Multi-modal beam prediction

Patent metadata

Field	Value
Publication number	US-12580628-B2
Application number	US-202318505966-A
Country	US
Kind code	B2
Filing date	Nov 9, 2023
Priority date	Nov 9, 2023
Publication date	Mar 17, 2026
Grant date	Mar 17, 2026

Abstract

A processor-implemented method for multimodal beam management implemented by a network device includes receiving, by the network device, a stream of inputs from one or more sensors. The network device generates a digital twin modeling an environment of a region observed by the one or more sensors. The digital twin includes one or more objects detected based on the stream of inputs. The network device manages a wireless communication signal beam for communicating with at least one user equipment (UE) in the region observed by the one or more sensors based at least in part on the digital twin.

First claim

What is claimed is:

1 . An apparatus comprising:
at least one memory; and at least one processor coupled to the at least one memory, the at least one processor configured to:
receive, by a network device, a stream of inputs from one or more sensors;
generate, by the network device, a digital twin modeling an environment of a region observed by the one or more sensors, the digital twin including one or more objects detected based on the stream of inputs;
manage, by the network device, a wireless communication signal beam for communicating with at least one user equipment (UE) in the region observed by the one or more sensors based at least in part on the digital twin;
generate, by the network device, a second predicted beam for the at least one UE to conduct uplink communications with the network device; and
transmit, by the network device, an indication of the second predicted beam to the at least one UE.

2 . The apparatus of claim 1 , in which the at least one processor is further configured to generate, by the network device, a predicted beam for conducting wireless communication with the at least one UE using the digital twin based on the stream of inputs from the one or more sensors.

3 . The apparatus of claim 1 , in which the one or more sensors includes one or more of a camera, an inertial measurement unit (IMU) sensor, a light detection and ranging (LiDAR) sensor, and a global positioning satellite (GPS) sensor.

4 . The apparatus of claim 1 , in which the network device manages the wireless communication signal beam based on a fusion of information from the stream of inputs.

5 . The apparatus of claim 1 , in which the at least one processor is further configured to:
track, by the network device, a location of an object of the one or more objects detected based on the stream of inputs, based on an object type; update, by the network device, the digital twin based on the tracking; and adapt, by the network device, the wireless communication signal beam based on the updated digital twin.

6 . The apparatus of claim 1 , in which the at least one processor is further configured to predict, by the network device, a blocker of a wireless communication signal to a first UE of the at least one UE based on the digital twin and a first location of an object relative to a second location of an antenna of the first UE.

7 . The apparatus of claim 6 , in which one or more of the first UE or the object are moving.

8 . The apparatus of claim 6 , in which the at least one processor is further configured to:
determine, by the network device, link degradation due to the object based on a degree of overlap of the first location and the second location; and adapt, by the network device, wireless communication with the at least one UE based on a link degradation type, the wireless communication being adapted by one of: adapting the wireless communication signal beam, changing a frequency for the wireless communication, or initiating a handover.

9 . The apparatus of claim 1 , in which the at least one processor is further configured to:
determine a channel state associated with the wireless communication signal beam; and supply the channel state as feedback to the digital twin.

10 . A processor-implemented method, implemented by a network device, the processor-implemented method comprising:
receiving, by the network device, a stream of inputs from one or more sensors; generating, by the network device, a digital twin modeling an environment of a region observed by the one or more sensors, the digital twin including one or more objects detected based on the stream of inputs; managing, by the network device, a wireless communication signal beam for communicating with at least one user equipment (UE) in the region observed by the one or more sensors based at least in part on the digital twin; generating, by the network device, a second predicted beam for the at least one UE to conduct uplink communications with the network device; and transmitting, by the network device, an indication of the second predicted beam to the at least one UE.

11 . The processor-implemented method of claim 10 , further comprising:
generating, by the network device, a predicted beam for conducting wireless communication with the at least one UE using the digital twin based on the stream of inputs from the one or more sensors.

12 . The processor-implemented method of claim 10 , in which the network device manages the wireless communication signal beam based on a fusion of information from the stream of inputs.

13 . The processor-implemented method of claim 10 , further comprising:
tracking, by the network device, a location of an object of the one or more objects detected based on the stream of inputs, based on an object type; updating, by the network device, the digital twin based on the tracking; and adapting, by the network device, the wireless communication signal beam based on the updated digital twin.

14 . The processor-implemented method of claim 10 , further comprising predicting, by the network device, a blocker of a wireless communication signal to a first UE of the at least one UE based on the digital twin and a first location of an object relative to a second location of an antenna of the first UE.

15 . The processor-implemented method of claim 14 , in which one or more of the first UE or the object are moving.

16 . The processor-implemented method of claim 14 , further comprising:
determining, by the network device, link degradation due to the object based on a degree of overlap of the first location and the second location; and adapting, by the network device, wireless communication with the at least one UE based on a link degradation type, the wireless communication being adapted by one of adapting the wireless communication signal beam, changing a frequency for the wireless communication, or initiating a handover.

17 . The processor-implemented method of claim 10 , further comprising:
determining a channel state associated with the wireless communication signal beam; and supplying the channel state as feedback to the digital twin.

Assignees

• Qualcomm Inc

Inventors

• Arnold Maximilian Wolfgang Martin
• Major Bence
• Behboodi Arash
• Ackermann Hanno
• Massoli Fabio Valerio
• Soriaga Joseph Binamira
• Porikli Fatih Murat

Classifications

• H04B7/0626

  Channel coefficients, e.g. channel state information [CSI] · CPC title

• G06N3/044

  Recurrent networks, e.g. Hopfield networks · CPC title

• G06N20/20

  Ensemble learning · CPC title

• G06N3/08

  Learning methods · CPC title

• G06N3/045

  Combinations of networks · CPC title