Relevant bibliographies by topics / QoT Estimation

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Academic literature on the topic 'QoT Estimation'

Author: Grafiati
Published: 4 June 2025
Last updated: 23 June 2025

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Journal articles on the topic "QoT Estimation"

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Zhou, Yuhang, Xiaoli Huo, Zhiqun Gu, et al. "Self-Attention Mechanism-Based Multi-Channel QoT Estimation in Optical Networks." Photonics 10, no. 1 (2023): 63. http://dx.doi.org/10.3390/photonics10010063.

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It is essential to estimate the quality of transmission (QoT) of lightpaths before their establishment for efficient planning and operation of optical networks. Due to the nonlinear effect of fibers, the deployed lightpaths influence the QoT of each other; thus, multi-channel QoT estimation is necessary, which provides complete QoT information for network optimization. Moreover, the different interfering channels have different effects on the channel under test. However, the existing artificial-neural-network-based multi-channel QoT estimators (ANN-QoT-E) neglect the different effects of the interfering channels in their input layer, which affects their estimation accuracy severely. In this paper, we propose a self-attention mechanism-based multi-channel QoT estimator (SA-QoT-E) to improve the estimation accuracy of the ANN-QoT-E. In the SA-QoT-E, the input features are designed as a sequence of feature vectors of channels that route the same path, and the self-attention mechanism dynamically assigns weights to the feature vectors of interfering channels according to their effects on the channel under test. Moreover, a hyperparameter search method is used to optimize the SA-QoT-E. The simulation results show that, compared with the ANN-QoT-E, our proposed SA-QoT-E achieves higher estimation accuracy, and can be directly applied to the network wavelength expansion scenarios without retraining.
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Tania, Panayiotou, Savva Giannis, Tomkos Ioannis, and Ellinas Georgios. "Decentralizing machine-learning-based QoT estimation for sliceable optical networks." IEEE/OSA Journal of Optical Communications and Networking 12, no. 7 (2020): 146–62. https://doi.org/10.1364/JOCN.387853.

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Dynamic network slicing has emerged as a promising and fundamental framework for meeting 5G’s diverse use cases. As machine learning (ML) is expected to play a pivotal role in the efficient control and management of these networks, in this work, we examine the ML-based quality-of-transmission (QoT) estimation problem under the dynamic network slicing context, where each slice has to meet a different QoT requirement. Specifically, we examine ML-based QoT frameworks with the aim of finding QoT model/s that are fine-tuned according to the diverse QoT requirements. Centralized and distributed frameworks are examined and compared according to their model accuracy, routing and spectrum allocation (RSA) accuracy, and CPU (training time) and RAM (memory) requirements.We show that the distributed QoT models outperform the centralized QoT model in accuracy and CPU usage. The RSA accuracy, i.e., measuring the accuracy of the models with regard to the QoT-aware RSA decisions, is sufficiently high for both frameworks. Regarding the RAM usage, as the distributed framework has to train in parallel several QoT models, it may require higher memory, especially as the number of diverse QoT requirements increases. This memory, however, tends to be reserved for a shorter period of time. Moreover, this work develops a dynamic multi-slice QoT-aware (RSA) framework that integrates the ML-based QoT models. The aim is to examine the network performance when the diverse QoT models are considered, as opposed to the state-of-the-art single-slice QoT-aware RSA approach where all connections/slices are provisioned according to a single QoT requirement. We show that the multi-slice QoT-aware RSA approach significantly improves network performance, a clear indicator that the commonly considered single-slice QoT-aware RSA approach may lead to connection overprovisioning.
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Hafsa, Maryam, Panayiotou Tania, and Ellinas Georgios. "Learning quantile QoT models to address uncertainty over unseen lightpaths." Computer Networks Volume 212, no. 108992 (2022): 1389–286. https://doi.org/10.1016/j.comnet.2022.108992.

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Uncertainty in quality-of-transmission (QoT) estimation is traditionally addressed through empirical, myopic margins, ignoring the fact that each unseen lightpath is subject to different levels of uncertainty. To address this limitation, in this work, deep quantile regression is leveraged to finer capture QoT estimation uncertainty through the inference of margins that act discriminative over the unseen lightpaths. Specifically, deep-quantile regression is applied to approximate QoT models capable of inferring the QoT of unseen lightpaths, according to a predefined level of certainty. Quantile models automatically account for the uncertainty during inference, without the need to consider additional empirical margins for decision-making (i.e., the margins are learned and considered upon inference). It is shown that quantile QoT models lead to significant margin reduction when compared to baseline myopic margin schemes, resulting in more confident and network efficient allocation decisions.
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Savva, Giannis, Tania Panayiotou, Ioannis Tomkos, and Georgios Ellinas. "Deep Graph Learning for QoT Estimation of Unseen Optical Sub-Network States: Capturing the Crosstalk Impact on the In-Service Lightpaths." Journal of Lightwave Technology 40, no. 4 (2021): 921–34. https://doi.org/10.1109/JLT.2021.3129646.

