Learning on the Fly: An RNN-Based Online Throughput Prediction Framework for UAV Communications

Abstract

This paper presents learning on the fly (LoF), a two-stage online framework to predict the achievable application-layer throughput in the downlink data communication from an unmanned aerial vehicle (UAV) to a ground access point. LoF is based on a recurrent neural network (RNN). While the UAV is flying, LoF trains the RNN with constantly observed throughput and in the meantime, makes throughput predictions for the near future. Both the training and prediction can concurrently run on a non-GPU device at the network edge (e.g., on the UAV). To this end, we design LoF with a lightweight RNN architecture and a customized training process by weighted sampling on a sliding window. We implement LoF using PyTorch. Numerical results show that LoF is able to achieve an average prediction accuracy of 87.65%, outperforming existing approaches in the literature.