@inproceedings{0dbd5be6aeb24721b4ec34cadac62492,
title = "LSTM Network-based SNR Estimator for DS-UWB Wireless Sensor Network",
abstract = "Reliability measurement is vital for wireless communication and SNR is an important indicator of communication reliability. This paper mainly focused on the SNR estimation for the direct-sequence ultra-wideband (DS-UWB) wireless sensor network. The system channel is contaminated by AWGN and MAI. We provide an SNR estimator based on LSTM, an RNN network mainly designed for long-term dependency problems that can remember information for an extended period. The LSTM network is trained to predict the SNR of the input signal that has through code mapping. Compared with the original signal, the mapping data can distinguish the MAI and AWGN of the received bits, improving the estimator's RMSE performance. The simulation experiments show that using LSTM to design an SNR estimator for the DS-UWB wireless network can draw attention due to its simplicity and effectiveness. Moreover, the performance of the estimator is more accurate using mapping data.",
keywords = "LSTM, Reliability, SNR estimation, ultra-wideband",
author = "Qingzhi Liu and Zhendong Yin and Mingyang Wu and Zhilu Wu and Zhijiang Xu",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 9th International Conference on Dependable Systems and Their Applications, DSA 2022 ; Conference date: 04-08-2022 Through 05-08-2022",
year = "2022",
doi = "10.1109/DSA56465.2022.00136",
language = "英语",
series = "Proceedings - 2022 9th International Conference on Dependable Systems and Their Applications, DSA 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "965--973",
booktitle = "Proceedings - 2022 9th International Conference on Dependable Systems and Their Applications, DSA 2022",
address = "美国",
}