Flexible analytics related procedures for optimized quality of service (QOS)

Abstract

A network entity may comprise a processor configured to receive a request from a policy control function (PCF) that includes a set of analytics identifiers (IDs). The processor may send, to a network data analytics function (NWDAF), a NWDAF analytics request corresponding to the set of analytics IDs, receive, from the NWDAF, an NWDAF analytics response corresponding to the set of analytics IDs. The NWDAF analytics response may include analytics ID outputs and / or a joint machine learning (ML) model analytics output for values of quality of service (QoS) parameters to be optimized. The processor may perform analytics aggregation using the received analytics ID outputs and / or a joint ML model analytics output, determine QoS parameters values based on output results of the analytics aggregation for different candidate values of QoS parameters, and send a response message to the PCF including the determined QoS parameters values.

Description

FLEXIBLE ANALYTICS RELATED PROCEDURES FOR OPTIMIZED QUALITY OF SERVICE (QOS)CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of United States Provisional Application No. 63 / 681 ,056 filed on August 8, 2024, the entire contents of which are incorporated herein by reference.BACKGROUND

[0002] New solutions have proposed that a certain logical entity that can be a Network Data Analytics Function (NWDAF) may perform Quality of Service (QoS) recommendations. The recommendations may be based on the use of multiple analytics to provide a certain output. An analytics consumer may be able to request multiple analytics Identifiers (IDs) from one or more analytics IDs producers. Such request may include information that may be different for each individual analytics ID. For example, for one analytics ID, analytics ID1 , analytics filter information may include a Single Network Slice Selection Assistance Information (S-NSSAI) value, whereas for a second analytics ID2, the analytics filter information may include an area of interest. In another example, different analytics IDs may be requested using dataset with different statistical properties. However, when requesting multiple analytics together, some other information may be provided for the whole group of requested analytics IDs. For example, analytics target period and / or preferred level of accuracy have the same value for all requested analytics IDs. In the case of preferred accuracy level, requiring that all the analytics IDs have the same (e.g., high) level of accuracy, may not be needed, and / or may induce delay in obtaining the requested analytics ID, since it may take more time, (e.g., for Machine Learning (ML) model training), to be able to obtain the level of accuracy for some analytics. The 5GS does not allow providing some parameters or information when requesting multiple analytics ID together, in a differentiated way (e.g., per analytics IDs). One way to be able to leverage multiple analytics output results may be to use the analytics aggregation capability for supporting NWDAFs.SUMMARY

[0003] A network entity may comprise a processor. The processor may be configured to receive a request from a policy control function (PCF) that includes a set of analytics identifiers (IDs). The set of analytics IDs may include, for example, an indication whether each analytics ID is mandatory, preferred and / or optional, an indication of preferred accuracy levels per analytics ID, and / or an indication whether each analytics ID was used in training jointly or separately. The processor may be configured to send, to a network data analytics function (NWDAF), a NWDAF analytics request corresponding to the set of analytics IDs. The processor may be configured to receive, from the NWDAF, an NWDAF analytics response corresponding to the set of analytics IDs. The NWDAF analytics response may include, for example, analytics ID outputs and / or a joint machine learning (ML) model analytics output for values of quality of service (QoS) parameters to be optimized. The processor may be configured to perform analytics aggregation using the received analytics ID outputs and / or a joint ML model analytics output. The processor may beconfigured to determine QoS parameters values based on output results of the analytics aggregation for different candidate values of QoS parameters. The processor may be configured to send a response message to the PCF including the determined QoS parameters values.

[0004] The request from the PCF may include, for example, one or more sets of analytics IDs. Each of the one or more sets of analytics IDs may include, for example, a preferred accuracy level(s) per analytics ID. The NWDAF analytics request may include, for example, accuracy level per analytics ID and / or supported delay per analytics ID.

[0005] The analytics IDs may include one or more of analytics ID1, analytics ID2, analytics ID3, and / or analytics ID4. In some examples, analytics ID1 may equal observed service experience (OSE), analytics ID2 may equal QoS sustainability, analytics ID3 may equal user data congestion, and / or analytics ID4 may equal data network (DN) performance.

[0006] The processor may be configured to determine, based on the indication whether each analytics IDs is mandatory, preferred and / or optional, which analytics IDs from the set of analytics IDs are to be used. The processor may be configured to determine that the analytics aggregation is required.

[0007] The NWDAF analytics request may indicate whether the joint ML model analytics output and / or the analytics aggregation may be used. The request from the PCF may include, for example, an optimization task and / or related optimization assistance information. The related optimization assistance information may include, for example, a set of features that may be used to reach an optimization goal.

[0008] The processor may be configured to determine that an analytics aggregation and / or joint ML model training may be required. The response message may include, for example, an achieved goal determined based on the QoS parameters, and / or determined features that are recommended to be used with parameters corresponding to the determined features.

[0009] A network entity may be configured to perform a method that includes one or more of the following steps. For example, the method may include receiving a request from a policy control function (PCF) that includes a set of analytics identifiers (IDs). The set of analytics IDs may include, for example, an indication whether each analytics ID is mandatory, preferred and / or optional, an indication of preferred accuracy levels per analytics ID, and / or an indication whether each analytics ID was used in training jointly or separately. The method may include sending, to a network data analytics function (NWDAF), a NWDAF analytics request corresponding to the set of analytics IDs. The method may include receiving, from the NWDAF, an NWDAF analytics response corresponding to the set of analytics IDs. The NWDAF analytics response may include, for example, analytics ID outputs and / or a joint machine learning (ML) model analytics output for values of quality of service (QoS) parameters to be optimized. The method may include performing analytics aggregation using the received analytics ID outputs and / or a joint ML model analytics output. The method may include determining QoS parameters values based on output results of the analytics aggregation for different candidate values of QoS parameters. The method may include sending a response message to the PCF including the determined QoS parameters values.

[0010] The request from the PCF may include, for example, one or more sets of analytics IDs. Each of the one or more sets of analytics IDs may include, for example, a preferred accuracy level(s) per analytics ID. The NWDAF analytics request may include, for example, accuracy level per analytics ID and / or supported delay per analytics ID.

[0011] The analytics IDs may include one or more of analytics ID1, analytics ID2, analytics ID3, and / or analytics ID4. In some examples, analytics ID1 may equal observed service experience (OSE), analytics ID2 may equal QoS sustainability, analytics ID3 may equal user data congestion, and / or analytics ID4 may equal data network (DN) performance.

[0012] The method may include determining, based on the indication whether each analytics IDs is mandatory, preferred and / or optional, which analytics IDs from the set of analytics IDs are to be used. The method may include determining that the analytics aggregation is required.

[0013] The NWDAF analytics request may indicate whether the joint ML model analytics output and / or the analytics aggregation may be used. The request from the PCF may include, for example, an optimization task and / or related optimization assistance information. The related optimization assistance information may include, for example, a set of features that may be used to reach an optimization goal.

[0014] The method may include determining that an analytics aggregation and / or joint ML model training may be required. The response message may include, for example, an achieved goal determined based on the QoS parameters, and / or determined features that are recommended to be used with parameters corresponding to the determined features.BRIEF DESCRIPTION OF THE DRAWINGS

[0015] FIG. 1A is a system diagram illustrating an example communications system in which one or more disclosed embodiments may be implemented.

[0016] FIG. 1 B is a system diagram illustrating an example wireless transmit / receive unit (WTRU) that may be used within the communications system illustrated in FIG. 1 A according to an embodiment.

[0017] FIG. 1C is a system diagram illustrating an example radio access network (RAN) and an example core network (CN) that may be used within the communications system illustrated in FIG. 1A according to an embodiment.

[0018] FIG. 1 D is a system diagram illustrating a further example RAN and a further example CN that may be used within the communications system illustrated in FIG. 1 A according to an embodiment.

[0019] FIG. 2A is a diagram illustrating an example of Enhanced QoS recommendation and / or optimization service using multiple analytics with more granular analytics reporting parameters and enhanced optimization capability by NWDAF, including example steps 1-5.

[0020] FIG. 2B is a diagram illustrating an example of Enhanced QoS recommendation and / or optimization service using multiple analytics with more granular analytics reporting parameters and enhanced optimization capability by NWDAF, including example steps 6-11.DETAILED DESCRIPTION

[0021] FIG. 1A is a diagram illustrating an example communications system 100 in which one or more disclosed embodiments may be implemented. The communications system 100 may be a multiple access system that provides content, such as voice, data, video, messaging, broadcast, etc., to multiple wireless users. The communications system 100 may enable multiple wireless users to access such content through the sharing of system resources, including wireless bandwidth. For example, the communications systems 100 may employ one or more channel access methods, such as code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal FDMA (OFDMA), single-carrier FDMA (SC-FDMA), zero-tail uniqueword DFT-Spread OFDM (ZT UW DTS-s OFDM), unique word OFDM (UW-OFDM), resource block-filtered OFDM, filter bank multicarrier (FBMC), and the like.

[0022] As shown in FIG. 1A, the communications system 100 may include wireless transmit / receive units (WTRUs) 102a, 102b, 102c, 102d, a RAN 104 / 113, a CN 106 / 115, a public switched telephone network (PSTN) 108, the Internet 110, and other networks 112, though it will be appreciated that the disclosed embodiments contemplate any number of WTRUs, base stations, networks, and / or network elements. Each of the WTRUs 102a, 102b, 102c, 102d may be any type of device configured to operate and / or communicate in a wireless environment. By way of example, the WTRUs 102a, 102b, 102c, 102d, any of which may be referred to as a “station” and / or a “STA", may be configured to transmit and / or receive wireless signals and may include a user equipment (UE), a mobile station, a fixed or mobile subscriber unit, a subscription-based unit, a pager, a cellular telephone, a personal digital assistant (PDA), a smartphone, a laptop, a netbook, a personal computer, a wireless sensor, a hotspot or Mi-Fi device, an Internet of Things (loT) device, a watch or other wearable, a head-mounted display (HMD), a vehicle, a drone, a medical device and applications (e.g., remote surgery), an industrial device and applications (e.g., a robot and / or other wireless devices operating in an industrial and / or an automated processing chain contexts), a consumer electronics device, a device operating on commercial and / or industrial wireless networks, and the like. Any of the WTRUs 102a, 102b, 102c and 102d may be interchangeably referred to as a WTRU. Further, any description herein that is described with reference to a UE may be equally applicable to a WTRU (or vice versa). For example, a WTRU may be configured to perform any of the processes or procedures described herein as being performed by a UE (or vice versa).

