IP Library › Granted Patent US 12694289
Granted Patent B2
US 12694289 · App. 18/004,255 · Granted Jul 28, 2026

Neural network or layer configuration indicator for a channel state information scheme

Inventors: Alexandros Manolakos (Escondido, CA); Pavan Kumar Vitthaladevuni (San Diego, CA); June Namgoong (San Diego, CA); Jay Kumar Sundararajan (San Diego, CA); Taesang Yoo (San Diego, CA); Hwan Joon Kwon (San Diego, CA); Krishna Kiran Mukkavilli (San Diego, CA); Tingfang Ji (San Diego, CA); Naga Bhushan (San Diego, CA)
Assignee: QUALCOMM Incorporated
G06N3/08H04B7/0479
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Quick Facts
Patent No.
US 12694289
App. No.
18/004,255
Granted
Jul 28, 2026
Kind
B2
Abstract

Methods, systems, and devices for wireless communications are described. A user equipment (UE) may train a first set of layers of a neural network based on channel estimates using a set of resources. The UE may generate a set of weights for the first set of layers of the neural network based on the training. The UE may receive, from a first network entity, an indication of an association between a first set of signaling and a second set of signaling based on the first set of layers of the neural network. The UE may receive the second set of signaling from a second network entity and process the second set of signaling using the set of weights for the first set of layers based on the association between the first set of signaling and the second set of signaling.

Claims (63)

1 . A method for wireless communications at a user equipment (UE), comprising:

training a first set of layers of a neural network based at least in part on channel estimates using a set of resources;

generating a set of weights for the first set of layers of the neural network based at least in part on the training;

receiving, from a first network entity, an indication of an association between a first set of one or more signals and a second set of one or more signals;

receiving the second set of one or more signals from a second network entity; and

processing the second set of one or more signals using the set of weights of the first set of layers based at least in part on the association between the first set of one or more signals and the second set of one or more signals,

wherein the indication of the association comprises a source identifier and a target identifier.

2 . The method of claim 1 , further comprising:

transmitting, to the first network entity, an indication of a quantity of layer-states that the UE is capable of storing, tracking, training, processing, or any combination thereof, for one or more of a component carrier, a band, or a band combination.

3 . The method of claim 1 , wherein processing the second set of one or more signals comprises:

decoding the second set of one or more signals using the set of weights for the first set of layers based at least in part on the association between the first set of one or more signals and the second set of one or more signals.

4 . The method of claim 1 , wherein processing the second set of one or more signals comprises:

demodulating the second set of one or more signals using the set of weights for the first set of layers based at least in part on the association between the first set of one or more signals and the second set of one or more signals.

5 . The method of claim 1 , wherein processing the second set of one or more signals comprises:

estimating a downlink channel from the second set of one or more signals using the set of weights for the first set of layers based at least in part on the association between the first set of one or more signals and the second set of one or more signals.

6 . The method of claim 1 , wherein processing the second set of one or more signals comprises:

compressing the second set of one or more signals using the set of weights for the first set of layers based at least in part on the association between the first set of one or more signals and the second set of one or more signals; and

training the first set of layers of the neural network, a second set of layers of the neural network, or both, based at least in part on compressing the second set of one or more signals.

7 . The method of claim 1 , wherein processing the second set of one or more signals comprises:

training a set of layers of a second neural network using the set of weights for the first set of layers of the neural network based at least in part on the association between the first set of one or more signals and the second set of one or more signals.

8 . The method of claim 1 , wherein the first set of one or more signals comprises one or more of a channel state information reference signal, a synchronization signal block, or a positioning reference signal, a demodulation reference signal, a tracking signal, a data channel, or a control channel.

9 . The method of claim 1 , wherein the second set of one or more signals comprises one or more of a channel state information reference signal, a synchronization signal block, or a positioning reference signal, a demodulation reference signal, a tracking signal, a data channel, or a control channel.

10 . The method of claim 1 , wherein the source identifier includes an identifier of the neural network.

11 . The method of claim 1 , wherein the target identifier includes an identifier of the second set of one or more signals, a procedure for the second set of one or more signals, or an identifier of a second neural network, or any combination thereof.

12 . The method of claim 1 , wherein the source identifier includes an identifier of a signal or procedure corresponding to at least the first set of one or more signals, an identifier of at least the first set of layers of the neural network, or any combination thereof.

13 . The method of claim 1 , wherein the indication of the association is received via higher-layer signals, a medium access control (MAC) control element (CE), downlink control information, or both.

