Super-resolution of Ray-tracing Channel Simulation via Attention Mechanism based Deep Learning Model

TL;DR

This paper introduces a deep learning super-resolution model with attention mechanisms to improve the efficiency and accuracy of ray-tracing channel simulations, reducing noise and computational cost.

Contribution

The paper proposes a novel CNN-based super-resolution model with residual and attention mechanisms for channel modeling, outperforming traditional RT in efficiency and noise reduction.

Findings

Achieves 2.82 dB MAE on PL with scale factor 2

Reduces RT simulation time to 52% of original

Outperforms vision transformer in speed and computational cost

Abstract

As an emerging approach, deep learning plays an increasingly influential role in channel modeling. Traditional ray tracing (RT) methods of channel modeling tend to be inefficient and expensive. In this paper, we present a super-resolution (SR) model for channel characteristics. Residual connection and attention mechanism are applied to this convolutional neural network (CNN) model. Experiments prove that the proposed model can reduce the noise interference generated in the SR process and solve the problem of low efficiency of RT. The mean absolute error of our channel SR model on the PL achieves the effect of 2.82 dB with scale factor 2, the same accuracy as RT took only 52\% of the time in theory. Compared with vision transformer (ViT), the proposed model also demonstrates less running time and computing cost in SR of channel characteristics.