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EP-4736510-A1 - METHODS AND SYSTEMS FOR AI MODEL DOWNLOAD FOR BEYOND 5G 3GPP SYSTEMS

EP4736510A1EP 4736510 A1EP4736510 A1EP 4736510A1EP-4736510-A1

Abstract

Embodiments of the disclosure relate to methods and systems for managing and sharing artificial intelligence (AI) models in wireless networks. The disclosure provides a method for a target base station to download and transmit configuration-specific sub-blocks of AI models to user equipment (UE) based on the UE's capabilities and previous configuration data. The server managing the AI models splits them into blocks based on input and configuration parameters, categorizing them as common or configuration-specific sub-blocks for optimized delivery. The UE receives these sub-blocks along with execution information and configures the AI model accordingly.

Inventors

  • KUMAR, ASHWINI
  • KADAMBAR, Sripada
  • CHAVVA, Ashok Kumar Reddy

Assignees

  • Samsung Electronics Co., Ltd.

Dates

Publication Date
20260506
Application Date
20250103

Claims (15)

  1. A method for sharing artificial intelligence (AI) models by a target base station, the method comprising: receiving (702) user equipment (UE) capability information from a UE; receiving (704) previous UE configuration data from a serving base station; downloading (706), from a server, one or more configuration specific sub-blocks corresponding to at least one AI model at least based on the previous UE configuration data and the UE capability information; and transmitting (708) one or more configuration specific sub-blocks along with channel state information (CSI) report configuration information to the UE.
  2. The method of claim 1, wherein the UE capability information at least includes one or more AI based operations supported by the UE and one or more AI model download capability.
  3. The method of claim 1, further comprising: receiving, from the UE, performance information of the at least one AI model.
  4. The method of claim 1, wherein downloading the one or more configuration specific sub-blocks corresponding to the at least one AI model further comprises: downloading the one or more configuration specific sub-blocks corresponding to the at least one AI model further based on AI operations supported by the target base station.
  5. A method for managing artificial intelligence (AI) models by a server, the method comprising: splitting an AI model into a plurality of blocks based on at least one input parameter and at least one configuration parameter; and categorizing the plurality of blocks as one or more common sub-blocks or one or more configuration specific sub-blocks for a base station at least based on dependency of the base station on the at least one input parameter and the at least one configuration parameter.
  6. The method of claim 5, further comprising: receiving, from the base station, a request for one or more sub-blocks, wherein the request for one or more sub-blocks comprises the one or more configuration specific sub-blocks or the one or more common sub-block; and transmitting the requested one or more sub-blocks to the base-station.
  7. The method of claim 5, further comprising: assigning a unique identifier comprising of a block number and a block identifier to the plurality of blocks of the AI model, wherein the unique identifier is stored in the AI model execution information for sharing with channel state information (CSI) report configuration information, wherein the block identifier indicates the flow of execution of the blocks of the AI model, and wherein the each block number corresponds to each block from among the plurality of blocks of the AI model.
  8. A method for loading artificial intelligence (AI) models by a user equipment (UE), the method comprising: transmitting UE capability information to a target base station; receiving one or more configuration specific sub-blocks corresponding to an AI model along with channel state information (CSI) report configuration information from the target base station, wherein the CSI report configuration information comprises AI model execution information; and configuring the AI model based on the one or more configuration specific sub-blocks and the AI model execution information within the CSI report configuration information.
  9. The method of claim 8, further comprising: receiving, from a serving base station, one or more common sub-blocks corresponding to an AI model, wherein the AI model is configured further based on the one or more common sub-blocks and the AI model execution information.
  10. The method of claim 8, further comprising: measuring performance parameters of the AI model based on outputs generated by the AI model; and transmitting performance information of the AI model to the target base station, wherein the performance information includes the performance parameters of the AI model.
  11. A base station (400) for sharing artificial intelligence (AI) models, the base station comprising: memory (403) storing instructions; and at least one processor (401), wherein the instructions, when executed by the at least one processor individually or collectively, cause the base station to: receive user equipment (UE) capability information from a UE; receive previous UE configuration data from a serving base station; download, from a server, one or more configuration specific sub-blocks corresponding to at least one AI model at least based on the previous UE configuration data and the UE capability information; and transmit one or more configuration specific sub-blocks along with channel state information (CSI) report configuration information to the UE.
  12. The base station of claim 11, wherein the UE capability information at least includes one or more AI based operations supported by the UE and one or more AI model download capability, wherein the instructions, when executed by the at least one processor individually or collectively, cause the base station to: receive, from the UE, performance information of the at least one AI model; and download the one or more configuration specific sub-blocks corresponding to the at least one AI model further based on AI operations supported by the base station.
  13. A server (500) for managing artificial intelligence (AI) models, the server comprising: memory (503) storing instructions; and at least one processor (501), wherein the instructions, when executed by the at least one processor individually or collectively, cause the server to: split an AI model into a plurality of blocks based on at least one input parameter and at least one configuration parameter; and categorize the plurality of blocks as one or more common sub-blocks or one or more configuration specific sub-blocks for a base station at least based on dependency of the base station on the at least one input parameter and the at least one configuration parameter.
  14. The server of claim 13, wherein the instructions, when executed by the at least one processor individually or collectively, cause the server to: receive, from the base station, a request for one or more sub-blocks, wherein the request for one or more sub-blocks comprises the one or more configuration specific sub-blocks or the common sub-block; transmit the requested one or more sub-blocks to the base-station; and assigning a unique identifier comprising of a block number and a block identifier to the plurality of blocks of the AI model, wherein the unique identifier is stored in the AI model execution information for sharing with channel state information (CSI) report configuration information, wherein the block identifier indicates the flow of execution of the blocks of the AI model, and wherein the each block number corresponds to each block from among the plurality of blocks of the AI model.
  15. A user equipment (UE) (600) for loading of artificial intelligence (AI) models, the UE comprising: memory (603) storing instructions; and at least one processor (601), wherein the instructions, when executed by the at least one processor individually or collectively, cause the UE to: transmit UE capability information to a target base station; receive one or more configuration specific sub-blocks corresponding to an AI model along with channel state information (CSI) report configuration information from the target base station, wherein the CSI report configuration information comprises AI model execution information; and configure the AI model based on the one or more configuration specific sub-blocks and the AI model execution information within the CSI report configuration information.

