Artificial neural networks for photonic applications—from algorithms to implementation: tutorial
This tutorial–review on applications of artificial neural networks in photonics targets a broad
audience, ranging from optical research and engineering communities to computer science …
audience, ranging from optical research and engineering communities to computer science …
Machine learning-based self-interference cancellation for full-duplex radio: Approaches, open challenges, and future research directions
In contrast to the long-held belief that wireless systems can only work in half-duplex mode,
full-duplex (FD) systems are able to concurrently transmit and receive information over the …
full-duplex (FD) systems are able to concurrently transmit and receive information over the …
Res-GAN for Behavioral Modeling and Pre-distortion of Power Amplifiers in OFDM-Based Satellite Communication System
In satellite communications, the power amplifier (PA) plays a vital role in enhancing the
transmission performance of signals. However, the non-linearity of PAs often distorts the …
transmission performance of signals. However, the non-linearity of PAs often distorts the …
Lightweight machine-learning model for efficient design of graphene-based microwave metasurfaces for versatile absorption performance
Graphene, as a widely used nanomaterial, has shown great flexibility in designing optically
transparent microwave metasurfaces with broadband absorption. However, the design of …
transparent microwave metasurfaces with broadband absorption. However, the design of …
Adaptive compensation of hardware impairments in digitally modulated radars using ML-based behavioral models
R Michev, Y Shu, D Werbunat, J Hasch… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
This work proposes novel compensation approaches for full radar transceiver (TRX) self-
calibration of the IQ modulator, power amplifier (PA), and IQ demodulator without the need of …
calibration of the IQ modulator, power amplifier (PA), and IQ demodulator without the need of …
Graph neural network-based node deployment for throughput enhancement
Y Yang, D Zou, X He - IEEE Transactions on Neural Networks …, 2023 - ieeexplore.ieee.org
The recent rapid growth in mobile data traffic entails a pressing demand for improving the
throughput of the underlying wireless communication networks. Network node deployment …
throughput of the underlying wireless communication networks. Network node deployment …
A learning-based methodology for microwave passive component design
Microwave passive component design is of particular interest to radio frequency (RF)
scholars and engineers. Although a plethora of studies have been carried out over multiple …
scholars and engineers. Although a plethora of studies have been carried out over multiple …
Modular neural network based models of high-speed link transceivers
In this article, we address the nonlinear behavioral modeling of transceivers using
feedforward neural networks (FNNs) such that each modular block functions independently …
feedforward neural networks (FNNs) such that each modular block functions independently …
Phase-normalized neural network for linearization of RF power amplifiers
A Fischer-Bühner, L Anttila… - IEEE Microwave and …, 2023 - ieeexplore.ieee.org
This letter proposes a methodology for phase-normalization of the complex-valued I/Q inputs
of a real-valued time delay neural network (RVTDNN). The normalization enables modeling …
of a real-valued time delay neural network (RVTDNN). The normalization enables modeling …
Block-Oriented Recurrent Neural Network for Digital Predistortion of RF Power Amplifiers
In this article, a novel block-oriented recurrent neural network (RNN) model is proposed for
behavioral modeling and digital predistortion (DPD) of radio frequency (RF) power …
behavioral modeling and digital predistortion (DPD) of radio frequency (RF) power …