US-12621689-B2 - Method and apparatus for monitoring and reporting AI model in wireless communication system

Abstract

The disclosure relates to a 6th generation (6G) communication system for optimizing a network while being applied in various industrial fields through connection between 5th generation (5G) or beyond 5G things and networks for supporting a higher data rate. A method performed by user equipment (UE) in a wireless communication system is provided, including receiving configuration information related to an artificial intelligence (AI) model from a base station; performing monitoring of a first AI model of the UE for encoding and decoding channel state information (CSI); and reporting a monitoring result to the base station. The first AI model includes a first encoder and a first decoder of the UE, and the first decoder may be related to a second AI model of the base station including a second decoder.

Inventors

• Changsung LEE
• Suhwook Kim
• Hyeondeok JANG

Assignees

• SAMSUNG ELECTRONICS CO., LTD.

Dates

Publication Date

20260505

Application Date

20221206

Priority Date

20220614

Claims (19)

1. 1 . A method performed by a user equipment (UE) in a wireless communication system, the method comprising: receiving, from a base station, configuration information related to a first artificial intelligence (AI) model of the UE and a second AI model of the base station, wherein the first AI model includes a first encoder of the UE and a first decoder of the UE; generating a plurality of channel state information (CSI) for monitoring the first AI model: compressing the plurality of CSI based on the first encoder: identifying whether at least one compressed CSI among the plurality of compressed CSI is a monitoring target, based on the configuration information: if the at least one compressed CSI is identified as the monitoring target, restoring the at least one compressed CSI based on the first decoder; and reporting, to the base station, a monitoring result of the first AI model based on comparing the at least one restored CSI and at least one corresponding CSI among the plurality of CSI, wherein the first decoder is related to the second AI model of the base station including a second decoder of the base station.
2. 2 . The method of claim 1 , wherein the first AI model includes a first autoencoder and the second AI model includes a second autoencoder.
3. 3 . The method of claim 1 , wherein the configuration information comprises at least one of an identifier of the first AI model, an interval for monitoring the first AI model, a method for monitoring the first AI model, or information on a type of reporting for the monitoring result of the first AI model.
4. 4 . The method of claim 1 , wherein comparing the measured at least one CSI and the restored at least one CSI comprises: identifying whether a difference between the measured at least one CSI and the restored at least one CSI is less than a first threshold value in the configuration information.
5. 5 . The method of claim 4 , further comprising: receiving, from the base station, a message requesting a change into a non-AI-based CSI feedback mode, in response to transmitting, during a third time period, wherein the monitoring result is transmitted when accuracy of the first AI model of the monitoring result is less than the first threshold value.
6. 6 . The method of claim 1 , wherein reporting the monitoring result of the first AI model to the base station comprises at least one of: reporting the monitoring result of the first AI model together with a report of CSI compressed by the first encoder; reporting the monitoring result of the first AI model when a specific event occurs; or periodically reporting the monitoring result of the first AI model, and wherein the monitoring result of the first AI model comprises accuracy of the first AI model and an identifier of the first AI model.
7. 7 . The method of claim 1 , further comprising: transmitting, to the base station, a message indicating a failure of the first AI model when accuracy of the first AI model is less than a second threshold value during a first time period.
8. 8 . The method of claim 7 , further comprising: transmitting, to the base station, a message indicating a change into a non-AI-based CSI feedback mode when the accuracy of the first AI model is less than a third threshold value during a second time period.
9. 9 . The method of claim 8 , further comprising: transmitting, to the base station, a message indicating a change into an AI-based CSI feedback mode when the UE operates in the non-AI-based CSI feedback mode, and when accuracy of the first AI model is greater than the third threshold value during the second time period.
10. 10 . The method of claim 1 , wherein the configuration information comprises information on a weight value and a structure of the second AI model.
11. 11 . A user equipment (UE) in a wireless communication system, the UE comprising: at least one transceiver; at least one processor communicatively coupled to the at least one transceiver; and at least one memory, communicatively coupled to the at least one processor, storing instructions executable by the at least one processor individually or in any combination to cause the UE to: receive, from a base station, configuration information related to a first artificial intelligence (AI) model of the UE and a second AI model of the base station, wherein the first AI model includes a first encoder of the UE and a first decoder of the UE, generate a plurality of channel state information (CSI) for monitoring the first AI model, compress the plurality of CSI based on the first encoder, identify whether at least one compressed CSI among the plurality of compressed CSI is a monitoring target, based on the configuration information, if the at least one compressed CSI is identified as the monitoring target, restore the at least one compressed CSI based on the first decoder, and report, to the base station, a monitoring result of the first AI model based on comparing the at least one restored CSI and at least one corresponding CSI among the plurality of CSI, wherein the first decoder is related to the second AI model of the base station including a second decoder of the base station.
12. 12 . The UE of claim 11 , wherein the first AI model includes a first autoencoder and the second AI model includes a second autoencoder.
13. 13 . The UE of claim 11 , wherein the configuration information comprises at least one of an identifier of the first AI model, an interval for monitoring the first AI model, a method for monitoring the first AI model, or information on a type of reporting for the monitoring result of the first AI model.
14. 14 . A method performed by a base station in a wireless communication system, the method comprising: transmitting, to a user equipment (UE), configuration information related to a first artificial intelligence (AI) model of the UE and a second AI model of the base station, wherein the first AI model includes a first encoder of the UE and a first decoder of the UE; and receiving, from the UE, a monitoring result, wherein the monitoring result is based on a comparison between at least one restored channel state information (CSI) and at least one corresponding CSI, the at least one restored CSI being restored from at least one compressed CSI which corresponds to a monitoring target, wherein the at least one compressed CSI is compressed from the at least one corresponding CSI among a plurality of CSI of the UE, wherein the first decoder is related to a second decoder included in the second AI model of the base station, and wherein the monitoring result of the first AI model is based on a comparison between a first output from the first encoder and a second output from the first decoder.
15. 15 . The method of claim 14 , wherein the first AI model includes a first autoencoder and the second AI model includes a second autoencoder.
16. 16 . The method of claim 14 , wherein the configuration information comprises at least one of an identifier of the first AI model, an interval for monitoring the first AI model, a method for monitoring the first AI model, or information on a type of reporting for the monitoring result of the first AI model.
17. 17 . A base station in a wireless communication system, the base station comprising: at least one transceiver; at least one processor communicatively coupled to the at least one transceiver; and at least one memory, communicatively coupled to the at least one processor, storing instructions executable by the at least one processor individually or in any combination to cause the base station to: transmit, to a user equipment (UE), configuration information related to a first artificial intelligence (AI) model of the UE and a second AI model of the base station, wherein the first AI model includes a first encoder of the UE and a first decoder of the UE; and receive, from the UE, a monitoring result, wherein the monitoring result is based on a comparison between at least one restored channel state information (CSI) and at least one corresponding CSI, the at least one restored CSI being restored from at least one compressed CSI which corresponds to a monitoring target, wherein the at least one compressed CSI is compressed from the at least one corresponding CSI among a plurality of CSI of the UE, wherein the first decoder is related to a second decoder included in the second AI model of the base station, and wherein the monitoring result of the first AI model is based on a comparison between a first output from the first encoder and a second output from the first decoder.
18. 18 . The base station of claim 17 , wherein the first AI model includes a first autoencoder and the second AI model includes a second autoencoder.
19. 19 . The base station of claim 17 , wherein the configuration information comprises at least one of an identifier of the first AI model, an interval for monitoring the first AI model, a method for monitoring the first AI model, or information on a type of reporting for the monitoring result of the first AI model.

