A Transformer-Based Signal Denoising Network for AoA Estimation in NLoS Environments
Junchen Liu, Tianyu Wang, Yuxiao Li, Cheng Li, Yi Wang, Yuan Shen
Abstract
The next-generation communications impose requirements on integrated sensing and communication. However, the non-line-of-sight propagation in indoor complex environments poses great challenges to common localization techniques. In this letter, we propose a signal denoising network based on the transformer and temporal attention to improve the angle-of-arrival estimation accuracy. In the proposed network, the channel impulse response is denoised and reconstructed to mitigate errors. Then, two database are constructed based on self-built ultra-wideband transceivers in indoor environments for validation. Results show that the proposed network outperforms other machine learning methods in terms of angle-of-arrival estimation accuracy.