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In this work, deep graph convolutional neural networks (DGCNN) are applied for estimating the quality-of-transmission (QoT) of unseen network states in elastic optical networks (EONs) in the presence of physical layer impairments (PLIs), including inter- and intra-channel crosstalk (XT). The objective is to find a DGCNN-QoT model that accurately estimates network state feasibility. A network state is considered feasible if the QoT of the in-service lightpaths and of the lightpath under provisioning is sufficient; that is, the DGCNN does not only infer about the feasibility of an unestablished lightpath but also whether the feasibility of the in-service lightpaths will be affected by the establishment of a new lightpath due to XT. As DGCNN model generalization over unseen graphs is known to be negatively affected by the number of possible graphs and their dimensionality, problem uncertainty and complexity is reduced by formulating the QoT estimation problem over sub-network states, capturing only the spatio-temporal correlations that are relevant to the unestablished lightpath at decision time. DGCNN model accuracy is compared to a state-of-the-art deep neural network (DNN) model trained only over per-lightpath information. It is shown that DGCNN achieves accuracies above 92%, while DNN performs poorly with accuracies as low as 77%, as it fails to infer about the feasibility of in-service connections; an indicator of the importance of explicitly considering during the QoT model training, not only the lightpath patterns, but also the network-state patterns capturing the XT effect. Importantly, it is demonstrated that deep graph learning is a promising approach towards accomplishing this objective.
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Mahajan, Ankush, Konstantinos Christodoulopoulos, Ricardo Martínez, Salvatore Spadaro, and Raül Muñoz. "Modeling EDFA Gain Ripple and Filter Penalties with Machine Learning for Accurate QoT Estimation." Journal of Lightwave Technology, 38, no. 9 (2020): 2616–29. https://doi.org/10.1109/JLT.2020.2975081.

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For reliable and efficient network planning and operation, accurate estimation of Quality of Transmission (QoT) before establishing or reconfiguring the connection is necessary. In optical networks, a design margin is generally included in a QoT estimation tool (Qtool) to account for modeling and parameter inaccuracies, ensuring the acceptable performance. In this article, we use monitoring information from an operating network combined with supervised machine learning (ML) techniques to understand the network conditions. In particular, we model the penalties generated due to i) Erbium Doped Fiber Amplifier (EDFA) gain ripple effect, and ii) filter spectral shape uncertainties at Reconfigurable Optical Add and Drop Multiplexer (ROADM) nodes. Enhancing the Qtool with the proposed ML regression models yields estimates for new or reconfigured connections that account for these two effects, resulting in more accurate QoT estimation and a reduced design margin. We initially propose two supervised ML regression models, implemented with Support Vector Machine Regression (SVMR), to estimate the individual penalties of the two effects and then a combined model. On Deutsche Telekom (DT) network topology with 12 nodes and 40 bidirectional links, we achieve a design margin reduction of ~1 dB for new connection requests.
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Ghasrizadeh, Sadegh, Prasunika Khare, Nelson Costa, et al. "Digital Twin-Assisted Lightpath Provisioning and Nonlinear Mitigation in C+L+S Multiband Optical Networks." Sensors 24, no. 24 (2024): 8054. https://doi.org/10.3390/s24248054.

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Multiband (MB) optical transmission targets increasing the capacity of operators’ optical transport networks. However, nonlinear impairments (NLI) affect each optical channel in the C+L+S bands differently, and, therefore, the routing and spectrum assignment (RSA) problem needs to be complemented with fast and accurate tools to consider the quality of transmission (QoT) within the provisioning process. This paper proposes a digital twin-assisted approach for lightpath provisioning to provide a complete solution for the RSA problem that ensures the required QoT in MB optical networks. The OCATA time domain digital twin is proposed, not only to estimate the QoT of a selected path but also to support the QoT-based channel assignment process. OCATA is based on a Deep Neural Network (DNN) to model the propagation of the optical signal. However, because of the different impacts of nonlinear noise on each channel and the large number of channels that need to be considered in C+L+S MB scenarios, OCATA needs to be adapted to make it scalable, while keeping its high accuracy and fast QoT estimation characteristics. In consequence, a complete methodology is proposed in this work that limits the number of channels being modeled to just a few. Moreover, OCATA-MB helps to mitigate NLI noise by programming the receiver at the provisioning time and thus with very little complexity compared to its equivalent implemented during the operation. NLI noise mitigation can be applied in the case when a lightpath cannot be provisioned because none of the available channels can provide the required QoT, making it an advantageous tool for reducing connection blocking. Exhaustive simulation results demonstrate the remarkable accuracy of OCATA-MB in estimating the QoT for any channel. Interestingly, by utilizing the proposed OCATA-MB-assisted lightpath provisioning approach, a reduction of the blocking ratio exceeding 50% when compared to traditional approaches is shown when NLI noise mitigation is not applied. If NLI mitigation is implemented, an additional over 50% blocking reduction is achieved.
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Ibrahimi, Memedhe, Hatef Abdollahi, Cristina Rottondi, et al. "Machine learning regression for QoT estimation of unestablished lightpaths." Journal of Optical Communications and Networking 13, no. 4 (2021): B92. http://dx.doi.org/10.1364/jocn.410694.

