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Kun Qiu

Publications and source records attributed to Kun Qiu.

At least 19 recordsLinked to original sources
arXiv2026

Quantum-interference metrology of dissipative Kerr solitons

Dissipative Kerr solitons in optical microresonators underpin chip-scale frequency combs with applications ranging from coherent telecommunications to precision spectroscopy. Yet the characterization of their intrinsic femtosecond temporal structure remains challenging, as the low pulse energy and broad spectral bandwidth necessitate optical amplification and careful dispersion compensation in conventional ultrafast diagnostics, both of which can significantly distort the waveform. Here we demonstrate a quantum-interference metrology of microcomb solitons based on Hong-Ou-Mandel interference. By attenuating the soliton stream to the single-photon level and measuring fourth-order interference, we directly retrieve near transform-limited pulse durations without amplification or dispersion management, remaining accurate even after propagation through 25 km of standard fiber. The same interferogram also provides direct access to the temporal separations in multi-soliton states by converting inter-soliton separations into additional interference dips at corresponding delays, enabling sub-picosecond characterization of their intracavity temporal structure. This quantum-inspired paradigm introduces a fundamentally new metrological approach that is immune to amplification and dispersion distortions, offering a powerful tool for the characterization of complex soliton physics.

quant-ph↗
arXiv2026

Agon: A Semi-Supervised Framework for Robust Satellite Interference Detection

The rapid expansion of non-geostationary orbit (NGSO) satellites alongside existing geostationary orbit (GSO) systems has intensified spectrum congestion and inter-system interference, placing stringent demands on real-time interference management to sustain reliable coexistence in next-generation communication networks. While existing machine learning (ML)-based reconstruction models have made strides, they remain constrained to an area under the curve (AUC) of 0.83 due to fixed thresholds, causing unacceptable false alarm rates that undermine critical link reliability. Additionally, their decoupled training paradigm neglects cross-domain dependencies, limiting time and frequency-domain AUCs to 0.83 and 0.71, respectively. To address these limitations, this paper introduces a semi-supervised satellite interference detection framework named Agon, employing a novel two-stage hybrid learning paradigm. Agon integrates masked autoencoder (MAE) pre-training of a dual attention transformer (DAT) with multi-task fine-tuning to optimize a direct binary classifier, effectively eliminating unstable thresholds. Furthermore, it incorporates high-order statistics (HOS)-augmented attention and wavelet regularization to bolster noise robustness and structural fidelity. Extensive validation on public NGSO-GSO dataset and a high-fidelity NGSO-NGSO dataset demonstrates that Agon achieves state-of-the-art (SOTA) detection performance, with a 25.3% improvement in AUC. Moreover, the multi-task learning (MTL) framework facilitates accurate modulation classification with accuracies exceeding 90%, while simultaneously maintaining optimal detection performance across diverse scenarios characterized by varying off-axis angles and interference-to-noise ratios (INRs).

cs.NI↗
arXiv2026

Aidos: A Hybrid Optimization Algorithm for Beam Hopping Scheduling in NGSO Mega-Constellations

With the rapid proliferation of non-geostationary orbit (NGSO) mega-constellations, beam hopping (BH) has become indispensable for resource scheduling in multi-satellite, multi-coverage scenarios. By dynamically adjusting spot beam power and pointing within each time slot, BH enables highly efficient spectrum utilization. A principal engineering challenge is the real-time generation of beam hopping time plans (BHTP). Traditional algorithms, such as the round-robin strategy, distribute beams evenly across all service cells in a round-robin fashion. However, real traffic follows a long-tail distribution; the most active 10% of hotspot cells generate more than 50% of the aggregate demand, making uniform allocation inadequate. To address this issue, existing frameworks adopt a genetic algorithm (GA), whose throughput is approximately 80.7% higher than the traditional baseline. Operational satellite footprints encompass more than 1,000 service cells. The GA requires 67.8 s to generate a BHTP for 1,127 cells. With a 550 km LEO satellite providing only a 300 s visibility window, multiple online recomputations are impractical. State-of-the-art algorithms, such as multi-agent deep reinforcement learning (MADRL), fail to converge once the cell count exceeds 200. To overcome these challenges, we propose a novel BH scheduling algorithm Aidos. The algorithm integrates traffic-aware random-key encoding into a multi-objective metaheuristic search, and then applies a sliding-window Beta resampling strategy during adaptive distribution evolution, to improve both the search efficiency and the solution quality of the BHTP. Experiments demonstrate that Aidos improves throughput by 79.2% and reduces latency by 99.45%. Its average computation time is 9.3 s, enabling online replanning within a 300 s satellite overpass window.

