Unsatisfied Today, Satisfied Tomorrow: a simulation framework for performance evaluation of crowdsourcing-based network monitoring

• URL: http://arxiv.org/abs/2010.16162v1
• Date: Fri, 30 Oct 2020 10:03:48 GMT
• Title: Unsatisfied Today, Satisfied Tomorrow: a simulation framework for performance evaluation of crowdsourcing-based network monitoring
• Authors: Andrea Pimpinella, Marianna Repossi, Alessandro Enrico Cesare Redondi
• Abstract summary: We propose an empirical framework tailored to assess the quality of the detection of under-performing cells. The framework simulates both the processes of satisfaction surveys delivery and users satisfaction prediction. We use the simulation framework to test empirically the performance of under-performing sites detection in general scenarios.
• Score: 68.8204255655161
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Network operators need to continuosly upgrade their infrastructures in order to keep their customer satisfaction levels high. Crowdsourcing-based approaches are generally adopted, where customers are directly asked to answer surveys about their user experience. Since the number of collaborative users is generally low, network operators rely on Machine Learning models to predict the satisfaction levels/QoE of the users rather than directly measuring it through surveys. Finally, combining the true/predicted user satisfaction levels with information on each user mobility (e.g, which network sites each user has visited and for how long), an operator may reveal critical areas in the networks and drive/prioritize investments properly. In this work, we propose an empirical framework tailored to assess the quality of the detection of under-performing cells starting from subjective user experience grades. The framework allows to simulate diverse networking scenarios, where a network characterized by a small set of under-performing cells is visited by heterogeneous users moving through it according to realistic mobility models. The framework simulates both the processes of satisfaction surveys delivery and users satisfaction prediction, considering different delivery strategies and evaluating prediction algorithms characterized by different prediction performance. We use the simulation framework to test empirically the performance of under-performing sites detection in general scenarios characterized by different users density and mobility models to obtain insights which are generalizable and that provide interesting guidelines for network operators.

Related papers

• Uniting contrastive and generative learning for event sequences models [51.547576949425604]
  This study investigates the integration of two self-supervised learning techniques - instance-wise contrastive learning and a generative approach based on restoring masked events in latent space. Experiments conducted on several public datasets, focusing on sequence classification and next-event type prediction, show that the integrated method achieves superior performance compared to individual approaches.
  arXiv  Detail & Related papers  (2024-08-19T13:47:17Z)
• Improving Network Interpretability via Explanation Consistency Evaluation [56.14036428778861]
  We propose a framework that acquires more explainable activation heatmaps and simultaneously increase the model performance. Specifically, our framework introduces a new metric, i.e., explanation consistency, to reweight the training samples adaptively in model learning. Our framework then promotes the model learning by paying closer attention to those training samples with a high difference in explanations.
  arXiv  Detail & Related papers  (2024-08-08T17:20:08Z)
• New User Event Prediction Through the Lens of Causal Inference [20.676353189313737]
  We propose a novel discrete event prediction framework for new users. Our method offers an unbiased prediction for new users without needing to know their categories. We demonstrate the superior performance of the proposed framework with a numerical simulation study and two real-world applications.
  arXiv  Detail & Related papers  (2024-07-08T05:35:54Z)
• An Adaptive Framework for Generalizing Network Traffic Prediction towards Uncertain Environments [51.99765487172328]
  We have developed a new framework using time-series analysis for dynamically assigning mobile network traffic prediction models. Our framework employs learned behaviors, outperforming any single model with over a 50% improvement relative to current studies.
  arXiv  Detail & Related papers  (2023-11-30T18:58:38Z)
• Data-driven Predictive Latency for 5G: A Theoretical and Experimental Analysis Using Network Measurements [0.7643181805997241]
  We present an analytical formulation of the user-plane latency as a Hypoexponential distribution. We test our predictive framework using data gathered from scenarios of vehicular mobility, dense-urban traffic, and social gathering events.
  arXiv  Detail & Related papers  (2023-07-05T14:39:47Z)
• Big-data-driven and AI-based framework to enable personalization in wireless networks [20.26379197206863]
  We propose a big-data-driven and AI-based personalization framework to integrate personalization into wireless networks. Based on each user's actual requirements and context, a multi-objective formulation enables the network to micromanage and optimize the provided user satisfaction levels simultaneously.
  arXiv  Detail & Related papers  (2023-06-08T02:30:55Z)
• Network Generalization Prediction for Safety Critical Tasks in Novel Operating Domains [0.0]
  We propose the task Network Generalization Prediction: predicting the expected network performance in novel operating domains. We describe the network performance in terms of an interpretable Context Subspace, and we propose a methodology for selecting the features of the Context Subspace that provide the most information about the network performance.
  arXiv  Detail & Related papers  (2021-08-17T01:55:54Z)
• Predicting Deep Neural Network Generalization with Perturbation Response Curves [58.8755389068888]
  We propose a new framework for evaluating the generalization capabilities of trained networks. Specifically, we introduce two new measures for accurately predicting generalization gaps. We attain better predictive scores than the current state-of-the-art measures on a majority of tasks in the Predicting Generalization in Deep Learning (PGDL) NeurIPS 2020 competition.
  arXiv  Detail & Related papers  (2021-06-09T01:37:36Z)
• Topological Uncertainty: Monitoring trained neural networks through persistence of activation graphs [0.9786690381850356]
  In industrial applications, data coming from an open-world setting might widely differ from the benchmark datasets on which a network was trained. We develop a method to monitor trained neural networks based on the topological properties of their activation graphs.
  arXiv  Detail & Related papers  (2021-05-07T14:16:03Z)
• SideInfNet: A Deep Neural Network for Semi-Automatic Semantic Segmentation with Side Information [83.03179580646324]
  This paper proposes a novel deep neural network architecture, namely SideInfNet. It integrates features learnt from images with side information extracted from user annotations. To evaluate our method, we applied the proposed network to three semantic segmentation tasks and conducted extensive experiments on benchmark datasets.
  arXiv  Detail & Related papers  (2020-02-07T06:10:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.