A Transformer-Based Signal Denoising Network for AoA Estimation in NLoS Environments

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.

Automated summary

The letter introduces a signal denoising network based on transformer and temporal attention to improve angle-of-arrival estimation accuracy, validated using two self-built ultra-wideband transceivers databases in indoor environments. Results indicate that the proposed network outperforms other machine learning methods in terms of angle-of-arrival estimation accuracy.