cs.NI↗
arXiv2026

Metasurface-assisted balanced-injection synchronization for turbulence-resilient long-haul chaotic free-space link

Optical chaotic synchronization between coupled nonlinear lasers underpins most chaos-based applications, including complex laser network dynamics, secure communication, key distribution, and reinforcement learning. In free-space links, however, chaotic synchronization is highly vulnerable to stochastic fluctuation induced by atmospheric turbulence, which results in temporal injection imbalances at symmetric receivers and triggers intermittent desynchronization. Here, we introduce a full Poincar\'e vector beam-enabled balanced-injection synchronization (BIS) mechanism, which passively mitigates coupling fluctuations and preserves injection symmetry through a complementary metasurface pair, without requiring any channel estimation or active control. Over a 3.2 km urban link under moderately strong turbulence, BIS suppresses coupling power fluctuations by a factor of 4.6 (from 0.4511 to 0.0975). It eliminates desynchronization events and increases the high-quality synchronization probability from 58.6% to 91.0%. This enables a record-high bit rate-distance product of 720 Gbps \cdot km, reducing communication interruption probability by up to 77% compared to Gaussian beam transmission. Our innovative strategy bridges the gap between nanophotonics and engineering optics, offering a new insight into advancing next-generation LiDAR, secure communication, and integrated sensing and communication systems in turbulent environments.

physics.optics↗
arXiv2025

Tyche: A Hybrid Computation Framework of Illumination Pattern for Satellite Beam Hopping

High-Throughput Satellites (HTS) use beam hopping to handle non-uniform and time-varying ground traffic demand. A significant technical challenge in beam hopping is the computation of effective illumination patterns. Traditional algorithms, like the genetic algorithm, require over 300 seconds to compute a single illumination pattern for just 37 cells, whereas modern HTS typically covers over 300 cells, rendering current methods impractical for real-world applications. Advanced approaches, such as multi-agent deep reinforcement learning, face convergence issues when the number of cells exceeds 40. In this paper, we introduce Tyche, a hybrid computation framework designed to address this challenge. Tyche incorporates a Monte Carlo Tree Search Beam Hopping (MCTS-BH) algorithm for computing illumination patterns and employs sliding window and pruning techniques to significantly reduce computation time. Specifically, MCTS-BH can compute one illumination pattern for 37 cells in just 12 seconds. To ensure real-time computation, we use a Greedy Beam Hopping (G-BH) algorithm, which provides a provisional solution while MCTS-BH completes its computation in the background. Our evaluation results show that MCTS-BH can increase throughput by up to 98.76%, demonstrating substantial improvements over existing solutions.

cs.NI↗
arXiv2025

Eunomia: A Multicontroller Domain Partitioning Framework in Hierarchical Satellite Network

With the rise of mega-satellite constellations, the integration of hierarchical non-terrestrial and terrestrial networks has become a cornerstone of 6G coverage enhancements. In these hierarchical satellite networks, controllers manage satellite switches within their assigned domains. However, the high mobility of LEO satellites and field-of-view (FOV) constraints pose fundamental challenges to efficient domain partitioning. Centralized control approaches face scalability bottlenecks, while distributed architectures with onboard controllers often disregard FOV limitations, leading to excessive signaling overhead. LEO satellites outside a controller's FOV require an average of five additional hops, resulting in a 10.6-fold increase in response time. To address these challenges, we propose Eunomia, a three-step domain-partitioning framework that leverages movement-aware FOV segmentation within a hybrid control plane combining ground stations and MEO satellites. Eunomia reduces control plane latency by constraining domains to FOV-aware regions and ensures single-hop signaling. It further balances traffic load through spectral clustering on a Control Overhead Relationship Graph and optimizes controller assignment via the Kuhn-Munkres algorithm. We implement Eunomia on the Plotinus emulation platform with realistic constellation parameters. Experimental results demonstrate that Eunomia reduces request loss by up to 58.3%, control overhead by up to 50.3\%, and algorithm execution time by 77.7% significantly outperforming current state-of-the-art solutions.

