In this paper, we propose a self-deployment approach for finding the optimal placement of extenders in which both the wireless back-haul and front-haul throughput of the extender are optimized. We present an artificial intelligence (AI) case based reasoning (CBR) framework that enables autonomous self-deployment in which the network can learn the environment by means of sensing and perception. New actions, i.e. extender positions, are created by problem-specific optimization and semi-supervised learning algorithms 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 in perceiving such a dense scenario and reason the extender deployment that achieves user quality of service (QoS). Throughput fairness and ubiquitous QoS satisfaction are achieved which provide a leap to apply AI-driven self-deployment in wireless networks.
@article{arxiv.1805.06217,
title = {Artificial Intelligence Inspired Self-Deployment of Wireless Networks},
author = {Erma Perenda and Ramy Atawia and Haris Gacanin},
journal= {arXiv preprint arXiv:1805.06217},
year = {2018}
}