Predictive adaptation of radio bearer configuration

Abstract

Disclosed are devices, methods, and systems for predictively adapting radio bearer configurations. One method (700) includes receiving (702) an expected quality of service profile pattern for at least one quality of service flow of at least one user equipment. The method (700) includes determining (704) a predictive adaptation of a radio bearer configuration based on the expected quality of service profile pattern, where the predictive adaptation comprises at least one radio bearer remapping to the at least one quality of service flow. The method (700) includes configuring (706) a predictive quality of service flow to radio bearer mapping pattern for an expected time window based on the predictive adaptation. The method (700) includes transmitting (708) the predictive quality of service flow to radio bearer mapping pattern to the at least one user equipment.

Description

TECHNICAL FIELD

[0001] The subject matter disclosed herein relates generally to wireless communications and more specifically to predictively adapting radio bearer configurations. BACKGROUND

[0002] The following abbreviations are defined herein, at least some of which are mentioned within the following description: Third Generation Partnership Project (“3GPP”), Fifth Generation (“5G”), 5G System (“5GS”), 5G QoS Indicator (“5QI”), Authentication, Authorization, and Accounting (“AAA”), Acknowledgment (“ACK”), Application Function (“AF”), Artificial Intelligence (“AI”), Authentication and Key Agreement (“AKA”), Aggregation Level (“AL”), Access and Mobility Management Function (“AMF”), Angle of Arrival (“AOA”), Angle of Departure (“AoD”), Access Point (“AP”), Application Programing Interface (“API”), Access Stratum (“AS”), Application Service Provider (“ASP”), Autonomous Uplink (“AUL”), Authentication Server Function (“AUSF”), Authentication Token (“AUTN”), Background Data (“BD”), Background Data Transfer (“BDT”), Beam Failure Detection (“BFD”), Beam Failure Recovery (“BFR”), Binary Phase Shift Keying (“BPSK”), Base Station (“BS”), Buffer Status Report (“BSR”), Bandwidth (“BW”), Bandwidth Part (“BWP”), Cell RNTI (“C-RNTI”), Carrier Aggregation (“CA”), Channel Access Priority Class (“CAPC”), Channel Busy Ratio (“CBR”), Contention-Based Random Access (“CBRA”), Clear Channel Assessment (“CCA”), Common Control Channel (“CCCH”), Control Channel Element (“CCE”), Cyclic Delay Diversity (“CDD”), Code Division Multiple Access (“CDMA”), Control Element (“CE”), Contention Free Random Access (“CFRA”), Configured Grant (“CG”), Closed Loop (“CL”), Core Network (“CN”), Coordinated Multipoint (“CoMP”), Class of Requirement (“CoR”), Channel Occupancy Time (“COT”), Cyclic Prefix (“CP”), Channel Quality Indicator (“CQI”), Cyclic Redundancy Check (“CRC”), Channel State Information (“CSI”), Channel State Information-Reference Signal (“CSI-RS”), Common Search Space (“CSS”), Control Resource Set (“CORESET”), Discrete Fourier Transform Spread (“DFTS”), Dual Connectivity (“DC”), Downlink Control Information (“DCI”), Downlink Feedback Information (“DFI”), Downlink (“DL”), Demodulation Reference Signal (“DMRS”), Data Network Name (“DNN”), Data Radio Bearer (“DRB”), Discontinuous Reception (“DRX”), Dedicated Short-Range Communications (“DSRC”), Downlink Pilot Time Slot (“DwPTS”), Enhanced Clear Channel Assessment (“eCCA”), Enhanced Mobile Broadband (“eMBB”), Evolved Node B (“eNB”), Enhanced V2X (“eV2X”), Extensible Authentication Protocol (“EAP”),enhanced ICIC (“eICIC”), effective isotropic radiated power (“EIRP”), evolved packet system (“EPS”), European Telecommunications Standards Institute (“ETSI”), frame based equipment (“FBE”), frequency division duplex (“FDD”), frequency division multiplex (“FDM”), frequency division multiple access (“FDMA”), frequency division orthogonal cover code (“FD-OCC”), frequency range 1 - sub 6 GHz band and / or 410 MHz to 7125 MHz (“FR1”), frequency range 2 - 24.25 GHz to 52.6 GHz (“FR2”), generic area description (“GAD”), guaranteed bit rate (“GBR”), guaranteed flow bit rate (“GFBR”), group long (“GL”), 5G NodeB or next generation NodeB (“gNB”), global navigation satellite system (“GNSS”), general packet radio service (“GPRS”), guard period (“GP”), global positioning system (“GPS”), generic public subscription identifier (“GPSI”), global system for mobile communications (“GSM”), global unique temporary UE identifier (“GUTI”), home AMF (“hAMF”), hybrid automatic repeat request (“HARQ”), home location register (“HLR”), handover (“HO”), home PLMN (“HPLMN”), home subscriber server (“HSS”), hashed expected response (“HXRES”), inter-cell interference coordination (“ICIC”), identity or identifier (“ID”), information element (“IE”), industrial internet of things (“IIOT”), international mobile equipment identity (“IMEI”), international mobile subscriber identity (“IMSI”), international mobile telecommunications (“IMT”), internet of things (“IoT”), intelligent transportation systems (“ITS”), key performance indicator (“KPI”), layer 1 (“L1”), layer 2 (“L2”), layer 3 (“L3”), license assisted access (“LAA”), local area data network (“LADN”), local area network (“LAN”), load based equipment (“LBE”), listen before talk (“LBT”), logical channel (“LCH”), logical channel group (“LCG”), logical channel prioritization (“LCP”), log likelihood ratio (“LLR”), level of automation (“LoA”), line of sight (“LOS”), long term evolution (“LTE”), LTE vehicle (“LTE-V”), multiple access (“MA”), medium access control (“MAC”), multimedia broadcast multicast service (“MBMS”), maximum bit rate (“MBR”), minimum communication range (“MCR”), modulation coding scheme (“MCS”), mobile edge computing (“MEC”), master information block (“MIB”), multiple input multiple output (“MIMO”), machine learning (“ML”), mobility management (“MM”), mobility management entity (“MME”),Master Node (“MN”), Mobile Network Operator (“MNO”), Mobile Originated (“MO”), Mean Opinion Score (“MOS”), Massive MTC (“mMTC”), Maximum Power Reduction (“MPR”), Multi-Radio Dual Connectivity (“MR-DC”), Machine Type Communication (“MTC”), Multi-User Shared Access (“MUSA”), Non-Access Stratum (“NAS”), Narrow Band (“NB”), Negative Acknowledgement (“NACK”) or (“NAK”), New Data Indicator (“NDI”), Network Entity (“NE”), Network Exposure Function (“NEF”), Network Exposure Function / Service Capability Exposure Function (“NEF / SCEF”), Network Function (“NF”), Non-LOS (“NLOS”), Next Generation (“NG”), NG 5G S-TMSI (“NG-5G-S-TMSI”), Neural Network (“NN”), Non-Orthogonal Multiple Access (“NOMA”), New Radio (“NR”), NR Unlicensed (“NR-U”), Network Repository Function (“NRF”), Network Scheduled Mode (“NS Mode”) (e.g., Network Scheduled Mode for V2X communication resource allocation - Mode-1 in NR V2X and Mode-3 in LTE V2X), Network Slice Instance (“NSI”), Network Slice Selection Assistance Information (“NSSAI”), Network Slice Selection Function (“NSSF”), Network Slice Selection Policy (“NSSP”), Operation, Administration, and Maintenance System or Operation and Maintenance Center (“OAM”), Orthogonal Frequency Division Multiplexing (“OFDM”), Open Loop (“OL”), Other System Information (“OSI”), Power Angular Spectrum (“PAS”), Physical Broadcast Channel (“PBCH”), Power Control (“PC”), UE-to-UE Interface (“PC5”), Principal Component Analysis (“PCA”), Policy and Charging Control (“PCC”), Primary Cell (“PCell”), Policy and Charging Rules Function (“PCRF”), Policy Control Function (“PCF”), Physical Cell Identity (“PCI”), Physical Downlink Control Channel (“PDCCH”), Packet Data Convergence Protocol (“PDCP”), Packet Data Network Gateway (“PGW”), Physical Downlink Shared Channel (“PDSCH”), Pattern Division Multiple Access (“PDMA”), Packet Data Unit (“PDU”), Physical Hybrid-ARQ Indicator Channel (“PHICH”), Power Headroom (“PH”), Power Headroom Report (“PHR”), Physical Layer (“PHY”), Public Land Mobile Network (“PLMN”), Precoding Matrix Index (“PMI”), Per-Packet Proximity Service Priority (“PPPP”), Per-Packet Proximity Service Reliability (“PPPR”), PC5 5QI (“PQIs”), Predictive QoS (“P-QoS”), Physical Random Access Channel (“PRACH”), Physical Resource Block (“PRB”),Proximity Service (“ProSe”), Positioning Reference Signal (“PRS”), Physical Sidelink Control Channel (“PSCCH”), Primary Secondary Cell (“PSCell”), Physical Sidelink Feedback Control Channel (“PSFCH”), Physical Uplink Control Channel (“PUCCH”), Physical Uplink Shared Channel (“PUSCH”), QoS Class Identifier (“QCI”), Quasi-Co-Location (“QCL”), QoS Flow Indicator (“QFI”), Quality of Experience (“QoE”), Quality of Service (“QoS”), Quadrature Phase Shift Keying (“QPSK”), Registration Area (“RA”), RA RNTI (“RA-RNTI”), Radio Access Network (“RAN”), Random (“RAND”), Radio Access Technology (“RAT”), Serving RAT (“RAT-1”) (with respect to Uu serving), Other RAT (“RAT-2”) (with respect to Uu non-serving), Random Access Procedure (“RACH”), Random Access Preamble Identifier (“RAPID”), Random Access Response (“RAR”), Resource Block Assignment (“RBA”), Resource Element Group (“REG”), Rank Indicator (“RI”), RAN Intelligent Controller (“RIC”), Radio Link Control (“RLC”), RLC Acknowledged Mode (“RLC-AM”), RLC Unacknowledged Mode / Transparent Mode (“RLC-UM / TM”), Radio Link Failure (“RLF”), Radio Link Monitoring (“RLM”), Radio Network Information (“RNI”), RNI Service (“RNIS”), Radio Network Temporary Identifier (“RNTI”), Reference Signal (“RS”), Recurrent Model (“RM”), Remaining Minimum System Information (“RMSI”), Radio Resource Control (“RRC”), Radio Resource Management (“RRM”), Resource Spread Multiple Access (“RSMA”), Reference Signal Received Power (“RSRP”), Received Signal Strength Indicator (“RSSI”), Round Trip Time (“RTT”), Receive (“RX”), Service Capability Exposure Function (“SCEF”), Sparse Code Multiple Access (“SCMA”), Scheduling Request (“SR”), Sounding Reference Signal (“SRS”), Single-Carrier Frequency Division Multiple Access (“SC-FDMA”), Secondary Cell (“SCell”), Secondary Cell Group (“SCG”), Shared Channel (“SCH”), Sidelink Control Information (“SCI”), Subcarrier Spacing (“SCS”), Service Data Unit (“SDU”), Security Anchor Function (“SEAF”), Service Enabler Architecture Layer (“SEAL”), Sidelink Feedback Content Information (“SFCI”), Serving Gateway (“SGW”), System Information Block (“SIB”), System Information Block Type 1 (“SIB1”), System Information Block Type 2 (“SIB2”), Subscriber Identity / Identification Module (“SIM”),Signal and interference plus noise ratio (“SINR”), sidelink (“SL”), service level agreement (“SLA”), sidelink synchronization signal (“SLSS”), session management (“SM”), session management function (“SMF”), secondary node (“SN”), special cell (“SpCell”), single-network slice selection assistance information (“S-NSSAI”), scheduling request (“SR”), signaling radio bearer (“SRB”), shortened TMSI (“S-TMSI”), shortened TTI (“sTTI”), synchronization signal (“SS”), sidelink CSI RS (“S-CSI RS”), sidelink PRS (“S-PRS”), sidelink SSB (“S-SSB”), synchronization signal block (“SSB”), subscription concealed identifier (“SUCI”), scheduled user equipment (“SUE”), supplementary uplink (“SUL”), user permanent identifier (“SUPI”), support vector machine (“SVM”), tracking area (“TA”), TA identifier (“TAI”), TA update (“TAU”), timing alignment timer (“TAT”), transport block (“TB”), transport block size (“TBS”), time division duplex (“TDD”), time division multiplex (“TDM”), time division orthogonal cover code (“TD-OCC”), temporary mobile subscriber identity (“TMSI”), time of flight (“ToF”), transmit power control (“TPC”), transmit receive point (“TRP”), transmission time interval (“TTI”), transmission (“TX”), uplink control information (“UCI”), unified data management function (“UDM”), unified data repository (“UDR”), user entity / device (mobile terminal) (“UE”) (e.g., V2X UE), UE autonomous mode (UE autonomous selection of V2X communication resources - e.g., Mode-2 in NR V2X and Mode-4 in LTE V2X. UE autonomous selection can or can not be based on resource sensing operations), uplink (“UL”), UL SCH (“UL-SCH”), universal mobile