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Panayiotou, Tania, Giannis Savva, Ioannis Tomkos, and Georgios Ellinas. "Decentralizing machine-learning-based QoT estimation for sliceable optical networks." Journal of Optical Communications and Networking 12, no. 7 (2020): 146. http://dx.doi.org/10.1364/jocn.387853.

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Khare, Prasunika, Nelson Costa, Marc Ruiz, et al. "Simulation and Modelling of C+L+S Multiband Optical Transmission for the OCATA Time Domain Digital Twin." Sensors 25, no. 6 (2025): 1948. https://doi.org/10.3390/s25061948.

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C+L+S multiband (MB) optical transmission has the potential to increase the capacity of optical transport networks, and thus, it is a possible solution to cope with the traffic increase expected in the years to come. However, the introduction of MB optical technology needs to come together with the needed tools that support network planning and operation. In particular, quality of transmission (QoT) estimation is needed for provisioning optical MB connections. In this paper, we concentrate on modelling MB optical transmission for provide fast and accurate QoT estimation and propose machine learning (ML) approaches based on neural networks, which can be easily integrated into an optical layer digital twin (DT) solution. We start by considering approaches that can be used for accurate signal propagation modelling. Even though solutions such as the split-step Fourier method (SSFM) for solving the nonlinear Schrödinger equation (NLSE) have limited application for QoT estimation during provisioning because of their very high complexity and time consumption, they could be used to generate datasets for ML model creation. However, even that can be hard to carry out on a fully loaded MB system with hundreds of channels. In addition, in MB optical transmission, interchannel stimulated Raman scattering (ISRS) becomes a major effect, which adds more complexity. In view of that, the fourth-order Runge–Kutta in the interaction picture (RK4IP) method, complemented with an adaptive step size algorithm to further reduce the computation time, is evaluated as an alternative to reduce time complexity. We show that RK4IP provided an accuracy comparable to that of the SSFM with reduced computation time, which enables its application for MB optical transmission simulation. Once datasets were generated using the adaptive step size RK4IP method, two ML modelling approaches were considered to be integrated in the OCATA DT, where models predict optical signal propagation in the time domain. Being able to predict the optical signal in the time domain, as it will be received after propagation, opens opportunities for automating network operation, including connection provisioning and failure management. In this paper, we focus on comparing the proposed ML modelling approaches in terms of the models’ general and QoT estimation accuracy.
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Sambo, Nicola, Yvan Pointurier, Filippo Cugini, Luca Valcarenghi, Piero Castoldi, and Ioannis Tomkos. "Lightpath Establishment Assisted by Offline QoT Estimation in Transparent Optical Networks." Journal of Optical Communications and Networking 2, no. 11 (2010): 928. http://dx.doi.org/10.1364/jocn.2.000928.

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Dissertations / Theses on the topic "QoT Estimation"

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Mahajan, Ankush. "Machine learning assisted QoT estimation for optical networks optimization." Doctoral thesis, Universitat Politècnica de Catalunya, 2021. http://hdl.handle.net/10803/672665.

