Zobrazeno 1 - 10
of 299
pro vyhledávání: '"Erol-Kantarci, Melike"'
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
The integration of unmanned aerial vehicles (UAVs) with mobile edge computing (MEC) and Internet of Things (IoT) technology in smart farms is pivotal for efficient resource management and enhanced agricultural productivity sustainably. This paper add
Externí odkaz:
http://arxiv.org/abs/2407.19657
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
In recent years, machine learning (ML) techniques have created numerous opportunities for intelligent mobile networks and have accelerated the automation of network operations. However, complex network tasks may involve variables and considerations e
Externí odkaz:
http://arxiv.org/abs/2406.04276
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
Integrated sensing and communications is a key enabler for the 6G wireless communication systems. The multiple sensing modalities will allow the base station to have a more accurate representation of the environment, leading to context-aware communic
Externí odkaz:
http://arxiv.org/abs/2406.18542
Large language models (LLMs), especially generative pre-trained transformers (GPTs), have recently demonstrated outstanding ability in information comprehension and problem-solving. This has motivated many studies in applying LLMs to wireless communi
Externí odkaz:
http://arxiv.org/abs/2405.11002
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
The Synchronization Signal Block (SSB) is a fundamental component of the 5G New Radio (NR) air interface, crucial for the initial access procedure of Connected and Automated Vehicles (CAVs), and serves several key purposes in the network's operation.
Externí odkaz:
http://arxiv.org/abs/2403.02645
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