telecommunications infrastructure planners

Abstract: Satellite-based quantum links are a promising enabler of long-distance entanglement distribution, overcoming the exponential losses of optical fibers. In uplink architectures, entangled photons are generated at ground stations and transmitted to orbiting satellites, reducing space-segment complexity but introducing significant atmospheric and geometric losses. Single-satellite dual-uplink configurations are additionally limited by the requirement of simultaneous visibility between both ground stations and the satellite. In this work, we investigate a dual-uplink dual-satellite network architecture and compare its performance with the single-satellite scheme under identical physical assumptions. The proposed topology introduces an inter-satellite link (ISL) that decouples the ground-station separation from the atmospheric uplink geometry. A realistic channel model is employed, incorporating geometric attenuation, atmospheric turbulence, background noise, and Bell-state measurement efficiency. The overall success probability and practical fidelity are evaluated as functions of satellite altitude and ground-station separation. Our results show that while the single-satellite configuration is suitable for regional-scale networks, it becomes infeasible for large separations due to geometric constraints. The proposed dual-satellite architecture mitigates this limitation and significantly extends the achievable communication distance, representing a key step toward scalable quantum internet infrastructures.

Abstract: Reconfigurable Intelligent Surfaces (RISs) offer a promising paradigm to mitigate blockage and extend millimeter-wave coverage in 6G multi-cell networks. However, dynamically allocating shared RIS infrastructure across competing base stations is an NP-hard problem posing severe scalability bottlenecks. In this paper, we propose a scalable framework combining Graph Neural Networks (GNNs) with Deep Reinforcement Learning (DRL) for dynamic shared RIS orchestration. By formulating allocation as a Markov Decision Process, we introduce a physical topology sparsification strategy that prunes dense channel matrices into a sparse tripartite graph. This pruning reduces edge density by 77% and removes representation noise, thereby improving global coverage probability while reducing computational complexity. Our relational message-passing architecture naturally generalizes to arbitrary network dimensions without model retraining. Furthermore, structural ablation studies reveal that physical path loss localizes surface dependencies, enabling a highly efficient localized graph design with linear computational scaling. Extensive simulations in dense urban environments demonstrate that under strict infrastructure budget constraints, the proposed GNN-DRL framework consistently outperforms greedy heuristic baselines by up to ~12% in coverage while delivering faster inference speed via GPU acceleration.

Abstract: In this work we consider the Maximal Independent Set (MIS) problem and the metric Steiner Forest problem in the sublinear time setting, under the adjacency/distance matrix query model. First, we give an algorithm that estimates the size of an MIS up to a multiplicative factor of $(1+\eps)$ using $\tO(n^{4/3}/\eps^2)$ queries. This improves the best previous algorithm by Mahabadi, Roghani, Tarnawski, and Vakilian (SODA 2026), which had a query complexity of $\tO(n^{3/2}/\eps^2)$. Via a reduction from that work, this would automatically imply the same improvement for the problem of estimating the metric Steiner Forest cost up to an $O(\log n)$ factor. However, as our second contribution, we consider the Steiner Forest problem directly and provide an algorithm with $\tO(n)$ query complexity that is very simple and does not proceed via MIS.

Abstract: Integrated Sensing and Communication (ISAC) will allow 6G networks to provide both sensing and communication services through shared infrastructure and spectrum resources. However, network slicing that views these services as distinct slices with distinct objectives and performance requirements will inevitably miss the emergent value of integrating them. By considering Sensing-Assisted Communication (SAC) and Communication-Assisted Sensing (CAS), which create interdependencies between services, we can observe trade-offs in resources dimensioned to support each slice and, ultimately, the ISAC slicing performance. As such, this paper proposes a performance analysis of an ISAC slicing framework that accounts for resource allocation and sensor density, as well as SAC and CAS, dimensioned to achieve slice-specific Service-Level Agreement (SLA) satisfaction. Numerical simulations, across different bandwidth allocations, sensor deployments, and demand scenarios, show that ISAC slicing performance is fundamentally constrained by sensing resolution, shaped by demand, and scaled by sensor deployment. These findings provide insights for the design of future ISAC slicing mechanisms.

Abstract: Emerging experimental quantum network deployments are based on collaborations between research institutions and rely on telecommunications fiber infrastructure. Currently, such networks consist of mostly Dual Input Repeaters (DIRs) and can be viewed as composed of (mostly) line graphs. In order to evaluate the performance of such networks (e.g., in terms of Entangled Pair Rate, EPR, and average Fidelity) we developed a custom quantum network simulation platform that takes into account both the quantum and classical network components. To support the verification of the simulation results, we develop a novel approach for performance analysis of DIRs. We compare the simulation results to the analytical results for DIRs and to prior results for repeaters with more than two inputs. We observe that although the expected memory utilization cannot be bounded, \emph{a small DIR memory size is sufficient and leads to minimal performance degradation}. For line networks with a single repeater and $3$ repeaters, we explore the dependency of incoming and outgoing memory size requirements on the number of nodes (network size), their connectivity (fiber length) and various quantum error settings (amplitude damping and phase flip channels). We evaluate the \emph{EPR} and the \emph{average Fidelity} for a sub-network of a deployed network with $3$ repeaters, using two alternative paths. We show that as the number of nodes increases or when the fiber lengths between nodes differ significantly, additional node memory is needed to maintain the same EPR (qubits are stored for longer duration to allow completion of entanglement across the network). We also show that the memory size should be above some minimum threshold to allow EP creation (EPR $>0$) as pair selection and swapping completion propagate across the network.