[0023] The communications systems 100 may also include a base station 114a and / or a base station 114b. Each of the base stations 114a, 114b may be any type of device configured to wirelessly interface with at least one of the WTRUs 102a, 102b, 102c, 102d to facilitate access to one or more communication networks, such as the CN 106 / 115, the Internet 110, and / or the other networks 112. By way of example, the base stations 114a, 114b may be a base transceiver station (BTS), a Node-B, an eNode B, a Home Node B, a Home eNode B, a gNB, a NR NodeB, a site controller, an access point (AP), a wireless router, and the like. While the base stations 114a, 114b are eachdepicted as a single element, it will be appreciated that the base stations 114a, 114b may include any number of interconnected base stations and / or network elements.

[0024] The base station 114a may be part of the RAN 104 / 113, which may also include other base stations and / or network elements (not shown), such as a base station controller (BSC), a radio network controller (RNC), relay nodes, etc. The base station 114a and / or the base station 114b may be configured to transmit and / or receive wireless signals on one or more carrier frequencies, which may be referred to as a cell (not shown). These frequencies may be in licensed spectrum, unlicensed spectrum, or a combination of licensed and unlicensed spectrum. A cell may provide coverage for a wireless service to a specific geographical area that may be relatively fixed or that may change over time. The cell may further be divided into cell sectors. For example, the cell associated with the base station 114a may be divided into three sectors. Thus, in one embodiment, the base station 114a may include three transceivers, i.e., one for each sector of the cell. In an embodiment, the base station 114a may employ multiple-input multiple output (MIMO) technology and may utilize multiple transceivers for each sector of the cell. For example, beamforming may be used to transmit and / or receive signals in desired spatial directions.

[0025] The base stations 114a, 114b may communicate with one or more of the WTRUs 102a, 102b, 102c, 102d over an air interface 116, which may be any suitable wireless communication link (e.g., radio frequency (RF), microwave, centimeter wave, micrometer wave, infrared (IR), ultraviolet (UV), visible light, etc.). The air interface 116 may be established using any suitable radio access technology (RAT).

[0026] More specifically, as noted above, the communications system 100 may be a multiple access system and may employ one or more channel access schemes, such as CDMA, TDMA, FDMA, OFDMA, SC-FDMA, and the like. For example, the base station 114a in the RAN 104 / 113 and the WTRUs 102a, 102b, 102c may implement a radio technology such as Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access (UTRA), which may establish the air interface 115 / 116 / 117 using wideband CDMA (WCDMA). WCDMA may include communication protocols such as High-Speed Packet Access (HSPA) and / or Evolved HSPA (HSPA+). HSPA may include High- Speed Downlink (DL) Packet Access (HSDPA) and / or High-Speed UL Packet Access (HSUPA).

[0027] In an embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement a radio technology such as Evolved UMTS Terrestrial Radio Access (E-UTRA), which may establish the air interface 116 using Long Term Evolution (LTE) and / or LTE-Advanced (LTE-A) and / or LTE-Advanced Pro (LTE-A Pro).

[0028] In an embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement a radio technology such as NR Radio Access , which may establish the air interface 116 using New Radio (NR).

[0029] In an embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement multiple radio access technologies. For example, the base station 114a and the WTRUs 102a, 102b, 102c may implement LTE radio access and NR radio access together, for instance using dual connectivity (DC) principles. Thus, the air interface utilized by WTRUs 102a, 102b, 102c may be characterized by multiple types of radio access technologies and / or transmissions sent to / from multiple types of base stations (e.g., a eNB and a g N B).

[0030] In other embodiments, the base station 114a and the WTRUs 102a, 102b, 102c may implement radio technologies such as IEEE 802.11 (i.e., Wireless Fidelity (WiFi), IEEE 802.16 (i.e., Worldwide Interoperability for Microwave Access (WiMAX)), CDMA2000, CDMA2000 1X, CDMA2000 EV-DO, Interim Standard 2000 (IS-2000), Interim Standard 95 (IS-95), Interim Standard 856 (IS-856), Global System for Mobile communications (GSM), Enhanced Data rates for GSM Evolution (EDGE), GSM EDGE (GERAN), and the like.

[0031] The base station 114b in FIG. 1 A may be a wireless router, Home Node B, Home eNode B, or access point, for example, and may utilize any suitable RAT for facilitating wireless connectivity in a localized area, such as a place of business, a home, a vehicle, a campus, an industrial facility, an air corridor (e.g., for use by drones), a roadway, and the like. In one embodiment, the base station 114b and the WTRUs 102c, 102d may implement a radio technology such as IEEE 802.11 to establish a wireless local area network (WLAN). In an embodiment, the base station 114b and the WTRUs 102c, 102d may implement a radio technology such as IEEE 802.15 to establish a wireless personal area network (WPAN). In yet another embodiment, the base station 114b and the WTRUs 102c, 102d may utilize a cellular-based RAT (e.g., WCDMA, CDMA2000, GSM, LTE, LTE-A, LTE-A Pro, NR etc.) to establish a picocell or femtocell. As shown in FIG. 1 A, the base station 114b may have a direct connection to the Internet 110. Thus, the base station 114b may not be required to access the Internet 110 via the CN 106 / 115.

[0032] The RAN 104 / 113 may be in communication with the CN 106 / 115, which may be any type of network configured to provide voice, data, applications, and / or voice over internet protocol (VoIP) services to one or more of the WTRUs 102a, 102b, 102c, 102d. The data may have varying quality of service (QoS) requirements, such as differing throughput requirements, latency requirements, error tolerance requirements, reliability requirements, data throughput requirements, mobility requirements, and the like. The CN 106 / 115 may provide call control, billing services, mobile location-based services, pre-paid calling, Internet connectivity, video distribution, etc., and / or perform high-level security functions, such as user authentication. Although not shown in FIG. 1 A, it will be appreciated that the RAN 104 / 113 and / or the CN 106 / 115 may be in direct or indirect communication with other RANs that employ the same RAT as the RAN 104 / 113 or a different RAT. For example, in addition to being connected to the RAN 104 / 113, which may be utilizing a NR radio technology, the CN 106 / 115 may also be in communication with another RAN (not shown) employing a GSM, UMTS, CDMA 2000, WiMAX, E-UTRA, or WiFi radio technology.

[0033] The CN 106 / 115 may also serve as a gateway for the WTRUs 102a, 102b, 102c, 102d to access the PSTN 108, the Internet 110, and / or the other networks 112. The PSTN 108 may include circuit-switched telephone networks that provide plain old telephone service (POTS). The Internet 110 may include a global system of interconnected computer networks and devices that use common communication protocols, such as the transmission control protocol (TCP), user datagram protocol (UDP) and / or the internet protocol (IP) in the TCP / IP internet protocol suite. The networks 112 may include wired and / or wireless communications networks owned and / or operated byother service providers. For example, the networks 112 may include another CN connected to one or more RANs, which may employ the same RAT as the RAN 104 / 113 or a different RAT.

[0034] Some or all of the WTRUs 102a, 102b, 102c, 102d in the communications system 100 may include multimode capabilities (e.g., the WTRUs 102a, 102b, 102c, 102d may include multiple transceivers for communicating with different wireless networks over different wireless links). For example, the WTRU 102c shown in FIG. 1A may be configured to communicate with the base station 114a, which may employ a cellular-based radio technology, and with the base station 114b, which may employ an IEEE 802 radio technology.

[0035] FIG. 1 B is a system diagram illustrating an example WTRU 102. As shown in FIG. 1 B, the WTRU 102 may include a processor 118, a transceiver 120, a transmit / receive element 122, a speaker / microphone 124, a keypad 126, a display / touchpad 128, non-removable memory 130, removable memory 132, a power source 134, a global positioning system (GPS) chipset 136, and / or other peripherals 138, among others. It will be appreciated that the WTRU 102 may include any sub-combination of the foregoing elements while remaining consistent with an embodiment.

[0036] The processor 118 may be a general purpose processor, a special purpose processor, a conventional processor, a digital signal processor (DSP), a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) circuits, any other type of integrated circuit (IC), a state machine, and the like. The processor 118 may perform signal coding, data processing, power control, I n put / output processing, and / or any other functionality that enables the WTRU 102 to operate in a wireless environment. The processor 118 may be coupled to the transceiver 120, which may be coupled to the transmit / receive element 122. While FIG. 1B depicts the processor 118 and the transceiver 120 as separate components, it will be appreciated that the processor 118 and the transceiver 120 may be integrated together in an electronic package or chip.

[0037] The transmit / receive element 122 may be configured to transmit signals to, or receive signals from, a base station (e.g., the base station 114a) over the air interface 116. For example, in one embodiment, the transmit / receive element 122 may be an antenna configured to transmit and / or receive RF signals. In an embodiment, the transmit / receive element 122 may be an emitter / detector configured to transmit and / or receive IR, UV, or visible light signals, for example. In yet another embodiment, the transmit / receive element 122 may be configured to transmit and / or receive both RF and light signals. It will be appreciated that the transmit / receive element 122 may be configured to transmit and / or receive any combination of wireless signals.

[0038] Although the transmit / receive element 122 is depicted in FIG. 1 B as a single element, the WTRU 102 may include any number of transmit / receive elements 122. More specifically, the WTRU 102 may employ MIMO technology. Thus, in one embodiment, the WTRU 102 may include two or more transmit / receive elements 122 (e.g., multiple antennas) for transmitting and receiving wireless signals over the air interface 116.

