Abstract
In this paper, we propose a self-deployment strategy for non-stationary wireless extenders, where both back-haul and front-haul links are optimized. We present an artificial intelligence (AI) case based reasoning (CBR) framework that enables self-deployment with learning the environment by means of sensing and perception. New actions, i.e., extender positions, are created by problem-specific optimization and semi-supervised learning that balance exploration and exploitation of the search space. An IEEE 802.11 standard compliant simulations are performed to evaluate the framework on a large scale and compare its performance against existing conventional coverage maximization approaches. Experimental evaluation is also performed in an enterprise environment to demonstrate the competence of the proposed AI-framework.
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