A Ray-tracing and Deep Learning Fusion Super-resolution Modeling Method for Wireless Mobile Channel

TL;DR

This paper introduces a deep learning-based super-resolution method fused with ray-tracing to accurately predict mobile channel characteristics, significantly reducing computational time for 5G and beyond wireless systems.

Contribution

It proposes a novel deep neural network model that enhances cluster characteristic prediction in ray-traced wireless channels, improving accuracy and efficiency over traditional methods.

Findings

Achieves 51% reduction in RMSE for cluster location prediction

Reduces computation time of ray tracing simulations significantly

Provides accurate channel impulse response reconstruction matching MPC

Abstract

Mobile channel modeling has always been the core part for design, deployment and optimization of communication system, especially in 5G and beyond era. Deterministic channel modeling could precisely achieve mobile channel description, however with defects of equipment and time consuming. In this paper, we proposed a novel super resolution (SR) model for cluster characteristics prediction. The model is based on deep neural networks with residual connection. A series of simulations at 3.5 GHz are conducted by a three-dimensional ray tracing (RT) simulator in diverse scenarios. Cluster characteristics are extracted and corresponding data sets are constructed to train the model. Experiments demonstrate that the proposed SR approach could achieve better power and cluster location prediction performance than traditional interpolation method and the root mean square error (RMSE) drops by 51% and 78% relatively. Channel impulse response (CIR) is reconstructed based on cluster characteristics, which could match well with the multi-path component (MPC). The proposed method can be used to efficiently and accurately generate big data of mobile channel, which significantly reduces the computation time of RT-only.