[0039] The transceiver 120 may be configured to modulate the signals that are to be transmitted by the transmit / receive element 122 and to demodulate the signals that are received by the transmit / receive element 122. As noted above, the WTRU 102 may have multi-mode capabilities. Thus, the transceiver 120 may include multiple transceivers for enabling the WTRU 102 to communicate via multiple RATs, such as NR and IEEE 802.11 , for example.

[0040] The processor 118 of the WTRU 102 may be coupled to, and may receive user input data from, the speaker / microphone 124, the keypad 126, and / or the display / touchpad 128 (e.g., a liquid crystal display (LCD) display unit or organic light-emitting diode (OLED) display unit). The processor 118 may also output user data to the speaker / microphone 124, the keypad 126, and / or the display / touchpad 128. In addition, the processor 118 may access information from, and store data in, any type of suitable memory, such as the non-removable memory 130 and / or the removable memory 132. The non-removable memory 130 may include random-access memory (RAM), read-only memory (ROM), a hard disk, or any other type of memory storage device. The removable memory 132 may include a subscriber identity module (SIM) card, a memory stick, a secure digital (SD) memory card, and the like. In other embodiments, the processor 118 may access information from, and store data in, memory that is not physically located on the WTRU 102, such as on a server or a home computer (not shown).

[0041] The processor 118 may receive power from the power source 134, and may be configured to distribute and / or control the power to the other components in the WTRU 102. The power source 134 may be any suitable device for powering the WTRU 102. For example, the power source 134 may include one or more dry cell batteries (e.g., nickel-cadmium (NiCd), nickel-zinc (NiZn), nickel metal hydride (NiMH), lithium-ion (Li-ion), etc.), solar cells, fuel cells, and the like.

[0042] The processor 118 may also be coupled to the GPS chipset 136, which may be configured to provide location information (e.g., longitude and latitude) regarding the current location of the WTRU 102. In addition to, or in lieu of, the information from the GPS chipset 136, the WTRU 102 may receive location information over the air interface 116 from a base station (e.g., base stations 114a, 114b) and / or determine its location based on the timing of the signals being received from two or more nearby base stations. It will be appreciated that the WTRU 102 may acquire location information by way of any suitable location-determination method while remaining consistent with an embodiment.

[0043] The processor 118 may further be coupled to other peripherals 138, which may include one or more software and / or hardware modules that provide additional features, functionality and / or wired or wireless connectivity. For example, the peripherals 138 may include an accelerometer, an e-compass, a satellite transceiver, a digital camera (for photographs and / or video), a universal serial bus (USB) port, a vibration device, a television transceiver, a hands free headset, a Bluetooth® module, a frequency modulated (FM) radio unit, a digital music player, a media player, a video game player module, an Internet browser, a Virtual Reality and / or Augmented Reality (VR / AR) device, an activity tracker, and the like. The peripherals 138 may include one or more sensors, the sensors may be one ormore of a gyroscope, an accelerometer, a hall effect sensor, a magnetometer, an orientation sensor, a proximity sensor, a temperature sensor, a time sensor; a geolocation sensor; an altimeter, a light sensor, a touch sensor, a magnetometer, a barometer, a gesture sensor, a biometric sensor, and / or a humidity sensor.

[0044] The WTRU 102 may include a full duplex radio for which transmission and reception of some or all of the signals (e.g., associated with particular subframes for both the UL (e.g., for transmission) and downlink (e.g., for reception) may be concurrent and / or simultaneous. The full duplex radio may include an interference management unit 139 to reduce and or substantially eliminate self-interference via either hardware (e.g., a choke) or signal processing via a processor (e.g., a separate processor (not shown) or via processor 118). In an embodiment, the WRTU 102 may include a half-duplex radio for which transmission and reception of some or all of the signals (e.g., associated with particular subframes for either the UL (e.g., for transmission) or the downlink (e.g., for reception)).

[0045] FIG. 1C is a system diagram illustrating the RAN 104 and the GN 106 according to an embodiment. As noted above, the RAN 104 may employ an E-UTRA radio technology to communicate with the WTRUs 102a, 102b, 102c over the air interface 116. The RAN 104 may also be in communication with the CN 106.

[0046] The RAN 104 may include eNode-Bs 160a, 160b, 160c, though it will be appreciated that the RAN 104 may include any number of eNode-Bs while remaining consistent with an embodiment. The eNode-Bs 160a, 160b, 160c may each include one or more transceivers for communicating with the WTRUs 102a, 102b, 102c over the air interface 116. In one embodiment, the eNode-Bs 160a, 160b, 160c may implement MIMO technology. Thus, the eNode-B 160a, for example, may use multiple antennas to transmit wireless signals to, and / or receive wireless signals from, the WTRU 102a.

[0047] Each of the eNode-Bs 160a, 160b, 160c may be associated with a particular cell (not shown) and may be configured to handle radio resource management decisions, handover decisions, scheduling of users in the UL and / or DL, and the like. As shown in FIG. 1 C, the eNode-Bs 160a, 160b, 160c may communicate with one another over an X2 interface.

[0048] The CN 106 shown in FIG. 1C may include a mobility management entity (MME) 162, a serving gateway (SGW) 164, and a packet data network (PDN) gateway (or PGW) 166. While each of the foregoing elements are depicted as part of the CN 106, it will be appreciated that any of these elements may be owned and / or operated by an entity other than the CN operator.

[0049] The MME 162 may be connected to each of the eNode-Bs 162a, 162b, 162c in the RAN 104 via an S1 interface and may serve as a control node. For example, the MME 162 may be responsible for authenticating users of the WTRUs 102a, 102b, 102c, bearer activation / deactivation, selecting a particular serving gateway during an initial attach of the WTRUs 102a, 102b, 102c, and the like. The MME 162 may provide a control plane function for switching between the RAN 104 and other RANs (not shown) that employ other radio technologies, such as GSM and / or WCDMA.

[0050] The SGW 164 may be connected to each of the eNode Bs 160a, 160b, 160c in the RAN 104 via the S1 interface. The SGW 164 may generally route and forward user data packets to / from the WTRUs 102a, 102b, 102c. The SGW 164 may perform other functions, such as anchoring user planes during inter-eNode B handovers, triggering paging when DL data is available for the WTRUs 102a, 102b, 102c, managing and storing contexts of the WTRUs 102a, 102b, 102c, and the like.

[0051] The SGW 164 may be connected to the PGW 166, which may provide the WTRUs 102a, 102b, 102c with access to packet-switched networks, such as the Internet 110, to facilitate communications between the WTRUs 102a, 102b, 102c and IP-enabled devices.

[0052] The ON 106 may facilitate communications with other networks. For example, the ON 106 may provide the WTRUs 102a, 102b, 102c with access to circuit-switched networks, such as the PSTN 108, to facilitate communications between the WTRUs 102a, 102b, 102c and traditional land-line communications devices. For example, the CN 106 may include, or may communicate with, an IP gateway (e.g., an IP multimedia subsystem (IMS) server) that serves as an interface between the CN 106 and the PSTN 108. In addition, the CN 106 may provide the WTRUs 102a, 102b, 102c with access to the other networks 112, which may include other wired and / or wireless networks that are owned and / or operated by other service providers.

[0053] Although the WTRU is described in FIGS. 1A-1 D as a wireless terminal, it is contemplated that in certain representative embodiments that such a terminal may use (e.g., temporarily or permanently) wired communication interfaces with the communication network.

[0054] In representative embodiments, the other network 112 may be a WLAN.

[0055] A WLAN in Infrastructure Basic Service Set (BSS) mode may have an Access Point (AP) for the BSS and one or more stations (ST As) associated with the AP. The AP may have an access or an interface to a Distribution System (DS) or another type of wired / wireless network that carries traffic in to and / or out of the BSS. T raffic to STAs that originates from outside the BSS may arrive through the AP and may be delivered to the STAs. Traffic originating from STAs to destinations outside the BSS may be sent to the AP to be delivered to respective destinations. Traffic between STAs within the BSS may be sent through the AP, for example, where the source STA may send traffic to the AP and the AP may deliver the traffic to the destination STA. The traffic between STAs within a BSS may be considered and / or referred to as peer-to-peer traffic. The peer-to-peer traffic may be sent between (e.g., directly between) the source and destination STAs with a direct link setup (DLS). In certain representative embodiments, the DLS may use an 802.11e DLS or an 802.11z tunneled DLS (TDLS). A WLAN using an Independent BSS (IBSS) mode may not have an AP, and the STAs (e.g., all of the STAs) within or using the IBSS may communicate directly with each other. The IBSS mode of communication may sometimes be referred to herein as an “ad-hoc" mode of communication.

[0056] When using the 802.11ac infrastructure mode of operation or a similar mode of operations, the AP may transmit a beacon on a fixed channel, such as a primary channel. The primary channel may be a fixed width (e.g., 20MHz wide bandwidth) or a dynamically set width via signaling. The primary channel may be the operating channel of the BSS and may be used by the STAs to establish a connection with the AP. In certain representative embodiments, Carrier Sense Multiple Access with Collision Avoidance (CSMA / CA) may be implemented, for example in in 802.11 systems. For CSMA / CA, the STAs (e.g., every STA), including the AP, may sense the primary channel. If the primary channel is sensed / detected and / or determined to be busy by a particular STA, the particular STA may back off. One STA (e.g., only one station) may transmit at any given time in a given BSS.

[0057] High Throughput (HT) STAs may use a 40 MHz wide channel for communication, for example, via a combination of the primary 20 MHz channel with an adjacent or nonadjacent 20 MHz channel to form a 40 MHz wide channel.

[0058] Very High Throughput (VHT) STAs may support 20MHz, 40 MHz, 80 MHz, and / or 160 MHz wide channels. The 40 MHz, and / or 80 MHz, channels may be formed by combining contiguous 20 MHz channels. A 160 MHz channel may be formed by combining 8 contiguous 20 MHz channels, or by combining two non-contiguous 80 MHz channels, which may be referred to as an 80+80 configuration. For the 80+80 configuration, the data, after channel encoding, may be passed through a segment parser that may divide the data into two streams. Inverse Fast Fourier Transform (IFFT) processing, and time domain processing, may be done on each stream separately. The streams may be mapped on to the two 80 MHz channels, and the data may be transmitted by a transmitting STA. At the receiver of the receiving STA, the above described operation for the 80+80 configuration may be reversed, and the combined data may be sent to the Medium Access Control (MAC).

