Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Gaigalas, Raimundas"'
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:
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:
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:
Soldati, Pablo, Ghadimi, Euhanna, Demirel, Burak, Wang, Yu, Gaigalas, Raimundas, Sintorn, Mathias
Artificial intelligence (AI) has emerged as a powerful tool for addressing complex and dynamic tasks in radio communication systems. Research in this area, however, focused on AI solutions for specific, limited conditions, hindering models from learn
Externí odkaz:
http://arxiv.org/abs/2306.06251
Autor:
Zhou, Hao, Elsayed, Medhat, Bavand, Majid, Gaigalas, Raimundas, Furr, Steve, Erol-Kantarci, Melike
Energy efficiency (EE) is one of the most important metrics for envisioned 6G networks, and sleep control, as a cost-efficient approach, can significantly lower power consumption by switching off network devices selectively. Meanwhile, the reconfigur
Externí odkaz:
http://arxiv.org/abs/2304.13226
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:
Dantas, Ycaro, Iturria-Rivera, Pedro Enrique, Zhou, Hao, Bavand, Majid, Elsayed, Medhat, Gaigalas, Raimundas, Erol-Kantarci, Melike
The growing adoption of mmWave frequency bands to realize the full potential of 5G, turns beamforming into a key enabler for current and next-generation wireless technologies. Many mmWave networks rely on beam selection with Grid-of-Beams (GoB) appro
Externí odkaz:
http://arxiv.org/abs/2302.00156