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1. A Multi-Resolution Channel Structure
Learning Estimation Method of
Geometry-Based Stochastic Model With
Multi-Scene
Abstract
Vehicle-to-vehicle (V2V) communication is the most typical application of the
Internet of Vehicles, and it has many envisaged applications, such as
driverless driving, collision warning and addressing traffic congestion. The
realization of future intellige
communication technology, which in turn relies on accurate V2V channel
estimation. As the possibilities that 5G technologies can be used for V2V
communications are increasingly being explored, corresponding
models used to describe the characteristics of V2V channels are also being
updated, so the new channel estimation algorithms needed to be developed.
Although the channel estimation method based on Convolutional Neural
Network (CNN) has achieved re
Resolution Channel Structure
Learning Estimation Method of
Based Stochastic Model With
vehicle (V2V) communication is the most typical application of the
Internet of Vehicles, and it has many envisaged applications, such as
driverless driving, collision warning and addressing traffic congestion. The
realization of future intelligent transportation systems (ITSs) will require V2V
communication technology, which in turn relies on accurate V2V channel
estimation. As the possibilities that 5G technologies can be used for V2V
communications are increasingly being explored, corresponding
models used to describe the characteristics of V2V channels are also being
updated, so the new channel estimation algorithms needed to be developed.
Although the channel estimation method based on Convolutional Neural
Network (CNN) has achieved remarkable success in communication problems
Resolution Channel Structure
Based Stochastic Model With
vehicle (V2V) communication is the most typical application of the
Internet of Vehicles, and it has many envisaged applications, such as
driverless driving, collision warning and addressing traffic congestion. The
nt transportation systems (ITSs) will require V2V
communication technology, which in turn relies on accurate V2V channel
estimation. As the possibilities that 5G technologies can be used for V2V
communications are increasingly being explored, corresponding channel
models used to describe the characteristics of V2V channels are also being
updated, so the new channel estimation algorithms needed to be developed.
Although the channel estimation method based on Convolutional Neural
markable success in communication problems

2. in recent years, it cannot be well adapted to V2V channels under different
scenarios and different modeling methods. In this article, a method is
proposed for learning and estimating the channel structure in the 5G NR
downlink that can adapt to the V2V channel under multi-scenes. The inherent
block sparsity characteristics of the channel structure is adopted to combine
the group lasso Alternating Direction Method of Multipliers (ADMM) algorithm
with CNN, and residual dense network is employed to estimate and refine the
V2V channel structure. Furthermore, the classifier and interpolation with
pooling method are used to estimate the V2V channel structure under multi-
scene and multi-resolution. Simulation results show that the performance of
this method is better than other deep learning based estimation algorithms.