US-20260129667-A1 - INDICATING CURRENT AND FUTURE ASSOCIATED IDENTIFIERS FOR BEAM PREDICTION

US20260129667A1US 20260129667 A1US20260129667 A1US 20260129667A1US-20260129667-A1

Abstract

Methods, systems, and devices for wireless communications are described. A user equipment (UE) may receive a control message that includes a list of associated identifiers (IDs), and the control message may further indicate whether each associated ID included in the list is a current associated ID or a future associated ID (e.g., whether a respective associated ID represents a current or future network-side additional condition). In some aspects, the information indicating whether an associated ID is a current of future associated ID may be conveyed to the UE explicitly or implicitly. In some examples, the UE may receive an indication of a priority across associated IDs included in the list. Additionally, or alternatively, the UE may receive an indication of a priority across inference configurations for one or more current associated IDs. The priority may be configured across associated IDs and used to determine which associated ID(s) to prioritize.

Inventors

Hamed PEZESHKI
Rajeev Kumar
Aziz Gholmieh

Assignees

QUALCOMM INCORPORATED

Dates

Publication Date: 20260507
Application Date: 20251105

Claims (20)

1 . A user equipment (UE), comprising: one or more memories storing processor-executable code; and one or more processors coupled with the one or more memories and individually or collectively operable to execute the code to cause the UE to: receive a first control message indicating: a set of associated identifiers, one or more configurations for prediction operations or one or more parameters associated with the prediction operations, and whether one or more of the set of associated identifiers are associated with a first set of additional conditions of a network entity or a second set of additional conditions; transmit a reporting message comprising an indication of whether at least one set of machine learning configurations or at least one set of prediction parameters from the one or more parameters is applicable for the prediction operations, wherein the at least one set of machine learning configurations or the at least one set of prediction parameters is applicable in accordance with the set of associated identifiers, the one or more configurations, the one or more parameters, whether the one or more of the set of associated identifiers are associated with the first set of additional conditions or the second set of additional conditions, or any combination thereof; receive a second control message activating one or more machine learning configurations in accordance with the reporting message; and use the one or more machine learning configurations for the prediction operations in accordance with the at least one set of machine learning configurations indicated by the reporting message.
2 . The UE of claim 1 , wherein the indication of the reporting message indicates whether: the at least one set of machine learning configurations or the at least one set of prediction parameters from the one or more parameters is ready for one or more prediction operations; the at least one set of machine learning configurations or the at least one set of prediction parameters from the one or more parameters is obtainable for one or more prediction operations after a duration; or the at least one set of machine learning configurations or the at least one set of prediction parameters from the one or more parameters is not applicable for one or more prediction operations.
3 . The UE of claim 1 , wherein the first set of additional conditions comprises a current set of additional conditions and the second set of additional conditions comprises a future set of additional conditions of the network entity.
4 . The UE of claim 1 , wherein the first set of additional conditions corresponds to a serving cell associated with the network entity and the second set of additional conditions corresponds to an adjacent cell associated with a second network entity.
5 . The UE of claim 1 , wherein the one or more configurations include the set of associated identifiers, and wherein the one or more configurations comprise one or more channel state information report configurations that include predictive information.
6 . The UE of claim 1 , wherein the prediction operations comprise a beam prediction or a CSI prediction, or any combination thereof.
7 . The UE of claim 1 , wherein the first control message indicates a priority of each of the one or more of the set of associated identifiers, and wherein the one or more processors are individually or collectively operable to cause the UE to: select at least one associated identifier from the one or more of the set of associated identifiers in accordance with the priority.
8 . The UE of claim 1 , wherein the first control message indicates a first associated identifier of the one or more of the set of associated identifiers for the first set of additional conditions, and the first control message further indicates a priority of one or more channel state information report configurations corresponding to the first associated identifier, a priority of one or more prediction parameters corresponding to the first associated identifier, or any combination thereof, and wherein the at least one set of machine learning configurations or the at least one set of prediction parameters indicated via the reporting message corresponds to the priority of the one or more channel state information report configurations, the priority of the one or more prediction parameters, or both.
9 . The UE of claim 1 , wherein the first control message comprises a respective flag for each associated identifier of the set of associated identifiers, and wherein the respective flag comprises a first value indicating that a corresponding associated identifier is associated with the first set of additional conditions or comprising a second value indicating that the corresponding associated identifier is associated with the second set of additional conditions of the network entity.