[0003] In certain wireless communication networks, radio bearer configurations can be used. SUMMARY

[0004] Methods for predictively adapting radio bearer configurations are disclosed. Devices and systems also perform the functions of the methods. One embodiment of a method includes receiving an expected quality of service profile pattern for at least one quality of service flow of at least one user equipment. In some embodiments, the method includes determining a predictive adaptation of a radio bearer configuration based on the expected quality of service profile pattern, where the predictive adaptation comprises at least one radio bearer remapping to the at least one quality of service flow. In various embodiments, the method includes configuring a predictive quality of service flow to radio bearer mapping pattern for an expected time window based on the predictive adaptation. In certain embodiments, the method includes transmitting the predictive quality of service flow to radio bearer mapping pattern to the at least one user equipment.

[0005] A device for predictively adapting radio bearer configurations includes a receiver that receives an expected quality of service profile pattern for at least one quality of service flow of at least one user equipment. In various embodiments, the device includes a processor that: determines a predictive adaptation of a radio bearer configuration based on the expected quality of service profile pattern, where the predictive adaptation comprises at least one radio bearer remapping to the at least one quality of service flow; and configures a predictive quality of service flow to radio bearer mapping pattern for an expected time window based on the predictive adaptation. In certain embodiments, the device includes a transmitter that transmits the predictive quality of service flow to radio bearer mapping pattern to the at least one user equipment.

[0006] In certain embodiments, a method for predictively adapting radio bearer configurations includes receiving a predictive quality of service flow to radio bearer mapping pattern, where the predictive quality of service flow to radio bearer mapping pattern is determined based on a predictive adaptation comprising at least one radio bearer remapping to at least one quality of service flow, and the predictive adaptation is determined based on an expected quality of service profile pattern for the at least one quality of service flow. In some embodiments, the method includes triggering a radio bearer modification based on the received predictive quality of service flow to radio bearer mapping pattern.

[0007] In various embodiments, an apparatus for predictively adapting radio bearer configuration includes a receiver that receives a predictive quality of service flow to radio bearer mapping pattern, wherein the predictive quality of service flow to radio bearer mapping pattern is determined based on a predictive adaptation that includes at least one radio bearer remapping to at least one quality of service flow, and the predictive adaptation is determined based on an expected quality of service profile pattern of the at least one quality of service flow. In certain embodiments, the apparatus includes a processor that triggers a radio bearer modification based on the received predictive quality of service flow to radio bearer mapping pattern. BRIEF DESCRIPTION OF DRAWINGS

[0008] A more particular description of the embodiments briefly described above will be rendered by reference to specific embodiments that are illustrated in the appended drawings. Understanding that these drawings depict only some embodiments and are not therefore to be considered to be limiting of the scope of the embodiments, the embodiments will be described and explained with additional specificity and detail through the use of the accompanying drawings in which:

[0009] Figure 1 is a schematic block diagram illustrating one embodiment of a wireless communication system for predictively adapting radio bearer configuration;

[0010] Figure 2 is a schematic block diagram illustrating one embodiment of an apparatus that can be used for predictively adapting radio bearer configuration;

[0011] Figure 3 is a schematic block diagram illustrating one embodiment of an apparatus that can be used for predictively adapting radio bearer configuration;

[0012] Figure 4 is a schematic block diagram illustrating one embodiment of an architecture for enabling edge applications;

[0013] Figure 5 is a diagram illustrating one embodiment of a communication for AI-capable QoS pattern remapping;

[0014] Figure 6 is a diagram illustrating one embodiment of a communication for predictive QFI to DRB reconfiguration in an MR-DC embodiment;

[0015] Figure 7 is a flow diagram illustrating one embodiment of a method for predictively adapting radio bearer configuration; and

[0016] Figure 8 is a flow diagram illustrating another embodiment of a method for predictively adapting radio bearer configuration. DETAILED DESCRIPTION

[0017] As those skilled in the art will appreciate, aspects of the embodiments can be embodied as a system, device, method or program product. Accordingly, embodiments can take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that can all generally be referred to herein as a "circuit," "module" or "system." Furthermore, embodiments can take the form of a program product embodied in one or more computer readable storage devices (storage devices) having computer readable code embodied thereon. The storage devices can be tangible, non-transitory, and / or non-transmission. The storage devices can not embody signals. In a certain embodiment, the storage devices only employ signals for accessing the code.

