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1. WO2020222998 - BEAM MANAGEMENT USING ADAPTIVE LEARNING

Publication Number WO/2020/222998
Publication Date 05.11.2020
International Application No. PCT/US2020/027648
International Filing Date 10.04.2020
IPC
H04B 7/06 2006.01
HELECTRICITY
04ELECTRIC COMMUNICATION TECHNIQUE
BTRANSMISSION
7Radio transmission systems, i.e. using radiation field
02Diversity systems; Multi-antenna systems, i.e. transmission or reception using multiple antennas
04using two or more spaced independent antennas
06at the transmitting station
H04B 7/08 2006.01
HELECTRICITY
04ELECTRIC COMMUNICATION TECHNIQUE
BTRANSMISSION
7Radio transmission systems, i.e. using radiation field
02Diversity systems; Multi-antenna systems, i.e. transmission or reception using multiple antennas
04using two or more spaced independent antennas
08at the receiving station
CPC
G06N 20/00
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
20Machine learning
G06N 3/006
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
004Artificial life, i.e. computers simulating life
006based on simulated virtual individual or collective life forms, e.g. single "avatar", social simulations, virtual worlds or particle swarm optimisation
G06N 3/0445
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
04Architectures, e.g. interconnection topology
0445Feedback networks, e.g. hopfield nets, associative networks
G06N 3/0454
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
04Architectures, e.g. interconnection topology
0454using a combination of multiple neural nets
G06N 3/0472
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
04Architectures, e.g. interconnection topology
0472using probabilistic elements, e.g. p-rams, stochastic processors
G06N 3/08
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
08Learning methods
Applicants
  • QUALCOMM INCORPORATED [US]/[US]
Inventors
  • LANDIS, Shay
  • TOUBOUL, Assaf
  • GUBESKYS, Arthur
  • GOROKHOV, Alexi Yurievitch
  • KHANDEKAR, Aamod Dinkar
Agents
  • YADEGAR-BANDARI, Fariba
Priority Data
16/400,86401.05.2019US
Publication Language English (EN)
Filing Language English (EN)
Designated States
Title
(EN) BEAM MANAGEMENT USING ADAPTIVE LEARNING
(FR) GESTION DE FAISCEAU EN UTILISANT L'APPRENTISSAGE ADAPTATIF
Abstract
(EN)
Certain aspects of the present disclosure provide techniques for beam management using adaptive learning. Certain aspects provide a method that can be performed by a node, such as user equipment (UE) or a base station (BS). The node determines one or more beams to utilize for a beam management procedure using adaptive learning. The node performs the beam management procedure using the determined one or more beams. In some aspects, the node uses an adaptive reinforcement learning algorithm to select beams for measurement in beam discovery procedure. The node may adaptive the beam management algorithm based on feedback associated with the beam selection, such as based on a throughput achieved using a beam pairing determined during the beam management procedure.
(FR)
Certains aspects de la présente invention concernent des techniques de gestion de faisceau en utilisant l'apprentissage adaptatif. Certains aspects concernent un procédé qui peut être mis en œuvre par un nœud, tel qu'un équipement d'utilisateur (UE) ou une station de base (BS). Le nœud détermine un ou plusieurs faisceaux à utiliser pour une procédure de gestion de faisceau en utilisant l'apprentissage adaptatif. Le nœud exécute la procédure de gestion de faisceau en utilisant lesdits faisceaux déterminés. Dans certains aspects, le nœud utilise un algorithme d'apprentissage de renforcement adaptatif pour sélectionner des faisceaux pour une mesure dans une procédure de découverte de faisceau. Le nœud peut adapter l'algorithme de gestion de faisceau sur la base d'une rétroaction associée à la sélection de faisceau, par exemple sur la base d'un débit obtenu à l'aide d'un appariement de faisceau déterminé pendant la procédure de gestion de faisceau.
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