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

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 functionalities that are essential for the wireless ecosystem, such as resource allocation, channel modeling, or network planning. This article explores how data-driven propagation models can be leveraged for the automated and expedient deployment of small cells in indoor environments. In particular, a generalizable data-driven propagation model is entwined with AI-based optimizers aiming at determining the optimal network topology with respect to some target key performance indicators. The data-driven model combines the accuracy of a high-performance propagation solver with the computational efficiency of deep neural networks, inferring the signal level spatial distribution based on physics-based information of the indoor geometry. The capability of the data-driven model to conduct multiple simulations within a few seconds is vital for the optimization procedure, enabling accurate network planning that would be extremely expensive to produce with a conventional indoor propagation tool, especially for ultra-dense networks where macro-base stations coexist with numerous small cells.