[0018] Certain of the functional units described in this specification can be labeled as modules, in order to more particularly emphasize their implementation independence. For example, a module can be implemented as a hardware circuit comprising custom very-large-scale integration ("VLSI") circuits or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, or other discrete components. A module can also be implemented in programmable hardware devices such as field programmable gate arrays, programmable array logic, programmable logic devices or the like.

[0019] Modules can also be implemented in code and / or software for execution by various types of processors. An identified module of code may, for instance, include one or more physical or logical blocks of executable code which may, for instance, be organized as an object, procedure or function. Nevertheless, the executables of an identified module need not be physically located together, but can include disparate instructions stored in different locations which, when joined logically together, include the module and achieve the stated purpose for the module.

[0020] Indeed, a module of code can be a single instruction, or many instructions, and can even be distributed over several different code segments, among different programs, and across several memory devices. Similarly, operational data can be identified within the modules and can be

[0021] Any combination of one or more computer readable medium can be utilized. The computer readable medium can be a computer readable storage medium. The computer readable storage medium can be a storage device storing the code. The storage device can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, holographic, micromechanical, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing.

[0022] More specific examples (a non-exhaustive list) of the storage device would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory ("RAM"), a read-only memory ("ROM"), an erasable programmable read-only memory ("EPROM" or Flash memory), a portable compact disc read-only memory ("CD-ROM"), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium can be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

[0023] Code for carrying out operations for embodiments can be any number of lines and can be written in any combination of one or more programming languages including an object oriented programming language such as Python, Ruby, Java, Smalltalk, C++, or the like, and conventional procedural programming languages, such as the "C" programming language, or the like, and / or machine languages such as assembly languages. The code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network ("LAN") or a wide area network ("WAN"), or the

[0024] Reference throughout this specification to "one embodiment", "an embodiment", or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, appearances of the phrases "in one embodiment", "in an embodiment", and similar language throughout this specification may, but not always, refer to the same embodiment, while other instances can refer to a different embodiment. The terms "including", "comprising", "having" and variations thereof mean "including but not limited to", unless expressly specified otherwise. Enumerated lists of items do not imply any order or require that all items be included in the same embodiment, unless expressly specified otherwise. The terms "a", "an" and "the" refer to "one or more" unless expressly specified otherwise.

[0025] Moreover, the described features, structures, or characteristics of the embodiments can be combined in any suitable manner. In the following description, numerous specific details are provided, such as examples of programming, software modules, user selections, network transactions, database queries, database structures, hardware modules, hardware circuits, hardware chips, etc., to provide a thorough understanding of the embodiments. One skilled in the relevant art will recognize, however, that the embodiments can be practiced without one or more of the specific details, or with other methods, components, materials, and so forth. In other instances, well-known structures, materials, or operations are not shown or described in detail in order to avoid obscuring aspects of the embodiments.

[0026] Aspects of the embodiments are described below with reference to flowchart illustrations and / or block diagrams of methods, apparatus, systems, and program product according to embodiments. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by code. The code can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0027] The code can also be stored in a storage device that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the storage device produce an article of manufacture including instructions which implement the function / act specified in the flowchart and / or block diagram block or blocks.

[0028] The code can also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the code which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0029] The flowchart illustrations and / or block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of devices, systems, methods and program product according to various embodiments. In this regard, each block in the flowchart illustrations and / or block diagrams can represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical functions.

[0030] It is also noted that the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks noted in succession can in fact be executed substantially concurrently or can sometimes be executed in the reverse order, depending upon the functionality involved. Other steps and methods can be conceived that are equivalent in function, logic, or effect to those described here.

[0031] Although various arrow types and line styles can be employed in the flowchart and / or block diagrams, these are understood to be merely illustrative of the logical flows of the depicted embodiments. In reality, there can be numerous items of

[0032] The description of the elements in each figure can refer to elements of previous figures. Like numbers refer to like elements throughout the several figures, including like elements of alternative embodiments.

[0033] Figure 1 Embodiments of a wireless communication system 100 for predictively adapting radio bearer configurations are depicted. In one embodiment, the wireless communication system 100 includes remote units 102 and network units 104. While Figure 1 Although a specific number of remote units 102 and network units 104 are depicted in the FIGs, one of skill in the art will recognize that any number of remote units 102 and network units 104 can be included in the wireless communication system 100.

[0034] In one embodiment, a remote unit 102 can include a computing device, such as a desktop computer, laptop computer, personal digital assistant (“PDA”), tablet computer, smart phone, smart television (e.g., a television connected to the Internet), set-top box, game console, security system (including security cameras), vehicle

[0035] The network units 104 can be distributed over a geographic region. In certain embodiments, a network unit 104 can also be referred to as, and / or can include one or more of, an access point, an access terminal, a base, a base station, a NodeB, an eNB, a gNB, a Home NodeB, a relay node, a device, a core network, an aerial server, a radio access node, an AP, an NR, a network entity, an AMF, a UDM, a UDR, a UDM / UDR, a PCF, a RAN, a NSSF, an OAM, a SMF, a UPF, an application function, or any other terminology used in the art. The network units 104 are generally part of a radio access network that includes one or more controllers that can be communicably coupled to one or more corresponding network units 104. The radio access network can be communicably coupled to one or more core networks, which can be coupled to other networks, like the Internet and public switched telephone networks, among other networks. These and other elements of radio access and core networks are not illustrated but are well known to those ordinarily skilled in the art.

[0036] In one implementation, the wireless communication system 100 complies with the NR protocols standardized in 3GPP, where the network units 104 transmit using an OFDM modulation scheme on the DL and the remote units 102 transmit on the UL using a SC-FDMA scheme or an OFDM scheme. More generally, however, the wireless communication system 100 can implement some other open or proprietary communication protocol, for example, WiMAX, IEEE 802.11 variants, GSM, GPRS, UMTS, LTE variants, CDMA2000, ZigBee, Sigfoxx, and other protocols. The present disclosure is not intended to be limited to the implementation of any particular wireless communication system architecture or protocol.

[0037] The network units 104 can serve a number of remote units 102 within a serving area, for example, a cell or a cell sector, via wireless communication links. The network units 104 transmit DL communication signals to serve the remote units 102 in the time, frequency, and / or space domains.

[0038] In various embodiments, the network unit 104 can receive an expected quality of service profile pattern for at least one quality of service flow of at least one user equipment. In various embodiments, the expected quality of service profile pattern is based on prescriptive and / or predictive data analysis. Predictive data analysis can be defined as a type of analysis that uses ML to determine what is likely to happen (e.g., QoS upgrade and / or downgrade). Prescriptive data analysis can be defined as a type of analysis that prescribes a certain action (e.g., QoS profile adaptation) to be taken, recommended, and / or implemented. In some embodiments, the network unit 104 determines a predictive adaptation to radio bearer configuration based on the expected quality of service profile pattern, where the predictive adaptation comprises at least one radio bearer remapping to at least one quality of service flow. In certain embodiments, the network unit 104 can configure a predictive quality of service flow to radio bearer mapping pattern for an expected time window based on the predictive adaptation. In various embodiments, the network unit 104 can transmit the predictive quality of service flow to radio bearer mapping pattern to the at least one user equipment. Thus, the network unit 104 can function to predictively adapt radio bearer configuration.

[0039] In certain embodiments, the remote unit 102 can receive a predictive quality of service flow to radio bearer mapping pattern, where the predictive quality of service flow to radio bearer mapping pattern is determined based on a predictive change comprising at least one radio bearer remapping to at least one quality of service flow, and the predictive adaptation is determined based on an expected quality of service profile pattern of the at least one quality of service flow. In some embodiments, the remote unit 102 can trigger a radio bearer modification based on the received predictive quality of service flow to radio bearer mapping pattern. Thus, the remote unit 102 can function to predictively adapt radio bearer configuration.

