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Qiaolun Zhang

Publications and source records attributed to Qiaolun Zhang.

10 recordsLinked to original sources
arXiv2026

Measurement-Device Placement in MDI-QKD Networks with Minimal Trusted Relays

Measurement-Device-Independent Quantum Key Distribution (MDI-QKD) removes detector side-channel vulnerabilities by delegating measurements to an untrusted relay that, when shared by several user pairs, acts as a Bell-state measurement (BSM) hub. Channel loss still limits the reach of MDI-QKD links, so long-range services rely on trusted relays for key forwarding. As MDI-QKD moves toward metropolitan deployment, a key network-planning question arises: how should BSM hubs be placed on existing fiber infrastructure to minimize the use of trusted relays? In this work, we formalize this challenge as the MDI-QKD Hub Deployment (MHD) problem. We first prove that the MHD problem is NP-hard, and then formulate it as a Mixed-Integer Linear Programming (MILP). To the best of our knowledge, this is the first formulation for MDI-QKD network planning that captures the structural features specific to MDI-QKD, namely a shared BSM hub, two-link user-to-hub routing, and loss-balancing constraints, while jointly determining hub placement, user-to-hub assignment, and trusted-relay demand allocation. Our solution accounts for realistic budget and capacity constraints, including practical insights from real-world deployments of commercial MDI-QKD solutions. Numerical evaluations on three metropolitan topologies (12 to 50 nodes) at realistic geographic distances show that topology structure governs trusted-relay demand: hub placement alone eliminates all trusted relays in compact urban meshes, while in sparse topologies, relaxing the loss-balancing constraint removes the dominant source of trusted-relay usage at no additional infrastructure cost. The key-rate threshold at which trusted relays first appear shifts monotonically with the network diameter, i.e., the largest shortest-path distance between any node pair in fiber kilometers, delineating the feasibility boundary of pure MDI-QKD deployment.

cs.NI↗
arXiv2026

LLMs and Optical Networks: A Symbiotic Relationship

This paper explores the emerging symbiosis between LLMs and optical networks. Massive LLMs require geo-distributed training, which demands advanced optical transport capabilities that require new key technical enablers, as WAN-aware CCL algorithms, ZR+ pluggables, and Hollow Core Fibers. Conversely, LLMs also enable new forms of autonomous network management.

cs.NI↗
arXiv2026

Multi-Modal Style Transfer-based Prompt Tuning for Efficient Federated Domain Generalization

Federated Domain Generalization (FDG) aims to collaboratively train a global model across distributed clients that can generalize well on unseen domains. However, existing FDG methods typically struggle with cross-client data heterogeneity and incur significant communication and computation overhead. To address these challenges, this paper presents a new FDG framework, dubbed FaST-PT, which facilitates local feature augmentation and efficient unseen domain adaptation in a distributed manner. First, we propose a lightweight Multi-Modal Style Transfer (MST) method to transform image embedding under text supervision, which could expand the training data distribution and mitigate domain shift. We then design a dual-prompt module that decomposes the prompt into global and domain prompts. Specifically, global prompts capture general knowledge from augmented embedding across clients, while domain prompts capture domain-specific knowledge from local data. Besides, Domain-aware Prompt Generation (DPG) is introduced to adaptively generate suitable prompts for each sample, which facilitates unseen domain adaptation through knowledge fusion. Extensive experiments on four cross-domain benchmark datasets, e.g., PACS and DomainNet, demonstrate the superior performance of FaST-PT over SOTA FDG methods such as FedDG-GA and DiPrompt. Ablation studies further validate the effectiveness and efficiency of FaST-PT.

