A DL-Enabled Relay Node Placement and Selection Framework in Multicellular Networks

ΜΠΑΡΤΣΙΩΚΑΣ, Ιωάννης

dc.contributor.author	ΜΠΑΡΤΣΙΩΚΑΣ, Ιωάννης
dc.date.accessioned	2023-10-17T19:20:47Z
dc.date.available	2023-10-17T19:20:47Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/58198
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.25894
dc.rights	Αναφορά Δημιουργού-Όχι Παράγωγα Έργα 3.0 Ελλάδα	*
dc.rights.uri	http://creativecommons.org/licenses/by-nd/3.0/gr/	*
dc.subject	Relay assisted transmission	en
dc.subject	machine learning	en
dc.subject	deep learning	en
dc.subject	Q-learning	en
dc.subject	5G networks	en
dc.subject	system level simulations	en
dc.title	A DL-Enabled Relay Node Placement and Selection Framework in Multicellular Networks	el
heal.type	journalArticle
heal.classification	Telecommunications	el
heal.contributorName	Bartsiokas, Ioannis
heal.contributorName	Gkonis, Panagiotis
heal.contributorName	Kaklamani, Dimitra
heal.contributorName	Venieris, Iakovos
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2023-06-28
heal.bibliographicCitation	I. A. Bartsiokas, P. K. Gkonis, D. I. Kaklamani and I. S. Venieris, "A DL-Enabled Relay Node Placement and Selection Framework in Multicellular Networks," in IEEE Access, vol. 11, pp. 65153-65169, 2023, doi: 10.1109/ACCESS.2023.3290482.	el
heal.abstract	The ever-increasing and diverse user demands as well as the need for uninterrupted access to the medium with minimum latency in dense machine type communication networks, are the key driving forces to a holistic network redesign. In this context, fifth-generation and beyond (5G/B5G) networks, incorporate various advanced physical layer techniques, such as relaying-assisted transmission, aiming to improve network performance and extend the coverage area of multicellular orientations. However, the deployment of such techniques in a cellular environment characterized by high interference levels and multi-variate channel representations, leads to increased computational complexity for radio resource management (RRM) tasks. Machine learning (ML), and especially Deep Learning (DL), is proposed as an efficient way to support end-to-end user applications in highly complex environments, since ML/DL models can relax the RRMassociated computational burden. In this paper, we consider the joint problem of relay node (RN) placement and selection subject to subcarrier allocation and power management constraints in 5G/B5G networks. Various DL-based methods are examined and combined to solve both sub-problems. The performance of these schemes is evaluated for various relaying-assisted transmission approaches, either considering known channel state information (CSI) or not. According to the derived results, total system energy efficiency (EE) and spectral efficiency (SE) can be improved by up to 30%, when considering only the DL-based RN placement scheme compared to state-of-the-art non-ML schemes. The deployment of the reinforcement learning (RL) model for RN selection, can improve EE up to 80%, while SE can be improved up to 75%, compared to a system with only DL-enabled RN placement.	en
heal.publisher	IEEE	en
heal.journalName	IEEE Access	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	false
dc.identifier.doi	https://doi.org/10.1109/ACCESS.2023.3290482	el