[0040] Figure 2 One embodiment of an apparatus 200 that can be used to predictively adapt radio bearer configuration is depicted. The apparatus 200 includes one embodiment of the remote unit 102. Furthermore, the remote unit 102 can include a processor 202, a memory 204, an input device 206, a display 208, a transmitter 210, a receiver 212, one or more network interfaces 214, and one or more application interfaces 216. In some embodiments, the input device 206 and the display 208 combine into a single device, such as a touchscreen. In certain embodiments, the remote unit 102 can not include any input device 206 and / or display 208. In various embodiments, the remote unit 102 can include one or more of the processor 202, the memory 204, the transmitter 210, and the receiver 212, and can not include the input device 206 and / or the display 208.

[0041] In one embodiment, the processor 202 can include any known controller capable of executing computer-readable instructions and / or capable of performing logical operations. For example, the processor 202 can be a microcontroller, a microprocessor, a central processing unit (“CPU”), a graphics processing unit (“GPU”), an auxiliary processing unit, a field programmable gate array (“FPGA”), or similar programmable controller. In some embodiments, the processor 202 executes instructions stored in the memory 204 to perform methods and routines described herein. The processor 202 is communicatively coupled to the memory 204, the input device 206, the display 208, the transmitter 210, and the receiver 212.

[0042] In one embodiment, the memory 204 is a computer-readable storage medium. In some embodiments, the memory 204 includes volatile computer storage media. For example, the memory 204 can include RAM, including dynamic RAM (“DRAM”), synchronous dynamic RAM (“SDRAM”), and / or static RAM (“SRAM”). In some embodiments, the memory 204 includes non-volatile computer storage media. For example, the memory 204 can include a hard disk drive, a flash memory, or any other suitable non-volatile computer storage device. In some embodiments, the memory 204 includes both volatile and non-volatile computer storage media. In some embodiments, the memory 204 also stores program code and related data, such as an operating system or other controller algorithms operating on the remote unit 102.

[0043] In one embodiment, the input device 206 can include any known computer input device, including a touch panel, buttons, a keyboard, a stylus, a microphone, or the like. In some embodiments, the input device 206 can be integrated with the display 208, for example, as a touch screen or similar touch-sensitive display. In some embodiments, the input device 206 includes a touch screen such that text can be input using a virtual keyboard displayed on the touch screen and / or by handwriting on the touch screen. In some embodiments, the input device 206 includes two or more different devices, for example a keyboard and a touch panel.

[0044] In one embodiment, display 208 can include any suitable electronic controllable display or display device. Display 208 can be designed to output visual, audible, and / or tactile signals. In some embodiments, display 208 includes an electronic display capable of outputting visual data to a user. For example, display 208 can include, but is not limited to, an LCD display, an LED display, an OLED display, a projector, or similar display device capable of outputting images, text, or the like to a user. As another, non-limiting, example, display 208 can include a wearable display, such as a smart watch, smart glasses, a heads-up display, or the like. Further, display 208 can be a component of a smart phone, a personal digital assistant, a television, a table computer, a notebook (laptop) computer, a personal computer, a vehicle dashboard, or the like.

[0045] In certain embodiments, display 208 includes one or more speakers for producing sound. For example, display 208 can produce an audible alert or notification (e.g., a beep or chime). In some embodiments, display 208 includes one or more tactile devices for producing vibrations, motion, or other tactile feedback. In some embodiments, all or portions of display 208 can be integrated with input device 206. For example, input device 206 and display 208 can form a touchscreen or similar touch-sensitive display. In other embodiments, display 208 can be located near input device 206.

[0046] In certain embodiments, receiver 212 can receive a predictive quality of service flow to radio bearer mapping pattern, where the predictive quality of service flow to radio bearer mapping pattern is determined based on a predictive adaptation that includes a re-mapping of at least one radio bearer to at least one quality of service flow, and the predictive adaptation is determined based on an expected quality of service profile pattern of the at least one quality of service flow. In some embodiments, processor 202 can trigger a radio bearer modification based on the received predictive quality of service flow to radio bearer mapping pattern.

[0047] Although only one transmitter 210 and one receiver 212 are illustrated, remote unit 102 can have any suitable number of transmitters 210 and receivers 212. The transmitter 210 and receiver 212 can be any suitable type of transmitters and receivers. In one embodiment, transmitter 210 and receiver 212 can be part of a transceiver.

[0048] Figure 3One embodiment of an apparatus 300 that can be used to predictively adapt radio bearer configurations is depicted. The apparatus 300 includes one embodiment of the network unit 104. Moreover, the network unit 104 can include a processor 302, a memory 304, an input device 306, a display 308, a transmitter 310, a receiver 312, one or more network interfaces 314, and one or more application interfaces 316. As can be appreciated, the processor 302, the memory 304, the input device 306, the display 308, the transmitter 310, and the receiver 312 can be substantially similar to the processor 202, the memory 204, the input device 206, the display 208, the transmitter 210, and the receiver 212 of the remote unit 102, respectively.

[0049] In certain embodiments, the receiver 312 can receive an expected quality of service profile pattern for at least one quality of service flow of at least one user equipment. In certain embodiments, the processor 302 can determine a predictive adaptation of a radio bearer configuration based on the expected quality of service profile pattern, where the predictive adaptation comprises at least one radio bearer remapping to at least one quality of service flow, and configure a predictive quality of service flow to radio bearer mapping pattern for an expected time window based on the predictive adaptation. In various embodiments, the transmitter 310 can transmit the predictive quality of service flow to radio bearer mapping pattern to the at least one user equipment. Although only one transmitter 310 and one receiver 312 are illustrated, the network unit 104 can have any suitable number of transmitters 310 and receivers 312. The transmitter 310 and the receiver 312 can be any suitable type of transmitters and receivers. In one embodiment, the transmitter 310 and the receiver 312 can be part of a transceiver.

[0050] In various embodiments, the minimum complexity and / or signaling load of the RAN can be utilized to configure the optimal QoS flow to radio bearer mapping for one or more ongoing sessions (e.g., if significant channel quality fluctuations are expected).

[0051] In some embodiments, prediction of QoS degradation on the network side and / or proactive application adaptation to facilitate service continuity can be performed (e.g., for URLLC communications) for some verticals (e.g., smart factories, V2X).

[0052] In certain embodiments, prediction of predictive maintenance and / or proactive application adaptation can be used (e.g., with application layer impact) for V2X and / or IIOT configurations.

[0053] In various embodiments, there can be a need to support predicted QoS to third parties, e.g., for V2X. In some embodiments, the 3GPP network can provide the expected QoS to the V2X application; however, the V2X application can provide the necessary data to enable QoS analytics. In certain embodiments, the predicted QoS can be applied to applications such as condition monitoring and predictive maintenance based on sensor data, but data analytics can also be used to optimize future parameter sets for a particular process.

[0054] In various embodiments, alternative QoS profile features can be used. Alternative QoS profiles can include one or more of the following: 1) an AF (e.g., service provider and / or vertical) can provide multiple application QoS levels for one requested session in the SLA; 2) the 5G network can map a session to original and lower priority alternative QoS profiles (e.g., original 5QI x > alternative 1 5QI y > alternative 2 5QI z); 3) the CN informs the RAN of the alternative QoS profiles mapped to the QoS flow; 4) the RAN checks whether the originally set QoS attributes cannot be achieved - if the originally set QoS attributes cannot be achieved, the RAN requests the CN to perform QoS degradation to one of the alternative QoS profiles; and / or 5) if alternative QoS profiles are used, the RAN can continuously check whether a higher priority alternative or original QoS profile can be achieved again to perform QoS upgrade (e.g., upgrade to a different alternative or originally selected QoS profile).

[0055] In some embodiments, the RAN can periodically check the implementation and / or non-implementation of QoS profiles for many QoS flows. In such embodiments, the RAN can (e.g., based on upper layer’s subscription or request) do the following for multiple flows: 1) the RAN can determine to perform QoS upgrade, QoS downgrade, or stay the same; 2) the RAN can determine which QoS profiles to upgrade or downgrade; and / or the RAN can determine whether other actions need to be performed at the RAN level to facilitate meeting GFBR (e.g., meeting scheduling requirements, implementing DRB modification).

[0056] In certain embodiments, there can be significant complexity and / or processing at the RAN node, e.g., for multiple QoS flows and / or multiple alternatives per flow. In various embodiments, the determination of QoS profile changes can be performed at the SMF. In such embodiments, the benefit compared to some embodiments can be negligible, considering the signaling requirements for multiple flows and frequent transitions.