Description

CROSS-REFERENCE TO RELATED APPLICATION(S) This application is based on and claims priority under 35 U.S.C. § 119 to Korean Patent Application No. 10-2022-0071974, which was filed in the Korean Intellectual Property Office on Jun. 14, 2022, the entire disclosure of which is incorporated herein by reference. BACKGROUND 1. Field The disclosure relates generally to a wireless communication system and, more particularly, to a method and apparatus for monitoring and reporting an artificial intelligence (AI) model in a wireless communication system. 2. Description of Related Art Considering the development of wireless communication from generation to generation, technologies have been developed for services targeting humans, such as voice calls, multimedia services, and data services. Following the commercialization of 5th generation (5G) communication systems, it is expected that the number of connected devices will exponentially grow. Increasingly, the devices and things will be connected to communication networks. Examples of connected things include vehicles, robots, drones, home appliances, displays, smart sensors connected to various infrastructures, construction machines, and factory equipment. Mobile devices are expected to evolve in various form-factors, such as augmented reality glasses, virtual reality headsets, and hologram devices. To provide various services by connecting hundreds of billions of devices and things in the 6th generation (6G) era, there are ongoing efforts to develop improved 6G communication systems, which may also be referred to as beyond-5G systems. 6G communication systems, which are expected to be commercialized around 2030, are expected to have a peak data rate of tera (1,000 giga)-level bit per second (bps) and a radio latency that is less than 100 μsec, and thus, should be 50 times as fast as 5G communication systems and have 1/10 the radio latency thereof. To accomplish such a high data rate and an ultra-low latency, it has been considered to implement 6G communication systems in a terahertz (THz) band (e.g., 95 gigahertz (GHz) to 3 THz bands). It is expected that, due to increased path loss and atmospheric absorption in the terahertz bands than those in mmWave bands introduced in 5G, technologies capable of securing the signal transmission distance (i.e., coverage) will become more crucial. Accordingly, it is important to develop, as technologies for securing the appropriate coverage, radio frequency (RF) elements, antennas, novel waveforms having a better coverage than orthogonal frequency division multiplexing (OFDM), beamforming and massive multiple-input multiple-output (MIMO), full dimensional MIMO (FD-MIMO), array antennas, and multiantenna transmission technologies such as large-scale antennas. In addition, new technologies are being discussed for improving the coverage of terahertz-band signals, such as metamaterial-based lenses and antennas, orbital angular momentum (OAM), and reconfigurable intelligent surface (RIS). To improve the spectral efficiency and the overall network performances, various technologies have been developed for 6G communication systems, including: a full-duplex technology for enabling an uplink transmission and a downlink transmission to simultaneously use the same frequency resource at the same time; a network technology for utilizing satellites, high-altitude platform stations (HAPS), etc., in an integrated manner; an improved network structure for supporting mobile base stations and the like and enabling network operation optimization and automation and the like; a dynamic spectrum sharing technology via collision avoidance based on a prediction of spectrum usage; use of AI in wireless communication for improvement of overall network operation by utilizing AI from a designing phase for developing 6G and internalizing end-to-end AI support functions; and a next-generation distributed computing technology for overcoming the limit of UE computing ability through reachable super-high-performance communication and computing resources (such as mobile edge computing (MEC), clouds, etc.) over the network. In addition, through designing new protocols to be used in 6G communication systems, developing mechanisms for implementing a hardware-based security environment and safe use of data, and developing technologies for maintaining privacy, attempts continue to strengthen the connectivity between devices, optimize the network, promote softwarization of network entities, and increase the openness of wireless communications. It is expected that research and development of 6G communication systems in hyper-connectivity, including person to machine (P2M) as well as machine to machine (M2M), will allow the next hyper-connected experience. Particularly, it is expected that services such as truly immersive extended Reality (XR), high-fidelity mobile hologram, and digital replica could be provided through 6G communication systems. In addition, services such as