Fuzzy-Logic Based Framework for Spectrum Availability Assessment in Cognitive Radio Systems

Matinmikko, Marja; Ser, Javier Del; Rauma, Tapio; Mustonen, Miia

doi:10.1109/jsac.2013.131117

Cited by 25 publications

(19 citation statements)

References 20 publications

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“…FIS using if-then rules will allow determining the output of the system. The authors in [22][23][24] and [25] applied fuzzy logic theory in cognitive radio to solve the following objectives: bandwidth allocation, interference and power management, spectrum availability assessment methods, and resource allocation. In [22], the authors have proposed a centralized fuzzy inference system that can allocate the available bandwidth among cognitive users considering traffic intensity, type, and quality of service priority.…”

Section: Fuzzy Logicmentioning

confidence: 99%

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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Section: Fuzzy Logicmentioning

confidence: 99%

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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“…Diferentes investigaciones (Abbas, Nasser y El Ahmad, 2015;Matinmikko, Del Ser, Rauma y Mustonen, 2013) han aplicado la teoría de lógi-ca difusa en la CR para resolver los problemas en función de la asignación del ancho de banda, estudiando de antemano la interferencia y la administración de la energía, los anteriores como métodos de evaluación en la correcta asignación del espectro. No obstante, diferentes resultados de estudios han detallado tópicos como la inferencia difusa centralizada, que asigna los anchos de banda correspondientes a la intensidad de tráfico y la prioridad del servicio.…”

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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“…There are different types and variants of Computational Intelligence (CI) and machine learning algorithms that can be used in CR such as genetic algorithms for optimization of transmission parameters [9], swarm intelligence for optimization of radio resource allocation [10], fuzzy logic system (FLS) for decision making [11] [12], neural network and hidden Markov model for prediction of spectrum holes; game theory, linear regression and linear predictors for spectrum occupancy prediction [13] , Bayesian inference based predictors, etc. Some of the CI methods are used for learning and prediction, some for optimization of certain transmission parameters while others for decision making [14].…”

Section: Related Workmentioning

confidence: 99%

“…As a result it has been used in CR research for decision making processes. In [11] an FL based decision-making system with a learning mechanism was developed for selection of optimum spectrum sensing techniques for a given band. Among these techniques are matched filtering, correlation detection, features detection, energy detection, and cooperative sensing.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Application of Artificial Neural Network and Support Vector Regression in Cognitive Radio Networks for RF Power Prediction Using Compact Differential Evolution Algorithm

Iliya¹,

Goodyer²,

Gow³

et al. 2015

Annals of Computer Science and Information Systems

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Abstract-Cognitive radio (CR) technology has emerged as a promising solution to many wireless communication problems including spectrum scarcity and underutilization. To enhance the selection of channel with less noise among the white spaces (idle channels), the a priory knowledge of Radio Frequency (RF) power is very important. Computational Intelligence (CI) techniques cans be applied to these scenarios to predict the required RF power in the available channels to achieve optimum Quality of Service (QoS). In this paper, we developed a time domain based optimized Artificial Neural Network (ANN) and Support Vector Regression (SVR) models for the prediction of real world RF power within the GSM 900, Very High Frequency (VHF) and Ultra High Frequency (UHF) FM and TV bands. Sensitivity analysis was used to reduce the input vector of the prediction models. The inputs of the ANN and SVR consist of only time domain data and past RF power without using any RF power related parameters, thus forming a nonlinear time series prediction model. The application of the models produced was found to increase the robustness of CR applications, specifically where the CR had no prior knowledge of the RF power related parameters such as signal to noise ratio, bandwidth and bit error rate. Since CR are embedded communication devices with memory constrain limitation, the models used, implemented a novel and innovative initial weight optimization of the ANN's through the use of compact differential evolutionary (cDE) algorithm variants which are memory efficient. This was found to enhance the accuracy and generalization of the ANN model.

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Fuzzy-Logic Based Framework for Spectrum Availability Assessment in Cognitive Radio Systems

Cited by 25 publications

References 20 publications

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

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

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

Application of Artificial Neural Network and Support Vector Regression in Cognitive Radio Networks for RF Power Prediction Using Compact Differential Evolution Algorithm

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