Power Control in 5G Heterogeneous Cells Considering User Demands Using Deep Reinforcement Learning - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2021

Power Control in 5G Heterogeneous Cells Considering User Demands Using Deep Reinforcement Learning

Résumé

Heterogeneous cells have been emerged as the dominant design approach for the deployment of 5G wireless networks . In this context, inter-cell interferences are expected to drastically affect the 5G targets, especially in terms of throughput experienced by the mobile users. This work proposes a novel Deep Reinforcement Learning (DRL) scheme, targeting at minimizing the difference between the allocated and requested user throughput through power regulation . The developed algorithm is employed in heterogeneous cells that are controlled in a centralized manner and validated for 5G-compliant channel models. First, the proposed learning framework of the DRL method is presented, mainly including the stabilization of the learning-related hyperparameters. Then, the DRL method is evaluated for several simulation scenarios and compared to well-established optimization methods for power allocation, namely the Water-filling and Weighted Minimum Mean Squared Error (WMMSE) algorithms, as well as a fixed power control scheme. The evaluation outcomes demonstrate the ability of the DRL framework in accurately approaching the user requirements, whereas the Water-filling and WMMSE solutions present large deviations from the user demands since they aim at the total network-wide throughput maximization.
Fichier principal
Vignette du fichier
517424_1_En_9_Chapter.pdf (822.71 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03788990 , version 1 (27-09-2022)

Licence

Paternité

Identifiants

Citer

Anastasios Giannopoulos, Sotirios Spantideas, Christos Tsinos, Panagiotis Trakadas. Power Control in 5G Heterogeneous Cells Considering User Demands Using Deep Reinforcement Learning. 17th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2021, Hersonissos, Crete, Greece. pp.95-105, ⟨10.1007/978-3-030-79157-5_9⟩. ⟨hal-03788990⟩
21 Consultations
5 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More