Resource allocation for cognitive radio network using particle swarm optimization

Behera, Subhrakanta; Seth, Dambarudhar

doi:10.1109/ecs.2015.7124992

Cited by 9 publications

(5 citation statements)

References 4 publications

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“…PSO is used to optimize power in [26] and spectrum allocation in [27][28][29]. Our proposed algorithm in this paper has the objective of maximizing the SINR for all secondary users (SUs).…”

Section: E Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Improved Resource Allocation Model for Reducing Interference Among Secondary Users in TV White Space for Broadband Services

et al. 2022

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In recent years, the Television White Space has attracted the interest of many researchers due to its propagation characteristics obtainable between 470MHz and 790MHz spectrum bands. However, aggregate interference increase when secondary users in the wireless network increase. The aggregate interference on the side of Primary Users has been extensively scrutinized. Therefore, resource allocation (power and spectrum) is crucial when designing the Television White Space network to avoid interferences from Secondary Users to Primary Users and among Secondary Users themselves. This paper proposes a resource allocation model that uses joint power and spectrum hybrid Particle Swarm Optimization, Firefly, and Genetic algorithm for reducing the aggregate interference among Secondary Users. The algorithm is integrated with the admission control algorithm so that; there is a possibility to remove some of the Secondary Users in the network whenever the Signal to Noise Interference Ratio threshold for Secondary and Primary Users is not met. We considered the infeasible system whereby all Secondary and Primary Users may not be supported simultaneously. Metrics such as Primary User Signal-to-noise ratio, sum throughput, and secondary user signal-to-noise ratio less than the threshold used to compare the performance of the proposed algorithm and the results show that the PSOFAGA with effective link gain ratio admission control has the best performance compared to particle swarm optimization, genetic algorithm, firefly algorithm, and PSOFAGA algorithm.

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“…PSO is used to optimize power in [26] and spectrum allocation in [27][28][29]. Our proposed algorithm in this paper has the objective of maximizing the SINR for all secondary users (SUs).…”

Section: E Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Improved Resource Allocation Model for Reducing Interference Among Secondary Users in TV White Space for Broadband Services

et al. 2022

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“…PSI algorithms are scalable, fault tolerant and moreover, they adapt to changes in real time. There are quite a few efforts [72][73][74][75] are found in the literature that utilizes PSI algorithms to solve RA problems.…”

Section: Particle Swarm Intelligence Algorithmsmentioning

confidence: 99%

Resource allocation strategies in cognitive radio D2D communication networks

Sultana¹

2021

Preprint

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Device-to-device (D2D) communication is developed as a new paradigm to enhance net- work performance according to LTE and WiMAX advanced standards. On the other hand, cognitive radio (CR) approach provides efficient spectral usage using intelligent wireless nodes. In this thesis, a number of optimal resource allocation strategies for D2D communi- cation networks are investigated using the CR approach. As a first step, the CR approach in radio access networks is introduced. In the second step, the taxonomy of the RA process in CRNs is provided. For radio resource allocation (RRA), the most crucial task is to associate a user with a particular serving base station, to assign the channel and to allocate the power efficiently. In this thesis, a subcarrier assignment scheme and a power allocation algorithm using geometric water-filling (GWF) is presented for orthogonal frequency division multiplexing (OFDM) based CRNs. This algorithm is proved to maximize the sum rate of secondary users by allocating power more efficiently. Then, the RA problem is studied to jointly employ CR technology and D2D communication in cellular networks in terms of spectral efficiency (SE) and energy efficiency (EE). In the first case, in terms of SE, a two-stage approach is considered to allocate the radio resource efficiently where a new adaptive subcarrier allocation (ASA) scheme is designed first and then a novel power allocation (PA) scheme is developed utilizing proven GWF approach that can compute exact solution with less computation. In the second case, in terms of EE, the power allocation problem of cellular networks that co-exist with D2D communication considering both underlay and overlay CR approaches are investigated. A proven power allocation algorithm based on GWF approach is utilized to solve the EE maximization problem which results in an “exact" and “low complexity" solution.

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“…[37] et al integrated auxiliary schemenonlinear one-leader-multiple follower (OLMF) and nonlinear bilevel OLMF with PSO in optimizing pricing during the spectrum sharing process. [32] et al presented PSO to optimize parameters such as signal-to-noise ratio, spectral efficiency and power consumption in a dynamic CAP. The authors mentioned that higher number of particle and iteration will improve the overall fitness.…”

Section: Related Workmentioning

confidence: 99%

“…Many optimization methods had been proposed in the past for interference minimization: ant colony optimization (ACO) [25], genetic algorithm (GA) [1,[26][27][28][29], particle swarm optimization (PSO) [1,[30][31][32], tabu search (TS) [33][34][35] et cetera. Evolutionary computing algorithm such as GA is said to be effective for CAP and is more computationalfriendlier [27,36].…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithms and Particle Swarm Optimization for Interference Minimization in Mobile Network Channel Assignment Problem

Keek¹,

Loh²,

Wong³

et al. 2021

IJIES

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Interference minimization in cellular network has and will always be top priority, whether in current or future generation of cellular technology. Therefore, cellular channel assignment problem (CAP) requires continuous study and research. This paper presents the study and comparison of Genetic Algorithm (GA) and Particle Swam Optimization (PSO) for CAP in minimizing interference. GA with three variants in term of population selectionroulette wheel selection (RWS), tournament selection (TS) and stochastic universal sampling (SUS) were studied, and then compared with classic PSO. Two CAPs were derived and used to comprehensively evaluate the performances of the PSO and GAs. It was found that GA-TS is ~11% and ~7% faster than GA-RWS and GA-SUS, respectively. Although the difference is small, but it allowed GA-TS to run for few more iterations and eventually achieved better interference minimization. Moreover, it was also found that GA-SUS has less noise and produce a more consistent result. On the other hand, PSO is slower than GA-TS, but has higher potential to converge on smaller minimum value.

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Resource allocation for cognitive radio network using particle swarm optimization

Cited by 9 publications

References 4 publications

Improved Resource Allocation Model for Reducing Interference Among Secondary Users in TV White Space for Broadband Services

Improved Resource Allocation Model for Reducing Interference Among Secondary Users in TV White Space for Broadband Services

Resource allocation strategies in cognitive radio D2D communication networks

Genetic Algorithms and Particle Swarm Optimization for Interference Minimization in Mobile Network Channel Assignment Problem

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