Deep reinforcement learning for combined coverage and resource allocation in UAV-aided RAN-slicing
| dc.contributor.author | Bellone, Lorenzo | en |
| dc.contributor.author | Galkin, Boris | en |
| dc.contributor.author | Traversi, Emiliano | en |
| dc.contributor.author | Natalizio, Enrico | en |
| dc.date.accessioned | 2023-11-03T09:58:10Z | |
| dc.date.available | 2023-11-03T09:58:10Z | |
| dc.date.issued | 2023-09-27 | en |
| dc.description.abstract | Network slicing is a well assessed approach enabling virtualization of the mobile core and radio access network (RAN) in the emerging 5th Generation New Radio. Slicing is of paramount importance when dealing with the emerging and diverse vertical applications entailing heterogeneous sets of requirements. 5G is also envisioning Unmanned Aerial Vehicles (UAVs) to be a key element in the cellular network standard, aiming at their use as aerial base stations and exploiting their flexible and quick deployment to enhance the wireless network performance. This work presents a UAV-assisted 5G network, where the aerial base stations (UAV-BS) are empowered with network slicing capabilities aiming at optimizing the Service Level Agreement (SLA) satisfaction ratio of a set of users. The users belong to three heterogeneous categories of 5G service type, namely, enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (URLLC), and massive machine-type communication (mMTC). A first application of multi-agent and multi-decision deep reinforcement learning for UAV-BS in a network slicing context is introduced, aiming at the optimization of the SLA satisfaction ratio of users through the joint allocation of radio resources to slices and refinement of the UAV-BSs 2-dimensional trajectories. The performance of the presented strategy have been tested and compared to benchmark heuristics, highlighting a higher percentage of satisfied users (at least 10.5% more) in a variety of scenarios. | en |
| dc.description.status | Peer reviewed | en |
| dc.description.version | Accepted Version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Bellone, L., Galkin, B., Traversi, E. and Natalizio, E. (2023) 'Deep reinforcement learning for combined coverage and resource allocation in UAV-aided RAN-slicing', 2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT), Pafos, Cyprus, 19-21 June, pp. 669-675. doi: 10.1109/DCOSS-IoT58021.2023.00106 | en |
| dc.identifier.doi | 10.1109/dcoss-iot58021.2023.00106 | en |
| dc.identifier.eissn | 2325-2944 | en |
| dc.identifier.endpage | 675 | en |
| dc.identifier.isbn | 979-8-3503-4649-7 | en |
| dc.identifier.isbn | 979-8-3503-4650-3 | en |
| dc.identifier.issn | 2325-2936 | en |
| dc.identifier.startpage | 669 | en |
| dc.identifier.uri | https://hdl.handle.net/10468/15182 | |
| dc.language.iso | en | en |
| dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en |
| dc.relation.ispartof | 2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT) | en |
| dc.rights | © 2023, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en |
| dc.subject | UAV aided RAN slicing | en |
| dc.subject | Network slicing | en |
| dc.subject | Multi agent deep reinforcement learning | en |
| dc.subject | 5GNR | en |
| dc.title | Deep reinforcement learning for combined coverage and resource allocation in UAV-aided RAN-slicing | en |
| dc.type | Conference item | en |
