Deep reinforcement learning for hybrid beamforming in multi-user millimeter wave wireless systems

Enrique M. Lizarraga, Gabriel N. Maggio, Alexis A. Dowhuszko

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

1 Sitaatiot (Scopus)
83 Lataukset (Pure)

Abstrakti

This paper proposes a Machine Learning (ML) algorithm for hybrid beamforming in millimeter-wave wireless systems with multiple users. The time-varying nature of the wireless channels is taken into account when training the ML agent, which identifies the most convenient hybrid beamforming matrix with the aid of an algorithm that keeps the amount of signaling information low, avoids sudden changes in the analog beamformers radiation patterns when scheduling different users (flashlight interference), and simplifies the hybrid beamformer update decisions by adjusting the phases of specific analog beamforming vectors. The proposed hybrid beamforming algorithm relies on Deep Reinforcement Learning (DRL), which represents a practical approach to embed the online adaptation feature of the hybrid beamforming matrix into the channel states of continuous nature in which the multiuser MIMO system can be. Achievable data rate curves are used to analyze performance results, which validate the advantages of DRL algorithms with respect to solutions relying on conventional/deterministic optimization tools.

AlkuperäiskieliEnglanti
OtsikkoProceedings of IEEE 93rd Vehicular Technology Conference, VTC 2021
KustantajaIEEE
Sivumäärä5
ISBN (elektroninen)9781728189642
DOI - pysyväislinkit
TilaJulkaistu - 15 kesäk. 2021
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaIEEE Vehicular Technology Conference - Helsinki, Suomi
Kesto: 25 huhtik. 202128 huhtik. 2021
Konferenssinumero: 93

Julkaisusarja

NimiIEEE Vehicular Technology Conference
Vuosikerta2021-April
ISSN (painettu)1550-2252

Conference

ConferenceIEEE Vehicular Technology Conference
LyhennettäVTC-Spring
Maa/AlueSuomi
KaupunkiHelsinki
Ajanjakso25/04/202128/04/2021

Sormenjälki

Sukella tutkimusaiheisiin 'Deep reinforcement learning for hybrid beamforming in multi-user millimeter wave wireless systems'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.

Siteeraa tätä