Deep Learning-Assisted Target Classification Using OTFS Signaling

Ziyu Yan1; Long Tan2; Xiaoqi Zhang1; Kecheng Zhang1; Ruoyu Zhou1

10.1109/ICCCWorkshops57813.2023.10233827

2023

2474-9133

979-8-3503-4541-4

2023 IEEE/CIC International Conference on Communications in China (ICCC Workshops)

1-6

10-12 Aug. 2023

Dalian, China

A new modulation method, orthogonal time frequency space (OTFS), can support reliable data transmission by representing the signal in the delay-Doppler (DD) domain for high-mobility applications. In particular, the parameters of in-environment reflectors can be obtained from the representation of wireless channels in the DD domain, making it possible to provide sensing capability. In this paper, we propose a deep learning (DL) based target classification method using OTFS signaling. In our approach, to enhance the network performance, a 2D correlation method is utilized to extract features for data preprocessing. Subsequently, inspired by the residual learning technique, a deep neural network incorporating the attention mechanism is designed to distinguish sensing targets from the coarse estimation results. Through simulation experiments, we demonstrate that our proposed network exhibits superior performance in terms of efficiency and accuracy for OTFS sensing applications.

OTFS Target classification Deep learning ResNet

第一

EI

20234014814220

Learning systems

Ergonomics and Human Factors Engineering:461.4

IEEE

1.Southern University of Science and Technology, Shenzhen, China
2.Taiyuan university of technology, Taiyuan, China

Ziyu Yan,Long Tan,Xiaoqi Zhang,et al. Deep Learning-Assisted Target Classification Using OTFS Signaling[C],2023:1-6.