Stochastic Evaluation of Indoor Wireless Network Performance with Data-Driven Propagation Models

Cell densification through the installation of smallcells and femtocells in indoor environments is an emerging solution to enhance the operation of wireless networks. The deployment of new components within the heart of the radio access network calls for expedient tools that assist and ensure their optimal placement within the existing network infrastructure. In this paper, we introduce metrics that can characterize indoor wireless network performance (IWNP) in terms of coverage and capacity, and we evaluate them via physics-based propagation models. In particular, we exploit a deterministic propagation model, i.e., a ray-tracer, as well as a novel machine learning-based propagation model. We demonstrate that data-driven propagation models can be leveraged for the rigorous evaluation of the IWNP metrics, yielding a remarkable computational efficiency compared to the conventional deterministic models. The use of physics-based site-specific propagation models allows for the particularities of each indoor geometry to be taken into account, and also makes feasible the consideration of uncertainties related to the indoor environment. In this case, the IWNP metrics are expressed as stochastic quantities and a stochastic solution is derived through an efficient polynomial chaos expansion representation, enabling on-the-fly computation of the IWNP metrics statistics.