cs.NI↗
arXiv2025

Hyperflex: A SIMD-based DFA Model for Deep Packet Inspection

Deep Packet Inspection (DPI) has been extensively employed for network security. It examines traffic payloads by searching for regular expressions (regex) with the Deterministic Finite Automaton (DFA) model. However, as the network bandwidth and ruleset size are increasing rapidly, the conventional DFA model has emerged as a significant performance bottleneck of DPI. Leveraging the Single-Instruction-Multiple-Data (SIMD) instruction to perform state transitions can substantially boost the efficiency of the DFA model. In this paper, we propose Hyperflex, a novel SIMD-based DFA model designed for high-performance regex matching. Hyperflex incorporates a region detection algorithm to identify regions suitable for acceleration by SIMD instructions across the whole DFA graph. Also, we design a hybrid state transition algorithm that enables state transition in both SIMD-accelerated and normal regions, and ensures seamless state transition across the two types of regions. We have implemented Hyperflex on the commodity CPU and evaluated it with real network traffic and DPI regexes. Our evaluation results indicate that Hyperflex reaches a throughput of 8.89Gbit/s, representing an improvement of up to 2.27 times over Mcclellan, the default DFA model of the prominent multi-pattern regex matching engine Hyperscan. As a result, Hyperflex has been successfully deployed in Hyperscan, significantly enhancing its performance.

cs.NI↗
arXiv2025

Unsupervised Dataset Cleaning Framework for Encrypted Traffic Classification

Traffic classification, a technique for assigning network flows to predefined categories, has been widely deployed in enterprise and carrier networks. With the massive adoption of mobile devices, encryption is increasingly used in mobile applications to address privacy concerns. Consequently, traditional methods such as Deep Packet Inspection (DPI) fail to distinguish encrypted traffic. To tackle this challenge, Artificial Intelligence (AI), in particular Machine Learning (ML), has emerged as a promising solution for encrypted traffic classification. A crucial prerequisite for any ML-based approach is traffic data cleaning, which removes flows that are not useful for training (e.g., irrelevant protocols, background activity, control-plane messages, and long-lived sessions). Existing cleaning solutions depend on manual inspection of every captured packet, making the process both costly and time-consuming. In this poster, we present an unsupervised framework that automatically cleans encrypted mobile traffic. Evaluation on real-world datasets shows that our framework incurs only a 2%~2.5% reduction in classification accuracy compared with manual cleaning. These results demonstrate that our method offers an efficient and effective preprocessing step for ML-based encrypted traffic classification.

cs.NI↗
arXiv2025

ReWeave: Traffic Engineering with Robust Path Weaving for Localized Link Failure Recover

Link failures occur frequently in Internet Service Provider (ISP) networks and pose significant challenges for Traffic Engineering (TE). Existing TE schemes either reroute traffic over vulnerable static paths, leading to performance degradation, or precompute backup routes for a broad range of failure scenarios, which introduces high overhead and limits scalability. Hence, an effective failure recovery mechanism is required to offer sufficient path diversity under constrained overhead, thereby ensuring robust and performant network operation. This paper presents ReWeave, a scalable and efficient link-level TE scheme that enables localized rerouting by equipping each link with a compact set of adjacent-only backup paths. Upon detecting a failure, only the routers at both ends of the failed link reroute traffic dynamically using SRv6-based detours, without controller intervention or full-path recomputation. Evaluation results on large-scale backbone networks demonstrate that ReWeave outperforms existing TE schemes in link failure scenarios. Compared to HARP, the state-of-the-art failure recovery scheme based on centralized control and dynamic traffic reallocation, our approach reduces the average maximum link utilization by 10.5%~20.1%, and lowers the worst-case utilization by 29.5%~40.9%. When compared with Flexile, a protection-based scheme that precomputes routes for multi-failure scenarios, ReWeave achieves a similarly low packet loss rate in 90% of failure cases, while maintaining a response speed comparable to the fastest router-based local rerouting schemes.

cs.NI↗
arXiv2025

Dora: A Controller Provisioning Strategy in Hierarchical Domain-based Satellite Networks

The rapid proliferation of satellite constellations in Space-Air-Ground Integrated Networks (SAGIN) presents significant challenges for network management. Conventional flat network architectures struggle with synchronization and data transmission across massive distributed nodes. In response, hierarchical domain-based satellite network architectures have emerged as a scalable solution, highlighting the critical importance of controller provisioning strategies. However, existing network management architectures and traditional search-based algorithms fail to generate efficient controller provisioning solutions due to limited computational resources in satellites and strict time constraints. To address these challenges, we propose a three-layer domain-based architecture that enhances both scalability and adaptability. Furthermore, we introduce Dora, a reinforcement learning-based controller provisioning strategy designed to optimize network performance while minimizing computational overhead. Our comprehensive experimental evaluation demonstrates that Dora significantly outperforms state-of-the-art benchmarks, achieving 10% improvement in controller provisioning quality while requiring only 1/30 to 1/90 of the computation time compared to traditional algorithms. These results underscore the potential of reinforcement learning approaches for efficient satellite network management in next-generation SAGIN deployments.

