Invention Grant
- Patent Title: Method for non-linear distortion immune end-to-end learning with autoencoder—OFDM
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Application No.: US17065949Application Date: 2020-10-08
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Publication No.: US11570030B2Publication Date: 2023-01-31
- Inventor: Alphan Sahin , David Matolak
- Applicant: University of South Carolina
- Applicant Address: US SC Columbia
- Assignee: University of South Carolina
- Current Assignee: University of South Carolina
- Current Assignee Address: US SC Columbia
- Agency: Burr & Forman LLP
- Agent Jeffrey H. Kamenetsky
- Main IPC: H04L27/26
- IPC: H04L27/26 ; G06N3/04

Abstract:
A new layer tailored for Artificial Intelligence-based communication systems to limit the instantaneous peak power for the signals that relies on manipulation of complementary sequences through neural networks. Disclosed is a method for providing non-linear distortion in end-to-end learning communication systems, the communication system comprising a transmitter and a receiver. The method includes mapping transmitted information bits to an input of a first neural network; controlling, by an output of the neural network, parameters of a complementary sequence (CS) encoder, producing an encoded CS; transmitting the encoded CS through an orthogonal frequency division multiplexing (OFDM) signal; processing, by Discrete Fourier Transform (DFT), the encoded CS, to produce a received information signal in a frequency domain; and processing, by a second neural network, the received information signal.
Public/Granted literature
- US20210111936A1 METHOD FOR NON-LINEAR DISTORTION IMMUNE END-TO-END LEARNING WITH AUTOENCODER - OFDM Public/Granted day:2021-04-15
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