AI-assisted Indoor Wireless Network Planning with Data-Driven Propagation Models

Abstract

Propelled by rapid advances in artificial intelligence (AI), the design and operation of 5G and beyond networks are anticipated to be radically different from those of legacy communication systems. Indeed, AI can be exploited to automate and optimize various essential functionalities of the wireless ecosystem, such as resource allocation, channel modeling, or network planning. This article explores how AI-driven propagation models can be leveraged for the automated and expedient deployment of small cells in indoor environments. To this end, we couple a generalizable data-driven propagation model with an AI-based optimizer to determine the optimal network topology with respect to a target key performance indicator. Our approach reduces the computational time of indoor wireless network design by two to three orders of magnitude, thus enabling accurate planning that would be extremely expensive to conduct using conventional indoor propagation tools and yielding significant gains in the resulting indoor planning quality and performance.