An End-to-End Deep Learning Framework for Wideband Signal Recognition
Author:
Affiliation:
1. Institute for Digital Communications, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany
2. Innovationszentrum für Telekommunikationstechnik GmbH IZT, Erlangen, Germany
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/6514899/10136722.pdf?arnumber=10136722
Reference69 articles.
1. Deep Learning Models for Wireless Signal Classification With Distributed Low-Cost Spectrum Sensors
2. Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation
3. Automatic Modulation Classification: A Deep Learning Enabled Approach
4. Path Aggregation Network for Instance Segmentation
5. LSTM Guided Modulation Classification and Experimental Validation for Sub-Nyquist Rate Wideband Spectrum Sensing
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