[0059] Sub 1 GHz modes of operation are supported by 802.11 af and 802.11ah. The channel operating bandwidths, and carriers, are reduced in 802.11 af and 802.11 ah relative to those used in 802.11 n, and 802.11ac.802.11 af supports 5 MHz, 10 MHz and 20 MHz bandwidths in the TV White Space (TVWS) spectrum, and 802.11 ah supports 1 MHz, 2 MHz, 4 MHz, 8 MHz, and 16 MHz bandwidths using non-TVWS spectrum. According to a representative embodiment, 802.11 ah may support Meter Type Control / Machine-Type Communications, such as MTC devices in a macro coverage area. MTC devices may have certain capabilities, for example, limited capabilities including support for (e.g., only support for) certain and / or limited bandwidths. The MTC devices may include a battery with a battery life above a threshold (e.g., to maintain a very long battery life).

[0060] WLAN systems, which may support multiple channels, and channel bandwidths, such as 802.11 n,802.11 ac, 802.11 af, and 802.11 ah, include a channel which may be designated as the primary channel. The primary channel may have a bandwidth equal to the largest common operating bandwidth supported by all STAs in the BSS. The bandwidth of the primary channel may be set and / or limited by a STA, from among all STAs in operating in a BSS, which supports the smallest bandwidth operating mode. In the example of 802.11 ah, the primary channel may be 1 MHz wide for STAs (e.g., MTC type devices) that support (e.g., only support) a 1 MHz mode, even if the AP, and other STAs in the BSS support 2 MHz, 4 MHz, 8 MHz, 16 MHz, and / or other channel bandwidth operating modes. Carrier sensing and / or Network Allocation Vector (NAV) settings may depend on the status of the primarychannel. If the primary channel is busy, for example, due to a STA (which supports only a 1 MHz operating mode), transmitting to the AP, the entire available frequency bands may be considered busy even though a majority of the frequency bands remains idle and may be available.

[0061] In the United States, the available frequency bands, which may be used by 802.11 ah, are from 902 MHz to 928 MHz. In Korea, the available frequency bands are from 917.5 MHz to 923.5 MHz. In Japan, the available frequency bands are from 916.5 MHz to 927.5 MHz. The total bandwidth available for 802.11 ah is 6 MHz to 26 MHz depending on the country code.

[0062] FIG. 1 D is a system diagram illustrating the RAN 113 and the ON 115 according to an embodiment. As noted above, the RAN 113 may employ an NR radio technology to communicate with the WTRUs 102a, 102b, 102c over the air interface 116. The RAN 113 may also be in communication with the ON 115.

[0063] The RAN 113 may include gNBs 180a, 180b, 180c, though it will be appreciated that the RAN 113 may include any number of gNBs while remaining consistent with an embodiment. The gNBs 180a, 180b, 180c may each include one or more transceivers for communicating with the WTRUs 102a, 102b, 102c over the air interface 116. In one embodiment, the gNBs 180a, 180b, 180c may implement MIMO technology. For example, gNBs 180a, 108b may utilize beamforming to transmit signals to and / or receive signals from the gNBs 180a, 180b, 180c. Thus, the gNB 180a, for example, may use multiple antennas to transmit wireless signals to, and / or receive wireless signals from, the WTRU 102a. In an embodiment, the gNBs 180a, 180b, 180c may implement carrier aggregation technology. For example, the gNB 180a may transmit multiple component carriers to the WTRU 102a (not shown). A subset of these component carriers may be on unlicensed spectrum while the remaining component carriers may be on licensed spectrum. In an embodiment, the gNBs 180a, 180b, 180c may implement Coordinated Multi-Point (CoMP) technology. For example, WTRU 102a may receive coordinated transmissions from gNB 180a and gNB 180b (and / or gNB 180c).

[0064] The WTRUs 102a, 102b, 102c may communicate with gNBs 180a, 180b, 180c using transmissions associated with a scalable numerology. For example, the OFDM symbol spacing and / or OFDM subcarrier spacing may vary for different transmissions, different cells, and / or different portions of the wireless transmission spectrum. The WTRUs 102a, 102b, 102c may communicate with gNBs 180a, 180b, 180c using subframe or transmission time intervals (TTIs) of various or scalable lengths (e.g., containing varying number of OFDM symbols and / or lasting varying lengths of absolute time).

[0065] The gNBs 180a, 180b, 180c may be configured to communicate with the WTRUs 102a, 102b, 102c in a standalone configuration and / or a non-standalone configuration. In the standalone configuration, WTRUs 102a, 102b, 102c may communicate with gNBs 180a, 180b, 180c without also accessing other RANs (e.g., such as eNode- Bs 160a, 160b, 160c). In the standalone configuration, WTRUs 102a, 102b, 102c may utilize one or more of gNBs 180a, 180b, 180c as a mobility anchor point. In the standalone configuration, WTRUs 102a, 102b, 102c may communicate with gNBs 180a, 180b, 180c using signals in an unlicensed band. In a non-standalone configurationWTRUs 102a, 102b, 102c may communicate with / connect to gNBs 180a, 180b, 180c while also communicating with / connecting to another RAN such as eNode-Bs 160a, 160b, 160c. For example, WTRUs 102a, 102b, 102c may implement DC principles to communicate with one or more gNBs 180a, 180b, 180c and one or more eNode-Bs 160a, 160b, 160c substantially simultaneously. In the non-standalone configuration, eNode-Bs 160a, 160b, 160c may serve as a mobility anchor for WTRUs 102a, 102b, 102c and gNBs 180a, 180b, 180c may provide additional coverage and / or throughput for servicing WTRUs 102a, 102b, 102c.

[0066] Each of the gNBs 180a, 180b, 180c may be associated with a particular cell (not shown) and may be configured to handle radio resource management decisions, handover decisions, scheduling of users in the UL and / or DL, support of network slicing, dual connectivity, interworki ng between NR and E-UTRA, routing of user plane data towards User Plane Function (UPF) 184a, 184b, routing of control plane information towards Access and Mobility Management Function (AMF) 182a, 182b and the like. As shown in FIG. 1 D, the gNBs 180a, 180b, 180c may communicate with one another over an Xn interface.

[0067] The ON 115 shown in FIG. 1 D may include at least one AMF 182a, 182b, at least one UPF 184a, 184b, at least one Session Management Function (SMF) 183a, 183b, and possibly a Data Network (DN) 185a, 185b. While each of the foregoing elements are depicted as part of the CN 115, it will be appreciated that any of these elements may be owned and / or operated by an entity other than the CN operator.

[0068] The AMF 182a, 182b may be connected to one or more of the gNBs 180a, 180b, 180c in the RAN 113 via an N2 interface and may serve as a control node. For example, the AMF 182a, 182b may be responsible for authenticating users of the WTRUs 102a, 102b, 102c, support for network slicing (e.g., handling of different PDU sessions with different requirements), selecting a particular SMF 183a, 183b, management of the registration area, termination of NAS signaling, mobility management, and the like. Network slicing may be used by the AMF 182a, 182b in order to customize CN support for WTRUs 102a, 102b, 102c based on the types of services being utilized WTRUs 102a, 102b, 102c. For example, different network slices may be established for different use cases such as services relying on ultra-reliable low latency (URLLC) access, services relying on enhanced massive mobile broadband (eMBB) access, services for machine type communication (MTC) access, and / or the like. The AMF 162 may provide a control plane function for switching between the RAN 113 and other RANs (not shown) that employ other radio technologies, such as LTE, LTE-A, LTE-A Pro, and / or non-3GPP access technologies such as WiFi.

[0069] The SMF 183a, 183b may be connected to an AMF 182a, 182b in the CN 115 via an N11 interface. The SMF 183a, 183b may also be connected to a UPF 184a, 184b in the CN 115 via an N4 interface. The SMF 183a, 183b may select and control the UPF 184a, 184b and configure the routing of traffic through the UPF 184a, 184b. The SMF 183a, 183b may perform other functions, such as managing and allocating WTRU IP address, managing PDU sessions, controlling policy enforcement and QoS, providing downlink data notifications, and the like. A PDU session type may be IP-based, non-IP based, Ethernet-based, and the like.

[0070] The UPF 184a, 184b may be connected to one or more of the gNBs 180a, 180b, 180c in the RAN 113 via an N3 interface, which may provide the WTRUs 102a, 102b, 102c with access to packet-switched networks, such as the Internet 110, to facilitate communications between the WTRUs 102a, 102b, 102c and IP-enabled devices. The UPF 184, 184b may perform other functions, such as routing and forwarding packets, enforcing user plane policies, supporting multi-homed PDU sessions, handling user plane QoS, buffering downlink packets, providing mobility anchoring, and the like.

[0071] The ON 115 may facilitate communications with other networks. For example, the CN 115 may include, or may communicate with, an IP gateway (e.g., an IP multimedia subsystem (IMS) server) that serves as an interface between the CN 115 and the PSTN 108. In addition, the CN 115 may provide the WTRUs 102a, 102b, 102c with access to the other networks 112, which may include other wired and / or wireless networks that are owned and / or operated by other service providers. In one embodiment, the WTRUs 102a, 102b, 102c may be connected to a local Data Network (DN) 185a, 185b through the UPF 184a, 184b via the N3 interface to the UPF 184a, 184b and an N6 interface between the UPF 184a, 184b and the DN 185a, 185b.

