M.Sc. Arantxa Villavicencio Paz
M.Sc. Arantxa Villavicencio Paz
DoktorandinMitglied der Forschungsgruppe Netzoptimierung, Planung und Techno-Ökonomie
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Arantxa Villavicencio Paz is PhD student at the Chair of Communication Networks at the Technical University of Chemnitz (TUC), and Research Engineer at the Advanced Technology group at Adtran Networks SE, Munich, Germany. She received her B. Sc. degree in Telecommunications Engineering from the Pontificia Universidad Católica del Perú, and her M.Sc. degree in Communications Engineering from the Technical University of Munich, Germany. At TUC, she is member of the research group on Network Optimization, Planning and Techno-Economics, investigating and developing mathematical and algorithmic methods for the long-term planning and techno-economic optimization of high-speed Metro/Core optical networks.
- Network Planning: Development of AI-based optimization methods for the strategic and technical planning of flexible and low-cost software-defined multilayer networks subject to deep uncertainty. The methods include approaches for life-cycle decision-making in long-term operated networks, optimizing tasks such as traffic routing, capacity dimensioning, topology planning and resilience. Application scenarios: Planning of resilient, sustainable and flexible Metro/Core ultra-wide-band (UWB) and space-division-multiplexed (SDM) IP-Optical systems.
- Network Techno-Economics: Development of algorithms for the optimization of the technical and economic efficiency and the Total Cost of Ownership (TCO) of advanced network architectures and services. The methods investigate the evaluation of Real Options (RO) for the life-cycle adaptation of network designs subject to deep uncertainties in the traffic demands and costs. The developed approaches optimize key performance indicators (KPIs), including QoS objectives and financial metrics such as the Payback Period (PB), the Net Present Value (NPV), the Internal Rate of Return (IRR) and the Return on Investment (ROI) of the network deployment plan.
- Optimization methods: The above-mentioned research areas apply, develop and extend mathematical methods in the domains of Linear Programming (LP), Meta-Heuristics, Deep-Reinforcement Learning (DRL) and Graph Neural Networks (GNNs).