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The tremendous increase in data traffic has spurred a rapid evolution of the optical networks for a reliable, affordable, cost effective and scalable network infrastructure. To meet some of these requirements, network operators are pushing toward disaggregation. Network disaggregation focuses on decoupling the traditional monolithic optical transport hardware into independent functional blocks that interoperate. This enables a relatively free market where the network operators/owners could choose the best-in-class equipment from different vendors overcoming the vendor lock-in, at better prices. In this multi-vendor disaggregation context, the used equipment would impact the physical layer and the overall network behavior. This results in increasing the uncertainty on the performance when compared to a traditional single vendor aggregated approach. For effective optical network planning, operation and optimization, it is necessary to estimate the Quality of Transmission (QoT) of the connections. Network designers are interested in accurate and fast QoT estimation for services to be established in a future or existing network. Typically, QoT estimation is performed using a Physical Layer Model (PLM) which is included in the QoT estimation tool or Qtool. A design margin is generally included in a Qtool to account for the modeling and parameter inaccuracies, to re-assure an acceptable performance. PLM accuracy is highly important as modeling errors translate into a higher design margin which in turn translate into wasted capacity or unwanted regeneration. Recently monitoring and machine learning (ML) techniques have been proposed to account for the actual network conditions and improving the accuracy of the PLM in single vendor networks. This in turn results in more accurate QoT estimation. The first part of the thesis focuses on the ML assisted accurate QoT estimation techniques. In this regard, we developed a model that uses monitoring information from an operating network combined with supervised ML regression techniques to understand the network conditions. In particular, we model the generated penalties due to i). EDFA gain ripple effect, and ii). filter spectral shape uncertainties at ROADM nodes. Furthermore, with the aim of improving the Qtool estimation accuracy in multi-vendor networks, we propose PLM extensions. In particular, we introduce four TP vendor dependent performance factors that capture the performance variations of multi-vendor TPs. To verify the potential improvement, we studied the following two use cases with the proposed PLM, to: i) optimize the transponders (TPs) launch power; and ii) reduce design margin in incremental planning. In consequence, the last part of this thesis aims at investigating and solving the issue of accuracy limitation of Qtool in dynamic optimization tasks. To keep the models aligned to the real conditions, the digital twin (DT) concept is gaining significant attention in the research community. The DT is more than a model of the system; it includes an evolving set of data, and a means to dynamically adjust the model. Based on the DT fundamentals, we devised and implemented an iterative closed control loop process that, after several intermediate iterations of the optimization algorithm, configures the network, monitors, and retrains the Qtool. For the Qtool retraining, we adopt a ML-based nonlinear regression fitting technique. The key advantage of this novel scheme is that whilst the network operates, the Qtool parameters are retrained according to the monitored information with the adopted ML model. Hence, the Qtool tracks the projected states intermediately calculated by the algorithm. This reduces the optimization time as opposed to directly probing and monitoring the network.<br>Las operadoras están impulsando el concepto de desagregación de red. Dicho concepto permite desacoplar el tradicional hardware de transporte óptico dispuesto de forma monolítica en bloques funcionales independientes que interoperan entre ellos. Como resultado, esta desagregación incentiva un mercado más abierto en el cual los operadores/propietarios de la red pueden elegir los mejores dispositivos de diferentes proveedores, eliminando el conocido como bloqueo/dependencia del proveedor, a precios más competitivos. En este contexto de desagregación con múltiples fabricantes, cada equipo afecta de forma independiente. Por lo tanto, la incertidumbre aumenta al compararlo con el rendimiento obtenido mediante un modelo más tradicional basado en agregación y dependiente de un único proveedor. Para una eficiente planificación y optimización de una red óptica, es necesario estimar la Quality of Transmission (QoT) de las conexiones. Los diseñadores de redes están interesados en una estimación precisa y rápida de la QoT para los servicios que se establezcan. Normalmente, la estimación de la QoT se realiza mediante un Physical Layer Model (PLM) que se incluye en la herramienta de estimación de la QoT o Qtool. Además, se incluye unos márgenes de diseño (design margin) dentro de la herramienta Qtool. Esto permite tener en cuenta las imprecisiones de modelado y de los parámetros y de esta forma asegurar un rendimiento aceptable. La precisión del PLM es muy importante, ya que los errores de modelado se traducen en un mayor design margin que, a su vez, se traduce en una pérdida de capacidad. Recientemente, importantes logros en la definición de PLMs para redes ópticas más precisos y rápidos se han alcanzado. Estos se basan en métodos tradicionales con soluciones analíticas o numéricas. La primera parte de la tesis se centra en las técnicas de estimación precisa de QoT asistidas por machine learning (ML). Se ha desarrollado un modelo que utiliza la información de monitorización de red combinada con técnicas de regresión ML supervisadas para comprender las condiciones de la red. En particular, se han modelado las penalizaciones generadas debido a: i) el efecto de gain ripple del EDFA, y ii) las incertidumbres de la forma espectral del filtro en los nodos ROADM. Además, con el objetivo de mejorar la precisión de la estimación del Qtool en redes que incluyen elementos de diferentes fabricantes (i.e., multi-proveedor), se han propuesto unas extensiones del PLM. Se han introducido cuatro factores de rendimiento dependientes del proveedor del transponder (TP) que capturan las variaciones de rendimiento de los TP de múltiples proveedores. Para verificar la mejora potencial, se han estudiado los siguientes dos casos de uso con el PLM propuesto: i) optimizar la potencia de lanzamiento de los TPs; y ii) reducir el design margin. La última parte de esta tesis ha tenido como objetivo investigar la cuestión de la limitación de la precisión del Qtool en las tareas de optimización dinámica. Para mantener los modelos alineados con las condiciones reales, el concepto de digital twin (DT) está ganando mucha atención. El DT incluye un conjunto de datos en evolución y un medio para ajustar dinámicamente el modelo. Basándonos en los fundamentos del DT, se ha ideado e implementado un proceso iterativo de bucle cerrado de control que, tras varias iteraciones intermedias del algoritmo de optimización, configura la red, supervisa y reentrena el Qtool. Para el reentrenamiento del Qtool, se ha adoptado una técnica de ajuste de regresión no lineal basada en ML. La principal ventaja es que, mientras la red funciona, los parámetros del Qtool se reentrenan según la información monitorizada con el modelo ML adoptado. Por lo tanto, el Qtool sigue los estados proyectados de forma intermedia calculados por el algoritmo. Esto reduce el tiempo de optimización en comparación con el sondeo y la monitorización directa<br>Teoria del senyal i comunicacions
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Knoll, Thomas Martin, and Marcus Eckert. "Improvement of network-based QoE estimation for TCP based streaming services." Universitätsbibliothek Chemnitz, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-147667.

