@inproceedings{33749a3ee5f74801beb05110e4dee538,
title = "Neural Network-Based Digital Predistortion and Self-Interference Cancellation in a Quadrature Balanced Full Duplex Transmitter",
abstract = "This work presents a neural network (NN) implementation of a digital self-interference cancellation (SIC) filter and a digital predistortion (DPD) linearizer in a quadrature balanced full duplex (FD) transceiver front-end. A quantitative description of the NNs design and functionality is laid out. The proposed algorithms were evaluated in measurements using a discrete-component quadrature balanced RF front-end and a 20 MHz 802.11ac WiFi signal with 10 dB peak-to-average power ratio (PAPR) around the center frequency of 2.4 GHz. At 13 dBm average transmit (TX) power, the NN-DPD corrects TX error vector magnitude (EVM) by 13 dB to the value of -41.5 dB. Total TX-RX isolation of 50 dB is demonstrated in the RF domain, out of which 20 dB is contributed by the passive TX-RX isolation and 30 dB by active TX leakage suppression using the NN SIC filter.",
keywords = "Digital predistortion, Full-duplex, Neural networks, Self-interference cancellation, Wireless communications",
author = "Erez Loebl and Nimrod Ginzberg and Emanuel Cohen",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems, COMCAS 2021 ; Conference date: 01-11-2021 Through 03-11-2021",
year = "2021",
doi = "10.1109/COMCAS52219.2021.9629003",
language = "الإنجليزيّة",
series = "2021 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems, COMCAS 2021",
pages = "252--254",
booktitle = "2021 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems, COMCAS 2021",
}