Publications associées à Duplex (canal de communication)

A Maximum-Likelihood-Based Two-User Receiver for LoRa Chirp Spread-Spectrum Modulation

Andreas Peter Burg, Alexios Konstantinos Balatsoukas Stimming, Orion Afisiadis, Joachim Tobias Tapparel, Mathieu Pierre Xhonneux

Long Range (LoRa) is an emerging low-power wide-area network technology offering long-range wireless connectivity to Internet of Things (IoT) devices. For energy efficiency reasons, LoRa end nodes implement a nonslotted ALOHA multiple access scheme to tran ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Enhancing the Reliability of Dense LoRaWAN Networks With Multi-User Receivers

Andreas Peter Burg, Joachim Tobias Tapparel, Mathieu Pierre Xhonneux

LoRaWAN is a low-power wireless technology that provides long-range connectivity to battery-powered Internet of Things (IoT) devices. To minimize the energy consumption of the IoT nodes, LoRaWAN networks use for the uplink a pure non-slotted ALOHA multiple ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Physical Layer Aspects of LoRa and Full-Duplex Wireless Transceivers

Orion Afisiadis

Wireless communications are currently faced with two main challenges. The first challenge stems from the enormous number of Internet of Things (IoT) devices that transmit very small amounts of data. The second challenge is the need for ever-increasing data ...

EPFL2020

Hardware Implementation of Neural Self-Interference Cancellation

Andreas Peter Burg, Alexios Konstantinos Balatsoukas Stimming, Andreas Toftegaard Kristensen, Yann Kurzo

In-band full-duplex systems can transmit and receive information simultaneously and on the same frequency band. However, due to the strong self-interference caused by the transmitter to its own receiver, the use of non-linear digital self-interference canc ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

On the Implementation Complexity of Digital Full-Duplex Self-Interference Cancellation

Andreas Peter Burg, Alexios Konstantinos Balatsoukas Stimming, Andreas Toftegaard Kristensen

In-band full-duplex systems promise to further increase the throughput of wireless systems, by simultaneously transmitting and receiving on the same frequency band. However, concurrent transmission generates a strong self-interference signal at the receive ...

IEEE2020

Identification of Non-Linear RF Systems Using Backpropagation

Andreas Peter Burg, Alexios Konstantinos Balatsoukas Stimming, Andreas Toftegaard Kristensen

In this work, we use deep unfolding to view cascaded non-linear RF systems as model-based neural networks. This view enables the direct use of a wide range of neural network tools and optimizers to efficiently identify such cascaded models. We demonstrate ...

IEEE2020

Wide band-pass FSS with reduced periodicity for energy efficient windows at higher frequencies

Josef Andreas Schuler, Luc Burnier, Jérémy Jacques Antonin Fleury

A simulation model and an experimental characterization of energy saving glazing transparent to mobile communication frequency up to 40 GHz is presented. A previous study showed that laser structured energy efficient windows with a frequency selective surf ...

SPRINGER HEIDELBERG2020

Wideband Multiport Antennas

Anja Skrivervik, Abdulkadir Uzun

In this paper, a wideband four port 2-6 GHz antenna is proposed. One-, two-, and four-port antennas are implemented and characterized between 2 and 6 GHz. The isolation between the ports is improved by connecting and optimizing the ground plane sections. T ...

2020

Advanced Machine Learning Techniques for Self-Interference Cancellation in Full-Duplex Radios

Andreas Peter Burg, Andreas Toftegaard Kristensen

In-band full-duplex systems allow for more efficient use of temporal and spectral resources by transmitting and receiving information at the same time and on the same frequency. However, this creates a strong self-interference signal at the receiver, makin ...

IEEE2019

Communication trade-offs for Local-SGD with large step size

Aymeric Daphnis Kévin Dieuleveut

Synchronous mini-batch SGD is state-of-the-art for large-scale distributed machine learning. However, in practice, its convergence is bottlenecked by slow communication rounds between worker nodes. A natural solution to reduce communication is to use the " ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2019