Predictive Modeling of Millimeter-Wave Vegetation-Scattering Effect Using Hybrid Physics-Based and Data-Driven Approach

System design and deployment of millimeter-wave (mmWave) wireless communication systems for outdoor coverage need sophisticated channel models to describe the wave interaction with illuminated physical objects. However, the conventional physical–statistical model cannot accurately predict the site-specific mmWave channel characteristics when involving complex system configurations and geometric information of transceiver relative locations. A framework of machine learning (ML) assisted channel modeling approach is proposed, in which the statistical models are leveraged for inter-cluster-level channel characterization and the propagation properties within each kind of clusters are predicted using a hybrid physics-based and data-driven approach. In particular, with a focus on the mmWave through-vegetation-scattering effect, a set of dedicated directional channel measurements and ray-tracing simulations is performed in an identical vegetated street canyon environment at 28 GHz for the performance evaluation of the proposed approach. Moreover, the training results and model validation in different environments show that comparing with the physical–statistical model, the proposed hybrid model, which adds the environment features to the artificial neural network (ANN) as inputs, has higher prediction accuracy and greater generalization ability in terms of the site-specific through-vegetation cluster parameters, such as vegetation attenuation, delay spread (DS), and angular spread.