Measurement-based Evaluation of Uplink Throughput Prediction

Motivated by the teleoperation and local map sharing vehicular communication use cases, we investigate whether uplink throughput can be predicted by different machine learning approaches. First, we perform measurements of the vehicle to infrastructure (V2I) uplink throughput in Munich, Germany. Then, we use the collected measurements to evaluate whether linear regression (LR), deep neural network (DNN), and random forest (RF) can predict the uplink throughput. Our results show that, while very easy to train, LR is overly simple in describing the relationship of the input features and the predicted uplink throughput. On the other hand, DNN and RF can provide a very good prediction of uplink throughput (below 0.5 Mbps mean absolute error for a 40 Mbps uplink connection), while requiring longer training. Irrespective of the employed model, our results show that the best indicator of uplink throughput is signal to interference and noise ratio (SINR). When location information is added to SINR, the prediction error can be further reduced, albeit slightly. In the absence of SINR, location information is the second best in predicting uplink throughput. However, it can be employed only for locations that were available in the training dataset. On the other hand, SINR allows for generalization to locations different to those observed in the training dataset.