Systems and methods for wireless signal configuration by a neural network

Patent metadata

Field	Value
Publication number	US-12021572-B2
Application number	US-202218080932-A
Country	US
Kind code	B2
Filing date	Dec 14, 2022
Priority date	May 15, 2019
Publication date	Jun 25, 2024
Grant date	Jun 25, 2024

Abstract

A wireless network can generate candidate signal configurations for physical transmissions to or from a user equipment (UE) in a radio environment. The generation of candidate signal configurations can be performed using a first neural network that is associated with the UE. These signal configurations can then be evaluated using a second neural network that is associated with the radio environment. The second neural network can be trained using measurements from previous physical transmissions in the radio environment. The trained second neural network generates a reward value that is associated with the candidate signal configurations. The first neural network is then trained using the reward values from the second neural network to produce improved candidate signal configurations. When a signal configuration that produces a suitable reward value is generated, this signal configuration can be used for the physical transmission in the radio environment.

First claim

The invention claimed is:

1. A method comprising:
collecting a plurality of data samples, each data sample, among the plurality of data samples, providing information associated with a respective physical transmission in a radio environment;
training, using a subset of the plurality of data samples, a first neural network associated with the radio environment, the training resulting in a trained first neural network;
obtaining, from a second neural network specific to a user equipment (UE):
a candidate signal configuration for a scheduled transmission to or from the UE in the radio environment;
information associated with a state of the UE; and
information associated with the radio environment;

inputting, into the trained first neural network:
the information associated with the state of the UE; and
the information associated with the radio environment; and

obtaining, from the trained first neural network, an evaluation of the candidate signal configuration for training the second neural network;
wherein the evaluation includes:
a metric representing a predicted effectiveness of the candidate signal configuration in the radio environment; and
information associated with a predicted state of the UE after performing the scheduled transmission to or from the UE using the candidate signal configuration.

2. The method of claim 1 , wherein the metric representing a predicted effectiveness of the candidate signal configuration in the radio environment is a metric representing a series of predicted effectiveness values over time of the candidate signal configuration in the radio environment.

3. The method of claim 1 , wherein the information associated with a predicted state of the UE after performing the scheduled transmission to or from the UE using the candidate signal configuration is information associated with a series of predicted states over time of the UE after performing the scheduled transmission to or from the UE using the candidate signal configuration.

4. The method of claim 1 , wherein the UE is a first UE, and at least one data sample of the plurality of data samples comprises information associated with a physical transmission to or from a second UE in the radio environment.

5. The method of claim 4 , wherein the information associated with the physical transmission to or from the second UE comprises:
a first state of the second UE before the physical transmission to or from the second UE;
transmission parameters associated with the physical transmission to or from the second UE;
a second state of the second UE after the physical transmission to or from the second UE; and
a second metric representing an effectiveness of the physical transmission to or from the second UE in the radio environment.

6. The method of claim 5 , wherein the second metric comprises a weighted sum of a plurality of measurements associated with the physical transmission to or from the second UE.

7. The method of claim 1 , wherein the collecting the plurality of data samples comprises:
receiving the plurality of data samples from a plurality of network devices; and
storing the plurality of data samples in a database.

8. The method of claim 7 , wherein the storing the plurality of data samples in the database comprises deleting a plurality of older data samples from the database.

9. The method of claim 7 , wherein the plurality of network devices comprises a base station and a plurality of UEs.

10. The method of claim 1 , wherein the UE is a first UE and the scheduled transmission is a first scheduled transmission, the method further comprising:
obtaining, from a third neural network associated with a second UE, a second candidate signal configuration for a second scheduled transmission in the radio environment; and
evaluating, using the trained first neural network, the second candidate signal configuration to produce an evaluation of the second candidate signal configuration for training the third neural network.

11. The method of claim 1 , wherein the method is performed ata base station.

12. An apparatus comprising:
a processor; and
at least one computer readable storage medium storing:
a database storing a plurality of data samples, each data sample, among the plurality of data samples, providing information associated with a respective physical transmission in a radio environment;
a first neural network associated with the radio environment; and
programming for execution by the processor, the programming including instructions to:
train, using a subset of the plurality of data samples, a first neural network associated with the radio environment, the training resulting in a trained first neural network;
obtain, from a second neural network specific to a user equipment (UE):
a candidate signal configuration for a scheduled transmission to or from the UE in the radio environment;
information associated with a state of the UE; and
information associated with the radio environment;

input, into the trained first neural network:
information associated with a state of the UE; and
information associated with the radio environment; and

obtain, from the trained first neural network, an evaluation of the candidate signal configuration for training the second neural network;

wherein the evaluation includes:
a metric representing a predicted effectiveness of the candidate signal configuration in the radio environment; and
information associated with a predicted state of the UE after performing the scheduled transmission to or from the UE using the candidate signal configuration.

13. The method of claim 12 , wherein the metric representing a predicted effectiveness of the candidate signal configuration in the radio environment is a metric representing a series of predicted effectiveness values over time of the candidate signal configuration in the radio environment.

14. The method of claim 12 , wherein the information associated with a predicted state of the UE after performing the scheduled transmission to or from the UE using the candidate signal configuration is information associated with a series of predicted states over time of the UE after performing the scheduled transmission to or from the UE using the candidate signal configuration.

15. The apparatus of claim 12 , wherein the UE is a first UE, and at least one data sample of the plurality of data samples comprises information associated with a physical transmission to or from a second UE in the radio environment.

16. The apparatus of claim 15 , wherein the information associated with the physical transmission to or from the second UE comprises:
a first state of the second UE before the physical transmission to or from the second UE;
transmission parameters associated with the physical transmission to or from the second UE;
a second state of the second UE after the physical transmission to or from the second UE; and
a second metric representing an effectiveness of the physical transmission to or from the second UE in the radio environment.

17. The apparatus of claim 16 , wherein the second metric comprises a weighted sum of a plurality of measurements associated with the physical transmission to or from the second UE.

18. The apparatus of claim 12 , wherein the UE is a first UE and the scheduled transmission is a first scheduled transmission, the programming further comprising instructions to:
obtain, from a third neural network associated with a second UE, a second candidate signal configuration for a second scheduled transmission in the radio environment;

Assignees

• Ge Yiqun
• Shi Wuxian
• Tong Wen
• Huawei Tech Co Ltd

Inventors

• Ge Yiqun
• Shi Wuxian
• Tong Wen

Classifications

• G06N3/09

  Supervised learning · CPC title

• G06N3/0442

  characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] · CPC title

• G06N3/092

  Reinforcement learning · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• H04L25/03165

  using neural networks · CPC title