[0057] In some embodiments, QoE is an indicator related to QoS, but QoE can indicate more information related to application side impact (e.g., which can be interpreted as application QoS requirements). In certain embodiments, QoE can be computed at the application layer and can refer to video related QoE scores, MOS scores (e.g., video MOS or custom), initial buffering, stall events, and / or stall ratio. In various embodiments, QoE targets can be pre-defined. In such embodiments, different targets can be negotiated at SLA agreements between MNO and verticals and / or customers. In some embodiments, QoE monitoring, upgrade decisions, and / or downgrade decisions can not be provided at the RAN. In certain embodiments, mapping of QoE to QoS profiles can be provided by upper layers (e.g., core network, application function).

[0058] In various embodiments, it can not be known what mechanisms the RAN node will check for possible QoS downgrades to adapt QoS provisioning to ensure service continuity (e.g., to avoid violating minimum agreed QoS).

[0059] In some embodiments, it can not be known how the RAN node will check for possible QoS upgrades to provide optimal QoS provisioning (e.g., to ensure maximum agreed QoS).

[0060] In certain embodiments, per-user QoS and / or QoE can be actively optimized in the RAN and can include one or more of the following: 1) how to actively and / or dynamically capture QoS downgrades at the RAN (e.g., QoS flow remapping to lower priority QoS profiles to ensure meeting per-UE QoE targets); and / or 2) how to actively and / or dynamically capture QoS upgrades at the RAN (e.g., QoS flow remapping to higher priority QoS profiles to optimize user and / or RAN performance).

[0061] In various embodiments, if for a given GBR QoS flow, notification control is enabled and the NG-RAN has received a list of alternative QoS profiles for the QoS flow and supports handling alternative QoS profiles, the following can apply: 1) if the NG-RAN determines that the GFBR, PDB, or PER of the QoS profile cannot be achieved, the NG-RAN can send a notification to the SMF indicating that the GFBR can no longer be guaranteed - before sending the notification to the SMF that the GFBR can no longer be guaranteed, the NG-RAN can check if the GFBR, PDB, and PER currently achieved by the NG-RAN match any of the alternative QoS profiles in the indicated order of precedence - if there is a match, the NG-RAN can indicate a reference to the matching alternative QoS profile; and / or 2) the NG-RAN can attempt to achieve the QoS profile and any alternative QoS profiles with higher priority than the current achieved scenario - to avoid signaling to the SMF too frequently, it can be assumed that the NG-RAN implementation can apply a hysteresis (e.g., via a configurable time interval).

[0062] In some embodiments, ETSI MEC can implement exposing APIs from the RAN to the MEC platform. In such embodiments, exposing APIs from the UE and / or RAN to service providers can involve UE location information, bandwidth management, and / or RNI. In various embodiments, RNI is a service that provides radio network related information to MEC applications and MEC platforms. In certain embodiments, the granularity of the radio network information can be adjusted based on parameters such as information per cell, per user equipment, per QoS class, or can be requested over a period of time. In some embodiments, MEC applications and / or MEC platforms can use RNI to optimize existing services and / or provide new types of services based on up-to-date information corresponding to radio conditions. In various embodiments, an application layer architecture can be used for edge services.

[0063] Figure 4is a schematic block diagram illustrating one embodiment of an architecture 400 for enabling edge applications. The architecture includes a UE 402, a 3GPP core network 404, an edge data network 406, and an edge configuration server 408. In particular, the UE 402 includes one or more application clients 410 and an edge enabler client 412. The one or more application clients 410 can communicate with the edge enabler client 412 via an EDGE-5 protocol 414. The edge data network includes one or more edge application servers 416 and one or more edge enabler servers 418. The one or more application clients 410 can communicate application data traffic 420 with the one or more edge application servers 416. The one or more edge application servers 416 can communicate with the 3GPP core network 404 via an EDGE-7 protocol 422. The edge enabler client 412 can communicate with the one or more edge enabler servers 418 via an EDGE-1 protocol 424. The edge enabler servers 418 can communicate with the 3GPP core network 404 via an EDGE-2 protocol 426. The edge enabler client 412 can communicate with the edge configuration server 408 via an EDGE-4 protocol 428. The edge configuration server 408 can communicate with the 3GPP core network 404 via an EDGE-8 protocol 430. The one or more edge application servers 416 can communicate with the one or more edge enabler servers 418 via an EDGE-3 protocol 432. The one or more edge enabler servers 418 can communicate with each other via an EDGE-9 protocol 434. The one or more edge enabler servers 418 can communicate with the edge configuration server 408 via an EDGE-6 protocol 436.

[0064] The one or more edge enabler servers 418 provide support functions needed by the one or more edge application servers 416 and the edge enabler client 412, such as: 1) providing configuration information to the edge enabler client 412 enabling the exchange of application data traffic with the one or more edge application servers 416; 2) interacting with the 3GPP core network 404 to access network functions capabilities directly (e.g., via a PCF) or indirectly (e.g., via a SCEF, NEF, and / or SCEF+NEF); and / or supporting the exposure of 3GPP network capabilities to the one or more edge application servers 416 via the EDGE-3 protocol 432.

[0065] The edge enabler client 412 provides support functions needed by the one or more application clients 410, such as retrieving and providing configuration information to enable the exchange of application data traffic 420 with the one or more edge application servers 416; and discovering the one or more edge application servers 416 available in the edge data network 406.

[0066] One or more edge application servers 416 are application servers that reside in the edge data network 406 and the one or more edge application servers 416 perform server functions.

[0067] One or more application clients 410 are clients that reside in the UE 402 that perform client functions.

[0068] In various embodiments, the control functionality of the RIC (e.g., RRC and / or RRM) can be collocated with the gNB or can be deployed for a cluster of gNBs. In such embodiments, the RRM and / or RRC functionality can be flexibly located at the CU and / or DU or a dedicated RIC controller (e.g., near-RT RIC and non-RT RIC) given the deployment and functional requirements (e.g., real-time, non-real-time, near-real-time) and slice isolation policies. In some embodiments, there can be QoE and / or QoS optimization as an AI-enabled feature deployed at the near-RT RIC (e.g., as a third-party xAPP or proprietary xAPP). In such embodiments, the QoS upgrade and / or downgrade can be assisted by using the RIC and the interface used can be an open API.

[0069] In certain embodiments, there can be a method for proactively configuring RAN resources and / or radio bearers to handle QoS flow to QoS profile adaptation (e.g., expected QoS degradations and / or upgrades that can reside at the AI-enabled RAN control unit). Such embodiments can include: 1) a RAN node (e.g., gNB, MN, SN) obtaining QoS flow configuration parameters based on expected QoS profile adaptation (e.g., QoS profile transitions over a period of time, time validity, changing applicable areas, enforcement and / or recommendation indications) of a QoS flow; 2) the RAN node can use RAN-level decisions (e.g., scheduling, QoS flow to DRB remapping) to decide whether to apply QoS flow remapping or whether to resolve any expected QoS changes; 3) if this can be achieved by RAN-level actions (e.g., actions provided by RAN control functionality related to, for example, RRM, MAC, and / or RRC functionality) (e.g., due to possible improvements in radio conditions, or low accuracy of AI and / or ML models), then the RAN node can determine a proactive DRB modification - expected QoS flow to DRB remapping pattern for a given time window (e.g., as long as the ongoing session is active at the serving cell or based on time validity and / or areas of applicability of predictive QoS adaptation) - this QoS flow to DRB remapping pattern can be a series of DRB remappings for a given time window and can also include a time counter of when each transition needs to be performed and time-DRB remapping can affect other QoS flows, so this can take into account the fact that DRB load is at an acceptable level; 4) the RRC functionality of the RAN node (e.g., DRB management at the gNB, MN, and / or CU) configures the SDAP protocol to map QoS flows to different DRBs remapping in future time instances and can send this information to the UE for UL DRBs (e.g., providing the UE with awareness of expected UL DRB remapping); and / or 5) the RAN node can notify other gNBs or RAN nodes (e.g., SN at MR-DC) for expected DRB remapping.

[0070] It should be noted that embodiments described herein can be described in relation to predictive QoS patterns, but can also apply to predictive QoE patterns. For example, in one embodiment, the RAN can convert QoE patterns to predictive QFI to DRB pattern remapping.

[0071] In a first embodiment, the AI functionality can provide a QoS profile adaptation pattern and the RAN can decide to proactively adapt DRB mapping to avoid changing QoS patterns.

[0072] Figure 5is a diagram illustrating one embodiment of communications 500 for AI-capable QoS profile remapping. The communications 500 include messages transmitted between a UE 502, a gNB 504 (e.g., RRC, RAN), an AMF and / or SMF 506, and an AI function 508 (e.g., RAN control function, AF, RIC, xAPP, MEC application). Each of the communications 500 described herein can include one or more messages.

