ABC-PLOSS: a software tool for path-loss minimisation in GSM telecom networks using artificial bee colony algorithm

Abstract

In this paper, we present an open-source software tool 'ABC-PLOSS', which is developed for use in optimisation processes. Path-loss optimisation deals with searching for the best set of operator-specific parameters in telecommunication that gives the least cost of operation. It is a primary issue that challenges mobile communication operators, particularly the global system mobile (GSM) operators in tuning mobile-base station networks for efficient and reliable operation. The tool uses a sequential processor architecture based on a swarm intelligence algorithm called artificial bee colony (ABC) and the cost-231 Hata path-loss model as cost function for path-loss minimisation (PLM). Using the ABC-PLOSS framework, the ABC algorithm is compared with two other existing and popular artificial intelligent (AI) algorithms called the genetic algorithm (GA) and particle swarm optimisation (PSO). Results of simulation studies show that this tool is indeed useful as it gives a competitive or lower path-loss estimate when compared with conventional techniques. It also shows that it is possible for the ABC to attain an estimated seven-fold and twofold path-loss improvement over the GA and the PSO techniques respectively. 21 Harcourt, Rivers State, and PhD degree in Computer Science from the University of Port Harcourt, Rivers State. His research has focused on design systems using artificial/machine intelligence models-neural networks, fuzzy logic systems and optimisation systems with the interest in bridging the gap between theory and practice in these fields. Emmanuel Ndidi Osegi obtained his HND in Electrical/Electronic Engineering from the Rivers State Polytechnic (now Ken-Saro Wiwa Polytechnic) Bori Nigeria and is currently studying a postgraduate degree in Information Technology in the National Open University of Nigeria (NOUN). His specific research has focused on the use of software engineering tools, complex machine learning/intelligence frameworks to design and implement physical and analytic computing systems, reasoning and predictive systems, control and automatics including robotics and artificial life systems.