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Progressive download video services, such as YouTube and podcasts, are responsible for a major part of the transmitted data volume in the Internet and it is expected, that they will also strongly affect mobile networks. Streaming video quality mainly depends on the sustainable throughput achieved during transmission. To ensure acceptable video quality in mobile networks (with limited capacity resources) the perceived quality by the customer (QoE) needs to be monitored by estimation. For that, the streaming video quality needs to be measured and monitored permanently. For TCP based progressive download we propose to extract the the video timestamps which are encoded within the payload of the TCP segments by decoding the video within the payload. The actual estimation is then done by play out buffer fill level calculations based on the TCP segment timestamp and their internal play out timestamp. The perceived quality for the user is derived from the number and duration of video stalls. Algorithms for decoding Flash Video, MP4 and WebM Video have already been implemented. After deriving the play out time it is compared to the timestamp of the respective TCP segment. The result of this comparison is an estimate of the fill level of the play out buffer in terms of play out time within the client. This estimation is done without access to the end device. The same measurement procedure can be applied for any TCP based progressive download Internet service. Video was simply taken as an example because of its current large share in traffic volume in operator networks.
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Parperis, Marios S. "Delay estimation and its QoS implications in voice over IP networks." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0024/MQ52394.pdf.

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Ferrari, Giovanna. "QoS control of E-business systems through performance modelling and estimation." Thesis, University of Newcastle Upon Tyne, 2007. http://hdl.handle.net/10443/2165.

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E-business systems provide the infrastructure whereby parties interact electronically via business transactions. At peak loads, these systems are susceptible to large volumes of transactions and concurrent users and yet they are expected to maintain adequate performance levels. Over provisioning is an expensive solution. A good alternative is the adaptation of the system, managing and controlling its resources. We address these concerns by presenting a model that allows fast evaluation of performance metrics in terms of measurable or controllable parameters. The model can be used in order to (a) predict the performance of a system under given or assumed loading conditions and (b) to choose the optimal configuration set-up for certain controllable parameters with respect to specified performance measures. Firstly, we analyze the characteristics of E-business systems. This analysis leads to the analytical model, which is sufficiently general to capture the behaviour of a large class of commonly encountered architectures. We propose an approximate solution which is numerically efficient and fast. By mean of simulation, we prove that its accuracy is acceptable over a wide range of system configurations and different load levels. We further evaluate the approximate solution by comparing it to a real-life E-business system. A J2EE application of non-trivial size and complexity is deployed on a 2-tier system composed of the JBoss application server and a database server. We implement an infrastructure fully integrated on the application server, capable of monitoring the E-business system and controlling its configuration parameters. Finally, we use this infrastructure to quantify both the static parameters of the model and the observed performance. The latter are then compared with the metrics predicted by the model, showing that the approximate solution is almost exact in predicting performance and that it assesses the optimal system configuration very accurately.
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Parperis, Marios S. (Marios Stavrou) Carleton University Dissertation Engineering Systems and Computer. "Delay estimation and its QoS implications in voice over IP networks." Ottawa, 2000.

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Srinivas, Sri Krishna. "On Added Value of Layer 4 ControlInformation for QoE Estimations." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-17059.

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Background: In the recent years, the focus of research has shifted to Quality of Experience(QoE) to maintain the user satisfaction levels and fulfill their expectations of the serviceoffered. Numerous work has been established in finding the relationship between the networklayer and QoE. But, it is fact that the transport layer is much closer to the end-user than thenetwork layer in the TCP/IP communication protocol suite. Thus, any changes in the degreeof satisfaction or degree of annoyance are directly reflected onto transport layer than on thenetwork layer. So, it becomes more significant to study the behavior of user satisfaction inrelation to transport layer than the network layer. Objectives: This research is to relate the behavior of TCP to QoE. The main considerations tobridge the gap between them are: (a) Analyzing the effects of using different versions of TCPon server and client side, (b) Monitoring and analyzing the intensity of TCP traffic in thereverse direction and (c) Investigating TCP control flags from client to server. Methods: QoE related parameters used in this research are: (a) Quality of video i.e., MOS, (b)Degree of disturbance caused by initial delay, (c) Degree of disturbance caused by jerkinessand (d) Degree of disturbance caused by freezes. Effects of network impairments like delay,jitter and packet loss are considered in this research. NetEm is used as the traffic emulationsoftware to shape the traffic. The packet capture analysis of traffic exchange is implementedusing tcpdump. Results and Conclusions: The aim of this research is to provide a passive-estimation methodto assess the user perceived performance. The results of this research provide valuablecontribution to service providers/operators to note the early warning signals from TCP reversetraffic to evaluate the decrease of user satisfaction level and try to cope or/and recover fromimpairments in the network. This research also provides a scope for future researchers toinvestigate other protocols both in transport and application layers.
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Niemelä, Markus. "Estimating Internet-scale Quality of Service Parameters for VoIP." Thesis, Linköpings universitet, Programvara och system, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-127360.