[0073] In certain embodiments, the 5GC (e.g., via the SMF of the AMF) can provide a N2 PDU session request with a list of QoS profiles for QoS flows. In a first communication 510 transmitted between the UE 502, the gNB 504, and the AMF and / or SMF 506, the PDU session establishment procedure can be triggered with multiple QoS levels.

[0074] In a second communication 512 transmitted between the gNB 504 and the AI function 508, the gNB 504 subscribes to the AI function 508 to be notified of the regulatory and / or predictive analytics corresponding to the expected profile of the QoS profile. In some embodiments, the gNB 504 performs a one-shot request to receive analytics and in this request, the gNB 504 configures the reporting (e.g., for format, accuracy, and / or periodicity) used by the AI function 508. In such embodiments, this is followed by a response (e.g., ACK / NACK) from the AI function 508 as an answer. In the request and / or subscription message, the gNB 504 can request the type of AI and / or ML model to be used, or expected accuracy and / or training configuration, and the AI function 508 can apply the most optimal algorithm. In the second communication 512, the gNB 504 can send a subscription message and / or can perform a one-shot request to receive P-QoS parameters from the AI function 508.

[0075] In a third communication 514 transmitted from the gNB 504 to the AI function 508, after subscribing and / or receiving a positive response for receiving predictive QoS profiles, the gNB 504 provides QoS parameters used by the AI function 508 to provide regulatory and / or predictive analytics. The QoS parameters can include a RAN UE ID, a QoS flow ID, a substitute QoS and / or priority, a hysteresis threshold, and / or a RNI. The gNB 504 can also provide radio parameters to support the AI function 508 in its analytics. The QoS parameters and / or radio parameters can be used to support online analytics at the AI function 508 using real-time measurements from the gNB 504. As can be appreciated, the radio parameters to be exposed depend on the deployment of the AI function 508 (which can impose latency limitations).

[0076] The AI function 508 can interact with an AI model designer (e.g., a third party) or a corresponding database (e.g., a database storing UE 502 and RAN related analytics) to obtain (e.g., fetch) data needed to perform AI model inference. The model can be trained and can be related to expected behavior of UEs (e.g., expected locations, traffic demand, handover sequence) and / or expected state of the RAN (e.g., expected performance degradation, expected UL and / or DL traffic demand, expected backhaul conditions, expected DRB load) for a given timeframe and / or area. The AI function 508 converts the trained AI model to QoS profiles for one or more corresponding QoS flows by considering a list of QoS profiles and their priorities, expected UE and / or RAN behavior, and / or a hysteresis threshold, which can impact the number of allowed transitions. The expected accuracy of the prediction can be configured for each area within a cell.

[0077] Based on this conversion, the AI function 508 can determine 516 a mapping of QoS flows to QoS profile patterns. The result from the AI function 508 can include a tuning of the hysteresis threshold for a given time and / or area (e.g., in a tunnel) to capture certain deep QoS degradations within a predefined hysteresis.

[0078] In a fourth communication 518 transmitted from the AI function 508 to the gNB 504, the AI function 508 sends a report with the predictive QoS patterns of the QoS flows to the gNB 504 (e.g., in a NOTIFY message if this is based on a subscription). The predictive QoS report includes one or more of the following: 1) one or more QoS flow IDs; 2) predictive QoS remapping patterns (e.g., in the form of a list (<QoS flow, QoS profile x, y,.., t1, t2,..>) or a table per flow (QoS profile x time instance)); 3) hysteresis tuning threshold; 4) hysteresis tuning reason; 5) accuracy of the prediction (e.g., per area, within a cell, per cell); 6) time of the prediction; 7) accuracy of the time prediction; 8) valid area of the prediction; 9) accuracy of the prediction; 10) accuracy of the prediction per area; 11) accuracy of the time prediction; 12) valid area of the prediction; 13) time range of the prediction; 14) an enforcement indicator (e.g., flag) indicating whether the QoS profile patterns need to be enforced or it can enable the RAN to take further actions (e.g., DRB remapping); 15) upgrade indication per QoS transition; and / or 16) downgrade indication per QoS transition.

[0079] The gNB 504 evaluates 520 that the QoS expectations can be fulfilled by RAN level actions (e.g., due to possible improvement of radio conditions, or low accuracy of the AI and / or ML model). The gNB 504 determines the proactive DRB modification, in particular the expected QoS flow to DRB remapping pattern, for a given time window (e.g., as long as the ongoing session is active at the serving cell or time validity / area based applicability of the predictive QoS adaptation).

[0080] Upon receiving the predictive QoS profile pattern (e.g., with enforcement flag = 0), the RRC of the gNB 504 performs 522 RRC reconfiguration and in particular SDAP reconfiguration to provide the UE 502 with the predicted QFI to DRB mapping. The gNB 504 indicates a list of QoS flows that are mapped to DRBs based on the predicted DRB remapping (e.g., as provided by the UE 502 or by the AI function 508). The transmission from the gNB 504 can include QFI values, time counter, timestamp, expected start and / or end of the predicted QFI mapping to this DRB (e.g., t = 0, mapped to DRB x after tl ms, mapped to DRB y after t2 ms).

[0081] In some embodiments, the adaptation of the QoS flow to DRB remapping pattern provided to the UE 502 (e.g., upon receiving the QoS expectation pattern by the AI function 508 or by the UE 502) for UL transmission can be as follows: in the DRB-ToAddMod field at RRC reconfiguration (e.g., at DRB configuration and / or modification), at SDAP_Config, include the expected QoS flow to DRB remapping in a given time window (e.g., predicted QoS flows to add). The predicted QoS flows to add can indicate a list of QoS flows that are mapped to DRBs based on the predicted DRB remapping (e.g., as provided by the UE 502 or by the AI function 508). This can include QFI values, time counter, timestamp, expected start and / or end of the predicted QFI mapping to this DRB.

[0082] In a fifth communication 524 transmitted from the gNB 504 to the UE 502, the gNB 504 sends an RRC reconfiguration message to the UE 502, which can be a conditional RRC reconfiguration applicable for the time instance of each QoS flow to UL DRB mapping. This message can include: 1) a conditional RRC reconfiguration flag - including the reason for the conditionality (e.g., predictive QoS adaptation); 2) a list and / or table of QoS flow to DRB remapping (e.g., DRB x, DRB y, DRB z); 3) a predictive QoS adaptation timer (e.g., time counter reset); 4) the time instance of each remapping occurrence (e.g., tl ms, t2 ms, t3 ms); and / or 4) configuration of evaluation by the UE (e.g., how and / or when to start and / or pause reevaluation).

[0083] The UE 502 performs 526 evaluation of the conditional reconfiguration (e.g., expected remapping to DRB x after tl, expected remapping to DRB y after t2). The UE 502 then reevaluates the expected DRB remapping and after a pre-defined timer triggers 528 and 530 QOS flow to DRB adaptation and sends RRC reconfiguration complete messages 536 and 538. If one of the future DRB remappings is not successful (e.g., due to lack of resources, sudden change of radio conditions), the UE 502 requests RRC reconnection establishment (e.g., with reason predictive QoS adaptation failure).

[0084] In a second embodiment, the AI function can provide a QoS profile adaptation mode and the RAN can decide to proactively adapt the DRB mapping to avoid changing the QoS mode.

[0085] In some embodiments, MR-DC can refer to a UE with multiple RX and / or TX capabilities, which can be configured to utilize resources provided by two different nodes connected via a non-ideal backhaul, where one node provides NR access and the other node provides E-UTRA or NR access. In such embodiments, one node acts as a MN and the other node acts as a SN. The MN and SN are connected via a network interface and at least the MN is connected to a core network.

[0086] In various embodiments, for MR-DC operation, the MN and SN can coordinate their UL and DL radio resources in a semi-static manner via UE-associated signaling.

[0087] In a second embodiment, one aspect can be that for a target UE, QoS flow to DRB modification in RRC level and resource coordination between the MN and the SN can be needed. This can be performed as explained in Figure 6

[0088] ​Figure 6 is a diagram illustrating one embodiment of communications 600 for predictive QFI to DRB reconfiguration in an MR-DC embodiment. The communications 600 include messages transmitted between a SN 602 and a MN 604. Each of the communications 600 described herein can include one or more messages.

[0089] Steps 510-522 can be performed at step 606.

[0090] In a first communication 608 transmitted from the MN 604 to the SN 602, the MN 604 can initiate SN 602 addition. Here, the addition of the SN 602 is configured with a predictive QoS to DRB remapping pattern for a target QoS flow. The first communication 608 can include QoS flow to DRB mapping information, such as a predictive QoS flow to UL DRB remapping pattern, timers, and / or counters.