cs.NI↗
arXiv2025

Stabilizing and Optimizing Inter-Shell Routing in LEO Networks with Integrated Routing Cost

The low Earth orbit (LEO) mega-constellation network (LMCN), which uses thousands of satellites across multi-shell architectures to deliver different services, is facing challenges in inter-shell routing stability due to dynamic network topologies and frequent inter-satellite link (ISL) switching. Existing strategies, such as the Minimum Hop Path set, prioritize minimizing hop counts to reduce latency, but ignore ISL switching costs, which leads to high instability. To overcome this, the Adaptive Path Routing Scheme introduces path similarity thresholds to reduce the ISL switching frequency between shells. However, the greedy approach of Adaptive Path Routing Scheme is often trapped in local optima, sacrificing inter-shell path distance efficiency. To address these limitations, we propose the Dynamic Programming-based Integrated Routing Cost (DP-IRC) algorithm, which is designed explicitly for inter-shell routing optimization. By formulating multi-shell paths as a multistage decision problem, DP-IRC balances hop counts and ISL stability through an Integrated Routing Cost (IRC) metric, combining inter-/intra-shell hops and switching costs. Experiments over 60 time slots with real-world Starlink and OneWeb configurations show that DP-IRC reduces inter-shell ISL switching rates by 39.1% and 22.0% compared to the Minimum Hop Path set strategy and Adaptive Path Routing Scheme, respectively, while still maintaining near-optimal end-to-end distances.

cs.NI↗
arXiv2025

Super-efficient optical frequency division referenced to {\mu}Hz Schawlow-Townes-linewidth quantum-noise-limited lasers

Optical frequency division (OFD) implements the conversion of ultra-stable optical frequencies into microwave frequencies through an optical frequency comb flywheel, generating microwave oscillators with record-low phase noise and time jitter. However, conventional OFD systems face significant trade-off between division complexity and noise suppression due to severe thermal noise and technical noise in the optical frequency references. Here, we address this challenge by generating common-cavity bi-color Brillouin lasers as the optical frequency references, which operate at the fundamental quantum noise limit with Schawlow-Townes linewidth on the 10 {\mu}Hz level. Enabled by these ultra-coherent reference lasers, our OFD system uses a dramatically simplified comb divider with an unprecedented small division factor of 10, and generates 10 GHz microwave signal with exceptional phase noise of -65 dBc/Hz at 1Hz, -155 dBc/Hz at 10 kHz, and -172 dBc/Hz at 10 MHz offset. Moreover, to fully harness the spectral purity of the OFD technology, here we implement broadband frequency synthesis directly referenced to the OFD oscillator, covering 5 to 20 GHz with millisecond tuning time. Our work redefines the trade-off between noise suppression and division complexity in OFD, paving the way for compact, high-performance microwave synthesis for next-generation atomic clocks, quantum sensors, and low-noise radar systems.

physics.optics↗
arXiv2025

DualAttWaveNet: Multiscale Attention Networks for Satellite Interference Detection

The escalating overlap between non-geostationary orbit (NGSO) and geostationary orbit (GSO) satellite frequency allocations necessitates accurate interference detection methods that address two pivotal technical gaps: computationally efficient signal analysis for real-time operation, and robust anomaly discrimination under varying interference patterns. Existing deep learning approaches employ encoder-decoder anomaly detectors that threshold input-output discrepancies for robustness. While the transformer-based TrID model achieves state-of-the-art performance (AUC: 0.8318, F1: 0.8321), its multi-head attention incurs prohibitive computation time, and its decoupled training of time-frequency models overlooks cross-domain dependencies. To overcome these problems, we propose DualAttWaveNet. A bidirectional attention fusion layer dynamically correlates time-domain samples using parameter-efficient cross-attention routing. A wavelet-regularized reconstruction loss enforces multi-scale consistency. We train the model on public dataset which consists of 48 hours of satellite signals. Experiments show that compared to TrID, DualAttWaveNet improves AUC by 12% and reduces inference time by 50% to 540ms per batch while maintaining F1-score.

eess.SP↗
arXiv2024

Efficient Satellite-Ground Interconnection Design for Low-orbit Mega-Constellation Topology

