Cognitive Radio and RF Communications Design Optimization using Genetic Algorithms

Hauris, J. F.; He, Donya; Michel, Geremy; Ozbay, Cahit

doi:10.1109/milcom.2007.4455236

Cited by 8 publications

(4 citation statements)

References 2 publications

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“…The CRs detect the licensed frequency band, and the BS announces whether the licensed band is available or not based on the data given by the CRs to the BS. 33 The energy detection technique is preferred by IEEE 802.22 because it is simple and has a low processing complexity. A number of additional strategies, aside from ''Energy Detection,'' have been proposed in the writing, for example,…”

Section: Spectrum Sensingmentioning

confidence: 99%

Fuzzy logic, genetic algorithms, and artificial neural networks applied to cognitive radio networks: A review

Alkhayyat

Abedi

Bagwari

et al. 2022

International Journal of Distributed Sensor Networks

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Cognitive radios are expected to play an important role in capturing the constantly growing traffic interest on remote networks. To improve the usage of the radio range, a cognitive radio hub detects the weather, evaluates the open-air qualities, and then makes certain decisions and distributes the executives’ space assets. The cognitive radio works in tandem with artificial intelligence and artificial intelligence methodologies to provide a flexible and intelligent allocation for continuous production cycles. The purpose is to provide a single source of information in the form of a survey research to enable academics better understand how artificial intelligence methodologies, such as fuzzy logics, genetic algorithms, and artificial neural networks, are used to various cognitive radio systems. The various artificial intelligence approaches used in cognitive radio engines to improve cognition capabilities in cognitive radio networks are examined in this study. Computerized reasoning approaches, such as fuzzy logic, evolutionary algorithms, and artificial neural networks, are used in the writing audit. This topic also covers cognitive radio network implementation and the typical learning challenges that arise in cognitive radio systems.

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Section: Spectrum Sensingmentioning

confidence: 99%

Fuzzy logic, genetic algorithms, and artificial neural networks applied to cognitive radio networks: A review

Alkhayyat

Abedi

Bagwari

et al. 2022

International Journal of Distributed Sensor Networks

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“…Hauris et al used genetic algorithms in [30] for RF parameter optimization in CR. The genes used are modulation and coding schemes, antenna parameters, transmit and receive antenna gains, receiver noise figure, transmit power, data rate, coding gain, bandwidth, and frequency.…”

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

123

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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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“…Recently learning based techniques like Hidden Markov process, ANN [30] and Genetic algorithm [31] are used for spectrum sensing.…”

Section: Learning/reasoning-based Sensingmentioning

confidence: 99%

Artificial Intelligence Based Model for Channel Status Prediction: A New Spectrum Sensing Technique for Cognitive Radio

Pattanayak¹,

Venkateswaran²,

Nandi³

2013

IJCNS

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The recent phenomena of tremendous growth in wireless communication application urge increasing need of radio spectrum, albeit it being a precious but natural resource. The recent technology under development to overview the situation is the concept of Cognitive Radio (CR). Recently the Artificial Intelligence (AI) tools are being considered for the topic. AI is the core of the cognitive engine that examines the external and internal environment parameters that leads to some postulations for QoS improvement. In this article, we propose a new Artificial Neural Network (ANN) model for detection of a spectrum hole. The model is trained with some pertinent features over a channel like SNR, channel capacity, bandwidth efficiency etc. The channel capacity status could be identified in a quantized index form. Some simulation results are presented.

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Cognitive Radio and RF Communications Design Optimization using Genetic Algorithms

Cited by 8 publications

References 2 publications

Fuzzy logic, genetic algorithms, and artificial neural networks applied to cognitive radio networks: A review

Fuzzy logic, genetic algorithms, and artificial neural networks applied to cognitive radio networks: A review

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

Artificial Intelligence Based Model for Channel Status Prediction: A New Spectrum Sensing Technique for Cognitive Radio

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