Zobrazeno 1 - 10
of 9 072
pro vyhledávání: '"Wireless traffic"'
With a broad range of emerging applications in 6G networks, wireless traffic prediction has become a critical component of network management. However, the dynamically shifting distribution of wireless traffic in non-stationary 6G networks presents s
Externí odkaz:
http://arxiv.org/abs/2408.10390
While smartphones and WiFi networks are bringing many positive changes to people's lives, they are susceptible to traffic analysis attacks, which infer user's private information from encrypted traffic. Existing traffic analysis attacks mainly target
Externí odkaz:
http://arxiv.org/abs/2408.07263
Autor:
Gao, Fuwei1 (AUTHOR) 2021317075@stu.sdnu.edu.cn, Zhang, Chuanting2 (AUTHOR) chuanting.zhang@sdu.edu.cn, Qiao, Jingping1 (AUTHOR) jingpingqiao@sdnu.edu.cn, Li, Kaiqiang1 (AUTHOR) 2022317069@stu.sdnu.edu.cn, Cao, Yi3 (AUTHOR) ise_caoy@ujn.edu.cn
Publikováno v:
Mathematics (2227-7390). Aug2024, Vol. 12 Issue 16, p2539. 14p.
Federated Learning (FL) offers a distributed framework to train a global control model across multiple base stations without compromising the privacy of their local network data. This makes it ideal for applications like wireless traffic prediction (
Externí odkaz:
http://arxiv.org/abs/2404.14389
Akademický článek
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Autor:
Habib, Md Arafat, Iturria-Rivera, Pedro Enrique, Ozcan, Yigit, Elsayed, Medhat, Bavand, Majid, Gaigalas, Raimundus, Erol-Kantarci, Melike
This paper introduces an innovative method for predicting wireless network traffic in concise temporal intervals for Open Radio Access Networks (O-RAN) using a transformer architecture, which is the machine learning model behind generative AI tools.
Externí odkaz:
http://arxiv.org/abs/2403.10808
Publikováno v:
Telecommunications Policy, 47(7), 102595 (2023)
The emergence of new wireless technologies, such as the Internet of Things, allows digitalizing new and diverse urban activities. Thus, wireless traffic grows in volume and complexity, making prediction, investment planning, and regulation increasing
Externí odkaz:
http://arxiv.org/abs/2310.14406
Publikováno v:
IEEE Access, Vol 12, Pp 130983-130994 (2024)
This paper designs a novel energy-efficient hybrid federated and centralized learning (HFCL) framework for training wireless traffic prediction models in aerial networks over distributed multi-access edge computing (MEC) servers where multiple networ
Externí odkaz:
https://doaj.org/article/38a4a2ea6da84ac88c6d6fd7476c05a3
Publikováno v:
Proceedings of the 2023 Workshop on Recent Advances in Resilient and Trustworthy ML Systems in Autonomous Networks; pp.17-28
Balancing the trade-off between accuracy and robustness is a long-standing challenge in time series forecasting. While most of existing robust algorithms have achieved certain suboptimal performance on clean data, sustaining the same performance leve
Externí odkaz:
http://arxiv.org/abs/2311.09790