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Location Optimization of WLAN Access Points Based on a Neural Network Model and Evolutionary Algorithms

In this article we intend to show the use of well-known evolutionary computation techniques - Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) - in an indoor propagation problem. Although these algorithms employ different strategies and computational efforts, they also share certain similarities. Their performance is compared with a genetic algorithm (GA), which is used as reference in this case. The ability of these algorithms to optimize access point locations using data derived from the neural network model of a particular Wireless Local Area Network (WLAN) is demonstrated. Better results are obtained by the PSO algorithm compared to the ACO algorithm. Although the ACO algorithm requires further work to optimize its parameters, improve the analysis of pheromone data and reduce computation time, the ant colony-based approach is useful for solving propagation problems.

Indoor propagation; complex indoor environment; signal strength prediction; WLAN; Neural network modelling; access point optimization; PSO; ACO.

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