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US-20260129002-A1 - METHODS AND APPARATUS FOR AI/ML TRAFFIC DETECTION

US20260129002A1US 20260129002 A1US20260129002 A1US 20260129002A1US-20260129002-A1

Abstract

The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. There is disclosed a first network entity included in a communication network, the first network entity comprising: a transmitter; a receiver; and a controller configured to: monitor traffic from a second network entity included in the communications network; and based on the traffic being associated with a type of an artificial intelligence/machine learning (AI/ML) operation, perform one or more operations to assist performance of the AI/ML operation.

Inventors

  • Tingyu Xin
  • David Gutierrez Estevez
  • Mahmoud Watfa

Assignees

  • SAMSUNG ELECTRONICS CO., LTD.

Dates

Publication Date
20260507
Application Date
20230925
Priority Date
20220930

Claims (20)

  1. 1 - 13 . (canceled)
  2. 14 . A method of a user plane function (UPF) included in a communications network, the method comprising: monitoring traffic from a user equipment (UE); and based on the traffic associated with an artificial intelligence/machine learning (AI/ML) operation, performing at least one operation to assist performance of the AI/ML operation.
  3. 15 . The method of claim 14 , wherein the AI/ML operation corresponds to one of: an AI/ML operation splitting between AI/ML endpoints; an AI/ML model or data distribution and sharing; and distributed or federated learning.
  4. 16 . The method of claim 14 , wherein performing at least one operation comprises: performing the at least one operation to assist performance of the AI/ML operation based on monitoring session inactivity related to the traffic, and monitoring traffic volume related to the traffic.
  5. 17 . The method of claim 14 , wherein the at least one operation comprises: applying a charging rate to traffic associated with the AI/ML operation based on policy set by an operator.
  6. 18 . The method of claim 14 , wherein the at least one operation comprises: mapping a 5G quality of service (QoS) identifier (5QI) corresponding to the AI/ML operation to QoS characteristics.
  7. 19 . The method of claim 18 , wherein the QoS characteristics comprise one or more of: a resource type including one of non-guaranteed bit rate (GBR) or delay-critical GBR, a packet delay budget, a packet error rate, a default maximum data burst volume, or a default averaging window.
  8. 20 . The method of claim 19 , wherein: the AI/ML operation is AI/ML model downloading, in case that the QoS characteristics comprise: the resource type of the non-GBR, the default maximum data burst volume of not available, and a default averaging window of not available; or the AI/ML operation is downlink (DL) split AI/ML image recognition, in case that the one or more QoS characteristics comprise: the resource type of the delay-critical GBR.
  9. 21 . The method of claim 16 , further comprising: reporting an event to a first network entity based on the monitoring of the session inactivity or the monitoring of the traffic volume.
  10. 22 . The method of claim 21 , wherein reporting the event comprises: transmitting an N 4 session report message to the first network entity to report the event; and/or wherein the event indicates the session inactivity, the traffic volume, the AI/ML operation and/or phase of the AI/ML operation.
  11. 23 . The method of claim 21 , wherein one or more of: the first network entity is one of a user plane function (UPF), a user equipment (UE), a session management function (SMF), an application function (AF), an application, a network data analytics function (NWDAF), or a network function (NF) configured to support the AI/ML operation; the communications network is a 5G network; and/or wherein the first network entity is included in a 5G core (5GC).
  12. 24 . A user plane function (UPF) entity included in a communication network, the UPF entity comprising: a transmitter; a receiver; and a controller configured to: monitor traffic from a user equipment (UE); and based on the traffic associated with an artificial intelligence/machine learning (AI/ML) operation, perform at least one operation to assist performance of the AI/ML operation.
  13. 25 . The UPF entity of claim 24 , wherein the AI/ML operation corresponds to one of: an AI/ML operation splitting between AI/ML endpoints; an AI/ML model or data distribution and sharing; and distributed or federated learning.
  14. 26 . The UPF entity of claim 24 , wherein the controller is further configured to: perform the at least one operation to assist performance of the AI/ML operation based on monitoring session inactivity related to the traffic, and monitoring traffic volume related to the traffic.
  15. 27 . The UPF entity of claim 24 , wherein the at least one operation comprises: apply a charging rate to traffic associated with the AI/ML operation based on policy set by an operator.
  16. 28 . The UPF entity of claim 24 , wherein the at least one operation comprises: map a 5G quality of service (QoS) identifier (5QI) corresponding to the AI/ML operation to QoS characteristics.
  17. 29 . The UPF entity of claim 28 , wherein the QoS characteristics comprise one or more of: a resource type including one of non-guaranteed bit rate (GBR) or delay-critical GBR, a packet delay budget, a packet error rate, a default maximum data burst volume, or a default averaging window.
  18. 30 . The UPF entity of claim 29 , wherein: the AI/ML operation is AI/ML model downloading, in case that the QoS characteristics comprise: the resource type of the non-GBR, the default maximum data burst volume of not available, and a default averaging window of not available; or the AI/ML operation is downlink (DL) split AI/ML image recognition, in case that the one or more QoS characteristics comprise: the resource type of the delay-critical GBR.
  19. 31 . The UPF entity of claim 26 , wherein the controller is further configured to: report an event to a first network entity based on the monitoring of the session inactivity or the monitoring of the traffic volume.
  20. 32 . The UPF entity of claim 31 , wherein the controller is further configured to: transmit an N 4 session report message to the first network entity to report the event, and/or wherein the event indicates the session inactivity, the traffic volume, the AI/ML operation and/or phase of the AI/ML operation.

Description

TECHNICAL FIELD Various embodiments of the present disclosure relate to methods, apparatus and/or systems for detecting artificial intelligence/machine learning (AI/ML) traffic. In particular, various embodiments of the present disclosure provide methods, apparatus and systems for determining, by a user plane function (UPF) or any 5GS network function (NF), that traffic from a user equipment (UE) or application will be or is associated with an AI/ML operation. Further, various embodiments of the present disclosure provide different methods for making this determination and/or performing one or more operations to assist the AI/ML operation. Further, in various embodiments of the present disclosure, information regarding the result of the determination is transmitted to a session management function (SMF) or any 5GS NF. Further, in various embodiments of the present disclosure, the NFs (or network entities) are included in a 3rd Generation Partnership Project (3GPP) 5th Generation (5G) New Radio (NR) communications network. BACKGROUND ART 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 6 GHz” bands such as 3.5 GHz, but also in “Above 6 GHz” bands referred to as mmWave including 28 GHz and 39 GHz. In addition, it has been considered to implement 6G mobile communication technologies (referred to as Beyond 5G systems) in terahertz bands (for example, 95 GHz to 3 THz 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