홈 › Papers

Data-driven Predictive Latency for 5G: A Theoretical and Experimental Analysis Using Network Measurements

2023-07-05 · Marco Skocaj, Francesca Conserva, Nicol Sarcone Grande, Andrea Orsi, Davide Micheli, Giorgio Ghinamo, Simone Bizzarri, Roberto Verdone

The advent of novel 5G services and applications with binding latency requirements and guaranteed Quality of Service (QoS) hastened the need to incorporate autonomous and proactive decision-making in network management procedures. The objective of our study is to provide a thorough analysis of predictive latency within 5G networks by utilizing real-world network data that is accessible to mobile network operators (MNOs). In particular, (i) we present an analytical formulation of the user-plane latency as a Hypoexponential distribution, which is validated by means of a comparative analysis with empirical measurements, and (ii) we conduct experimental results of probabilistic regression, anomaly detection, and predictive forecasting leveraging on emerging domains in Machine Learning (ML), such as Bayesian Learning (BL) and Machine Learning on Graphs (GML). We test our predictive framework using data gathered from scenarios of vehicular mobility, dense-urban traffic, and social gathering events. Our results provide valuable insights into the efficacy of predictive algorithms in practical applications.

📄 PDF Abstract BibTeX arXiv:2307.02329

Code (0)

등록된 구현이 없습니다.

Tasks

Anomaly Detection Decision Making Management

Methods 이 논문이 사용한 방법론

Golden Queue Managers 설명 없음

Predictive Pipelined Decoding: A Compute-Latency Trade-off for Exact LLM Decoding

2023-07-12 · Seongjun Yang, Gibbeum Lee, Jaewoong Cho, Dimitris Papailiopoulos 외

This paper presents "Predictive Pipelined Decoding (PPD)," an approach that speeds up greedy decoding in Large Language Models (LLMs) while maintaining the exact same output as the original decoding. Unlike conventional …

The PUR-1 Cyber-Physical Digital Twin

2026-08-31 · Vasileios Theos, Jonah Lau, Konstantinos Gkouliaras, Zachery Dahm 외 arxiv

Digital twin technologies have the potential to improve operational flexibility and responsiveness capabilities of nuclear systems. To provide decision support, cyber event characterization, state estimation, predictive …

Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks

2020-02-22 · Changyang She, Rui Dong, Zhouyou Gu, Zhanwei Hou 외

In the future 6th generation networks, ultra-reliable and low-latency communications (URLLC) will lay the foundation for emerging mission-critical applications that have stringent requirements on end-to-end delay and rel…

Deep Reinforcement Learning Edge-computing Federated Learning Reinforcement Learning+1

End-to-End Data Quality-Driven Framework for Machine Learning in Production Environment

2025-12-16 · Firas Bayram, Bestoun S. Ahmed, Erik Hallin arxiv

This paper introduces a novel end-to-end framework that efficiently integrates data quality assessment with machine learning (ML) model operations in real-time production environments. While existing approaches treat dat…

Implicit predictors in regularized data-driven predictive control

2023-07-20 · Manuel Klädtke, Moritz Schulze Darup

We introduce the notion of implicit predictors, which characterize the input-(state)-output prediction behavior underlying a predictive control scheme, even if it is not explicitly enforced as an equality constraint (as …