Reinforcement Learning for Antennas' Electric Tilts Optimization in Self Organizing Networks

Wireless networks are becoming more and more complex. New radio access technologies and cell densification have provided increased efficiency and met the needs of an increasing traffic demand. On the other hand, such increment in complexity poses new challenges to legacy network management and optimization solutions. Traditionally, network optimization has been performed by applying static, rule-based schemes. Both in academia and industry, there is a consensus about the need to transit from static optimization approaches to more automated, dynamic, cognitive techniques. In this paper we embrace this perspective and we propose a set of cognitive algorithms for network performance optimization. We leverage the electric antenna tilt to achieve improved network performance. The proposed algorithms rely on reinforcement learning principles and are validated in a simulated environment.