Base Station Placement for Dynamic Traffic Load Using Evolutionary Algorithms

  • Authors:

  • N. Lakshminarasimman;S. Baskar;A. Alphones;M. Willjuice Iruthayarajan

  • Affiliations:

  • Department of Computer Science and Engineering, K.L.N. College of Engineering, Madurai, India;Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai, India 625015;Division of Communication Engineering, Office: S2.2-B2-19, School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang, Singapore;Department of Electrical and Electronic Engineering, National Engineering College, Kovilpatti, India

  • Venue:
  • Wireless Personal Communications: An International Journal
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

In this paper, dynamic traffic load is considered to determine optimal location of base station (BS) using evolutionary optimization algorithms. The various parameters such as site coordinates (x, y), transmitting power, height and tilt are taken as design parameters for BS placement. Coverage maximization and cost minimization are considered as two conflicting objectives with inequality constraints such as handover, traffic demand and overlap. RGA and MNSGA-II algorithms are used to solve single objective and multiobjective BS placement problem respectively. A $$2 \times 2\, \text{ km}^{2}$$ synthetic test system is discretized as hexagonal cell structure for simulation purposes. Receiving field strength for all service testing points is calculated using simulations and path loss is calculated using Hata model. In dynamic traffic model, both vehicle and pedestrian movements in up and side directions are considered. Dynamic movement is achieved by randomly moving vehicles and pedestrians for a fixed speed in each sample time. The results show that the RGA is able to determine the optimal BS location after considering the dynamic traffic load and satisfying inequality constraints for both coverage maximization and cost objectives. MNSGA-II algorithm gives well distributed pareto-front for the multiobjective BS placement in single simulation run. The simulation results reveal that the proposed dynamic traffic model is suitable for the real world BS placement problem.