Channel assignment using differential evolution algorithm in cognitive radio networks

Latif, et al.

doi:10.21833/ijaas.2017.08.023

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…Differential Evolution approaches include Da Silva Maximiano et al, who assign frequencies to base stations in Global System for Mobile Communications (GSM) using DE for minimising interference [50], [51]. Differential Evolution is also used for CA in DSA CR networks by Latif et al [52] and Anumandla et al [53]. In Latif's work, the objectives considered are fairness and utility, while interference with PUs and other SUs is considered only as a constraint.…”

Section: Related Work: Metaheuristic Algorithms For Ca In Dsa Wmnsmentioning

confidence: 99%

Comparison of Metaheuristic Algorithms for Interface-Constrained Channel Assignment in a Hybrid Dynamic Spectrum Access – Wi-Fi Infrastructure WMN

et al. 2022

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In this work, we evaluate the application of four different metaheuristic optimisation algorithms for solving the channel assignment problem in a multi-radio multi-channel Wireless Mesh Network (WMN) using Dynamic Spectrum Access (DSA). The work advances a near optimal channel assignment in a WMN that uses DSA by applying soft computing methods. While CA in a WMN is wellstudied, and CA for secondary user cognitive radio networks has also been studied in the literature, CA for our specific scenario of an infrastructure DSA-WMN is novel. This scenario poses new challenges because nodes are spread out geographically and so may have different allowed channels and experience different levels of external interference in different channels. A solution must meet two conflicting requirements simultaneously: 1) to avoid interference within the network and with external interference sources, and 2) maintain connectivity within the network; all while staying within the radio interface constraint, i.e., only assigning as many channels to a node as it has radios. Our method is unique in that it is protocol-agnostic, being able to avoid interference from external sources that use different protocols and standards. We present a novel algorithm, used alongside the metaheuristic optimisation algorithms, which ensures the feasibility of solutions in the search space. Average Signal to Interference and Noise Ratio (SIN R) over the network is used as the performance measure, with the goal of optimisation being to find the highest average SIN R. This is a more realistic performance measure than the binary on/off conflict-based measures most common in the literature. Our energy-based method also has the unique advantage that it is protocol-agnostic, being able to avoid interference from external sources that use different protocols and standards. The algorithms that are compared in this work are Simulated Annealing (SA), the Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), and Differential Evolution (DE). These algorithms were evaluated through the use of simulation in Network Simulator 3. Various parameters were experimented with for each of the employed algorithms. The resultant best set of parameters was used for the comparison of the four metaheuristic algorithms. It was found that the population-based algorithms (PSO, GA, and DE) all perform satisfactorily for this problem, although DE is superior to the others. SA can give acceptable solutions, but performs poorly in comparison to the population-based algorithms. The paper also considers the computational complexity of the methods. It is found that SA and DE perform well in this regard.

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Section: Related Work: Metaheuristic Algorithms For Ca In Dsa Wmnsmentioning

confidence: 99%

Comparison of Metaheuristic Algorithms for Interface-Constrained Channel Assignment in a Hybrid Dynamic Spectrum Access – Wi-Fi Infrastructure WMN

et al. 2022

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“…To reduce the computational complexity of optimization approaches, heuristic algorithms are gaining the attention of the researchers. Heuristic approaches are easy to implement and flexible for NP complete problems [17]. Joint optimization of EE and spectral efficiency are considered as non-convex and NP hard problem in CR-IoT systems [18].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Pareto Optimal Resource Allocation Scheme in Cognitive Radio-Based Internet of Things Networks

Latif

Akraam

Karamat

et al. 2022

Sensors

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The high data rates detail that internet-connected devices have been increasing exponentially. Cognitive radio (CR) is an auspicious technology used to address the resource shortage issue in wireless IoT networks. Resource optimization is considered a non-convex and nondeterministic polynomial (NP) complete problem within CR-based Internet of Things (IoT) networks (CR-IoT). Moreover, the combined optimization of conflicting objectives is a challenging issue in CR-IoT networks. In this paper, energy efficiency (EE) and spectral efficiency (SE) are considered as conflicting optimization objectives. This research work proposed a hybrid tabu search-based stimulated algorithm (HTSA) in order to achieve Pareto optimality between EE and SE. In addition, the fuzzy-based decision is employed to achieve better Pareto optimality. The performance of the proposed HTSA approach is analyzed using different resource allocation parameters and validated through simulation results.

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“…Centralized techniques for spectrum access typically involve running optimization algorithms to reach the desired channel allocation strategy. Different approaches have been proposed to solve such an optimization problem, such as particle swarm optimization [18,19], ant colony optimization [20], genetic and evolutionary algorithms [12,21,22], fuzzy logic [23], and neural networks [24]. However, centralized optimization techniques suffer from some disadvantages, such as a single point-of-failure, extra implementation complexity, sensitivity to node or link failure, and the need to preestablish a proper CCC between the cognitive BSs and the controller.…”

Section: Introductionmentioning

confidence: 99%

Sensing Device Management for History‐Based Spectrum Sharing in Cognitive Radio Networks

Hawa

Jubair

Al‐Zubi

et al. 2018

Wireless Communications and Mobile Computing

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A novel approach to managing a fully distributed cognitive radio network (CRN) is presented. This approach builds on the concept of history-based spectrum access, in which cognitive base stations (BSs) independently estimate the system load using history records and adaptively swap their occupied spectrum bands to ensure allocation fairness and high overall throughput. In addition, cognitive BSs monitor primary user (PU) behavior in order to avoid interfering with active PUs. In this work, we address two issues that afflict history-based access: the first is the high cost of the sensing devices needed at each cognitive BS to be able to independently draw conclusions about the status of the CRN and the second is the unreliability inherent in practical sensing hardware (such as energy detectors). Simulation results show that the proposed technique manages to solve the two abovementioned issues without any noticeable drop in performance and without sacrificing the distributed nature of the protocol.

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Channel assignment using differential evolution algorithm in cognitive radio networks

Cited by 6 publications

References 13 publications

Comparison of Metaheuristic Algorithms for Interface-Constrained Channel Assignment in a Hybrid Dynamic Spectrum Access – Wi-Fi Infrastructure WMN

Comparison of Metaheuristic Algorithms for Interface-Constrained Channel Assignment in a Hybrid Dynamic Spectrum Access – Wi-Fi Infrastructure WMN

An Efficient Pareto Optimal Resource Allocation Scheme in Cognitive Radio-Based Internet of Things Networks

Sensing Device Management for History‐Based Spectrum Sharing in Cognitive Radio Networks

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