New Approaches in Cognitive Radios using Evolutionary Algorithms

Tuberquia, Miguel; Hernández, César

doi:10.11591/ijece.v8i3.pp1636-1646

Cited by 7 publications

(5 citation statements)

References 48 publications

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“…The unpredictability of wireless signals [2][3][4][5][6][7][8][9][10] motivates the sensing algorithms to develop more methods to analyze different signal types. This is because the wireless signals often contain unknown parameters; such as: amplitude, phase, time delay, and frequency.…”

Section: Introductionmentioning

confidence: 99%

Verification analysis for the reliable analytical multi-taper detector in next generation network

2020

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Multi-taper method (MTM) acts as an effective detector of spectrum sensing in Cognitive radio networks. In this paper, an analytical study was proposed in which reliable, simple, and computationally efficient mathematical terms for the mean and variance of the probability density function (PDF) were derived using the MTM technique. The closed-form expressions for the probability of detection and false alarm for the MTM detector were obtained accordingly. The proposed analytical study was evaluated by intensive simulations using MATLAB. Different simulation techniques were proposed to verify the derived analysis. The existence of White Gaussian Noise was assumed. Important aspects of spectrum detection in cognitive radio networks were included such as, receiver operating characteristics, detection rate versus signal to noise ratio (SNR), and the minimum desired sample points for a specific performance. A comparison was completed with the energy detection technique and all of the results suggested that the proposed paradigm is both credible and powerful under all the parameters considered in the simulation.

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Section: Introductionmentioning

confidence: 99%

Verification analysis for the reliable analytical multi-taper detector in next generation network

2020

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“…The authors in [10] proposed a new algorithm for handoff optimization in cognitive radio networks by segregating the WRAN into multiple cells. An algorithm for making decision based on multiple criteria was proposed in [11] for channel selection and spectrum decision function. The concept of optimization in cognitive radio with cuckoo search was introduced in [12] which focus on efficient spectrum sensing technique.…”

Section: Related Workmentioning

confidence: 99%

Ingenious Method for Conducive Handoff Appliance in Cognitive Radio Networks

Dhivya

Ramaswami

2018

IJECE

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Wireless communications deployed in the current epoch claims ceaseless connection among its users thereby leading to the investigation of Cognitive Radio Networks (CRN) which enables to make use of unallocated spectrum optimally and provides uninterrupted connection. Establishing interminable connectivity during the handoff process in spectrum mobility of CRN is a challenging task. This paper elucidates the optimization of handoff process carried out in CRN by incorporating an intelligent method. This includes fuzzy logic wherein the handoff parameters are processed thereby indicating the need of handoff. The proffered method also comprises of a part of genetic algorithm which yields fitness value for reducing the handoff occurrences and enhancing the overall performance of the system is promoted using cuckoo search which decides the mobile node from which the handoff process has to initiate based on the priority generated. This technique ensures that decision is taken ahead of link failure rather than range failure which are the key point in comparison to the existing system. Results obtained through the simulation are satisfactory in terms of delay, throughput, number of failed handoff and handoffs performed in comparison to the existing fuzzy based handoff process in CRN.

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“…10% of the fastest supercomputers in the world [7]. This paper focuses on GA in the ad hoc network, GA is efficient for our problem, based on principles of natural selection [8], they are being applied successfully to find acceptable solutions to problems in business, engineering, and science [4]. Our challenge is to change the design of meta-heuristics GA with parallelism, to take advantage of GPU for solving large-scale complex problems in ad hoc network with a view to high effectiveness and efficiency in a short time.…”

Section: Introductionmentioning

confidence: 99%

Parallel genetic approach for routing optimization in large ad hoc networks

Khankhour

Abdoun

Abouchabaka

2022

IJECE

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<span>This article presents a new approach of integrating parallelism into the genetic algorithm (GA), to solve the problem of routing in a large ad hoc network, the goal is to find the shortest path routing. Firstly, we fix the source and destination, and we use the variable-length chromosomes (routes) and their genes (nodes), in our work we have answered the following question: what is the better solution to find the shortest path: the sequential or parallel method?. All modern systems support simultaneous processes and threads, processes are instances of programs that generally run independently, for example, if you start a program, the operating system spawns a new process that runs parallel elements to other programs, within these processes, we can use threads to execute code simultaneously. Therefore, we can make the most of the available central processing unit (CPU) cores. Furthermore, the obtained results showed that our algorithm gives a much better quality of solutions. Thereafter, we propose an example of a network with 40 nodes, to study the difference between the sequential and parallel methods, then we increased the number of sensors to 100 nodes, to solve the problem of the shortest path in a large ad hoc network.</span>

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New Approaches in Cognitive Radios using Evolutionary Algorithms

Cited by 7 publications

References 48 publications

Verification analysis for the reliable analytical multi-taper detector in next generation network

Verification analysis for the reliable analytical multi-taper detector in next generation network

Ingenious Method for Conducive Handoff Appliance in Cognitive Radio Networks

Parallel genetic approach for routing optimization in large ad hoc networks

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