Device using neural network for combining cellular communication with sensor data

Patent metadata

Field	Value
Publication number	US-12537746-B2
Application number	US-202218279984-A
Country	US
Kind code	B2
Filing date	Mar 1, 2022
Priority date	Mar 1, 2021
Publication date	Jan 27, 2026
Grant date	Jan 27, 2026

Abstract

A method includes receiving an information block as an input to a transmitter neural network, receiving, as an input to the transmitter neural network, sensor data from one or more sensors, processing the information block and sensor data at the transmitter neural network to generate an output, and controlling an RF transceiver based on the output to generate an RF signal ( 134 ) for wireless transmission. Another method includes receiving a first output from an RF transceiver as a first input to a receiver neural network, receiving, as a second input to the receiver neural network, a set of sensor data from one or more sensors, processing the first input and the second input at the receiver neural network to generate an output, and processing the output to generate an information block representative of information communicated by a data sending device.

First claim

What is claimed is:

1 . A computer-implemented method, in a data sending device, comprising:
receiving a first information block as an input to a transmitter neural network of the data sending device, the first information block representing a set of information bits to be transmitted to a data receiving device; receiving, as an input to the transmitter neural network, a first set of sensor data from a first set of one or more sensors of the data sending device; processing the first information block and the first set of sensor data at the transmitter neural network to generate a first output; and controlling the data sending device based on the first output to generate a first RF signal for wireless transmission to the data receiving device.

2 . The method of claim 1 , further comprising one of:
identifying a neural network architectural configuration to be implemented by the data sending device based on sensor capabilities of at least one of the data sending device or the data receiving device and implementing the neural network architectural configuration for the transmitter neural network; or transmitting an indication of sensor capabilities of the data sending device to the data receiving device, receiving, from the data receiving device, a representation of a neural network architectural configuration that is based on the sensor capabilities of the data sending device, and implementing the neural network architectural configuration for the transmitter neural network.

3 . The method of claim 1 , further comprising:
receiving, from the data receiving device, a representation of sensor capabilities of the data receiving device; determining a neural network architectural configuration-to be implemented by a receiver neural network of the data receiving device based on the sensor capabilities of the data receiving device; and transmitting, to the data receiving device, a representation of the neural network architectural configuration.

4 . The method of claim 3 , further comprising:
participating in joint training of the neural network architectural configuration for the transmitter neural network with a neural network architectural configuration for a receiver neural network of the data receiving device.

5 . The method of claim 1 , further comprising:
receiving, from the data receiving device, a representation of a sensor configuration identifying the first set of one or more sensors from a plurality of sensors of the data sending device; and selectively activating the first set of one or more sensors based on the sensor configuration.

6 . The method of claim 1 , further comprising at least one of:
receiving, from the data receiving device, a representation of time resources or frequency resources to be utilized by a sensor of the first set of one or more sensors that operates in a licensed frequency spectrum; or transmitting to the data receiving device a representation of time resources or frequency resources to be utilized by a sensor of the data receiving device that operates in a licensed frequency spectrum.

7 . The method of claim 1 , further comprising:
determining that at least one sensor of the first set of one or more sensors is unavailable; implementing a neural network architectural configuration at the transmitter neural network of the data sending device based on sensor capabilities of the data sending device that exclude the at least one sensor that is unavailable; receiving a second information block as an input to the transmitter neural network of the data sending device; receiving, as an input to the transmitter neural network, a second set of sensor data from the first set of one or more sensors of the data sending device; processing the second information block and the second set of sensor data at the transmitter neural network to generate a second output; and controlling an RF transceiver of the data sending device based on the second output to generate a second RF signal for wireless transmission to the data receiving device.

8 . The method of claim 7 , wherein controlling the data sending device based on the first output comprises controlling at least one of: a scheduling decision; a handover decision for the data receiving device based on the first output; or a beam management operation of the RF transceiver based on the first output.

9 . A computer-implemented method, in a data receiving device, comprising:
receiving a first output from a radio frequency transceiver of the data receiving device as a first input to a receiver neural network of the data receiving device; receiving, as a second input to the receiver neural network, a first set of sensor data from a first set of one or more sensors of the data receiving device; processing the first input and the second input at the receiver neural network to generate a second output; and processing the second output at the data receiving device to generate a first information block representative of information communicated by a data sending device.

10 . The method of claim 9 , further comprising one of:
identifying a neural network architectural configuration to be implemented by the data receiving device based on sensor capabilities of the data receiving device and implementing the neural network architectural configuration for the receiver neural network; or transmitting an indication of sensor capabilities of the data receiving device to the data sending device, receiving, from the data sending device, a representation of a neural network architectural configuration that is based on the sensor capabilities of the data receiving device, and implementing the neural network architectural configuration for the receiver neural network.

11 . The method of claim 9 , further comprising:
receiving, from the data sending device, a representation of sensor capabilities of the data sending device; determining a neural network architectural configuration to be implemented by a transmitter neural network of the data sending device based on the sensor capabilities of the data sending device; and transmitting, to the data sending device, a representation of the neural network architectural configuration.

12 . The method of claim 11 , further comprising:
participating in joint training of the neural network architectural configuration for the receiver neural network with a neural network architectural configuration for a transmitter neural network of the data sending device.

13 . The method of claim 9 , further comprising:
receiving, from the data sending device, a representation of a sensor configuration identifying the first set of one or more sensors from a plurality of sensors of the data receiving device; and selectively activating the first set of one or more sensors based on the sensor configuration.

14 . The method of claim 9 , further comprising one of:
receiving, from the data sending device, a representation of time resources or frequency resources to be utilized by a sensor of the first set of one or more sensors that operates in a licensed frequency spectrum; or transmitting to the data sending device a representation of time resources or frequency resources to be utilized by a sensor of the data sending device that operates in a licensed frequency spectrum.

15 . The method of claim 9 , further comprising:
determining that at least one sensor of the first set of one or more sensors is unavailable; implementing a neural network architectural configuration at the receiver neural network of the data receiving device based on sensor capabilities of the data receiving

Assignees

• Google Llc

Inventors

• Wang Jibing
• Stauffer Erik Richard

Classifications

• H04W36/326

  by proximity to another entity · CPC title

• H04W72/51

  based on terminal or device properties · CPC title

• H04W36/00837

  Determination of triggering parameters for hand-off · CPC title

• H04L41/16 Primary

  using machine learning or artificial intelligence · CPC title

• H04B7/06952

  Selecting one or more beams from a plurality of beams, e.g. beam training, management or sweeping · CPC title