ESP: a new standard cell placement package using simulated evolution
DAC '87 Proceedings of the 24th ACM/IEEE Design Automation Conference
Optimum positioning of base stations for cellular radio networks
Wireless Networks
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
A Heuristic Approach for Antenna Positioning in Cellular Networks
Journal of Heuristics
ENCON: an evolutionary algorithm for the antenna placement problem
Computers and Industrial Engineering - Special issue: Focussed issue on applied meta-heuristics
A comparison of randomized and evolutionary approaches for optimizing base station site selection
Proceedings of the 2004 ACM symposium on Applied computing
On the optimality of facility location for wireless transmission infrastructure
Computers and Industrial Engineering
Comparison and evaluation of multiple objective genetic algorithms for the antenna placement problem
Mobile Networks and Applications
A permutation-coded evolutionary strategy for multi-objective GSM network planning
Journal of Heuristics
Dynamic Crowding Distance?A New Diversity Maintenance Strategy for MOEAs
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 01
The infrastructure efficiency of cellular wireless networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Differential evolution algorithm with strategy adaptation for global numerical optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Base Station Placement for Dynamic Traffic Load Using Evolutionary Algorithms
Wireless Personal Communications: An International Journal
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In this paper, various parameters of cellular base station (BS) placement problem such as site coordinates, transmitting power, height and tilt angle are determined using evolutionary multiobjective algorithm to obtain better compromised solutions. The maximization of service coverage and minimization of cost are considered as conflicting objectives by satisfying inequality constraints such as handover, traffic demand and overlap. For the purpose of simulation, a 15 脳 15 Km2 synthetic test system is discretized as hexagonal cell structure and necessary simulations are carried out to calculate receiving field strength at various points. The path loss is calculated using Hata model. To improve the diversity and uniformity of the obtained nondominated solutions, controlled elitism and dynamic crowding distance operators are introduced in non-dominated sorting genetic algorithm-II (NSGA-II) and are designated as modified NSGA-II (MNSGA-II). The optimal placement for BS is determined using MNSGA-II and NSGA-II. The effect of maximum number of function evaluations, handover and overlap on the performances of the algorithms is studied. A better distributed Pareto-front is obtained in MNSGA- II when compared with NSGA- II. The results reveal that, increasing of overlap percentage not only increases the coverage but also increases the overlap and handover error. The coverage percentage is indirectly proportional to the number of antennas involved in the handover constraint. The simulation results reveal that the proposed technique is more suitable for real-world BS placement problem.