Spectrum optimization in Cognitive Radios using elitism in genetic algorithms

Moghal, Mohammad Riaz; Khan, Mubbashar Altaf; Bhatti, Hassan A

doi:10.1109/icet.2010.5638381

Cited by 10 publications

(12 citation statements)

References 6 publications

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“…The chromosome comprises of four parameters in the order mentioned, ie, the frequency, the power, the bit error rate (BER), and the modulation technique, as shown in Table . The first ten bits of the chromosome represent the frequency gene, the next six bits are dedicated for the power gene, the following three bits represent the BER gene, whereas the last two bits in the chromosome structure stand for the modulation gene …”

Section: Ga Methodologymentioning

confidence: 99%

“…The frequency band considered in this research is in the range 0‐10 MHz . There is no theoretical reason for the selection of this particular band and it has been considered just for the experimental and analysis purposes.…”

Section: Ga Methodologymentioning

confidence: 99%

“…The data and assumptions are similar to those in the carrot and stick rule‐based secondary spectrum trade model . The PU activity is assumed to be a random value above 30% of the maximum capacity, and the eligibility criterion for the sales to the SSM is assumed as the 10% of the current SSM demand. Hourly bidding with a complete bidding horizon H comprising of 24 bidding instants is assumed, as given by

H = false{1, 2, 3, …, N false} .

…”

Section: Simulation Of Qos Optimization‐based Secondary Spectrum Tradmentioning

confidence: 99%

“…Cognitive radio (CR) technology offers a solution to the spectrum scarcity issues using the dynamic spectrum access . Research claims that the radio spectrum resource is heavily underutilized due to the static allocation and on an average, almost 70% of it is being wasted . The CR proposes to improve the spectrum utilization efficiency through its opportunistic access in the underutilized portions that exist as whitespaces in the already allocated spectrum.…”

Section: Introductionmentioning

confidence: 99%

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QoS optimization‐based dynamic secondary spectrum access model

Khan

Jamali

2018

Trans Emerging Tel Tech

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Cognitive radio (CR) is believed to be the future communication technology that provides the solution to the radio spectrum scarcity problem. Quality of service (QoS) and secondary radio spectrum pricing are two important aspects of the ongoing research on CR. The primary users (PUs) have guaranteed QoS while the secondary users (SUs) can make only opportunistic access to the radio spectrum. The SUs coexist with the PUs on a noninterfering basis and must vacate the bands under their use, if required by the PUs. Good QoS can be exercised by an allocation of whitespaces with QoS appropriate to the SUs that minimize the mobility probability. If the pricing issues are not addressed properly, they may result in economic viability challenges and/or pose existential threats to the CR technology. This research proposes a QoS optimization‐based dynamic secondary spectrum access model using genetic algorithm along with a pricing and a payment methodology for the sales to secondary spectrum markets. The trade‐off between the spectrum allocation and the economic parameters for the secondary radio spectrum trade is considered. An extensive simulation is created to analyze the QoS optimization and pricing for the secondary radio spectrum trade. The results indicate that the profitability and sales in the secondary spectrum market are based on the fitness of the offered QoS with respect to that requested by the SUs. An increase in the QoS provided to the SUs is realized at the cost of a slight increase in the unit cost of the secondary spectrum.

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Section: Ga Methodologymentioning

confidence: 99%

Section: Ga Methodologymentioning

confidence: 99%

H = false{1, 2, 3, …, N false} .

…”

Section: Simulation Of Qos Optimization‐based Secondary Spectrum Tradmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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QoS optimization‐based dynamic secondary spectrum access model

Khan

Jamali

2018

Trans Emerging Tel Tech

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“…In [29], the authors addressed spectrum optimization in CRs using elitism in genetic algorithms. They used four parameters for the chromosome structure representation: frequency, power, bit error rate, and modulation scheme.…”

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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