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With the rising popularity of Voice over IP (VoIP) services, understanding the effects of a global network on Quality of Service is critical for the providers of VoIP applications. This thesis builds on a model that analyzes the round trip time, packet delay jitter, and packet loss between endpoints on an Autonomous System (AS) level, extending it by mapping AS pairs onto an Internet topology. This model is used to produce a mean opinion score estimate. The mapping is introduced to reduce the size of the problem in order to improve computation times and improve accuracy of estimates. The results of testing show that estimating mean opinion score from this model is not desirable. It also shows that the path mapping does not affect accuracy, but does improve computation times as the input data grows in volume.
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Nocito, Carlos Daniel. "A Network Conditions Estimator for Voice Over IP Objective Quality Assessment." Scholarly Repository, 2011. http://scholarlyrepository.miami.edu/oa_theses/292.

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Objective quality evaluation is a key element for the success of the emerging Voice over IP (VoIP) technologies. Although there are extensive economic incentives for the convergence of voice, data, and video networks, packet networks such as the Internet have inherent incompatibilities with the transport of real time services. Under this paradigm, network planners and administrators are interested in ongoing mechanisms to measure and ensure the quality of these real time services. Objective quality assessment algorithms can be broadly divided into a) intrusive (methods that require a reference signal), and b) non intrusive (methods that do not require a known reference signal). The latter group, typically requires knowledge of the network conditions (level of delay, jitter, packet loss, etc.), and that has been a very active area of research in the past decade. The state of the art methods for objective non-intrusive quality assessment provide high correlations with the subjective tests. Although good correlations have been achieved already for objective non-intrusive quality assessment, the current large voice transport networks are in a hybrid state, where the necessary network parameters cannot easily be observed from the packet traffic between nodes. This thesis proposes a new process, the Network Conditions Estimator (NCE), which can serve as bridge element to real-world hybrid networks. Two classifications systems, an artificial neural network and a C4.5 decision tree, were developed using speech from a database collected from experiments under controlled network conditions. The database was composed of a group of four female speakers and three male speakers, who conducted unscripted conversations without knowledge about the details of the experiment. Using mel frequency cepstral coefficients (MFCCs) as the feature-set, an accuracy of about 70% was achieved in detecting the presence of jitter or packet loss on the channel. This resulting classifier can be incorporated as an input to the E-Model, in order to properly estimate the QoS of a network in real time. Additionally, rather than just providing an estimation of subjective quality of service provided, the NCE provides an insight into the cause for low performance.
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Oularbi, Mohamed Rabie. "Identification de Systèmes OFDM et Estimation de la QoS : Application à la Radio Opportuniste." Phd thesis, Ecole Nationale Supérieure des Télécommunications de Bretagne - ENSTB, 2011. http://tel.archives-ouvertes.fr/tel-00661753.

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Le schéma de modulation OFDM est très répandu de nos jours (WiFi, WiMAX, \dots) et préconisé comme couche physique pour de nombreux réseaux futurs (3GPP/LTE, IEEE 802.22). Ainsi cette coexistence de réseaux OFDM fait que l'environnement radio est de nos jours hétérogène. Afin de tirer partie de cette hétérogénéité et de satisfaire le concept de ''\textit{Always Best connected}'', il a été imaginé des terminaux multistandards capables de basculer de manière transparente d'un réseau à un autre à la recherche du réseau offrant la qualité de service la plus satisfaisante. Ce processus de basculement entre standards est appelé ''\textit{vertical handover}''. Avant de déclencher un \textit{vertical handover} le terminal se doit d'identifier les réseaux actifs qui l'entourent et estimer la qualité de service disponible sur chaque réseau. Ainsi, dans le cadre de cette thèse nous proposons dans un premier temps des algorithmes d'identification de systèmes OFDM. Dans un second temps, nous nous intéressons à la qualité de service disponible sur les réseaux détectés, nous avons ainsi proposé des estimateurs de métriques de qualité de service dédiés à des réseaux basés sur les schémas d'accès multiples OFDMA et CSMA/CA. Certaines de ces métriques ont été validées expérimentalement sur la plate-forme RAMMUS de TELECOM Bretagne. Toutes les techniques proposées dans le cadre de cette thèse, sont des approches passives à faible coût de calcul qui ne nécessitent aucune connexion au point d'accès, permettant ainsi une économie en temps et en énergie.
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PORCU, SIMONE. "Estimation of the QoE for video streaming services based on facial expressions and gaze direction." Doctoral thesis, Università degli Studi di Cagliari, 2021. http://hdl.handle.net/11584/308985.

