all AI news
Enhancing Automatic Modulation Recognition for IoT Applications Using Transformers
March 26, 2024, 4:42 a.m. | Narges Rashvand, Kenneth Witham, Gabriel Maldonado, Vinit Katariya, Nishanth Marer Prabhu, Gunar Schirner, Hamed Tabkhi
cs.LG updates on arXiv.org arxiv.org
Abstract: Automatic modulation recognition (AMR) is critical for determining the modulation type of incoming signals. Integrating advanced deep learning approaches enables rapid processing and minimal resource usage, essential for IoT applications. We have introduced a novel method using Transformer networks for efficient AMR, designed specifically to address the constraints on model size prevalent in IoT environments. Our extensive experiments reveal that our proposed method outperformed advanced deep learning techniques, achieving the highest recognition accuracy.
abstract advanced amr applications arxiv cs.lg deep learning eess.sp iot networks novel processing recognition transformer transformers type usage
More from arxiv.org / cs.LG updates on arXiv.org
Digital Over-the-Air Federated Learning in Multi-Antenna Systems
2 days, 12 hours ago |
arxiv.org
Bagging Provides Assumption-free Stability
2 days, 12 hours ago |
arxiv.org
Jobs in AI, ML, Big Data
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Research Scientist, Demography and Survey Science, University Grad
@ Meta | Menlo Park, CA | New York City
Computer Vision Engineer, XR
@ Meta | Burlingame, CA