SRLoRa: Neural-enhanced LoRa Weak Signal Decoding with Multi-gateway Super Resolution-Reference-Cited by-同舟云学术

SRLoRa: Neural-enhanced LoRa Weak Signal Decoding with Multi-gateway Super Resolution

Published:2023-10-16 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Twenty-fourth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing
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Author:

Du Jialuo¹^ORCID,Ren Yidong²^ORCID,Zhu Zhui³^ORCID,Li Chenning²^ORCID,Cao Zhichao⁴^ORCID,Ma Qiang¹^ORCID,Liu Yunhao³^ORCID

Affiliation:

1. School of Software, Tsinghua University, Beijing, Beijing, China

2. Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, United States of America

3. Department of Automation, Tsinghua University, Beijing, Beijing, China

4. Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, USA

Publisher

ACM

Reference32 articles.

1. Amani Al-Shawabka , Philip Pietraski , Sudhir B. Pattar , Francesco Restuccia , and Tommaso Melodia . 2021 . DeepLoRa: Fingerprinting LoRa Devices at Scale Through Deep Learning and Data Augmentation . In Proceedings of ACM MobiHoc. Amani Al-Shawabka, Philip Pietraski, Sudhir B. Pattar, Francesco Restuccia, and Tommaso Melodia. 2021. DeepLoRa: Fingerprinting LoRa Devices at Scale Through Deep Learning and Data Augmentation. In Proceedings of ACM MobiHoc.

2. LoRa Alliance. 2023. LoRa. https://lora-alliance.org. Accessed 19-Aug-2023. LoRa Alliance. 2023. LoRa. https://lora-alliance.org. Accessed 19-Aug-2023.

3. Justin Chan , Anran Wang , Arvind Krishnamurthy , and Shyamnath Gollakota . 2019 . Deepsense: Enabling carrier sense in low-power wide area networks using deep learning. arXiv preprint arXiv:1904.10607 (2019). Justin Chan, Anran Wang, Arvind Krishnamurthy, and Shyamnath Gollakota. 2019. Deepsense: Enabling carrier sense in low-power wide area networks using deep learning. arXiv preprint arXiv:1904.10607 (2019).

4. Kelvin CK Chan , Xintao Wang , Ke Yu , Chao Dong , and Chen Change Loy . 2021 . Understanding deformable alignment in video super-resolution . In Proceedings of the AAAI conference on artificial intelligence , Vol. 35 . 973--981. Kelvin CK Chan, Xintao Wang, Ke Yu, Chao Dong, and Chen Change Loy. 2021. Understanding deformable alignment in video super-resolution. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 973--981.

5. Charm: Exploiting Geographical Diversity through Coherent Combining in Low-Power Wide-Area Networks