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As the multimedia technologies evolve, the need to control their quality becomes even more important making the Quality of Experience (QoE) measurements a key priority. Machine Learning (ML) can support this task providing models to analyse the information extracted by the multimedia. It is possible to divide the ML models applications in the following categories: 1) QoE modelling: ML is used to define QoE models which provide an output (e.g., perceived QoE score) for any given input (e.g., QoE influence factor). 2) QoE monitoring in case of encrypted traffic: ML is used to analyze passive traffic monitored data to obtain insight into degradations perceived by end-users. 3) Big data analytics: ML is used for the extraction of meaningful and useful information from the collected data, which can further be converted to actionable knowledge and utilized in managing QoE. The QoE estimation quality task can be carried out by using two approaches: the objective approach and subjective one. As the two names highlight, they are referred to the pieces of information that the model analyses. The objective approach analyses the objective features extracted by the network connection and by the used media. As objective parameters, the state-of-the-art shows different approaches that use also the features extracted by human behaviour. The subjective approach instead, comes as a result of the rating approach, where the participants were asked to rate the perceived quality using different scales. This approach had the problem of being a time-consuming approach and for this reason not all the users agree to compile the questionnaire. Thus the direct evolution of this approach is the ML model adoption. A model can substitute the questionnaire and evaluate the QoE, depending on the data that analyses. By modelling the human response to the perceived quality on multimedia, QoE researchers found that the parameters extracted from the users could be different, like Electroencephalogram (EEG), Electrocardiogram (ECG), waves of the brain. The main problem with these techniques is the hardware. In fact, the user must wear electrodes in case of ECG and EEG, and also if the obtained results from these methods are relevant, their usage in a real context could be not feasible. For this reason, my studies have been focused on the developing of a Machine Learning framework completely unobtrusively based on the Facial reactions.
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Book chapters on the topic "QoT Estimation"

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Khan, Ihtesham, Muhammad Bilal, and Vittorio Curri. "Cross-Train: Machine Learning Assisted QoT-Estimation in Un-used Optical Networks." In Lecture Notes in Electrical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5692-7_9.

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Díaz-Montiel, Alan A., and Marco Ruffini. "A Performance Analysis of Supervised Learning Classifiers for QoT Estimation in ROADM-Based Networks." In Optical Network Design and Modeling. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38085-4_51.

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Anjali, Tricha, Caterina Scoglio, Jaudelice C. de Oliveira, et al. "A New Path Selection Algorithm for MPLS Networks Based on Available Bandwidth Estimation." In From QoS Provisioning to QoS Charging. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45859-x_20.

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Maryni, Piergiulio, and Giovanni Pacifici. "Real-Time Estimation of the Link Capacity in Multimedia Networks." In Building QoS into Distributed Systems. Springer US, 1997. http://dx.doi.org/10.1007/978-0-387-35170-4_11.

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Baaquie, Belal Ehsan, and Leong-Chuan Kwek. "Phase Estimation and quantum Fourier Transform (qFT)." In Quantum Computers. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7517-2_8.

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Odarchenko, Roman, and Tetiana Dyka. "QoE Estimation Methodology for 5G Use Cases." In Lecture Notes in Electrical Engineering. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92435-5_16.

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Krieger, Udo R., Natalia M. Markovitch, and Norbert Vicari. "Analysis of World Wide Web Traffic by Nonparametric Estimation Techniques." In Performance and QoS of Next Generation Networking. Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0705-7_4.

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Taboada, Ianire, Fidel Liberal, and Jose Oscar Fajardo. "Delay Modeling for 3G Mobile Multimedia Services QoE Estimation." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29479-2_6.

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Rodríguez, Guillermo, Cristian Mateos, and Sanjay Misra. "Exploring Web Service QoS Estimation for Web Service Composition." In Communications in Computer and Information Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59506-7_15.

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Varela, Martín, and Jukka–Pekka Laulajainen. "Terminal–Side QoE Estimations for Cross–Layer Network Control." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21560-5_12.

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Conference papers on the topic "QoT Estimation"

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Zhai, Zhiqun, Liang Dou, Sai Chen, et al. "Improving QoT Estimation Accuracy in Production Networks: A Data-Driven Approach to Address OLS Imperfections." In Optical Fiber Communication Conference. Optica Publishing Group, 2025. https://doi.org/10.1364/ofc.2025.tu3i.4.

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We explore QoT estimation across three phases of optical network planning. Utilizing deployed OCHs, we demonstrate that a data-driven approach enhances estimation precision by accounting OLS imperfections in the Service Expansion Phase.
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He, Yan, Zhiqun Zhai, Sai Chen, et al. "Transceiver Penalty and Amplifier Noise Figure Characterization for Accurate QoT Estimation in Hyperscale Disaggregated DCI Networks." In Optical Fiber Communication Conference. Optica Publishing Group, 2025. https://doi.org/10.1364/ofc.2025.m1j.6.

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We demonstrated that experimentally characterizing the input/frequency-dependent amplifier noise figure and input power dependent transceiver penalty can reduce the RMSE of QoT estimation from 0.662dB to 0.287dB in hyperscale disaggregated DCI production networks.
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Kruse, Lars Eike, and Stephan Pachnicke. "Joint QoT Estimation and Soft-Failure Localization using Variational Autoencoder." In 2023 International Conference on Optical Network Design and Modeling (ONDM). IEEE, 2023. http://dx.doi.org/10.23919/ondm57372.2023.10144854.

