Artificial Intelligence Inspired Self-Deployment of Wireless Networks

TL;DR

This paper introduces an AI-based self-deployment framework for wireless extenders that autonomously optimizes placement to enhance throughput and QoS, demonstrated through simulations and real-world experiments.

Contribution

The paper presents a novel AI case-based reasoning framework enabling autonomous, environment-aware deployment of wireless extenders for improved network performance.

Findings

Achieves throughput fairness and QoS satisfaction in dense scenarios

Outperforms conventional coverage maximization approaches

Demonstrates effectiveness in enterprise environments

Abstract

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.