[0072] In view of Figures 1 A-1 D, and the corresponding description of Figures 1 A-1 D, one or more, or all, of the functions described herein with regard to one or more of: WTRU 102a-d, Base Station 114a-b, eNode-B 160a-c, MME 162, SGW 164, PGW 166, gNB 180a-c, AMF 182a-ab, UPF 184a-b, SMF 183a-b, DN 185a-b, and / or any other device(s) described herein, may be performed by one or more emulation devices (not shown). The emulation devices may be one or more devices configured to emulate one or more, or all, of the functions described herein. For example, the emulation devices may be used to test other devices and / or to simulate network and / or WTRU functions.

[0073] The emulation devices may be designed to implement one or more tests of other devices in a lab environment and / or in an operator network environment. For example, the one or more emulation devices may perform the one or more, or all, functions while being fully or partially implemented and / or deployed as part of a wired and / or wireless communication network in order to test other devices within the communication network. The one or more emulation devices may perform the one or more, or all, functions while being temporarily implemented / deployed as part of a wired and / or wireless communication network. The emulation device may be directly coupled to another device for purposes of testing and / or may performing testing using over-the-air wireless communications.

[0074] The one or more emulation devices may perform the one or more, including all, functions while not being implemented / deployed as part of a wired and / or wireless communication network. For example, the emulation devices may be utilized in a testing scenario in a testing laboratory and / or a non-deployed (e.g., testing) wired and / or wireless communication network in order to implement testing of one or more components. The one or more emulation devices may be test equipment. Direct RF coupling and / or wireless communications via RF circuitry (e.g., which may include one or more antennas) may be used by the emulation devices to transmit and / or receive data.

[0075] A PCF (Policy Control Function) can be triggered to request QoS (Quality of Service) related optimization from an NWDAF (Network Data Analytics Function). The PCF request may include information related to the analytics IDs that may be used for the optimization process. The PCF may also include more granular analytics reporting information, such as per analytics ID (Identifier) preferred level of accuracy. The PCF may include optimization assistance information. For example, the PCF may include an optimization method. For example, the PCF may include optimization goal. For example, the PCF may include parameters / features to be considered. For example, the PCF may include candidate parameters value. A NWDAF that receives a PCF request may determine whether and / or how an analytics combination process may be performed. The NWDAF may determine to use separately and / or jointly trained analytics ID for the optimization task. The NWDAF may also coordinate the joint ML model training, analytics aggregation using information provided by the PCF, and / or using NWDAF discovery and selection procedures. Once the combined analytics outputs are ready, the NWDAF may perform the QoS related optimization task. The NWDAF may send the determined optimized QoS related features and / or parameters to the PCF, potentially including the achieved goal. The PCF may use the recommendation of the NWDAF to either determine QoS parameters and / or determine to activate and configure features such as Reflective QoS and / or SMF (Session Management Function) bulk processing for QoS flow change related requests.

[0076] In some examples, The NWDAF and / or a first NWDAF may receive a request for analytics (e.g., using analytics IDs to identify the requested analytics), from a PCF. Then this first NWDAF may request another NWDAF and / or second NWDAF to provide these analytics. The first NWDAF may process the PCF request and not necessarily produce the analytics itself but may ask other NWDAF to produce the analytics for it. Not every NWDAF supports producing and / or providing all the analytics or analytics IDs. In some examples, the concept of the NWDAF hosting an analytics logical function (AnLF), and / or the NWDAF supporting model training logical function (MTLF) may be implemented. The first NWDAF may be assumed to support the AnLF aspect, and then may reach out to other NWDAFs that have a MTLF logic to produce the request analytics. If the first NWDAF has both AnLF and / or MTLF functionality, and supports producing all required analytics requested from PCF, then the first NWDAF may not need to interact with other NWDAFs.

[0077] A NWDAF that supports analytics aggregation capability may leverage different analytics output provided from different NWDAFs to perform some sort of aggregation of the outputs. For example, if NWDAF Identifier 1 (ID1), ID2 and ID3, serve different areas of interest (Aol), and if an analytics consumer requests some analytics over the total Aol that covers all three Aols, then an aggregator NWDAF may be able to request the analytics output from the different NWDAFs for each Aol, and then aggregate the outputs together (e.g., by averaging the analytics output of the Aols). The NWDAF aggregator may have the information of “supported Delay per Analytics ID”, which indicate how much time it takes for the NWDAF aggregator to provide requested analytics ID from different NWDAFs and aggregating their analytics outputs. The supported delay per analytics ID of an aggregator NWDAF may be impacted by the delay it takes for the NWDAFs used whose analytics output will be used for aggregation. The supported delayper analytics ID for the aggregator NWDAF may also depend on some other information related to the NWDAF that will provide analytics ID (e.g., in a certain Aera of Interest). If a request for aggregated analytics includes parameter "time when analytics information is needed” (e.g., T1 that is lower than the supported delay per analytics ID of the aggregator NWDAF), then the aggregator NWDAF may reject the analytics request or analytics subscription. It may be the case that some analytics outputs do not have the same priority and / or importance when they are to be used for aggregation of analytics. In this case, it may be beneficial to allow for a more flexible request for multiple analytics and / or aggregated analytics, to provide suitable analytics outputs within acceptable conditions (e.g., time). The 5GS does not allow for such flexibility when requesting multiple analytics together, and / or when some analytics are to be used together using aggregation, in a case where one analytics output may not be available or may take lot of time to be available.

[0078] A NWDAF may be configured to coordinate the ML (Machine Learning) model training, analytics combination and / or optimization QoS related parameters. A NWDAF entity may perform the following actions. For example, a NWDAF entity may receive a request from a consumer network function (NF) (e.g., PCF to performs QoS related optimization). The request may include a set of analytics IDs that may be used for QoS optimization. The request may include a set of analytics IDs that can be used. The request may include an indication whether each analytics ID is mandatory, preferred, and / or optional. The request may include an indication of a dependency relationship between the analytics IDs. The request may include desired and / or preferred level(s) of accuracy per analytics ID. The request may include an indication whether analytics IDs are trained jointly or separately. The request may include optimization assistance information. Optimization assistance information may include optimization goal (e.g., in the form of target output related to analytics IDs to be optimized), and / or as a function of the analytics IDs outputs. Optimization assistance information may include an optimization method. Optimization assistance information may include time when the optimization result is needed. Optimization assistance information may include QoS parameters that may be used for the optimization task. Optimization assistance information may include candidate parameters domain and / or values.

[0079] A NWDAF entity may determine, based on PCF request, that analytics combination is required. A NWDAF entity may determine, based on PCF request, that optimization of parameters based on such analytics is required. A NWDAF entity may determine, based on PCF request, initial configuration parameters for the analytics IDs, combination task and / or optimization task (e.g., aggregation method).

[0080] A NWDAF entity may perform NWDAF discovery and selection based on information described herein. For example, a NWDAF entity may perform NWDAF discovery and selection based on the analytics IDs requested and / or the information related to the analytics IDs such as the analytics reporting parameters (e.g., supported Delay per analytics ID and / or accuracy level per analytics ID). A NWDAF entity may perform NWDAF discovery and selection based on authorization from candidate NWDAFs to provide analytics that will be used to serve the PCF. A NWDAFentity may perform NWDAF discovery and selection based on candidate analytics capability for joint ML model training.

[0081] A NWDAF entity may configure the ML model training process, the analytics determination process, and / or the analytics aggregation and optimization processes. A NWDAF entity may receive from the selected NWDAFs the analytics ID outputs and / or joint ML model analytics output, for the candidate values of the QoS parameters to be optimized. A NWDAF entity may determine, based on the result of individual and / or jointly trained analytics obtained from the NWDAFs, to perform analytics aggregation, using aggregation method. A NWDAF entity may use the output results of the analytics aggregation and / or combination for the different candidate values of QoS parameters to determine optimal QoS parameters values and / or QoS related features parameters.

[0082] A NWDAF entity may send a response message to the PCF including the determined optimal features and / or QoS parameters values. The determined optimal features and / or QoS parameters values may include the target output achieved value using these optimal parameters.

[0083] Flexible analytics and recommendations related procedure to optimize QoS may be implemented. For example, when an analytics or service consumer requests a service (e.g., a recommendation and / or optimization and / or combined analytics) that involves multiples analytics IDs, such a request may be enhanced to account for more differentiated parameters and / or information for each analytics ID.

[0084] In the context of leveraging one or more analytics to provide either statistics, inference and / or a prediction to better assist consumer such as PCF, to enhance QoS and policy control, an NWDAF may be further tasked with determining and providing recommendations to the consumer, and / or perform optimizations that use the leveraged analytics output to determine desired, optimal parameters (e.g., QoS parameters) that achieve a certain optimization objective (e.g., expected QoE).

[0085] A consumer NF (Network Function), such as PCF, may request from an NWDAF to perform optimization based on analytics IDs. The consumer NF may send a request to the NWDAF using existing NWDAF services and / or via a new service. The consumer NF may include in the request the analytics IDs that are requested to be used by the NWDAF for the optimization. For example, the consumer NF may include analytics ID1 (e.g., observed service experience (OSE)), analytics ID2 (e.g., QoS sustainability), analytics ID3 (e.g., user data congestion), and / or analytics ID4 (e.g., DN performance).

[0086] In some examples, for a certain set of analytics or set of analytics ID, the PCF request may further include a preferred level of accuracy per analytics ID. In some examples, the PCF may provide different sets of analytics IDs, each with different preferred accuracy per analytics ID.

[0087] In some examples, the PCF request may include an indication whether an analytics ID is required, preferred or optional. In some examples, a first NWDAF may need to determine, based on availability of analytics IDs which analytics IDs to use. In some examples, the NWDAF may not determine further whether an analytics ID is mandatoryand / or preferred, but may use the indication provided by the PCF and availability of the analytics, to determine which analytics ID to use.

[0088] So, it is not something in the PCF request that is additional to claim 1 , but rather an action of the first NWDAF based on that input from PCF.

[0089] The consumer NF may include an indication whether the analytics need to be determined and / or trained separately, and / or jointly. For example, the consumer NF may indicate that joint ML training is needed and / or preferred. If that is the case, a joint ML model that may be trained to produce output using the dataset and features of each involved analytics IDs may be needed. The consumer NF may indicate a priority between joint analytics determination or separate determination.

