A Location-Aware Hybrid Deep Learning Framework for Dynamic Near-Far Field Channel Estimation in Low-Altitude UAV Communications

TL;DR

This paper presents a location-aware hybrid deep learning framework for dynamic channel estimation in low-altitude UAV communications, effectively capturing near and far field variations and incorporating real-time positional data.

Contribution

It introduces a novel hybrid deep learning model combining CNNs, BiLSTM, and self-attention, integrated with real-time location priors, to improve near-far field channel estimation accuracy.

Findings

Achieves at least 30.25% reduction in NMSE compared to benchmarks.

Effectively models both spatial and temporal channel variations.

Outperforms existing methods in dynamic UAV communication scenarios.

Abstract

In low altitude UAV communications, accurate channel estimation remains challenging due to the dynamic nature of air to ground links, exacerbated by high node mobility and the use of large scale antenna arrays, which introduce hybrid near and far field propagation conditions. While conventional estimation methods rely on far field assumptions, they fail to capture the intricate channel variations in near-field scenarios and overlook valuable geometric priors such as real-time transceiver positions. To overcome these limitations, this paper introduces a unified channel estimation framework based on a location aware hybrid deep learning architecture. The proposed model synergistically combines convolutional neural networks (CNNs) for spatial feature extraction, bidirectional long short term memory (BiLSTM) networks for modeling temporal evolution, and a multihead self attention mechanism to enhance focus on discriminative channel components. Furthermore, real-time transmitter and receiver locations are embedded as geometric priors, improving sensitivity to distance under near field spherical wavefronts and boosting model generalization. Extensive simulations validate the effectiveness of the proposed approach, showing that it outperforms existing benchmarks by a significant margin, achieving at least a 30.25% reduction in normalized mean square error (NMSE) on average.