An Environment-Data-Physics Driven Model for 6G V2V Urban Channels

The performance of the sixth-generation (6G) vehicle-to-vehicle (V2V) communication systems will be significantly improved, but they are also confronted with many technical challenges like massive terminal access and low transmission delay. A fundamental and difficult problem is how to establish an intelligent 6G V2V channel model with high accuracy, low complexity, and generality. In this paper, we propose a dynamic V2V channel model in complicated urban scenarios driven by effective environment information, channel data, and physical statistics. To begin with, the bimodal features representing the environment information are extracted from vector maps by a set of fully automatic algorithms. Heuristic graph datasets are constructed using features coupled with locations and ground-truth large-scale parameters (LSPs), i.e., the channel data reflecting realistic statistical properties. Then, we design a novel network based on attention-assisted graph convolution and pooling layers, which enables us to perform prediction for path loss, delay spread, and angular spreads. Compared with convolutional neural networks-based methods, the proposed LSPs prediction model can reduce both the number of trainable parameters and the FLOPs by two orders of magnitude with higher accuracy. Moreover, the predicted LSPs are next fed into multi-link V2V simulations based on physical statistics. Dynamic channel impulse response generation is implemented based on a spatially consistent geometrical modeling methodology. Eventually, we validate our model by comparing key channel characteristics with those of the ground-truth values, and better agreements are shown compared with existing methods.