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1. Resource Placement for Rate and Fidelity Maximization in Quantum Networks

   https://doi.org/10.1109/TQE.2024.3432390  

   Pouryousef, Shahrooz; Shapourian, Hassan; Shabani, Alireza; Kompella, Ramana; Towsley, Don (January 2024, IEEE Transactions on Quantum Engineering)

   Existing classical optical network infrastructure cannot be immediately used for quantum network applications due to photon loss. The first step toward enabling quantum networks is the integration of quantum repeaters into optical networks. However, the expenses and intrinsic noise inherent in quantum hardware underscore the need for an efficient deployment strategy that optimizes the placement of quantum repeaters and memories. In this article, we present a comprehensive framework for network planning, aiming to efficiently distribute quantum repeaters across existing infrastructure, with the objective of maximizing quantum network utility within an entanglement distribution network. We apply our framework to several cases including a preliminary illustration of a dumbbell network topology and real-world cases of the SURFnet and ESnet. We explore the effect of quantum memory multiplexing within quantum repeaters, as well as the influence of memory coherence time on quantum network utility. We further examine the effects of different fairness assumptions on network planning, uncovering their impacts on real-time network performance. 

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2. Domain adaptation and transfer learning for failure detection and failure-cause identification in optical networks across different lightpaths [Invited]

   https://doi.org/10.1364/JOCN.438269  

   Musumeci, Francesco; Venkata, Virajit_Garbhapu; Hirota, Yusuke; Awaji, Yoshinari; Xu, Sugang; Shiraiwa, Masaki; Mukherjee, Biswanath; Tornatore, Massimo (December 2021, Journal of Optical Communications and Networking)

   Optical network failure management (ONFM) is a promising application of machine learning (ML) to optical networking. Typical ML-based ONFM approaches exploit historical monitored data, retrieved in a specific domain (e.g., a link or a network), to train supervised ML models and learn failure characteristics (a signature) that will be helpful upon future failure occurrence in that domain. Unfortunately, in operational networks, data availability often constitutes a practical limitation to the deployment of ML-based ONFM solutions, due to scarce availability of labeled data comprehensively modeling all possible failure types. One could purposely inject failures to collect training data, but this is time consuming and not desirable by operators. A possible solution is transfer learning (TL), i.e., training ML models on a source domain (SD), e.g., a laboratory testbed, and then deploying trained models on a target domain (TD), e.g., an operator network, possibly fine-tuning the learned models by re-training with few TD data. Moreover, in those cases when TL re-training is not successful (e.g., due to the intrinsic difference in SD and TD), another solution is domain adaptation, which consists of combining unlabeled SD and TD data before model training. We investigate domain adaptation and TL for failure detection and failure-cause identification across different lightpaths leveraging real optical SNR data. We find that for the considered scenarios, up to 20% points of accuracy increase can be obtained with domain adaptation for failure detection, while for failure-cause identification, only combining domain adaptation with model re-training provides significant benefit, reaching 4%–5% points of accuracy increase in the considered cases. 

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3. Adaptive Privacy Preserving Deep Learning Algorithms for Medical Data

   Zhang, Xinyue; Ding, Jiahao; Wu, Maoqiang; Wong, Stephen TC.; Nguyen, Hien V.; Pan, Miao (January 2021, IEEE Winter Conference on Applications of Computer Vision)

   null (Ed.)

   Deep learning holds a great promise of revolutionizing healthcare and medicine. Unfortunately, various inference attack models demonstrated that deep learning puts sensitive patient information at risk. The high capacity of deep neural networks is the main reason behind the privacy loss. In particular, patient information in the training data can be unintentionally memorized by a deep network. Adversarial parties can extract that information given the ability to access or query the network. In this paper, we propose a novel privacy-preserving mechanism for training deep neural networks. Our approach adds decaying Gaussian noise to the gradients at every training iteration. This is in contrast to the mainstream approach adopted by Google's TensorFlow Privacy, which employs the same noise scale in each step of the whole training process. Compared to existing methods, our proposed approach provides an explicit closed-form mathematical expression to approximately estimate the privacy loss. It is easy to compute and can be useful when the users would like to decide proper training time, noise scale, and sampling ratio during the planning phase. We provide extensive experimental results using one real-world medical dataset (chest radiographs from the CheXpert dataset) to validate the effectiveness of the proposed approach. The proposed differential privacy based deep learning model achieves significantly higher classification accuracy over the existing methods with the same privacy budget. 

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4. Dynamic network simulation using DeepRMSA in Elastic Optical Networks

   https://doi.org/10.1109/ANTS56424.2022.10227763  

   Hu, Boyang; Majid, Mohammad Jafar; Ramamurthy, Byrav (December 2022, 2022 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS))

   In optical networks, simulation is a cost-efficient and powerful way for network planning and design. It helps researchers and network designers quickly obtain preliminary results on their network performance and easily adjust the design. Unfortunately, most optical simulators are not open-source and there is currently a lack of optical network simulation tools that leverage machine learning techniques for network simulation. Compared to Wavelength Division Multiplexing (WDM) networks, Elastic Optical Networks (EON) use finer channel spacing, a more flexible way of using spectrum resources, thus increasing the network spectrum efficiency. Network resource allocation is a popular research topic in optical networks. In EON, this problem is classified as Routing, Modulation and Spectrum Allocation (RMSA) problem, which aims to allocate sufficient network resources by selecting the optimal modulation format to satisfy a call request. SimEON is an open-source simulation tool exclusively for EON, capable of simulating different EON setup configurations, designing RMSA and regenerator placement/assignment algorithms. It could also be extended with proper modelings to simulate CapEx, OpEx and energy consumption for the network. Deep learning (DL) is a subset of Machine Learning, which employs neural networks, large volumes of data and various algorithms to train a model to solve complex problems. In this paper, we extended the capabilities of SimEON by integrating the DeepRMSA algorithm into the existing simulator. We compared the performance of conventional RMSA and DeepRMSA algorithms and provided a convenient way for users to compare different algorithms’ performance and integrate other machine learning algorithms. 

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5. Physical-layer impairment estimation for arbitrary spectral-shaped signals in optical networks

   https://doi.org/10.1364/OE.451806  

   Xu, Yuxin; Brandt-Pearce, Maite (May 2022, Optics Express)

   In long-haul fiber-optic networks, precise modeling of physical-layer impairments (PLIs) is crucial to optimizing network resource usage while ensuring adequate transmission quality. In order to accurately estimate PLIs, many mathematical models have been proposed. Among them, the so-called Gaussian noise (GN) model is one of the most accurate and simple enough to use on complex continental-size networks. However, the closed-form GN model assumes that the signals can be represented as having rectangular spectra, leading to a significant estimation error in typical cases when this assumption is violated. We propose the component-wise Gaussian noise (CWGN) PLI model that can account for arbitrary spectral-shaped demands. The CWGN model is computationally simple and suitable for most network management approaches. Results indicate that the CWGN model can prevent as much as a 136% overestimation of the PLIs resulting from the closed-form GN model applied to network lightpaths containing cascaded filters. 

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