Invention Grant
- Patent Title: Deep learning-based polymorphic platform
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Application No.: US17604476Application Date: 2020-02-24
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Publication No.: US11949544B2Publication Date: 2024-04-02
- Inventor: Tommaso Melodia , Francesco Restuccia
- Applicant: Northeastern University
- Applicant Address: US MA Boston
- Assignee: Northeastern University
- Current Assignee: Northeastern University
- Current Assignee Address: US MA Boston
- Agency: Verrill Dana, LLP
- International Application: PCT/US2020/019411 2020.02.24
- International Announcement: WO2020/236236A 2020.11.26
- Date entered country: 2021-10-18
- Main IPC: H04L27/34
- IPC: H04L27/34 ; G06N3/04 ; G06N3/063 ; G06N3/08 ; H04L27/38

Abstract:
A polymorphic platform for wireless communication systems is provided that employs trained classification techniques to determine physical layer parameters from a transmitter at a receiver. The system includes a learning module to determine transmitted physical layer parameters of the signal using a trained classification module, such as a deep learning neural network. The trained classification module receives I/Q input samples from receiver circuitry and processes the I/Q input samples to determine transmitted physical layer parameters from the transmitter. The system includes a polymorphic processing unit that demodulates data from the signal based on the determined transmitted parameters.
Public/Granted literature
- US20220217035A1 Deep Learning-Based Polymorphic Platform Public/Granted day:2022-07-07
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