[0090] The consumer NF may include, for each involved analytics ID, an attribute that indicates whether the analytics ID is mandatory, preferred, and / or optional for the generation or determination of the combined analytics.

[0091] For example, the consumer NF may indicate that analytics ID1 and ID2 are mandatory, which means that if analytics ID1 and analytics ID2 are not available or may take time to be available which goes beyond the time when the result is requested, then the NWDAF may determine to not perform the analytics combination and the optimization and may reject the consumer NF request.

[0092] For example, the analytics ID3 and analytics ID4 may be described as preferred and optional, respectively. In this case, the NWDAF may try to obtain analytics ID3 output in a reasonable time, but it may not reject consumer NF request as it may combine analytics ID1 and ID2 to perform the optimization. The NWDAF in this case may accommodate analytics ID3 and, for example, tolerate a bit more delay in obtaining the analytics ID3 output, in order to use it in the analytics output combination. The NWDAF may check whether analytics ID4 are available within the determined time window, and if not available the NWDAF may determine not to use analytics ID4 (e.g., without trying to check whether adding slightly more delay in order to obtain analytics ID4 output).

[0093] The consumer NF may indicate whether there is a conditional dependency and / or relationship between the analytics IDs in terms of obtaining analytics, and / or between the analytics IDs. For example, an analytics ID3 may need to be obtained if analytics ID1 is used. In another example, analytics ID3 may be preferred when analytics ID1 is to be used, whereas analytics ID3 becomes necessary when analytics ID1 has not been used for the combined analytics output. The conditional dependency between the analytics IDs may be used to determine which analytics IDs to be used for the analytics aggregation process. For example, if analytics ID3 is to be selected if analytics ID1 is selected for the aggregation, and if the analytics ID1 has not been selected for the aggregated analytics (e.g., due to a high delay for the analytics ID1 output to be provided), then analytics ID3 may also not be selected for the analytics aggregation (e.g., analytics ID2 and ID4 are selected).

[0094] The request may include a desired or preferred accuracy level per analytics ID. For example, the consumer NF may indicate that the desired and / or preferred accuracy level for analytics ID1 and / or ID2 is “high”, whereas the desired and / or preferred level of accuracy for analytics ID3 and / or ID4 is “medium”.

[0095] The consumer NF request may include an accuracy category or class, (for example “low", “medium”, and / or “high" categories). The consumer NF request may represent the desired accuracy level. The accuracy level value may be a percentage value and / or an interval value. The request may include different accuracy level values for different analytics IDs. Additionally, the request may include multiple accuracy level values for each analytics IDs with its condition. For example, for analytics ID1 , an initial desired accuracy level value can be 95% or more. If this initial level cannot be achieved, then the next desired and / or minimum required accuracy level value may be 75% and so on.

[0096] The consumer NF request may include a different output strategy for different analytics IDs. For example, for analytics ID1 and / or ID2, a binary output strategy may be required, meaning that the NWDAF analytics producer may need to provide analytics IDs ID1 and / or ID2 outputs when the preferred level of accuracy is met. The request may include for analytics ID3 and ID4 conditional gradient output strategy. This may mean that the analytics producer may need to provide analytics ID3 and / or ID4 outputs within the requested time period, and / or whenever the analytics ID1 and ID2 are available and / or provided.

[0097] The consumer NF may include optimization assistance information related to the recommendation and / or optimization task. The optimization assistance information may represent parameters. The optimization assistance information may represent attributes that assist the NWDAF to perform the optimization task. The optimization assistance information may include a target output to be optimized. For example, a target output may be Quality of experience metric or QoS sustainability metric and so on.

[0098] In case one analytics ID is used for the optimization task, such as Observed service experience analytics, a target output may be the average and / or predicted quality of service experience for the traffic of interest. In the case of QoS sustainability analytics, a target output to be optimized (e.g., minimized) may be the QoS change occurrence.

[0099] In the case of multiple analytics IDs that are requested to be used to provide a recommendation or to optimize a target output, the target output may be a function of the individual target outputs of the involved analytics IDs.

[0100] For example, if the PCF requests QoS recommendation and / or QoS related optimization using analytics ID1 equals observed service experience (OSE) and analytics ID2 equals QoS sustainability with target outputs predicted service experience, and / or QoS change future occurrence, respectively, then a target output when using both analytics IDs together may be the product of predicted service experience output multiplied by predicted QoS change occurrence.

[0101] In another example, the target output may use one or more of the individual target outputs, while the other analytics ID may be used as validity constraints. For example, to determine QoS parameters using the OSE as the target output to be optimized and / or the QoS sustainability as a validity constraint, the optimization task may mean , for example, find QoS parameters that maximize or achieve a desired observed service experience value X, while such QoS parameters have a QoS sustainability analytic output that is higher than a value Y.

[0102] The consumer NF may provide a certain function and / or reference to a certain function the NWDAF may use to determine what the target output and validity constraints to be used, and / or which analytics IDs to be used for each aspect. For example, if separate Model training and analytics IDs is to be used, then NWDAFs may train two ML models, modell using features A, B and with labelled output variable X, and / or model2 using features B, C and with labelled output variable Y. The aggregator NWDAF may use the output values x of X from the modell and y of Y from model2 and / or may combined the two analytics outputs using a reference function (e.g. , product, hence using the output x multiplied by y). In another example, the NWDAF may request a joint model training. This may be achieved by requesting training a model3 using features A, B, C, and with labelled output variable Z equals X multiplied by Y. for instance, this may be achieve using coordination between different NWDAFs and / or Federated learning procedures. The aggregator NWDAF may then use the model3 and the result joint analytics output z, to perform optimization and / or determining optimal QoS. For example, the request may include target output function = product; arguments = target output analytics ID1 , ID2; desired level = A or more; validity constraints arguments = analytics ID3, domain = [c..d],

[0103] The optimization assistance information may also include the (QoS) parameters to be considered for the optimization task, and / or the domain of these parameters. For example, the optimization assistance information may indicate that the target output equals QoE is to be optimized with respect to the bit rate, packet delay budget and / or packet error rate.

[0104] The consumer NF may include a discrete finite set of possible values for the candidate parameters to be considered. For example, the consumer NF may include a range value and / or a step value for each parameter, to guide the NWDAF on how to span the parameter values range. For instance, for QoS parameter packet delay budget (PDB), a candidate value range may be [5ms. to 25ms.], with a spanning step of 1ms. In this case, all values from 5ms to 25ms with increment of 1 ms, may be considered to find an optimal PDB value for the target output to be maximized.

[0105] The optimization assistance information may further include an optimization method to use by the NWDAF. The consumer NF request may include a time when the optimization result (e.g., optimal QoS values) is needed. For example, Figures 2A & 2B illustrate an example enhanced QoS recommendation and / or optimization service using multiple analytics with more granular analytics reporting parameters and enhanced optimization capability by NWDAF.

[0106] At step 218 of FIG. 2A, the analytics, recommendations and / or optimization consumer (e.g., a PCF 202) may send a request to an NWDAF 204 to request assistance information from an NWDAF. The request may use an existing NWDAF service, such as Nnwdaf_Analyticslnfo (e.g., Nnwdaf_Analyticslnfo_Create service operation) and / or Nnwdaf_AnalyticsSubscription services (e.g., Nnwdaf_AnalyticsSubscription_Subscribe service operation). For instance, the existing services may be enhanced to include further information in the request message, including optimization and / or recommendation assistance information described above and more granular analytics reportingparameters, such as per analytics ID accuracy levels. The request for assistance information and / or optimization may also be represented using a new analytics ID, for example, analytics ID = QoSOptimization analytics ID.

[0107] The request may use a new service, for example named Nnwdaf_Optimizationlnfo or Nnwdaf_OptimizationSubscription services, and the request includes the information described above. The PCF request may include the following parameters. For example, the request may Include a set of analytics IDs that may be used. The request may include an indication whether each analytics ID is mandatory, preferred, and / or optional. The request may include an indication of a dependency relationship between the analytics IDs. The request may include desired and / or preferred level(s) of accuracy per analytics ID. The request may include optimization assistance information. For instance, optimization assistance information may include optimization goal (e.g., in the form of target output related to analytics IDs to be optimized), and / or as a function of the analytics IDs outputs. Optimization assistance information may include optimization method. Optimization assistance information may include time when the optimization result is needed. Optimization assistance information may include QoS parameters that may be used for the optimization task. Optimization assistance information may include candidate parameters domain and / or values.

[0108] At 218 of FIG. 2A, a NWDAF 204 may receive the PCF request. The NWDAF 204 may authorize the request message. The NWDAF 204 may use the information received from the PCF 202 to determine the tasks that need to be performed in relation to the request.

[0109] The NWDAF 204 may use the fact that the request used, for example, a new NWDAF service operation such as Nnwdaf_Optimizationlnfo_Request to determine that an optimization task based on analytics IDs needs to be performed. Alternatively, or additionally, the NWDAF 204 may use the fact that new analytics ID is requested, such as “QoSOptimization" analytics ID, to determine that an optimization task may be needed. Alternatively, or additionally, the NWDAF 204 may determine based on the information provided in the request, such as optimization indication and optimization assistance information, that an optimization task may be needed.

[0110] The NWDAF 204 may use the target set of analytics IDs as well as the corresponding analytics filter information, analytics reporting targets, optimization goal and optimization method provided at 218, to determine whether one or multiple analytics ID may be required for this request. For example, if the target set of analytics IDs includes OSE and QoS sustainability, and / or optimization goal indicates optimizing the product of the predicted OSE and predicted QoS sustainability, and other parameters, then the NWDAF 204 may determine that combining analytics ID or analytics aggregation may be needed.

[0111] Alternatively, or additionally, if the optimization goal includes the OSE, and the QoS sustainability is used as a validity constraint (e.g., the goal is to find QoS parameters that meet desired OSE, while their predicted QoS sustainability meets a certain level), the NWDAF 204 may determine that multiple analytics ID need to be obtained, yet no analytics aggregation may be needed.

