Self-X Design of Wireless Networks: Exploiting Artificial Intelligence and Guided Learning

TL;DR

This paper introduces a Self-X AI-driven framework for autonomous optimization of wireless mesh networks, jointly optimizing extender placement and channel configuration to improve throughput and resilience.

Contribution

It presents a novel Self-X framework that combines environment sensing and intelligent learning to achieve near-optimal network configuration in NP-hard problems.

Findings

Fast convergence demonstrated in simulations

High throughput achieved in dynamic conditions

Framework deployed on real devices for autonomous operation

Abstract

In this work, we develop a framework that jointly decides on the optimal location of wireless extenders and the channel configuration of extenders and access points (APs) in a Wireless Mesh Network (WMN). Typically, the rule-based approaches in the literature result in limited exploration while reinforcement learning based approaches result in slow convergence. Therefore, 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. Domain experience was provided to guide the agent while exploring and exploiting the set of possible actions in the environment. Thus, it guarantees a low-cost learning and achieves a near-optimal network configuration addressing the non-deterministic polynomial-time hardness (NP-hard) problem of joint channel assignment and location optimization in WMNs. Extensive simulations are run to validate its fast convergence, high throughput and resilience to dynamic interference conditions. We deploy the framework on off-the-shelf wireless devices to enable autonomous self-optimization and self-deployment, using APs and wireless extenders.