Evolutionary algorithms in high-dimensional radio access network optimization

This article describes the project result of modeling and optimizing Radio Access Network. We have proposed a solution for controlling a large number of antennas in the conditions of engineering constraints and a large search space dimension. For estimating the performance, a virtual environment has been developed, that allows changing the parameters of Radio Access antennas to control the coverage and signal quality for all User Equipments. To optimize the Radio Access network, we have analyzed DE, CMA-ES, MOS, self-adaptive surrogate CMA-ES, lq-CMA-ES, BIPOP CMA-ES, sep-CMA-ES, lm-CMA-ES, HMO-CMA-ES, JADE, PSO, which have been adapted to the constraints of the task. To reduce dimension, graph clustering methods - Spectral clustering, Label propagation, Markov Clustering - are compared in dividing the network into groups. The experiments illustrate the efficiency of optimizing a large Radio Access network by the cluster approach.