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D’Ingillo, Rocco, Andrea D’Amico, Renato Ambrosone, Stefano Straullu, Francesco Aquilino, and Vittorio Curri. "Enhancing Optical Multiplex Section QoT Estimation Using Scalable Gray-box DNN." In 2024 IEEE Photonics Conference (IPC). IEEE, 2024. https://doi.org/10.1109/ipc60965.2024.10799669.

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Lechowicz, Piotr, Carlos Natalino, and Paolo Monti. "QoT Estimation with Margin-Driven Transfer Learning in Time-Varying Optical Networks." In Optical Fiber Communication Conference. Optica Publishing Group, 2025. https://doi.org/10.1364/ofc.2025.m1j.5.

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Estimating transmission quality in an optical network is critical for resource efficiency but challenging due to the infrastructure time-varying state. We propose a transfer learning solution to adapt a data-driven model to network changes.
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Mano, Toru, Andrea D'Amico, Emanuele Virgillito, et al. "Modeling Transceiver BER-OSNR Characteristic for QoT Estimation in Short-Reach Systems." In 2023 International Conference on Optical Network Design and Modeling (ONDM). IEEE, 2023. http://dx.doi.org/10.23919/ondm57372.2023.10144894.

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Tang, Yu, Yan Shi, Shikui Shen, et al. "Digital Twin-Assisted QoT Estimation for a Fieldtrial Hybrid 800G Optical Transmission System." In 2024 Asia Communications and Photonics Conference (ACP) and International Conference on Information Photonics and Optical Communications (IPOC). IEEE, 2024. https://doi.org/10.1109/acp/ipoc63121.2024.10809457.

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Chen, Jinming, and Maïté Brandt-Pearce. "Machine Learning for QoT Estimation to Adapt to Non-uniform or Unknown Parameters." In Optical Fiber Communication Conference. Optica Publishing Group, 2025. https://doi.org/10.1364/ofc.2025.w2a.34.

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When faced with unknown and non-uniform physical parameters in networks, we employ a DNN to predict the OSNR by incorporating them into input features, enhancing the prediction accuracy of existing models by over 1.5 dB.
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Ouyang, Zhenlin, Xiaokang Chen, Zhengyong Liu, Xiaoliang Chen, and Zuqing Zhu. "Overview of ML-aided QoT Estimation in Optical Networks: A Perspective of Model Generalization." In 2024 IEEE 24th International Conference on Communication Technology (ICCT). IEEE, 2024. https://doi.org/10.1109/icct62411.2024.10946528.

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Amani, Mojtaba, Changiz Ghobadi, Javad Nourinia, and Mehdi Habibi. "QoT Estimation of a Commercial DWDM Transmission System Using Artificial Neural Network and Its Feature Importance Analysis." In 2024 11th International Symposium on Telecommunications (IST). IEEE, 2024. https://doi.org/10.1109/ist64061.2024.10843503.

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Reports on the topic "QoT Estimation"

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Andrade, Raúl, and Lorena Alcázar. Quality of Life in Urban Neighborhoods in Metropolitan Lima, Peru. Inter-American Development Bank, 2008. http://dx.doi.org/10.18235/0011272.

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This paper presents the results of the estimations of a quality of life (QoL) index focusing on three dimensions: individual factors, urban factors, and civil society. The study was mainly based on information collected through a survey applied in three districts of Lima: La Victoria, Los Olivos and Villa El Salvador. These districts are relatively similar in terms of income, although Villa El Salvador has a larger percentage of poor households. The results show that various indicators have different impacts on QoL. Two findings stand out. First, variables related to participation in civil society are statistically significant in all specifications used. Second, in La Victoria and Los Olivos, QoL is determined largely by indicators in the individual sphere, while the civil society sphere is more important in Villa El Salvador.
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Uribe-Terán, Carlos, Diego F. Grijalva, and Iván Gachet. How do Trade Restrictions Affect Market Diversity? Inter-American Development Bank, 2025. https://doi.org/10.18235/0013490.

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This paper examines the impact of safeguard import tariffs on market diversity in Ecuador from 2015 to 2017. Using firm-level data, we estimate the effects of tariffs on revenue and market shares in the Manufacturing and Wholesale &amp; Retail (W&amp;R) sectors with a Quantile Treatment Effect on the Treated (QTT) estimator. We also assess firm exit probabilities and pass-through effects on prices through a difference-in-differences approach. By linking firm-level QTT estimates to industry-level diversity measures, we construct counterfactual revenue distributions to quantify the effect on market concentration. We find that tariffs disproportionately reduced revenue and market shares for smaller firms, significantly increasing exit rates and reducing market diversity, with stronger effects in W&amp;R. While tariffs did not generate broad inflationary pressures, they induced short-term pass-through effects that further strained smaller firms. These sector-specific price responses reinforced market consolidation, accelerating the decline in market diversity.
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