10 . The UE of claim 1 , wherein the set of associated identifiers are indicated by the first control message in accordance with an order of respective associated identifiers, and wherein the order is indicative of whether a respective associated identifier is associated with the first set of additional conditions of the network entity or is associated with the second set of additional conditions of the network entity.
11 . The UE of claim 1 , wherein the one or more processors are individually or collectively further operable to cause the UE to: transmit a message comprising an indication that one or more of the at least one set of machine learning configurations are no longer applicable for one or more additional prediction operations for the prediction.
12 . The UE of claim 11 , wherein the message is transmitted via radio resource control signaling, via medium access control-control element signaling, via uplink control information, or any combination thereof, in accordance with the one or more of the at least one set of machine learning configurations being previously indicated as applicable.
13 . The UE of claim 11 , wherein the indication that the one or more of the at least one set of machine learning configurations are no longer applicable is based at least in part on whether the one or more of the set of associated identifiers are associated with the first set of additional conditions of the network entity or the second set of additional conditions, a priority of each of the one or more of the set of associated identifiers, whether an associated identifier corresponding to the one or more of the at least one set of machine learning configurations is associated with a neighboring cells, or any combination thereof.
14 . The UE of claim 1 , wherein the one or more processors are individually or collectively further operable to cause the UE to: obtain a machine learning model that corresponds to at least one associated identifier of the one or more of the set of associated identifiers in accordance with the first control message indicating that the one or more of the set of associated identifiers is associated with the first set of additional conditions.
15 . The UE of claim 1 , wherein the set of associated identifiers are indicated via the one or more configurations for prediction operations.
16 . A method for wireless communications by a user equipment (UE), comprising: receiving a first control message indicating: a set of associated identifiers, one or more configurations for prediction operations or one or more parameters associated with the prediction operations, and whether one or more of the set of associated identifiers are associated with a first set of additional conditions of a network entity or a second set of additional conditions; transmitting a reporting message comprising an indication of whether at least one set of machine learning configurations or at least one set of prediction parameters from the one or more parameters is applicable for the prediction operations, wherein the at least one set of machine learning configurations or the at least one set of prediction parameters is applicable in accordance with the set of associated identifiers, the one or more configurations, the one or more parameters, whether the one or more of the set of associated identifiers are associated with the first set of additional conditions or the second set of additional conditions, or any combination thereof; receiving a second control message activating one or more machine learning configurations in accordance with the reporting message; and using the one or more machine learning configurations for the prediction operations in accordance with the at least one set of machine learning configurations indicated by the reporting message.
17 . The method of claim 16 , wherein the indication of the reporting message indicates whether: the at least one set of machine learning configurations or the at least one set of prediction parameters from the one or more parameters is ready for one or more prediction operations; the at least one set of machine learning configurations or the at least one set of prediction parameters from the one or more parameters is obtainable for one or more prediction operations after a duration; or the at least one set of machine learning configurations or the at least one set of prediction parameters from the one or more parameters is not applicable for one or more prediction operations.
18 . The method of claim 16 , wherein the first set of additional conditions comprises a current set of additional conditions and the second set of additional conditions comprises a future set of additional conditions of the network entity.
19 . The method of claim 16 , wherein the first set of additional conditions corresponds to a serving cell associated with the network entity and the second set of additional conditions corresponds to an adjacent cell associated with a second network entity.
20 . A non-transitory computer-readable medium storing code for wireless communications, the code comprising instructions executable by one or more processors to: receive a first control message indicating: a set of associated identifiers, one or more configurations for prediction operations or one or more parameters associated with the prediction operations, and whether one or more of the set of associated identifiers are associated with a first set of additional conditions of a network entity or a second set of additional conditions; transmit a reporting message comprising an indication of whether at least one set of machine learning configurations or at least one set of prediction parameters from the one or more parameters is applicable for the prediction operations, wherein the at least one set of machine learning configurations or the at least one set of prediction parameters is applicable in accordance with the set of associated identifiers, the one or more configurations, the one or more parameters, whether the one or more of the set of associated identifiers are associated with the first set of additional conditions or the second set of additional conditions, or any combination thereof; receive a second control message activating one or more machine learning configurations in accordance with the reporting message; and use the one or more machine learning configurations for the prediction operations in accordance with the at least one set of machine learning configurations indicated by the reporting message.

