Traffic-Aware UAV Placement using a Generalizable Deep Reinforcement Learning Methodology

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2022

Autores

Almeida, EN (Autor) Outra; Campos, R (Autor) FEUP; Manuel Ricardo (Autor) FEUP

Ata de Conferência Internacional

Título: IEEE Symposium on Computers and Communications, ISCC 2022, Rhodes, Greece, June 30 - July 3, 2022

Páginas: 1-6

Indexação

ISI Web of Knowledge - 0 Citações

Scopus

Outras Informações

ID Authenticus: P-00W-WJP

DOI: 10.1109/iscc55528.2022.9912770

Abstract (EN): Unmanned Aerial Vehicles (UAVs) acting as Flying Access Points (FAPs) are being used to provide on-demand wireless connectivity in extreme scenarios. Despite ongoing research, the optimization of UAVs' positions according to dynamic users' traffic demands remains challenging. We propose the Traffic-aware UAV Placement Algorithm (TUPA), which positions a UAV acting as FAP according to the users' traffic demands, in order to maximize the network utility. Using a DRL approach enables the FAP to autonomously learn and adapt to dynamic conditions and requirements of networking scenarios. Moreover, the proposed DRL methodology allows TUPA to generalize knowledge acquired during training to unknown combinations of users' positions and traffic demands, with no additional training. TUPA is trained and evaluated using network simulator ns-3 and ns3-gym framework. The results demonstrate that TUPA increases the network utility, compared to baseline solutions, increasing the average network utility up to 4x in scenarios with heterogeneous traffic demands.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6