[0091] In a second communication 610 transmitted from the SN 602 to the MN 604, the SN 602 transmits a SN addition acknowledgement to the MN 604.

[0092] In a third communication 612 transmitted from the MN 604 to the SN 602, the MN 604 can initiate SN 602 modification. The SN 602 is added and made aware by the third communication 612 of the predictive QoS to DRB remapping pattern for the target QoS flow. The third communication 612 can include QoS flow to DRB mapping information, such as a predictive QoS flow to UL DRB remapping pattern, timers, and / or counters.

[0093] In a fourth communication 614 transmitted from the SN 602 to the MN 604, the SN 602 transmits a SN modification acknowledgement to the MN 604.

[0094] In a fifth communication 616 transmitted from the MN 604 to the SN 602, the MN 604 transmits a notification control indication. The notification control indication can be initiated by the MN 604 or the SN 602 and can be used to indicate that the GFBR for one or several QoS flows can no longer be achieved or can be achieved again. The P-QoS pattern together with the configuration and possible QoS flow to DRB remapping can be signaled to the SN 602 as part of the notification control indication.

[0095] After the fifth communication 616, the MN 604 or the SN 602 can signal information to the respective UE (e.g., as in steps 524-538 described above).

[0096] With certain embodiments described herein, the gNB can not need to continuously check for QoS degradations and / or upgrades. This is not only offloaded to the AI functionality, but due to the fact that this is based on mobility and / or traffic prediction, this enables less frequent (e.g., one-time) checks and / or QoS verification for potential future QoS changes.

[0097] Figure 7 FIG. 7 is a flow diagram illustrating one embodiment of a method 700 for predictively adapting radio bearer configurations. In some embodiments, the method 700 is performed by an apparatus, such as the network unit 104. In certain embodiments, the method 700 can be performed by a processor executing program code, for example, a microcontroller, a microprocessor, a CPU, a GPU, an auxiliary processing unit, a FPGA, or the like.

[0098] In various embodiments, the method 700 includes receiving 702 an expected quality of service profile pattern for at least one quality of service flow of at least one user equipment. In some embodiments, the method 700 includes determining 704 a predictive adaptation of a radio bearer configuration based on the expected quality of service profile pattern, where the predictive adaptation comprises at least one radio bearer remapping to the at least one quality of service flow. In various embodiments, the method 700 includes configuring 706 a predictive quality of service flow to radio bearer mapping pattern for an expected time window based on the predictive adaptation. In various embodiments, the method 700 includes transmitting 708 the predictive quality of service flow to radio bearer mapping pattern to the at least one user equipment.

[0099] In certain embodiments, the method 700 further includes transmitting a request for receiving an expected quality of service profile pattern, where the request is a one-time request or a subscription to receive the expected quality of service profile pattern as a service. In some embodiments, the expected quality of service profile pattern includes: a quality of service flow identifier; a session identifier; a user equipment identifier; an application identifier; a predictive quality of service remapping pattern, where the predictive quality of service remapping pattern includes a list of quality of service profile adaptation sequences within an expected time window, a table of quality of service profile adaptation sequences within an expected time window, or a combination thereof; a hysteresis tuning threshold; a hysteresis tuning cause; a prediction accuracy; a prediction time; a validity area; a time range, a time window, or a combination thereof, where the time range, the time window, or the combination thereof includes a time instance for each expected quality of service profile adaptation; an enforcement indication; an upgrade indication for each quality of service profile adaptation for a given time instance; a downgrade indication for each quality of service profile adaptation for a given time instance; or some combination thereof. In various embodiments, the predictive quality of service flow to radio bearer mapping includes: a conditional radio resource reconfiguration indication; a cause for the conditional radio resource reconfiguration; a re-mapping of at least one quality of service flow to a radio bearer; a time instance corresponding to each quality of service flow to radio bearer re-mapping of the at least one quality of service flow to radio bearer re-mapping; a re-evaluation configuration; or some combination thereof.

[0100] In one embodiment, the predictive quality of service flow to radio bearer mapping is configured using a service data adaptation protocol configuration. In certain embodiments, the service data adaptation protocol configuration includes an indication of the predictive quality of service flow to radio bearer mapping. In some embodiments, transmitting the predictive quality of service flow to radio bearer mapping to the at least one user equipment includes transmitting the predictive quality of service flow to radio bearer mapping to the at least one user equipment via radio resource control signaling. In various embodiments, the method 700 further includes transmitting a predictive quality of service flow to radio bearer mapping pattern for the at least one user equipment to a secondary radio access node serving the at least one user equipment.

[0101] Figure 8 FIG. 8 is a flow chart illustrating another embodiment of a method 800 for predictively adapting radio bearer configurations. In some embodiments, the method 800 is performed by a device, such as the remote unit 102. In certain embodiments, the method 800 can be performed by a processor (e.g., a microcontroller, a microprocessor, a CPU, a GPU, an auxiliary processing unit, a FPGA, or the like) executing program code.

[0102] In various embodiments, the method 800 includes receiving 802 a predictive quality of service flow to radio bearer mapping pattern, where the predictive quality of service flow to radio bearer mapping pattern is determined based on a predictive change including a re-mapping of at least one radio bearer to at least one quality of service flow, and the predictive adaptation is determined based on an expected quality of service profile pattern of the at least one quality of service flow. In some embodiments, the method 800 includes triggering 804 a radio bearer modification based on the received predictive quality of service flow to radio bearer mapping pattern.

[0103] In certain embodiments, the predictive quality of service flow to radio bearer mapping pattern includes a first quality of service flow to radio bearer mapping occurring at a first time and a second quality of service flow to radio bearer mapping occurring at a second time. In some embodiments, receiving the predictive quality of service flow to radio bearer mapping pattern includes receiving the predictive quality of service flow to radio bearer mapping pattern from a master node. In various embodiments, receiving the predictive quality of service flow to radio bearer mapping pattern includes receiving the predictive quality of service flow to radio bearer mapping pattern from a secondary node. In one embodiment, receiving the predictive quality of service flow to radio bearer mapping pattern includes receiving the predictive quality of service flow to radio bearer mapping pattern via radio resource control signaling.

[0104] In one embodiment, a method includes receiving an expected quality of service profile pattern for at least one quality of service flow of at least one user equipment, determining a predictive adaptation to a radio bearer configuration based on the expected quality of service profile pattern, where the predictive adaptation includes a re-mapping of at least one radio bearer to the at least one quality of service flow, configuring a predictive quality of service flow to radio bearer mapping pattern for an expected time window based on the predictive adaptation, and transmitting the predictive quality of service flow to radio bearer mapping pattern to the at least one user equipment.

[0105] In certain embodiments, the method further includes transmitting a request for receiving the expected quality of service profile pattern, where the request is a one-time request or a subscription to service for receiving the expected quality of service profile pattern.

[0106] In some embodiments, the expected quality of service profile pattern comprises: a quality of service flow identifier; a session identifier; a user equipment identifier; an application identifier; a predictive quality of service remapping pattern, wherein the predictive quality of service remapping pattern comprises a list of quality of service profile adaptation sequences within an expected time window, a table of quality of service profile adaptation sequences within an expected time window, or a combination thereof; a hysteresis tuning threshold; a hysteresis tuning cause; a prediction accuracy; a prediction time; a valid area; a time range, a time window, or a combination thereof, wherein the time range, the time window, or the combination thereof comprises a time instance of each expected quality of service profile adaptation; an enforcement indication; an upgrade indication of each quality of service profile adaptation for a given time instance; a downgrade indication of each quality of service profile adaptation for a given time instance; or some combination thereof.

[0107] In various embodiments, the predictive quality of service flow to radio bearer mapping comprises: a conditional radio resource reconfiguration indication; a cause of the conditional radio resource reconfiguration; at least one quality of service flow to radio bearer re-mapping; a time instance corresponding to each quality of service flow to radio bearer re-mapping of the at least one quality of service flow to radio bearer re-mapping; a re-evaluation configuration; or some combination thereof.

[0108] In one embodiment, the predictive quality of service flow to radio bearer mapping is configured using a service data adaptation protocol configuration.

[0109] In certain embodiments, the service data adaptation protocol configuration comprises an indication of the predictive quality of service flow to radio bearer mapping.

[0110] In some embodiments, transmitting the predictive quality of service flow to radio bearer mapping to the at least one user equipment comprises transmitting the predictive quality of service flow to radio bearer mapping to the at least one user equipment via radio resource control signaling.