Description

METHODS AND SYSTEMS FOR AI MODEL DOWNLOAD FOR BEYOND 5G 3GPP SYSTEMS The disclosure generally relates to the field of mobile communication systems. More particularly, the disclosure relates to a method and system for sharing artificial intelligence (AI) models. The integration of artificial intelligence (AI) into wireless systems represents a significant leap in enhancing the capacity, efficiency, and adaptability of communication networks. Within the framework of the 3rd generation partnership project (3GPP), AI has the potential to address several critical challenges in wireless communication, such as dynamic channel conditions, resource allocation, and optimization of system performance. In the context of 3GPP Release-18, AI is becoming a central component of future wireless systems, with study items being initiated to explore novel AI-based use cases. The applications of AI in wireless communication systems may broadly be categorized into one-sided and two-sided operations. One-sided operations involve the deployment of an AI model at either a base station (BS) or a user equipment (UE), where AI-based operations, such as channel state information (CSI) prediction, are performed independently. In contrast, two-sided operations involve a collaborative deployment of AI models at both the BS and the UE, as exemplified by CSI compression, where both entities work together to optimize system performance. The deployment of AI models in wireless systems is subject to strategic considerations to optimize performance and address inherent limitations. Once AI models are trained, it is critical to ensure that they are deployed on the appropriate devices either at the BS or the UE. For operations such as CSI compression or prediction, where real-time decision-making is essential, it is preferable for the trained AI models to reside on the UE, reducing latency in the execution of AI-based tasks. However, UEs are often constrained by limited memory capacity, which makes it impractical to store a wide range of AI models. To address this challenge, 3GPP has proposed, within the scope of Release-18, that UEs should be capable of either storing AI models locally or downloading them from the BS as needed. Storing AI models at the BS presents a viable solution, given the BS's higher computational power and memory resources compared to the UE. The BS may efficiently train AI models based on a broad spectrum of network data and dynamic field scenarios, ensuring that the models remain up-to-date and optimized. On the other hand, UEs may benefit from downloading AI models from the BS, allowing them to access the latest models tailored to evolving network conditions. This dynamic model distribution approach enhances system efficiency by ensuring that UEs may perform AI-based tasks without the limitations posed by local storage capacity. However, the process of downloading and configuring AI models introduces potential challenges, particularly when frequent configuration changes occur, such as during handovers or carrier aggregation (CA) scenarios. In situations where the UE switches between BSs with different reporting periodicities or operates across multiple component carriers, the need to download and reconfigure models may lead to inefficiencies, increased bandwidth consumption, and potential service disruptions. Addressing these challenges requires a more intelligent approach to AI model management. A potential solution to these inefficiencies involves optimizing the AI model download process by leveraging the commonalities between models for similar configurations. Rather than downloading entirely new models for each configuration change, an intelligent approach could involve selectively transferring the differentiating components of the models. This approach would reduce bandwidth requirements, accelerate the reconfiguration process, and enhance the responsiveness of the wireless network. While the integration of AI into wireless systems holds significant promise for improving network efficiency and adaptability, there remain challenges associated with the deployment, download, and reconfiguration of AI models. Therefore, there is a need for a more efficient and intelligent method of managing AI model downloads and configurations in wireless networks. The information disclosed in this background of the disclosure section is only for enhancement of understanding of the general background of the disclosure and may not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art. The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles. The same numbers are used throughout the figures to reference like features and components. Some embodiments of at least one of device and methods in accordance w