Generalizable Machine-Learning Based Modeling of Radiowave Propagation
in Stadiums
Abstract
Providing high throughput and quality of service in modern stadiums
necessitates the placement of hundreds of access points (APs).
Optimizing the locations of APs in such venues via measurements requires
significant resources. Even simulation methods, such as ray-tracing, can
be computationally costly. We provide a solution to this problem by
building a propagation model based on machine learning (ML) that rapidly
predicts received signal strengths in stadiums. We train the model with
a small set of simulated data generated by a ray-tracer. We use input
features, such as the electrical distance between the transmitter and
the receiver and the antenna gain along the direct path between the two,
to generalize to new transmitter locations, antenna patterns and stadium
geometries. Geometry and pattern generalization have not been included
in existing propagation models for stadiums. Finally, we present a novel
sampling approach for the input features in a given stadium, ensuring
the computational efficiency and accuracy of the ML model. The results
demonstrate the accuracy of our propagation model for new transmitter
locations, patterns and stadiums. The trained model is also considerably
faster than a ray-tracer, making it an efficient tool for resource
planning tasks, such as optimal placement of APs.