TY - JOUR
T1 - An Environment-Data-Physics Driven Model for 6G V2V Urban Channels
AU - Zhang, Kaien
AU - Zhang, Yan
AU - Cheng, Xiang
AU - Fei, Zesong
AU - Chen, Mingyu
AU - Ji, Zijie
N1 - Publisher Copyright:
© 2002-2012 IEEE.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - Channel model
KW - environment information
KW - map-based feature extraction
KW - vehicle-to-vehicle (V2V) communication
UR - http://www.scopus.com/inward/record.url?scp=105004892003&partnerID=8YFLogxK
U2 - 10.1109/TWC.2025.3564675
DO - 10.1109/TWC.2025.3564675
M3 - Article
AN - SCOPUS:105004892003
SN - 1536-1276
JO - IEEE Transactions on Wireless Communications
JF - IEEE Transactions on Wireless Communications
ER -