Learning-Based Estimate-then-Predict Channel Tracking for Cellular-Connected UAV

Estimating air-to-ground (A2G) channel for a cellular-connected unmanned aerial vehicle (UAV) requires frequent pilot transmission due to its high mobility. To reduce the pilot overhead, researchers have attempted to predict future channels based on the historical ones by leveraging learning techniques. However, most existing works are limited to sequentially forecasting subsequent channels, suffering from the error accumulation problem that consequently hampers the prediction accuracy. To address this issue, this paper proposes a novel learning-based estimate-then-predict scheme for A2G channel tracking. In this scheme, the UAV transmits limited pilots, and the base station (BS) first performs channel estimation by exploiting the received pilots and then predicts a series of subsequent channels concurrently. Specifically, in the estimation phase, we propose a least-squares feedforward neural network (LS-FNN) to fuse the benefits of LS in high signal-to-noise ratio (SNR) regime and FNN in low SNR regime. In the prediction phase, a multi-time-interval long-short-term-memory (MTI-LSTM) network is proposed for concurrent channel prediction. A distinctive difference from prior works is that layer normalization is employed to greatly increase the prediction accuracy at no cost of additional neurons. Simulation results corroborate the superior performance of our proposed scheme over the state-of-the-art benchmarks.