[0112] The NWDAF 204 uses the determination that an analytics aggregation task may be required, and that an optimization task may be required, and / or obtaining analytics ID task may be required, to perform an NWDAF selection. For example, the NWDAF 204 may be triggered to select an NWDAF that has an analytics aggregation capability. Additionally, if NWDAFs have an optimization capability that is either part of the analytics aggregation capability and / or an additional capability, then the NWDAFs may register such optimization capability, and the NWDAF 204 may select the appropriate NWDAF(s).

[0113] For example, in FIG. 2A the NWDAF 204 that receives the PCF request has the analytics aggregation capability and the optimization capability, hence the NWDAF aggregator (e.g., NWDAF 204) selection may not be included.

[0114] Even though the NWDAF 204 may not select a NWDAF with analytics aggregation capability since it does support the feature, the aggregator NWDAF may still need to discover and select other NWDAFs in order to obtain the analytics IDs of interest, based on PCF assistance request.

[0115] The NWDAF 204 may use the received information in the request, together with the information from other NWDAFs related to analytics IDs, and may determine which analytics IDs from the target analytics IDs (e.g., provided by the PCF) needs to be used. For example, the NWDAF 204 may use the indication from the PCF request on whether an analytics ID is "mandatory” for the derivation of the final result (e.g., aggregation and / or then optimization), to determine that this analytics ID may be required.

[0116] The aggregator NWDAF (e.g., NWDAF 204) may be able to resolve any conflict or inconsistent values of parameters in different analytic IDs from different NWDAFs. For example, a WTRU location from one NWDAF may be in the form of GPS and may be Tracking Area ID from another NWDAF.

[0117] The NWDAF 204 may use priority whether an analytics ID may be required or optional, together with the analytics accuracy information for each analytics ID, and / or the time when the output result may be needed, to determine whether an analytics ID may need to be obtained and the level of accuracy to use when requesting this analytics ID.

[0118] For example, if the time when the result is needed is T1, then the aggregator NWDAF (e.g., NWDAF 204) may determine than the time when the analytics are needed is approximately T1 - Taggreg - Toptim, where Taggreg is the time needed for the aggregator NWDAF (e.g., NWDAF 204) to aggregate the analytics, and Toptim is the time needed by the aggregator NWDAF (e.g., NWDAF 204) to perform the optimization task. The NWDAF 204 may then use the determined time when the analytics are needed, to determine whether analytics may be provided by that time, or around that time, using the preferred level of accuracy provided by the PCF 202 for this analytics ID.

[0119] The aggregator NWDAF (e.g, NWDAF 204) may use the optimization assistance information as well as the set of analytics IDs and related parameters, to determine which aggregation method to use. For example, if analytics ID1 and ID2 are to be obtained within the time the output result is needed, then the aggregation method may use theproduct of the two outputs of these analytics. When analytics ID1 , ID2 and ID3 are determined to be obtained for the aggregated result, then an aggregated output result may be (output 1 multiplied by output 2) divided by output 3.

[0120] The NWDAF 204 may use the optimization assistance information provided by the PCF 202 to set the parameters for the optimization task. For example, for the optimization task, if the NWDAF 204 is provided with candidate QoS parameters value in a range [a..b] with a step 0.1, then the NWDAF 204 may determine to use a larger step (e.g., 0.2) in order to provide the optimization result within the time needed. The aggregator NWDAF may also determine the ML model(s) that can be used for each analytics ID. Once the aggregator NWDAF (e.g., NWDAF 204) determines the necessary information for the aggregation and optimization tasks based on analytics, the NWDAF 204 may perform NWDAF selection in order to obtain the required analytics IDs. For example, in FIG. 2A the aggregator NWDAF (e.g., NWDAF 204) selects NWDAF 1 206 to provide analytics IDs set 1 and selects NWDAF 2 208 to provide analytics IDs set 2. The aggregator NWDAF (e.g., NWDAF 204) may also take into consideration the authorization of the analytics ID producer NWDAF(s) of the PCF 202 to obtain their analytics ID(s).

[0121] At 222 of FIG. 2A, the NWDAF 204 may send a request to NWDAF 1 206 to obtain analytics IDs set 1. The NWDAF 204 may use the Nnwdaf_analyticslnfo_Request service operation. The NWDAF 204 may include a desired accuracy level for each analytics ID of set 1 . The NWDAF 204 may include the ML model(s) that may be used to generate the analytics ID of interest.

[0122] At 224 of FIG. 2A, the NWDAF 204 may send a request to NWDAF 2 206 to obtain analytics ID set 2. The NWDAF 204 may use the Nnwdaf_analyticslnfo_Request service operation and / or include a desired accuracy level for each analytics ID of set 2. The NWDAF 204 may also include the ML model(s) that may be used to generate these analytics. The NWDAF 204 may also use service Nnwdaf_analyticsSubscription to obtain such analytics.

[0123] At 226 of FIG. 2A, once the analytics ID outputs are ready, NWDAF 1 206 provides the requested analytics IDs output to the aggregator NWDAF (e.g., NWDAF 204). At 228 of FIG. 2B, once the analytics ID outputs are ready, NWDAF 2 208 provides the requested analytics IDs output to the aggregator NWDAF (e.g., NWDAF 204).

[0124] From step 222 to step 228 of FIGs. 2A and 2B, the aggregator NWDAF (e.g., NWDAF 204) may determine based on PCF request that a joint model training may be needed to obtain the combined analytics. For instance, the aggregator NWDAF (e.g., NWDAF 204) may coordinate model training between different NWDAFs (e.g., NWDAF1 and / or NWDAF2) to perform joint model training using the dataset related to analytics ID1 and / or the dataset related to analytics ID2. The aggregator NWDAF (e.g., NWDAF 204) may further perform analytics aggregation, by averaging obtained analytics over different attributes, such as different Aols.

[0125] At 230 of FIG. 2B, once the NWDAF 204 obtains the requested analytics IDs outputs, the NWDAF 204 may use these analytics outputs to perform analytics aggregation. The NWDAF 204 may use the aggregation method determined at 220 for the aggregation of these analytics.

[0126] At 232 of FIG. 2B, once the analytics aggregation is performed, the NWDAF 204 may perform the optimization task, based on the aggregated analytic output, using the optimization parameters. For example, whenthe NWDAF 204 is provided with different PDB candidate values (e.g., PDB equals 5ms, 10ms, 15ms, 20ms, 25ms), the NWDAF 204 may use the aggregated and / or combined analytics output and evaluates the value of the output, when the PDB value takes one of the candidate values. The NWDAF 204 may select the PDB values that allow the service experience to meet a service desired level. The NWDAF 204 may provide a sorted list of optimal parameters values, depending on whether the output is at maximum or minimum level (e.g., descending or ascending sorting). The NWDAF 204 may provide a range of values of the parameter, to indicate that any value from that range satisfies the desired outcome (e.g., PDB is less than 15ms).

[0127] At 234 of FIG. 2B, the aggregator NWDAF (e.g., NWDAF 204) may send a response message to the PCF. This response message may include one or more recommendations determined by the NWDAF 204. This may include optimized QoS related parameters values, and / or additionally the value(s) of the achieved optimization goal (e.g., QoE) achieved using the optimized parameters.

[0128] At 236 of FIG. 2B, once the PCF 202 receives the optimization and / or recommendation result(s), the PCF 202 may use the obtained recommendations, together with local policies, to determine the (QoS) parameters values and / or features that may be used for the PDU Session and the QoS flow that carries the traffic flow of interest. For example, the PCF 202 may select one combination of QoS parameters value (e.g., PDB, pocket error rate (PER), etc.), for the QoS flow in question.

[0129] At 238 of FIG. 2B, the PCF 202 may trigger the activation and configuration of the determined parameters (e.g., QoS parameters). For example, once the PCF 202 determines the QoS parameters for the QoS flow for the traffic of interest, the PCF 202 may generate and / or update the policy and charging control (PCC) rules related to the traffic flow and send the PCC rules to the session management function (SMF) 214. The SMF 214 may then be triggered to perform a packet data unit (PDU) Session modification procedure with the WTRU 210, to update QoS flow parameters.

[0130] A PCF 202 may be configured to request QoS related recommendations with a more granular assistance information, and / or to perform QoS related features based on recommendations results.

[0131] A PCF entity may perform the following actions. A PCF entity may send a request to an NWDAF to perform QoS related optimization. The request includes a set of analytics IDs that may be used for QoS optimization. For example, the request may include a set of analytics IDs that can be used. The request may include an indication whether each analytics ID is mandatory, preferred, and / or optional. The request may include an indication of a dependency relationship between the analytics IDs. The request may include desired and / or preferred level(s) of accuracy per analytics ID. The request may include an indication whether analytics IDs are trained jointly or separately. The request may include optimization assistance information. For instance, optimization assistance information may include optimization goal (e.g., in the form of target output related to analytics IDs to be optimized), and / or as a function of the analytics IDs outputs. Optimization assistance information may include optimization method. Optimization assistance information may include time when the optimization result is needed. Optimizationassistance information may include QoS parameters that may be used for the optimization task. Optimization assistance information may include candidate parameters domain and / or values.

[0132] A PCF entity may receive a response message from the NWDAF including the determined optimal features and / or QoS parameters values that may include the target output achieved value using these optimal parameters.

[0133] A PCF entity may determine, based on the received optimization result and / or local policy, to determine the QoS related parameters and / or features to be used for the PDU Session (and / or QoS flow) of interest. The parameters and / or features may include QoS parameters value for the QoS flow of interest (e.g., PDB, allocation and retention priority (ARP), PER). The parameters and / or features may include an indication whether to use Reflective QoS attribute, and the related optimal parameters (e.g., reflective QoS Attribute (RQA) timer). The parameters and / or features may include an indication whether to use and / or activate PDU Set feature for the traffic of interest, and the corresponding optimal PDU Set QoS parameters. The parameters and / or features may include an indication whether to use bulk processing for QoS flow change requests by the SMF, and their corresponding optimal parameters (e.g., optimal waiting time, number of requests, group size).

