Efficient rate adaptation for 802.11af TVWS vehicular access via deep learning

Enabling connected vehicles is becoming an essential demand for modern mobility world. To envision the Internet of Vehicles (IoVs) and better support the transmission of the explosive vehicular data, the Ultra High Frequency(UHF) spectrum resource in TV White Space (TVWS) band is re-utilized to provide cognitively wireless access for vehicle users. The TVWS access can provide high throughput and wide coverage due to its wide bandwidth and penetrability. However, there remains a critical issue for vehicles to dynamically change the link rate for the egress frames to adapt to the channel variance. In this paper, we investigate deep learning based rate adaptation (RA) scheme for the vehicle users accessing to the TVWS band. We utilize a series of the recent Signal-to-Noise (SNR) records, and modeled the RA as a Time Series Classification (TSC) problem, which is solved by Deep Learning (DL) models to classifies the collected SNR series to the optimal rate selection for the frame to be transmitted. We compared three different DL models and show that the performance of the DL based RAs outperform conventional RAs. Such results could provide insightful guidance for applying machine learning in the RA problem for wireless vehicular access.