This paper proposes an advanced resource allocation technique based on multi-objective optimization (MOO) to jointly optimize spectrum and power, mitigating nonlinear impairments and enhancing network performance, thereby improving the optical signal-to-noise ratio.
The article concludes that this concept can provide a theoretical basis for routing in hybrid communication systems and can naturally extend to lossy transmission models, dynamic network scenarios, and integrated network-control problems.
The findings show that OBS-GraphSyn-2025 offers a scalable, interpretable, and reproducible complexity-aware metric for evaluating routing-state in OBS-inspired networks while abstracting from the constraints of the optical-layer.
As practical quantum networks approach large-scale deployment, the need for efficient user-to-user frequency allocation is increasing, yet current approaches only provide partial solutions to the routing and spectrum allocation problem for an arbitrary quantum network. We address this challenge for repeater-less flex-grid quantum networks based on hyperentangled photons using an efficient three-stage pipeline combining leading tools in classical networking with recent advances in numerical optimization. First, double instantiations of Yen's algorithm obtain low-loss route candidates between each pair of users and the entanglement sources. Second, the advanced process optimizer (APOPT) obtains frequency channel allocations that maximize distribution rates under fidelity constraints. Finally, the constraint programming solver using satisfiability methods (CP-SAT) assigns specific frequency bins to each link, ensuring that there is no contention between frequencies from different sources. We numerically demonstrate this approach on a representative ring network and a Manhattan incumbent local exchange carrier topology, realizing significant improvements over prior genetic algorithm approaches in speed, accuracy, and scalability. Overall, this pipeline provides an efficient heuristic workflow for optimizing broadband entanglement distribution, applicable to arbitrarily connected quantum networks integrated within the existing lightwave infrastructure.
This paper presents detailed algorithm for calculating L-LSR coefficient, and shows that L-LSR algorithm not only performs better than OSPF, but also has verySignificant performance improvement over the other LSR family of algorithms.
This work proposes Double-Channel Graph Attention (DCGA), an end-to-end reinforcement learning framework that isolates network reachability and demand-service logic into separate graph channels and constructs valid routes using a simulator-coupled, constraint-informed decoder.
Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI's neural network before their chatbot ever says a word.
PhD student Rachel Sava, winner of the Envisioning the Future of Computing Prize, explores transformative improvements and dystopian risks of neural technology.