14 . The method of claim 1 , wherein the first set of one or more signals corresponds to a first component carrier and the second set of one or more signals corresponds to a second component carrier, or the first set of one or more signals corresponds to a first band and the second set of one or more signals corresponds to a second band, or the first set of one or more signals corresponds to a first band combination and the second set of one or more signals corresponds to a second band combination, or the first set of one or more signals corresponds to a first frequency range and the second set of one or more signals corresponds to a second frequency range, or any combination thereof.

15 . The method of claim 1 , wherein the first set of layers of the neural network comprises one or more residual neural network layers or one or more convolutional neural network layers, or any combination thereof.

16 . The method of claim 1 , further comprising:

receiving a third set of one or more signals from the second network entity; and

processing the third set of one or more signals using the set of weights.

17 . The method of claim 1 , wherein the second network entity is another UE, a base station, a transmission and reception point, a server, the first network entity or any combination thereof.

18 . A method for wireless communications at a network entity comprising:

receiving, from a user equipment (UE) an indication of a set of trained layers of a neural network based at least in part on channel estimates over a set of resources;

identifying a set of weights for the set of trained layers of the neural network based at least in part on the indication of the set of trained layers;

transmitting, to the UE, an indication of an association between a first set of one or more signals and a second set of one or more signals based at least in part on the set of trained layers of the neural network; and

transmitting the second set of one or more signals to the UE,

wherein the indication of the association comprises a source identifier and a target identifier.

19 . The method of claim 18 , further comprising:

receiving, from the UE, an indication of a quantity of layer-states that the UE is capable of storing, tracking, training, processing, or any combination thereof, for one or more of a component carrier, a band, or a band combination.

20 . The method of claim 18 , wherein the first set of one or more signals comprises one or more of a channel state information reference signal, a synchronization signal block, or a positioning reference signal.

21 . The method of claim 18 , wherein the second set of one or more signals comprises one or more of a channel state information reference signal, a synchronization signal block, or a positioning reference signal.

22 . The method of claim 18 , wherein the source identifier includes an identifier of the neural network, an identifier of at least the set of trained layers of the neural network, an identifier of a signal or procedure corresponding to at least the first set of one or more signals, or any combination thereof.

23 . The method of claim 18 , wherein the target identifier includes an identifier of the second set of one or more signals, a procedure for the second set of one or more signals, or both.

24 . The method of claim 18 , wherein transmitting the indication of the association comprises:

transmitting the indication of the association via a medium access control (MAC) control element (CE), downlink control information, or any combination thereof.

25 . The method of claim 18 , wherein the first set of one or more signals corresponds to a first component carrier and the second set of one or more signals corresponds to a second component carrier, or the first set of one or more signals corresponds to a first band and the second set of one or more signals corresponds to a second band, or the first set of one or more signals corresponds to a first frequency range and the second set of one or more signals corresponds to a second frequency range, or any combination thereof.

26 . The method of claim 18 , wherein the set of trained layers of the neural network comprises one or more residual neural network layers or one or more convolutional neural network layers, or any combination thereof.

27 . An apparatus for wireless communications at a user equipment (UE), comprising memory coupled to a processor, wherein the processor is configured to:

train a first set of layers of a neural network based at least in part on channel estimates using a set of resources;

generate a set of weights for the first set of layers of the neural network based at least in part on the training;

receive, from a first network entity, an indication of an association between a first set of one or more signals and a second set of one or more signals;

receive the second set of one or more signals from a second network entity; and

process the second set of one or more signals using the set of weights for the first set of layers based at least in part on the association between the first set of one or more signals and the second set of one or more signals,

wherein the indication of the association comprises a source identifier and a target identifier.

28 . An apparatus for wireless communications at a network entity comprising memory coupled to a processor, wherein the processor is configured to:

receive, from a user equipment (UE) an indication of a set of trained layers of a neural network based at least in part on channel estimates over a set of resources;

identify a set of weights for the set of trained layers of the neural network based at least in part on the indication of the set of trained layers;

transmit, to the UE, an indication of an association between a first set of one or more signals and a second set of one or more signals based at least in part on the set of trained layers of the neural network; and

transmit the second set of one or more signals to the UE,

wherein the indication of the association comprises a source identifier and a target identifier.

29 . The apparatus of claim 27 , wherein the source identifier includes an identifier of the neural network, an identifier of a signal or procedure corresponding to at least the first set of one or more signals, an identifier of at least the first set of layers of the neural network, or any combination thereof.

30 . The apparatus of claim 27 , wherein the target identifier includes an identifier of the second set of one or more signals, a procedure for the second set of one or more signals, or an identifier of a second neural network, or any combination thereof.