Map-based Channel Modeling and Generation for U2V mmWave Communication

TL;DR

This paper presents a novel 3D non-stationary channel model for UAV-to-vehicle mmWave communications, incorporating stochastic and deterministic parameter generation, validated by simulations and measurements.

Contribution

It introduces a comprehensive 3D channel model considering scattering, trajectory, and antenna arrays, with a new method for intra-path parameter generation and reduced complexity via digital map reconstruction.

Findings

Model accurately reproduces non-stationary U2V channels

Generated properties match theoretical and measured data

Proposed methods reduce computational complexity

Abstract

Unmanned aerial vehicle (UAV) aided millimeter wave (mmWave) technologies have a promising prospect in the future communication networks. By considering the factors of three-dimensional (3D) scattering space, 3D trajectory, and 3D antenna array, a non-stationary channel model for UAV-to-vehicle (U2V) mmWave communications is proposed. The computation and generation methods of channel parameters including interpath and intra-path are analyzed in detail. The inter-path parameters are calculated in a deterministic way, while the parameters of intra-path rays are generated in a stochastic way. The statistical properties are obtained by using a Gaussian mixture model (GMM) on the massive ray tracing (RT) data. Then, a modified method of equal areas (MMEA) is developed to generate the random intra-path variables. Meanwhile, to reduce the complexity of RT method, the 3D propagation space is reconstructed based on the user-defined digital map. The simulated and analyzed results show that the proposed model and generation method can reproduce non-stationary U2V channels in accord with U2V scenarios. The generated statistical properties are consistent with the theoretical and measured ones as well.