Zobrazeno 1 - 10
of 835
pro vyhledávání: '"Kilper, A"'
Autor:
Vasan, Vivek, Agrawal, Anuj, Nico-Katz, Alexander, Horgan, Jerry, Bash, Boulat A., Kilper, Daniel C., Ruffini, Marco
We consider quantum networks, where entangled photon pairs are distributed using fibre optic links from a centralized source to entangling nodes. The entanglement is then stored (via an entanglement swap) in entangling nodes' quantum memories until u
Externí odkaz:
http://arxiv.org/abs/2411.07410
Autor:
McAleese, Hannah, Agrawal, Anuj, Vasan, Vivek, Campbell, Conall J., Hawkins, Adam G., Kilper, Daniel C., Paternostro, Mauro, Ruffini, Marco
Distributing entanglement over long distances remains a challenge due to its fragility when exposed to environmental effects. In this work, we compare various entanglement distribution protocols in a realistic noisy fiber network. We focus specifical
Externí odkaz:
http://arxiv.org/abs/2411.07306
Autor:
Dzaferagic, Merim, Ruffini, Marco, Slamnik-Krijestorac, Nina, Santos, Joao F., Marquez-Barja, Johann, Tranoris, Christos, Denazis, Spyros, Kyriakakis, Thomas, Karafotis, Panagiotis, DaSilva, Luiz, Pandey, Shashi Raj, Shiraishi, Junya, Popovski, Petar, Jensen, Soren Kejser, Thomsen, Christian, Pedersen, Torben Bach, Claussen, Holger, Du, Jinfeng, Zussman, Gil, Chen, Tingjun, Chen, Yiran, Tirupathi, Seshu, Seskar, Ivan, Kilper, Daniel
Multiple visions of 6G networks elicit Artificial Intelligence (AI) as a central, native element. When 6G systems are deployed at a large scale, end-to-end AI-based solutions will necessarily have to encompass both the radio and the fiber-optical dom
Externí odkaz:
http://arxiv.org/abs/2407.01544
Autor:
Bali, Rohan, Tittelbaugh, Ashley N., Jenkins, Shelbi L., Agrawal, Anuj, Horgan, Jerry, Ruffini, Marco, Kilper, Daniel C., Bash, Boulat A.
We investigate resource allocation for quantum entanglement distribution over an optical network. We characterize and model a network architecture that employs a single quasi-deterministic time-frequency heralded Einstein-Podolsky-Rosen (EPR) pair so
Externí odkaz:
http://arxiv.org/abs/2404.08744
Autor:
Bali, Rohan, Tittelbaugh, Ashley, Jenkins, Shelbi L., Agrawal, Anuj, Horgan, Jerry, Ruffini, Marco, Kilper, Daniel, Bash, Boulat A.
We investigate resource allocation for quantum entanglement distribution over an optical network. We characterize and model a network architecture that employs a single quasideterministic time-frequency heralded EPR-pair source, and develop a routing
Externí odkaz:
http://arxiv.org/abs/2311.14613
Autor:
Nishizawa, Hideki, Mano, Toru, De Lima, Thomas Ferreira, Huang, Yue-Kai, Wang, Zehao, Ishida, Wataru, Kawashima, Masahisa, Ip, Ezra, D'Amico, Andrea, Okamoto, Seiji, Inoue, Takeru, Anazawa, Kazuya, Curri, Vittorio, Zussman, Gil, Kilper, Daniel, Chen, Tingjun, Wang, Ting, Asahi, Koji, Takasugi, Koichi
We propose an approach to estimate the end-to-end GSNR accurately in a short time when a data center interconnect (DCI) network operator receives a service request from users, not by measuring the GSNR at the operational route and wavelength for the
Externí odkaz:
http://arxiv.org/abs/2309.07359
Efficient use of spectral resources will be an important aspect of converged access network deployment. This work analyzes the performance of variable bandwidth Analog Radio-over-Fiber signals transmitted in the unfilled spectral spaces of telecom-gr
Externí odkaz:
http://arxiv.org/abs/2308.10637
We present a novel ML framework for modeling the wavelength-dependent gain of multiple EDFAs, based on semi-supervised, self-normalizing neural networks, enabling one-shot transfer learning. Our experiments on 22 EDFAs in Open Ireland and COSMOS test
Externí odkaz:
http://arxiv.org/abs/2308.02233
Ultra-low end-to-end latency is one of the most important requirements in 5G networks and beyond to support latency-critical applications. Cloud-RAN and MEC are considered as the key driving technology that can help reduce end-to-end latency. However
Externí odkaz:
http://arxiv.org/abs/2303.06505
Autor:
Müller, Jasper, Slyne, Frank, Kaeval, Kaida, Troia, Sebastian, Fehenberger, Tobias, Elbers, Jörg-Peter, Kilper, Daniel C., Ruffini, Marco, Mas-Machuca, Carmen
SNR margins between partially and fully loaded DWDM systems are estimated without detailed knowledge of the network. The ML model, trained on simulation data, achieves accurate predictions on experimental data with an RMSE of 0.16 dB.
Comment: T
Comment: T
Externí odkaz:
http://arxiv.org/abs/2302.08275