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EP-4740326-A1 - METHODS AND APPARATUS FOR SITE-SPECIFIC AND IMPLICIT AI MODEL REFINEMENT FOR 3GPP SYSTEMS

EP4740326A1EP 4740326 A1EP4740326 A1EP 4740326A1EP-4740326-A1

Abstract

The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. Embodiments disclosed herein relate to methods (600) and systems (400) for refining Artificial Intelligence (AI) models in wireless systems. The methods (600) provide site-specific and implicit AI model refinement, where site-specific dataset collection is performed as a part of operational 3GPP procedure, and new models are trained or existing models are refined for CSI prediction and CSI compression using the collected site-specific dataset. The methods (600) include training of CSI prediction and CSI compression models for UEs with the ability to download new models from the base station (402) and also for UEs without the capability.

Inventors

  • KADAMBAR, Sripada
  • CHAVVA, Ashok Kumar Reddy
  • KUMAR, ASHWINI
  • BAL, Samar Ranjan

Assignees

  • Samsung Electronics Co., Ltd.

Dates

Publication Date
20260513
Application Date
20240806

Claims (15)

  1. A method performed by a base station (402) in a wireless communication system, the method comprising: receiving, from at least one UE (404), at least one user equipment (UE) capability information; obtaining AI models based on the at least one UE capability information; selecting at least one AI model among the AI models, and a channel state information (CSI) configuration for the at least one UE (404); and training the at least one AI model for performing at least one CSI operation, based on the CSI configuration.
  2. The method (600) of claim 1, wherein the at least one UE capability information comprises at least one of a computational complexity capability for each AI based functionality, or capability to download and deploy new AI models from the base station (402) during the at least one CSI operation, and wherein the AI based functionality comprises at least one of an AI compression, an AI prediction, an AI model download, and an AI model selection.
  3. The method (600) of claim 1, wherein the at least one AI model, and the CSI configuration for the at least one UE (404) are selected based on at least one of the at least one UE capability information, a site-specific dataset collection, at least one scheduling requirements, or at least one AI model training requirements, wherein the at least one scheduling requirements comprises at least one reporting periodicities to the at least one UE (404) for an AI based CSI prediction case, and wherein the at least one AI model training requirements comprises at least one feedback bit configuration for the at least one UE (404) for a CSI reporting in an AI compression case.
  4. The method (600) of claim 1, further comprising: training the at least one AI model of a high complexity using the site-specific dataset collection; and transferring the trained at least one AI model to the at least one UE (404) for execution, subject to capability of the at least one UE (404) to download and deploy at least one new AI models.
  5. The method (600) of claim 1, wherein the method (600) comprises: training a part of the at least one AI model that is executed on a network side using the site-specific dataset collection; and deploying the trained part of the at least one AI model that is executed on the network side during an AI operation, while leaving the at least one AI model at a UE side untouched.
  6. The method (600) as claimed in claim 1, further comprising: selecting at least one new CSI configuration for refinement; and training at least one new AI model using the site-specific dataset collection, for the at least one new CSI configuration.
  7. The method (600) of claim 1, further comprising: selecting at least one existing CSI configuration for refinement, wherein the base station (402) selects the at least one existing CSI configuration, based on higher accuracy of CSI reporting for the site-specific dataset creation that meets at least one dataset quality requirements; and training the at least one new AI model using the site-specific dataset collection, for the at least one existing CSI configuration.
  8. The method (600) of claim 6, further comprising: identifying a set of AI model configurations and respective complexity classes for training at least one new AI models based on a UE distribution and throughput requirements, wherein the set of AI model configurations per complexity class is a list of complexity classes and payload sizes of the at least one new AI models that are to be trained.
  9. A base station (402) in a wireless communication system, the base station (402) comprising: a transceiver; and at least one processor (406) coupled with the transceiver and configured to: receive, from at least one UE (404), at least one user equipment (UE) capability information, obtain AI models based on the at least one UE capability information, select at least one AI model from the downloaded among the AI models, and a channel state information (CSI) configuration for the at least one UE (404), and train the at least one AI model for performing at least one CSI operation, based on the CSI configuration.
  10. The base station (402) of claim 9, wherein the at least one UE capability information comprises at least one of a computational complexity capability for each AI based functionality, or capability to download and deploy new AI models from the base station (402) during the at least one CSI operation, and wherein the AI based functionality comprises at least one of an AI compression, an AI Prediction, an AI model download, and an AI model selection.
  11. The base station (402) of claim 9, wherein the at least one AI model, and the CSI configuration for the at least one UE (404) are selected based on at least one of the at least one UE capability information, a site-specific dataset collection, at least one scheduling requirements, and at least one AI model training requirements, wherein the at least one scheduling requirements comprises at least one reporting periodicities to the at least one UE (404) for an AI based CSI prediction case, and wherein the at least one AI model training requirements comprises at least one feedback bit configuration for the at least one UE (404) for a CSI reporting in an AI compression case.
  12. The base station (402) of claim 9, wherein the at least one processor (406) is further configured to: train the at least one AI model of a high complexity using the site-specific dataset collection, and transfer the trained at least one AI model to the at least one UE (404) for execution, subject to capability of the at least one UE (404) to download and deploy at least one new AI models.
  13. The base station (402) of claim 9, wherein the at least one processor (406) is further configured to: train a part of the at least one AI model that is executed on a network side using the site-specific dataset collection, and deploy the trained part of the at least one AI model that is executed on the network side during an AI operation, while leaving the at least one AI model at a UE side untouched.
  14. The base station (402) of claim 9, wherein the at least one processor (406) is further configured to: select at least one new CSI configuration for refinement, and train at least one new AI model using the site-specific dataset collection, for the at least one new CSI configuration.
  15. The base station (402) of claim 13, wherein the at least one processor (406) is further configured to: select at least one existing CSI configuration for refinement, wherein the base station (402) selects the at least one existing CSI configuration, based on higher accuracy of CSI reporting for the site-specific dataset creation that meets at least one dataset quality requirements, and train the at least one new AI model using the site-specific dataset collection, for the at least one existing CSI configuration.