Description

CROSS REFERENCES The present Application for Patent claims benefit of U.S. Provisional Patent. Application No. 63/717,750 by PEZESHKI et al., entitled “INDICATING CURRENT AND FUTURE ASSOCIATED IDENTIFIERS FOR BEAM PREDICTION,” filed Nov. 7, 2024, assigned to the assignee hereof, and expressly incorporated herein. FIELD OF TECHNOLOGY The following relates to wireless communications, including indicating current and future associated identifiers for beam prediction. BACKGROUND Wireless communications systems are widely deployed to provide various types of communication content such as voice, video, packet data, messaging, broadcast, and so on. These systems may be capable of supporting communication with multiple users by sharing the available system resources (e.g., time, frequency, and power). Examples of such multiple-access systems include fourth generation (4G) systems such as Long Term Evolution (LTE) systems, LTE-Advanced (LTE-A) systems, or LTE-A Pro systems, and fifth generation (5G) systems which may be referred to as New Radio (NR) systems. These systems may employ technologies such as code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal FDMA (OFDMA), or discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-S-OFDM). A wireless multiple-access communications system may include one or more base stations, each supporting wireless communication for communication devices, which may be known as user equipment (UE). SUMMARY The systems, methods, and devices of this disclosure each have several innovative aspects, no single one of which is solely responsible for the desirable attributes disclosed herein. A method for wireless communication by a user equipment (UE) is described. The method may include receiving a first control message indicating, a set of associated identifiers, one or more configurations for prediction operations or one or more parameters associated with the prediction operations, whether one or more of the set of associated identifiers are associated with a first set of additional conditions of a network entity or a second set of additional conditions, transmitting a reporting message including an indication of whether at least one set of machine learning configurations or at least one set of prediction parameters from the one or more parameters is applicable for the prediction operations, where the at least one set of machine learning configurations or the at least one set of prediction parameters is applicable in accordance with the set of associated identifiers, the one or more configurations, the one or more parameters, whether the one or more of the set of associated identifiers are associated with the first set of additional conditions or the second set of additional conditions, or any combination thereof, receiving a second control message activating one or more machine learning configurations in accordance with the reporting message, and using the one or more machine learning configurations for the prediction operations in accordance with the at least one set of machine learning configurations indicated by the reporting message. A UE for wireless communication is described. The UE may include one or more memories storing processor executable code, and one or more processors coupled with the one or more memories. The one or more processors may individually or collectively be operable to execute the code to cause the UE to receive a first control message indicating, a set of associate identifiers, one or more configurations for prediction operations or one or more parameters associate with the prediction operations, whether one or more of the set of associate identifiers are associated with a first set of additional conditions of a network entity or a second set of additional conditions, transmit a reporting message including an indication of whether at least one set of machine learning configurations or at least one set of prediction parameters from the one or more parameters is applicable for the prediction operations, where the at least one set of machine learn configurations or the at least one set of prediction parameters is applicable in accordance with the set of associated identifiers, the one or more configurations, the one or more parameters, whether the one or more of the set of associated identifiers are associated with the first set of additional conditions or the second set of additional conditions, or any combination thereof, receive a second control message activating one or more machine learning configurations in accordance with the reporting message, and used the one or more machine learning configurations for the prediction operations in accordance with the at least one set of machine learning configurations indicated by the reporting message. Another UE for wireless communication is described. The UE may include means for receiving a first control message indi