Machine Learning-Based 3D Channel Modeling for U2V mmWave Communications

TL;DR

This paper introduces a machine learning-based 3D channel model for UAV-to-Vehicle mmWave communications, capturing complex 3D effects and validated through ray-tracing simulations and theoretical analysis.

Contribution

It proposes a novel ML-integrated 3D channel modeling approach for U2V mmWave links, incorporating 3D rotations and using GAN and BPNN trained on ray-tracing data.

Findings

Generated PDP and DPSD match ray-tracing results

Model captures impact of 3D rotations on channel properties

Validates effectiveness with theoretical and measurement data

Abstract

Unmanned aerial vehicle (UAV) millimeter wave (mmWave) technologies can provide flexible link and high data rate for future communication networks. By considering the new features of three-dimensional (3D) scattering space, 3D velocity, 3D antenna array, and especially 3D rotations, a machine learning (ML) integrated UAV-to-Vehicle (U2V) mmWave channel model is proposed. Meanwhile, a ML-based network for channel parameter calculation and generation is developed. The deterministic parameters are calculated based on the simplified geometry information, while the random ones are generated by the back propagation based neural network (BPNN) and generative adversarial network (GAN), where the training data set is obtained from massive ray-tracing (RT) simulations. Moreover, theoretical expressions of channel statistical properties, i.e., power delay profile (PDP), autocorrelation function (ACF), Doppler power spectrum density (DPSD), and cross-correlation function (CCF) are derived and analyzed. Finally, the U2V mmWave channel is generated under a typical urban scenario at 28 GHz. The generated PDP and DPSD show good agreement with RT-based results, which validates the effectiveness of proposed method. Moreover, the impact of 3D rotations, which has rarely been reported in previous works, can be observed in the generated CCF and ACF, which are also consistent with the theoretical and measurement results.