Description

METHODS AND APPARATUS FOR SITE-SPECIFIC AND IMPLICIT AI MODEL REFINEMENT FOR 3GPP SYSTEMS Embodiments disclosed herein relate to wireless communication networks, and more particularly to refining Artificial Intelligence (AI) models in wireless systems. 5G mobile communication technologies define broad frequency bands such that high transmission rates and new services are possible, and can be implemented not only in “Sub 6GHz” bands such as 3.5GHz, but also in “Above 6GHz” bands referred to as mmWave including 28GHz and 39GHz. In addition, it has been considered to implement 6G mobile communication technologies (referred to as Beyond 5G systems) in terahertz (THz) bands (for example, 95GHz to 3THz bands) in order to accomplish transmission rates fifty times faster than 5G mobile communication technologies and ultra-low latencies one-tenth of 5G mobile communication technologies. At the beginning of the development of 5G mobile communication technologies, in order to support services and to satisfy performance requirements in connection with enhanced Mobile BroadBand (eMBB), Ultra Reliable Low Latency Communications (URLLC), and massive Machine-Type Communications (mMTC), there has been ongoing standardization regarding beamforming and massive MIMO for mitigating radio-wave path loss and increasing radio-wave transmission distances in mmWave, supporting numerologies (for example, operating multiple subcarrier spacings) for efficiently utilizing mmWave resources and dynamic operation of slot formats, initial access technologies for supporting multi-beam transmission and broadbands, definition and operation of BWP (BandWidth Part), new channel coding methods such as a LDPC (Low Density Parity Check) code for large amount of data transmission and a polar code for highly reliable transmission of control information, L2 pre-processing, and network slicing for providing a dedicated network specialized to a specific service. Currently, there are ongoing discussions regarding improvement and performance enhancement of initial 5G mobile communication technologies in view of services to be supported by 5G mobile communication technologies, and there has been physical layer standardization regarding technologies such as V2X (Vehicle-to-everything) for aiding driving determination by autonomous vehicles based on information regarding positions and states of vehicles transmitted by the vehicles and for enhancing user convenience, NR-U (New Radio Unlicensed) aimed at system operations conforming to various regulation-related requirements in unlicensed bands, NR UE Power Saving, Non-Terrestrial Network (NTN) which is UE-satellite direct communication for providing coverage in an area in which communication with terrestrial networks is unavailable, and positioning. Moreover, there has been ongoing standardization in air interface architecture/protocol regarding technologies such as Industrial Internet of Things (IIoT) for supporting new services through interworking and convergence with other industries, IAB (Integrated Access and Backhaul) for providing a node for network service area expansion by supporting a wireless backhaul link and an access link in an integrated manner, mobility enhancement including conditional handover and DAPS (Dual Active Protocol Stack) handover, and two-step random access for simplifying random access procedures (2-step RACH for NR). There also has been ongoing standardization in system architecture/service regarding a 5G baseline architecture (for example, service based architecture or service based interface) for combining Network Functions Virtualization (NFV) and Software-Defined Networking (SDN) technologies, and Mobile Edge Computing (MEC) for receiving services based on UE positions. As 5G mobile communication systems are commercialized, connected devices that have been exponentially increasing will be connected to communication networks, and it is accordingly expected that enhanced functions and performances of 5G mobile communication systems and integrated operations of connected devices will be necessary. To this end, new research is scheduled in connection with eXtended Reality (XR) for efficiently supporting AR (Augmented Reality), VR (Virtual Reality), MR (Mixed Reality) and the like, 5G performance improvement and complexity reduction by utilizing Artificial Intelligence (AI) and Machine Learning (ML), AI service support, metaverse service support, and drone communication. Furthermore, such development of 5G mobile communication systems will serve as a basis for developing not only new waveforms for providing coverage in terahertz bands of 6G mobile communication technologies, multi-antenna transmission technologies such as Full Dimensional MIMO (FD-MIMO), array antennas and large-scale antennas, metamaterial-based lenses and antennas for improving coverage of terahertz band signals, high-dimensional space multiplexing technology using OAM (Orbital Angular Momentum), and RIS (Rec