[0134] A PCF entity may perform PDU Session modification procedure to update the QoS parameters of the QoS flow of interest. For example, A PCF entity may configure the SMF with bulk session management (SM) request processing feature, and / or user plane function (UPF) with RQA feature and / or related parameters (e.g., RQA timer).

[0135] Leveraging QoS related recommendations to determine to use certain QoS related features may be implemented. For example, when a consumer NF (e.g., PCF) requests assistance information from an NWDAF to provide optimized QoS parameters, the PCF may provide the NWDAF with different input information that reflects the types of analytics IDs that may be needed to perform such recommendation. The PCF may also provide the NWDAF with different performance aspects for the analytics ID. For example, the PCF may also provide preferred level of accuracy per analytics ID, in addition to information related to the goal or desired output that needs to be considered for the optimization. The PCF may also provide information about the parameters that may be optimized, their domains, an optimization strategy, etc.

[0136] The PCF may provide such information when requesting recommendations from the NWDAF, but the PCF may not conclude that some features may be considered to achieve the desired goal, and eventually what parameters of the feature may be optimized in order to achieve the target goal.

[0137] When the PCF requests a recommendation in order to achieve a certain goal, that the PCF may also include some features that may also be considered in order to achieve the desired goal. The NWDAF may use PCF request to indicate whether certain feature(s) may be used, eventually with their corresponding optimized parameters, in order to achieve the desired goal.

[0138] An example of such features may be the use of Reflective QoS. For instance, when the application traffic may have a dynamic traffic characteristic for a short period of time, it may be beneficial to use optimal QoS parameters for the QoS flow that carries the traffic of interest. It may be beneficial to use optimal QoS parameters forthe QoS flow that achieve or meet a certain service experience and / or QoS sustainability level, in order to reduce control plane signaling related to frequent QoS flow parameters change. When there is a change in traffic characteristics for a short period of time, there may be some QoS flow change requests that take place.

[0139] In this context, the use of the Reflective QoS feature may be helpful in order to accommodate the change in QoS flow parameters to accommodate the traffic characteristics for a short period of time, without incurring a lot of signaling related to the QoS flow change. The current analytics provided by the NWDAF do not necessarily take into consideration whether the Reflective QoS Attribute has been used, and / or the related RQA parameters (e.g., RQA timer).

[0140] Existing analytics such as OSE or QoS sustainability may be enhanced to include features such as RQA may be taken into consideration when determining and obtaining such analytics. For example, the Observed service experience analytics ID involves data collected from application function (AF) related to quality of experience, and / or data collected from different NFs, such as used 5QI, application type and / or etc. The enhanced analytics may include aspects related to whether RQA has been used for the traffic flow. The enhanced analytics may include the data volume of the traffic flow when RQA has been used. The enhanced analytics may include the QoS Flow Identifier (QFI) that was used for the RQA traffic. The enhanced analytics may include the RQA duration. The enhanced analytics may include the RQA timer that was used for RQA.

[0141] The NWDAF may train a ML model using RQA related data as well as existing input data for the enhanced analytics of interest (e.g., OSE) to provide statistics, predictions, and / or recommendations on whether to activate the RQA for the traffic flow, and / or eventually optimal RQA related parameters (e.g., RQA timer).

[0142] The NWDAF may provide certain predictions related to QoS, in the form of recommendations. The PCF, instead of being triggered to modify QoS, may instead be triggered to use and setup other features, such as determining to activate RQA and / or determine the RQA timer.

[0143] For example, the PDU Set feature and related parameters may contribute to a better service experience when using optimal PDU Set parameters. For example, it may be determined whether the PDU Set feature is activated for the traffic flow of interest. For example, it may be determined whether the PDU Set Integrated handling needs to activate or not, in order to achieve a more sustainable QoS that provides a desired quality of experience.

[0144] For example, the PCF may be triggered to configure the SMF to perform some bulk processing of request for QoS flow change. For instance, the SMF may be configured with a certain waiting time and / or a minimum number of QoS flow change requests in a certain time window (e.g., to start a PDU Session modification request). The SMF may include the different QoS flow change requests for the PDU Sessions of interest in the same PDU Session modification request instead of sending multiple PDU Session modification requests (e.g., for each QoS flow change request). The PCF may determine with the assistance of the NWDAF whether the bulk processing by SMF for QoS flow change request needs to be activated and / or determine an optimal parameter. For example, the PCF may determine an optimal parameter such as waiting time. The PCF may determine an optimal parameter such asminimum number of requests per period of time. Minimum number of requests per period of time may include using SMF load or SMF load analytics. Minimum number of requests per period of time may include predicted service experience and / or QoS flow change related SM signaling reduction given a certain wait time by the SMF. The PCF may determine with the assistance of the NWDAF whether the bulk processing by SMF for QoS flow change request needs to be activated and / or determine an optimal parameter to allow for low QoS flow change related requests and still achieve a desired goal (e.g, quality of experience).

[0015] The wait time may be determined based on signaling load at SMF. For example, the wait time may be inferred and / or predicted using some analytics that learn the impact of the wait time on the performance of the network and / or the service experience.

Claims

CLAIMS:1 . A network entity comprising: a processor configured to: receive a request from a policy control function (PCF) that comprises a set of analytics identifiers(IDs), wherein the set of analytics IDs comprises an indication whether each analytics ID is mandatory, preferred or optional, an indication of preferred accuracy levels per analytics ID, and an indication whether each analytics ID was used in training jointly or separately; send, to a network data analytics function (NWDAF), a NWDAF analytics request corresponding to the set of analytics IDs; receive, from the NWDAF, an NWDAF analytics response corresponding to the set of analytics IDs, wherein the NWDAF analytics response comprises analytics ID outputs or a joint machine learning (ML) model analytics output for values of quality of service (QoS) parameters to be optimized; perform analytics aggregation using the received analytics ID outputs or a joint ML model analytics output; determine QoS parameters values based on output results of the analytics aggregation for different candidate values of QoS parameters; and send a response message to the PCF including the determined QoS parameters values.

2. The network entity of claim 1 , wherein the request from the PCF further comprises one or more sets of analytics IDs, wherein each of the one or more sets of analytics IDs comprises a preferred accuracy level(s) per analytics ID.

3. The network entity of claim 1 , wherein the NWDAF analytics request further comprises accuracy level per analytics ID or supported delay per analytics ID.

4. The network entity of claim 1 , wherein the analytics IDs comprise one or more of analytics ID1 , analytics ID2, analytics ID3, or analytics ID4, wherein analytics ID1 equals observed service experience (OSE), analytics ID2 equals QoS sustainability, analytics ID3 equals user data congestion, or analytics ID4 equals data network (DN) performance.

5. The network entity of claim 1 , wherein the network entity determines, based on the indication whether each analytics IDs is mandatory, preferred or optional, which analytics IDs from the set of analytics IDs are to be used.

6. The network entity of claim 5, wherein the processor is further configured to determine that the analytics aggregation is required.

7. The network entity of claim 1 , wherein the NWDAF analytics request indicates whether the joint ML model analytics output or the analytics aggregation is to be used.

8. The network entity of claim 1 , wherein the request from the PCF further comprises an optimization task and related optimization assistance information, wherein the related optimization assistance information comprises a set of features that is used to reach an optimization goal.

9. The network entity of claim 1 , wherein the processor is further configured to determine that an analytics aggregation or joint ML model training is required.

10. The network entity of claim 1 , wherein the response message further comprises an achieved goal determined based on the QoS parameters, or determined features that are recommended to be used with parameters corresponding to the determined features.

11. A method performed by a wireless transmit / receive unit (WTRU), the method comprising: receive a request from a policy control function (PCF) that comprises a set of analytics identifiers(IDs), wherein the set of analytics IDs comprises an indication whether each analytics ID is mandatory, preferred or optional, an indication of preferred accuracy levels per analytics ID, and an indication whether each analytics ID was used in training jointly or separately; send, to a network data analytics function (NWDAF), a NWDAF analytics request corresponding to the set of analytics IDs; receive, from the NWDAF, an NWDAF analytics response corresponding to the set of analytics IDs, wherein the NWDAF analytics response comprises analytics ID outputs or a joint machine learning (ML) model analytics output for values of quality of service (QoS) parameters to be optimized; perform analytics aggregation using the received analytics ID outputs or a joint ML model analytics output; determine QoS parameters values based on output results of the analytics aggregation for different candidate values of QoS parameters; and send a response message to the PCF including the determined QoS parameters values.

12. The method of claim 11 , wherein the request from the PCF further comprises one or more sets of analytics IDs, wherein each of the one or more sets of analytics IDs comprises a preferred accuracy level(s) per analytics ID.

13. The method of claim 11 , wherein the NWDAF analytics request further comprises accuracy level per analytics ID or supported delay per analytics ID.

14. The method of claim 11 , wherein the analytics IDs comprise one or more of analytics ID1 , analytics ID2, analytics ID3, or analytics ID4, wherein analytics ID1 equals observed service experience (OSE), analytics ID2 equals QoS sustainability, analytics ID3 equals user data congestion, or analytics ID4 equals data network (DN) performance.

15. The method of claim 11 , wherein the method further comprises determining, based on the indication whether each analytics IDs is mandatory, preferred or optional, which analytics IDs from the set of analytics IDs are to be used.

16. The method of claim 15, wherein the method further comprises determining that the analytics aggregation is required.

17. The method of claim 11 , wherein the NWDAF analytics request indicates whether the joint ML model analytics output or the analytics aggregation is to be used.

18. The method of claim 11 , wherein the request from the PCF further comprises an optimization task and related optimization assistance information, wherein the related optimization assistance information comprises a set of features that is used to reach an optimization goal.

19. The method of claim 11 , wherein the method further comprises determining that an analytics aggregation or joint ML model training is required.

20. The method of claim 11 , wherein the response message further comprises an achieved goal determined based on the QoS parameters, or determined features that are recommended to be used with parameters corresponding to the determined features.

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