Distributed self optimization techniques for heterogeneous network environments using active antenna tilt systems

Abstract

Active antenna systems in 4G and upcoming 5G networks offer the ability to electronically steer an antenna beam in any desired direction. This unique feature makes them a suitable candidate for realizing self organizing network (SON) architectures in 5G for optimizing of key performance indicators like throughput, file transfer time etc. In this paper, we aim to analyse the effect of increasing number of input variables and complexity of learning techniques on the performance of the network. We compare performance of simple stochastic cellular learning automata (SCLA) technique with only one input to comparatively complex Q-learning technique with two or more inputs. We use FTP flow based 5G network simulator for our work. The SON architecture model proposed, is distributed with optional inter cell communication. Simulation results reveal that increasing complexity of learning process does not necessarily benefit the system performance. The simple SCLA technique shows more robust performance compared to Q-learning case. However, within the same technique increasing the number of input variables does benefit the system, indicating that a complex technique can ultimately prove beneficial in complicated scenarios provided it is able to quickly process and adapt to the environment.