asignación de espectro para redes inalámbricas cognitivas. Conclusiones: La implementación de una técnica o del conjunto de las mismas depende de las necesidades en procesamiento de la señal, compensaciones en los tiempos de respuesta, disponibilidad de las muestras, capacidad de almacenamiento, capacidad de aprendizaje, robustez, entre otras.Palabras Clave: aprendizaje autonomo, asignacion dinamica de espectro, inteligencia artificial, radio cognitivas, redes inalambricas. ABSTRACTObjective: The objective of this work is to study the applications of different techniques of artificial intelligence and autonomous learning in the dynamic allocation of spectrum for cognitive wireless networks, especially the distributed ones. Method: The development of this work was done through the study and analysis of some of the most relevant publications in current literature through the search in indexed international journals in ISI and Scopus. Results: the most relevant techniques of artificial intelligence and autonomous learning were determined. Also, the ones with more applicability in the allocation of spectrum for cognitive wireless networks were determined, too. . Tecnicas inteligentes en la asignacion de espectro dinamica para redes inalambricas cognitivasSmart techniques in the dynamic spectrum alocation for cognitive wireless networks

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Learning and Reasoning in Cognitive Radio Networks

Cited by 86 publications

References 59 publications

Statistical spectrum occupancy prediction for dynamic spectrum access: a classification

Statistical spectrum occupancy prediction for dynamic spectrum access: a classification

Recent advances on artificial intelligence and learning techniques in cognitive radio networks

Tecnicas inteligentes en la asignacion de espectro dinamica para redes inalambricas cognitivas

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