The low-orbit mega-constellation network (LMCN) is an important part of the space-air-ground integrated network system. An effective satellite-ground interconnection design can result in a stable constellation topology for LMCNs. A naive solution is accessing the satellite with the longest remaining service time (LRST), which is widely used in previous designs. The Coordinated Satellite-Ground Interconnecting (CSGI), the state-of-the-art algorithm, coordinates the establishment of ground-satellite links (GSLs). Compared with existing solutions, it reduces latency by 19% and jitter by 70% on average. However, CSGI only supports the scenario where terminals access only one satellite and cannot fully utilize the multi-access capabilities of terminals. Additionally, CSGI's high computational complexity poses deployment challenges. To overcome these problems, we propose the Classification-based Longest Remaining Service Time (C-LRST) algorithm. C-LRST supports the actual scenario with multi-access capabilities. It adds optional paths during routing with low computational complexity, improving end-to-end communications quality. We conduct our 1000s simulation from Brazil to Lithuania on the open-source platform Hypatia. Experiment results show that compared with CSGI, C-LRST reduces the latency and increases the throughput by approximately 60% and 40%, respectively. In addition, C-LRST's GSL switching number is 14, whereas CSGI is 23. C-LRST has better link stability than CSGI.

cs.NI↗
arXiv2024

LEO Satellite Networks Assisted Geo-distributed Data Processing

Nowadays, the increasing deployment of edge clouds globally provides users with low-latency services. However, connecting an edge cloud to a core cloud via optic cables in terrestrial networks poses significant barriers due to the prohibitively expensive building cost of optic cables. Fortunately, emerging Low Earth Orbit (LEO) satellite networks (e.g., Starlink) offer a more cost-effective solution for increasing edge clouds, and hence large volumes of data in edge clouds can be transferred to a core cloud via those networks for time-sensitive big data tasks processing, such as attack detection. However, the state-of-the-art satellite selection algorithms bring poor performance for those processing via our measurements. Therefore, we propose a novel data volume aware satellite selection algorithm, named DVA, to support such big data processing tasks. DVA first takes into account both the data size in edge clouds and satellite capacity to finalize the selection, thereby preventing congestion in the access network and reducing transmitting duration. Extensive simulations validate that DVA has a significantly lower average access network duration than the state-of-the-art satellite selection algorithms in a LEO satellite emulation platform.

cs.NI↗
arXiv2024

Hurry: Dynamic Collaborative Framework For Low-orbit Mega-Constellation Data Downloading

Low-orbit mega-constellation network, which utilize thousands of satellites to provide a variety of network services and collect a wide range of space information, is a rapidly growing field. Each satellite collects TB-level data daily, including delay-sensitive data used for crucial tasks, such as military surveillance, natural disaster monitoring, and weather forecasting. According to NASA's statement, these data need to be downloaded to the ground for processing within 3 to 5 hours. To reduce the time required for satellite data downloads, the state-of-the-art solution known as CoDld, which is only available for small constellations, uses an iterative method for cooperative downloads via inter-satellite links. However, in LMCN, the time required to download the same amount of data using CoDld will exponentially increase compared to downloading the same amount of data in a small constellation. We have identified and analyzed the reasons for this degradation phenomenon and propose a new satellite data download framework, named Hurry. By modeling and mapping satellite topology changes and data transmission to Time-Expanded Graphs, we implement our algorithm within the Hurry framework to avoid degradation effects. In the fixed data volume download evaluation, Hurry achieves 100% completion of the download task while the CoDld only reached 44% of download progress. In continuous data generation evaluation, the Hurry flow algorithm improves throughput from 11% to 66% compared to the CoDld in different scenarios.

cs.NI↗
arXiv2024

Plotinus: A Satellite Internet Digital Twin System

The development of an integrated space-air-ground network (SAGIN) requires sophisticated satellite Internet emulation tools that can handle complex, dynamic topologies and offer in-depth analysis. Existing emulation platforms struggle with challenges like the need for detailed implementation across all network layers, real-time response, and scalability. This paper proposes a digital twin system based on microservices for satellite Internet emulation, namely Plotinus, which aims to solve these problems. Plotinus features a modular design, allowing for easy replacement of the physical layer to emulate different aerial vehicles and analyze channel interference. It also enables replacing path computation methods to simplify testing and deploying algorithms. In particular, Plotinus allows for real-time emulation with live network traffic, enhancing practical network models. The evaluation result shows Plotinus's effective emulation of dynamic satellite networks with real-world devices. Its adaptability for various communication models and algorithm testing highlights Plotinus's role as a vital tool for developing and analyzing SAGIN systems, offering a cross-layer, real-time and scalable digital twin system.

cs.NI↗