Leveraging diverse propagation and context for multi-modal vehicular applications

Vehicular wireless channels have a high degree of variability, presenting a challenge for vehicles and infrastructure to remain connected. The emergence of the white space bands for data usage enables increased flexibility for vehicular networks with distinct propagation characteristics across frequency bands from 450 MHz to 6 GHz. Since wireless propagation largely depends on the environment in operation, a historical understanding of the frequency bands´ performance in a given environment could expedite band selection as vehicles transition across diverse scenarios. In this paper, we leverage knowledge of in-situ operation across frequency bands with real-time measurements of the activity level to select the the band with the highest throughput. To do so, we perform a number of experiments in typical vehicular topologies. With two models based on machine learning algorithms and an in-situ training set, we predict the throughput based on: (i.) prior performance for similar context information (e.g., SNR, GPS, relative speed, and link distance), and (ii.) real-time activity level and relative channel quality per band. In the field, we show that training on a repeatable route with these machine learning techniques can yield vast performance improvements from prior schemes.