Artificial Intelligence Driven Optimization of Channel and Location in Wireless Networks

In this work, we develop a framework that jointly decides on the optimal location of wireless extenders
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Wireless extenders are wireless devices used to extend the coverage area of a main access point; they are also called mesh points, since the main access point refers to a mesh gateway in a WMN. and the channel configuration of wireless extenders and access points (APs) in a Wireless Mesh Network (WMN). 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.