Artificial Intelligence with Guided Learning for Self-optimization of Wireless Networks

In this work, we develop a framework that jointly decides on the optimal location of wireless extenders and the channel configuration of wireless extenders and access points (APs). Artificial Intelligence (AI) is adopted to support network autonomy and to capture insights on system and environment evolution. We propose a Self-X (self-optimizing and self-learning) framework that encapsulates both environment and intelligent agent to reach optimal operation through sensing, perception, reasoning and learning in a truly autonomous fashion. The agent derives adequate knowledge from previous actions, improving the quality of future decisions. Extensive simulations are run to validate its fast convergence, improved throughput and resilience to dynamic interference conditions.