Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Ozcan, Yigit"'
Autor:
Salehi, Shavbo, Zhou, Hao, Elsayed, Medhat, Bavand, Majid, Gaigalas, Raimundas, Ozcan, Yigit, Erol-Kantarci, Melike
Network slicing is a pivotal paradigm in wireless networks enabling customized services to users and applications. Yet, intelligent jamming attacks threaten the performance of network slicing. In this paper, we focus on the security aspect of network
Externí odkaz:
http://arxiv.org/abs/2410.05153
Autor:
Habib, Md Arafat, Zhou, Hao, Iturria-Rivera, Pedro Enrique, Ozcan, Yigit, Elsayed, Medhat, Bavand, Majid, Gaigalas, Raimundas, Erol-Kantarci, Melike
Traffic Steering is a crucial technology for wireless networks, and multiple efforts have been put into developing efficient Machine Learning (ML)-enabled traffic steering schemes for Open Radio Access Networks (O-RAN). Given the swift emergence of n
Externí odkaz:
http://arxiv.org/abs/2409.20391
Autor:
Salehi, Shavbo, Iturria-Rivera, Pedro Enrique, Elsayed, Medhat, Bavand, Majid, Gaigalas, Raimundas, Ozcan, Yigit, Erol-Kantarci, Melike
In 5G networks, network slicing has emerged as a pivotal paradigm to address diverse user demands and service requirements. To meet the requirements, reinforcement learning (RL) algorithms have been utilized widely, but this method has the problem of
Externí odkaz:
http://arxiv.org/abs/2408.10376
Autor:
Habib, Md Arafat, Rivera, Pedro Enrique Iturria, Ozcan, Yigit, Elsayed, Medhat, Bavand, Majid, Gaigalas, Raimundus, Erol-Kantarci, Melike
Intent-based network automation is a promising tool to enable easier network management however certain challenges need to be effectively addressed. These are: 1) processing intents, i.e., identification of logic and necessary parameters to fulfill a
Externí odkaz:
http://arxiv.org/abs/2406.06059
Autor:
Iturria-Rivera, Pedro Enrique, Gaigalas, Raimundas, Elsayed, Medhat, Bavand, Majid, Ozcan, Yigit, Erol-Kantarci, Melike
Extended Reality (XR) services will revolutionize applications over 5th and 6th generation wireless networks by providing seamless virtual and augmented reality experiences. These applications impose significant challenges on network infrastructure,
Externí odkaz:
http://arxiv.org/abs/2405.15872
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
Autor:
Zhang, Han, Elsayed, Medhat, Bavand, Majid, Gaigalas, Raimundas, Ozcan, Yigit, Erol-Kantarci, Melike
Federated learning (FL) allows distributed participants to train machine learning models in a decentralized manner. It can be used for radio signal classification with multiple receivers due to its benefits in terms of privacy and scalability. Howeve
Externí odkaz:
http://arxiv.org/abs/2401.11039
Intent-driven Intelligent Control and Orchestration in O-RAN Via Hierarchical Reinforcement Learning
Autor:
Habib, Md Arafat, Zhou, Hao, Iturria-Rivera, Pedro Enrique, Elsayed, Medhat, Bavand, Majid, Gaigalas, Raimundas, Ozcan, Yigit, Erol-Kantarci, Melike
rApps and xApps need to be controlled and orchestrated well in the open radio access network (O-RAN) so that they can deliver a guaranteed network performance in a complex multi-vendor environment. This paper proposes a novel intent-driven intelligen
Externí odkaz:
http://arxiv.org/abs/2307.02754
Autor:
Zhang, Han, Zhou, Hao, Elsayed, Medhat, Bavand, Majid, Gaigalas, Raimundas, Ozcan, Yigit, Erol-Kantarci, Melike
Traffic steering (TS) is a promising approach to support various service requirements and enhance transmission reliability by distributing network traffic loads to appropriate base stations (BSs). In conventional cell-centric TS strategies, BSs make
Externí odkaz:
http://arxiv.org/abs/2304.11282
Autor:
Habib, Md Arafat, Zhou, Hao, Iturria-Rivera, Pedro Enrique, Elsayed, Medhat, Bavand, Majid, Gaigalas, Raimundas, Ozcan, Yigit, Erol-Kantarci, Melike
In 5G non-standalone mode, an intelligent traffic steering mechanism can vastly aid in ensuring smooth user experience by selecting the best radio access technology (RAT) from a multi-RAT environment for a specific traffic flow. In this paper, we pro
Externí odkaz:
http://arxiv.org/abs/2301.07818