cs.DC↗
arXiv2025

Routing and Wavelength Assignment with Minimal Attack Radius for QKD Networks

Quantum Key Distribution (QKD) can distribute keys with guaranteed security but remains susceptible to key exchange interruption due to physical-layer threats, such as high-power jamming attacks. To address this challenge, we first introduce a novel metric, namely Maximum Number of Affected Requests (maxNAR), to quantify the worst-case impact of a single physical-layer attack, and then we investigate a new problem of Routing and Wavelength Assignment with Minimal Attack Radius (RWA-MAR). We formulate the problem using an Integer Linear Programming (ILP) model and propose a scalable heuristic to efficiently minimize maxNAR. Our approach incorporates key caching through Quantum Key Pools (QKPs) to enhance resilience and optimize resource utilization. Moreover, we model the impact of different QKD network architectures, employing Optical Bypass (OB) for optical switching of quantum channels and Trusted Relay (TR) for secure key forwarding. Moreover, a tunable parameter is designed in the heuristic to guide the preference for OB or TR, offering enhanced adaptability and dynamic control in diverse network scenarios. Simulation results confirm that our method significantly outperforms the baseline in terms of security and scalability.

quant-ph↗
arXiv2025

Cost and Power-Consumption Analysis for Power Profile Monitoring with Multiple Monitors per Link in Optical Networks

As deploying large amounts of monitoring equipment results in elevated cost and power consumption, novel low-cost monitoring methods are being continuously investigated. A new technique called Power Profile Monitoring (PPM) has recently gained traction thanks to its ability to monitor an entire lightpath using a single post-processing unit at the lightpath receiver. PPM does not require to deploy an individual monitor for each span, as in the traditional monitoring technique using Optical Time-Domain Reflectometer (OTDR). In this work, we aim to quantify the cost and power consumption of PPM (using OTDR as a baseline reference), as this analysis can provide guidelines for the implementation and deployment of PPM. First, we discuss how PPM and OTDR monitors are deployed, and we formally state a new Optimized Monitoring Placement (OMP) problem for PPM. Solving the OMP problem allows to identify the minimum number of PPM monitors that guarantees that all links in the networks are monitored by at least $n$ PPM monitors (note that using $n>1$ allows for increased monitoring accuracy). We prove the NP-hardness of the OMP problem and formulate it using an Integer Linear Programming (ILP) model. Finally, we also devise a heuristic algorithm for the OMP problem to scale to larger topologies. Our numerical results, obtained on realistic topologies, suggest that the cost (and power) of one PPM module should be lower than 2.6 times that of one OTDR for nation-wide and 10.2 times for continental-wide topology.

cs.NI↗
arXiv2025

Multi-Failure Localization in High-Degree ROADM-based Optical Networks using Rules-Informed Neural Networks

To accommodate ever-growing traffic, network operators are actively deploying high-degree reconfigurable optical add/drop multiplexers (ROADMs) to build large-capacity optical networks. High-degree ROADM-based optical networks have multiple parallel fibers between ROADM nodes, requiring the adoption of ROADM nodes with a large number of inter-/intra-node components. However, this large number of inter-/intra-node optical components in high-degree ROADM networks increases the likelihood of multiple failures simultaneously, and calls for novel methods for accurate localization of multiple failed components. To the best of our knowledge, this is the first study investigating the problem of multi-failure localization for high-degree ROADM-based optical networks. To solve this problem, we first provide a description of the failures affecting both inter-/intra-node components, and we consider different deployments of optical power monitors (OPMs) to obtain information (i.e., optical power) to be used for automated multi-failure localization. Then, as our main and original contribution, we propose a novel method based on a rules-informed neural network (RINN) for multi-failure localization, which incorporates the benefits of both rules-based reasoning and artificial neural networks (ANN). Through extensive simulations and experimental demonstrations, we show that our proposed RINN algorithm can achieve up to around 20 higher localization accuracy compared to baseline algorithms, incurring only around 4.14 ms of average inference time.

cs.NI↗
arXiv2024

Poster: Flexible Scheduling of Network and Computing Resources for Distributed AI Tasks

Many emerging Artificial Intelligence (AI) applications require on-demand provisioning of large-scale computing, which can only be enabled by leveraging distributed computing services interconnected through networking. To address such increasing demand for networking to serve AI tasks, we investigate new scheduling strategies to improve communication efficiency and test them on a programmable testbed. We also show relevant challenges and research directions.

cs.NI↗