Browse by UCL people
Group by: Type | Date
Number of items: 7.
Article
Matzner, R;
Semrau, D;
Luo, R;
Zervas, G;
Bayvel, P;
(2021)
Making intelligent topology design choices: understanding structural and physical property performance implications in optical networks [Invited].
Journal of Optical Communications and Networking
, 13
(8)
, Article D53. 10.1364/jocn.423490.
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Matzner, Robin;
Ahuja, Akanksha;
Sadeghi Yamchi, Rasoul;
Doherty, Michael;
Beghelli, Alejandra;
Savory, Seb;
Bayvel, Polina;
(2025)
Topology Bench: systematic graph-based benchmarking for core optical networks.
Journal of Optical Communications and Networking
, 17
(1)
pp. 7-27.
10.1364/jocn.534477.
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Matzner, Robin;
Luo, Ruijie;
Zervas, Georgios;
Bayvel, Polina;
(2023)
Intelligent performance inference: A graph neural network approach to modeling maximum achievable throughput in optical networks.
APL Machine Learning
, 1
(2)
, Article 026112. 10.1063/5.0137426.
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 |
Proceedings paper
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Bayvel, P;
Luo, R;
Matzner, R;
Semrau, D;
Zervas, G;
(2021)
Intelligent design of optical networks: which topology features help maximise throughput in the nonlinear regime?
In:
2020 European Conference on Optical Communications, ECOC 2020.
IEEE: Brussels, Belgium.
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Matzner, R;
Luo, R;
Zervas, G;
Bayvel, P;
(2022)
Expanding Graph Neural Networks for Ultra-Fast Optical Core Network Throughput Prediction to Large Node Scales.
In:
2022 European Conference on Optical Communication, ECOC 2022.
IEEE: Basel, Switzerland.
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Matzner, Robin;
Henrique, Buglia;
Polina, Bayvel;
(2024)
Evolving Optical Core Networks: Understanding the Impact of
Topology Redesign using Space and Wavelength Domains on
Network Throughput.
In:
49th European Conference on Optical Communications (ECOC 2023).
(pp. pp. 1043-1046).
IET: Glasgow, UK.
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Thesis
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Matzner, Robin Michael;
(2025)
Maximising Achievable Throughput in Optical Network Design.
Doctoral thesis (Ph.D), UCL (University College London).
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