A modified hopfield network for mobility management

Taheri, Javid; Zomaya, Albert Y.

doi:10.1002/wcm.582

Cited by 17 publications

(3 citation statements)

References 25 publications

(42 reference statements)

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“…Though the algorithm in not meta-heuristic but for the performance evaluation of the algorithm the results were compared with 8,13,14,29,32,33 which are all meta-heuristic algorithm based manuscripts.…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence‐based novel scheme for location area planning in cellular networks

Vimal

Singh

Qamar

et al. 2020

Computational Intelligence

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Planning of location area (LA) in cellular networks plays a vital role in utilizing the resources efficiently and economically. Location update and paging costs directly affect the cost to the service provider. When the size of the Mobile Switching Centre (MSC) is large, the paging cost becomes maximum. Though, no location update is required for this case. On the contrary, paging cost shoots high if each cell forms individual LA, but in this case, location update cost shoots to the maximum value. Therefore, deducing the network to the optimal amount of LA's is an intractable combinatorial optimization problem. In this paper, we intend to optimize the partitions of the MSC service area into an optimal number of LA, to curtail the total location management cost. We propose a novel scheme bearing in mind cell attributes of the network to deduce an optimum number of location areas, leading to minimum cost per call.It is revealed by the results of this work, attained after simulating the scenario on MATLAB 2018a that Cell Attributes Based Algorithm is a very promising scheme for cellular networks, and it outperforms the existing algorithms in a practical scenario.

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Section: Resultsmentioning

confidence: 99%

Artificial intelligence‐based novel scheme for location area planning in cellular networks

Vimal

Singh

Qamar

et al. 2020

Computational Intelligence

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“…(2017a,b). The Hopfield centric results of Taheri and Zomaya (2007b) were found to be superior as compared to that of GA. Hybrid swarm intelligence algorithms are formulated in Chaurasia and Singh (2015) to approach the network management issue.…”

Section: Introductionmentioning

confidence: 94%

Reporting cell planning-based cellular mobility management using a Binary Artificial Bat algorithm

Swayamsiddha

Prateek

Singh

et al. 2019

Heliyon

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This paper attempts to present a novel application of Binary Artificial Bat algorithm for more effective location management in cellular networks. The location management is a mobility management task, which involves tracking of the mobile stations to locate their exact positions so that an incoming call or data can be routed to the intended mobile user. The location management cost comprises of the costs incurred by two processes, namely location registration and location search. This work focuses on network cost optimization, using Binary Artificial Bat algorithm for reporting cell planning strategy, which has not been reported yet. Results of the proposed algorithm have been compared with that of Binary Particle Swarm Optimization (BPSO) and Binary Differential Evolution (BDE) for some reference and realistic networks. The proposed approach is found to perform as good as other state-of-art techniques reported in the literature in terms of accuracy in solution, but it shows perceptible improvement in convergence speed.

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“…A combined genetic-neural and Hybrid GA-PSO algorithms reports optimal mobility management [18,19]. The location management is also addressed using simulated annealing and modified Hopfield approach [20,21]. A hybrid swarm intelligence approach involving artificial bee colony is proposed for registration area planning problem [22].…”

Section: Introductionmentioning

confidence: 99%

Optimal Reporting Cell Planning with Binary Differential Evolution Algorithm for Location Management Problem

Swayamsiddha¹,

Parija²,

Sahu³

et al. 2017

IJISA

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Abstract-This paper presents binary differential evolution based optimal reporting cell planning (RCP) for location management in wireless cellular networks. The significance of mobile location management (MLM) in wireless communication has evolved drastically due to tremendous rise in the number of mobile users with the constraint of limited bandwidth. The total location management cost involves signaling cost due to location registration and location search and a trade-off between these two gives optimal location management cost. The proposed binary differential evolution (BDE) algorithm is used to determine the optimal reporting cell planning configuration such that the overall mobility management cost is minimized. Evidently, from the simulation result the proposed technique works well for the reference networks in terms of optimal cost and convergence speed. Further the applicability of the BDE is also validated for the realistic network of BSNL (Bharat Sanchar Nigam Limited), Odisha.

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A modified hopfield network for mobility management

Cited by 17 publications

References 25 publications

Artificial intelligence‐based novel scheme for location area planning in cellular networks

Artificial intelligence‐based novel scheme for location area planning in cellular networks

Reporting cell planning-based cellular mobility management using a Binary Artificial Bat algorithm

Optimal Reporting Cell Planning with Binary Differential Evolution Algorithm for Location Management Problem

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