Differential evolution for solving the mobile location management

Almeida-Luz, Sónia M.; Vega‐Rodríguez, Miguel A.; Gómez–Pulido, Juan A.; Sánchez‐Pérez, Juan M.

doi:10.1016/j.asoc.2009.11.031

Cited by 35 publications

(33 citation statements)

References 6 publications

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“…Considering now the same VLR administered area but with lower number of cells per location area (9,7,6), so that the number of location areas increases to 6, the results obtained are very similar: β 1 =41/(41+24)=0.63 and β 2 =24/(41+24)=0.37. Now taking a VLR area composed of 7·7 hexagonal cells, with 5 location areas of 11, 10 and 9 cells, the results obtained are:…”

Section: Simple Algorithmsupporting

confidence: 66%

“…Special attention must be paid to the efficient use of the scarce radio resources. Within Mobility Management, Location Management enables the roaming of the user in the coverage area, with the main tasks involved being location update and paging [1][2][3][4][5][6]. The location update procedure consists of informing the network about every new location the mobile terminal enters, while paging is employed by the network to deliver incoming calls to the user.…”

Section: Introductionmentioning

confidence: 99%

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Resource Optimization in Mobile Communication Networks With User Profile-Based Algorithms

Martin¹

2012

IJCNC

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Section: Simple Algorithmsupporting

confidence: 66%

Section: Introductionmentioning

confidence: 99%

Resource Optimization in Mobile Communication Networks With User Profile-Based Algorithms

Martin¹

2012

IJCNC

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“…The experimental simulations are conducted taking different networks of size 4x4, 6x6 and 8x8. The optimal parameters that are used for BDE algorithm for solving RCP problem are: Population size= 175, C r = 0.15, F=0.5, b=20 [17]. We DE/best/1/exp variant is used for optimal results.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

“…The weight attributes for call arrival weight and location update weight for 6x6 cells configuration is shown in Table 2 which is available in [4,17]. The column 1 shows the cell number, the column 2 indicates the frequency of location updates in that particular cell and the column 3 corresponds to frequency of arrival of calls.…”

Section: Experiments 1-cost Analysis Using the Reference Data Availablmentioning

confidence: 99%

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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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An efficient tabu search for cell planning problem in mobile communication

Lin

Liao

Lin

et al. 2014

Wireless Communications

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During the past decades, mobile communication is in the vigorous development, where the cell planning problem (CPP) is one of impressive research issues. CPP has been proved to be NP-Complete, and many works develop intelligent heuristic search strategies to solve it. Among many factors to affect the cell planning, the major one is the signaling cost, where the location management is critical to the cost. In this paper, we focus on how to tackle CPP such that the signaling cost can be minimized. We adopt a meta-heuristic iterative search algorithm, Tabu Search (TS), to deal with the cell planning issue for the base station and propose novel designs to improve the TS capability, including initialization and neighbor swap strategy. The simulation results reveal that our TS outperforms traditional TS, genetic algorithms, and several previous works in CPP.

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Differential evolution for solving the mobile location management

Cited by 35 publications

References 6 publications

Resource Optimization in Mobile Communication Networks With User Profile-Based Algorithms

Resource Optimization in Mobile Communication Networks With User Profile-Based Algorithms

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

An efficient tabu search for cell planning problem in mobile communication

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