Abstract
When designing modern cellular networks, it is challenging to account for many contradictory criteria and constantly changing external conditions of the networks (e.g., traffic). We need to solve multicriteria problems with high-dimensional vectors of parameters. A prerequisite to solution of these problems is correct determination of the feasible solution set, which is directly related to the statement of optimization problem. This is a major challenge in all multicriteria engineering optimization problems and represents significant difficulties for the expert. In this paper, we show how to define the feasible solution set for cellular network optimal design problems and thus answer the fundamental question of where to search for optimal solutions in such problems. We use the Parameter Space Investigation (PSI) method implemented in the Multicriteria Optimization and Vector Identification (MOVI) software system and apply it to a mathematical model of cellular network. In addition to developing methodology for stating and solving the problem of multicriteria optimization of cellular network, we have found that 1) defining the feasible solution set is directly related to the correct statement of the optimization problem, 2) once the feasible solution set has been determined, the criteria convolution can be applied to find the optimal solution in the feasible solution set, 3) it is possible to perform online tuning of the cellular network parameters.
Highlights
One of the distinguishing characteristics of cellular networks is difficulty of their design given a large number of contradictory criteria and constantly changing external conditions of the networks
We show how to define the feasible solution set for cellular network optimal design problems and answer the fundamental question of where to search for optimal solutions in such problems
We use the Parameter Space Investigation (PSI) method implemented in the Multicriteria Optimization and Vector Identification (MOVI) software system and apply it to a mathematical model of cellular network
Summary
One of the distinguishing characteristics of cellular networks is difficulty of their design given a large number of contradictory criteria and constantly changing external conditions of the networks (e.g., traffic). In addition to the difficulties in determining the feasible solution set, choice of the best solution from a Pareto set is non-trivial when this set contains a large number of solutions In some cases, these difficulties can be mitigated by applying criteria convolution as described below. We consider applying the Pareto Space Investigation (PSI) method as an attempt to improve existing techniques of network design and management by constructing and analyzing the feasible solution set. Jedidi et al [10] argue that it makes sense to consider cells overlap and cells geometry as criteria for real-life network optimization They aim at finding the whole Pareto front of this bi-criteria problem using a version of Multiobjective Evolutionary Algorithm. To the best of our knowledge, most commercial cellular network planning and optimization tools are based on some form of scalarization such as weighted sum [12,13]
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