Multi-Agent Deep Reinforcement Learning For Distributed Handover Management In Dense MmWave Networks

Fiche du document

Date

4 mai 2020

Type de document
Périmètre
Langue
Identifiants
Relations

Ce document est lié à :
info:eu-repo/semantics/altIdentifier/doi/10.1109/ICASSP40776.2020.9052936

Collection

Archives ouvertes

Licence

info:eu-repo/semantics/OpenAccess



Sujets proches En

Administration

Citer ce document

Mohamed Sana et al., « Multi-Agent Deep Reinforcement Learning For Distributed Handover Management In Dense MmWave Networks », HAL-SHS : sciences de l'information, de la communication et des bibliothèques, ID : 10.1109/ICASSP40776.2020.9052936


Métriques


Partage / Export

Résumé En

The dense deployment of millimeter wave small cells combined with directional beamforming is a promising solution to enhance the network capacity of the current generation of wireless communications. However, the reliability of millimeter wave communication links can be affected by severe pathloss, blockage, and deafness. As a result, mobile users are subject to frequent handoffs, which deteriorate the user throughput and the battery lifetime of mobile terminals. To tackle this problem, our paper proposes a deep multi-agent reinforcement learning framework for distributed handover management called RHando (Reinforced Handover). We model users as agents that learn how to perform handover to optimize the network throughput while taking into account the associated cost. The proposed solution is fully distributed, thus limiting signaling and computation overhead. Numerical results show that the proposed solution can provide higher throughput compared to conventional schemes while considerably limiting the frequency of the handovers.

document thumbnail

Par les mêmes auteurs

Sur les mêmes sujets

Sur les mêmes disciplines

Exporter en