[0111] In various embodiments, the method further comprises transmitting a predictive quality of service flow to radio bearer mapping pattern of the at least one user equipment to a secondary radio access node serving the at least one user equipment.

[0112] In one embodiment, an apparatus comprises a receiver that receives an expected quality of service profile pattern for at least one quality of service flow of at least one user equipment; a processor that determines a predictive adaptation of radio bearer configuration based on the expected quality of service profile pattern, wherein the predictive adaptation comprises at least one radio bearer remapping to the at least one quality of service flow, and configures a predictive quality of service flow to radio bearer mapping pattern for an expected time window based on the predictive adaptation; and a transmitter that transmits the predictive quality of service flow to radio bearer mapping pattern to the at least one user equipment.

[0113] In certain embodiments, the transmitter transmits a request for receiving the expected quality of service profile pattern, wherein the request is a one-time request or a subscription to a service that receives the expected quality of service profile pattern.

[0114] In some embodiments, the expected quality of service profile pattern comprises a quality of service flow identifier; a session identifier; a user equipment identifier; an application identifier; a predictive quality of service remapping pattern, wherein the predictive quality of service remapping pattern comprises a list of quality of service profile adaptation sequences within an expected time window, a table of quality of service profile adaptation sequences within an expected time window, or a combination thereof; a hysteresis tuning threshold; a hysteresis tuning reason; a prediction accuracy; a prediction time; a valid area; a time range, a time window, or a combination thereof, wherein the time range, the time window, or the combination thereof comprises a time instance of each expected quality of service profile adaptation; an enforcement indication; an upgrade indication of each quality of service profile adaptation for a given time instance; a downgrade indication of each quality of service profile adaptation for a given time instance; or some combination thereof.

[0115] In various embodiments, the predictive quality of service flow to radio bearer mapping comprises a conditional radio resource reconfiguration indication; a reason of the conditional radio resource reconfiguration; a re-mapping of at least one quality of service flow to a radio bearer; a time instance corresponding to each of the re-mapping of at least one quality of service flow to a radio bearer; a re-evaluation configuration; or some combination thereof.

[0116] In one embodiment, the predictive quality of service flow to radio bearer mapping is configured using a service data adaptation protocol configuration.

[0117] In certain embodiments, the service data adaptation protocol configuration comprises an indication of the predictive quality of service flow to radio bearer mapping.

[0118] In some embodiments, the transmitter transmitting the predictive quality of service flow to radio bearer mapping to the at least one user equipment comprises the transmitter transmitting the predictive quality of service flow to radio bearer mapping to the at least one user equipment via radio resource control signaling.

[0119] In various embodiments, the transmitter transmitting the predictive quality of service flow to radio bearer mapping pattern to the at least one user equipment comprises the transmitter transmitting the predictive quality of service flow to radio bearer mapping pattern to the at least one user equipment via radio resource control signaling.

[0120] In one embodiment, a method comprises: receiving a predictive quality of service flow to radio bearer mapping pattern, wherein the predictive quality of service flow to radio bearer mapping pattern is determined based on a predictive change comprising a re-mapping of at least one radio bearer to at least one quality of service flow, and a predictive adaptation is determined based on an expected quality of service profile pattern of the at least one quality of service flow; and triggering a radio bearer modification based on the received predictive quality of service flow to radio bearer mapping pattern.

[0121] In certain embodiments, the predictive quality of service flow to radio bearer mapping pattern comprises a first quality of service flow to radio bearer mapping occurring at a first time and a second quality of service flow to radio bearer mapping occurring at a second time.

[0122] In some embodiments, receiving the predictive quality of service flow to radio bearer mapping pattern comprises receiving the predictive quality of service flow to radio bearer mapping pattern from a primary node.

[0123] In various embodiments, receiving the predictive quality of service flow to radio bearer mapping pattern comprises receiving the predictive quality of service flow to radio bearer mapping pattern from a secondary node.

[0124] In one embodiment, receiving the predictive quality of service flow to radio bearer mapping pattern comprises receiving the predictive quality of service flow to radio bearer mapping pattern via radio resource control signaling.

[0125] In one embodiment, an apparatus comprises: a receiver that receives a predictive quality of service flow to radio bearer mapping pattern, wherein the predictive quality of service flow to radio bearer mapping pattern is determined based on a predictive adaptation comprising a re-mapping of at least one radio bearer to at least one quality of service flow, and the predictive adaptation is determined based on an expected quality of service profile pattern of the at least one quality of service flow; and a processor that triggers a radio bearer modification based on the received predictive quality of service flow to radio bearer mapping pattern.

[0126] In certain embodiments, the predictive quality of service flow to radio bearer mapping pattern includes a first quality of service flow to radio bearer mapping occurring at a first time and a second quality of service flow to radio bearer mapping occurring at a second time.

[0127] In some embodiments, the receiver receiving the predictive quality of service flow to radio bearer mapping pattern includes the receiver receiving the predictive quality of service flow to radio bearer mapping pattern from the primary node.

[0128] In various embodiments, the receiver receiving the predictive quality of service flow to radio bearer mapping pattern includes the receiver receiving the predictive quality of service flow to radio bearer mapping pattern from the secondary node.

[0129] In one embodiment, the receiver receiving the predictive quality of service flow to radio bearer mapping pattern includes the receiver receiving the predictive quality of service flow to radio bearer mapping pattern via radio resource control signaling.

[0130] Embodiments can be practiced in other specific forms. The described embodiments are to be considered in all respects only as illustrative and not restrictive. The scope of the application is, therefore, indicated by the appended claims rather than by the foregoing description. All changes that come within the meaning and range of equivalency of the claims are to be embraced within their scope.

Claims

1. A method comprising: Receive a desired quality of service profile pattern for at least one quality of service flow for at least one user equipment, wherein the desired quality of service profile pattern includes a quality of service profile adjustment sequence within a desired time window. Predictive adaptation of radio bearer configuration is determined based on the expected quality of service profile pattern, wherein the predictive adaptation includes remapping of at least one radio bearer to the at least one quality of service flow. Based on the predictive adaptation, a predictive quality of service flow to radio bearer mapping pattern is configured for the expected time window, wherein the predictive quality of service flow to radio bearer mapping pattern includes a first quality of service flow to radio bearer mapping occurring at a first time and a second quality of service flow to radio bearer mapping occurring at a second time. and The predictive quality of service stream is transmitted to the at least one user equipment via a mapping pattern of radio bearers.

2. The method of claim 1, further comprising sending a request to receive the expected quality of service profile pattern, wherein the request is a one-time request or to receive the expected quality of service profile pattern as a subscription to a service.

3. The method according to claim 1, wherein the expected service quality profile mode includes: Quality of Service Flow Identifier; Session identifier; User equipment identifier; Application identifier; A predictive quality of service remapping pattern, wherein the predictive quality of service remapping pattern includes a list of quality of service profile adjustment sequences within the expected time window, a table of the quality of service profile adjustment sequences within the expected time window, or a combination thereof. Hysteresis tuning threshold; Causes of delayed tuning; The accuracy of the prediction; Predicted time; Effective area; A time range, a time window, or a combination thereof, wherein the time range, the time window, or the combination thereof includes a time instance of each expected quality of service profile adjustment; Enforcement instructions; Upgrade instructions for each Quality of Service profile adjustment for a given time instance; Degradation instructions for each Quality of Service profile for a given time instance; or A certain combination of them.

4. The method of claim 1, wherein the mapping of the predictive quality of service flow to the radio bearer comprises: Conditional radio resource reconfiguration instruction; Reasons for conditional radio resource reallocation; At least one quality of service stream is remapped to the radio bearer; A time instance, which corresponds to each of the at least one remappings of quality of service flow to radio bearer; Reassess the configuration; or A certain combination of them.

5. The method of claim 1, wherein the mapping of the predictive quality of service flow to radio bearers is configured using a service data adaptation protocol configuration.

6. The method of claim 5, wherein the service data adaptation protocol configuration includes an indication of the mapping of the predictive quality of service flow to the radio bearer.

7. The method of claim 1, wherein transmitting the predictive quality of service flow-to-radio bearer mapping to the at least one user equipment comprises transmitting the predictive quality of service flow-to-radio bearer mapping to the at least one user equipment via radio resource control signaling.

8. The method of claim 1, further comprising transmitting the predictive quality of service of the at least one user equipment to a radio bearer mapping mode to an auxiliary radio access node serving the at least one user equipment.

9. An apparatus comprising: A receiver that receives a desired quality of service profile pattern for at least one quality of service stream for at least one user equipment, wherein the desired quality of service profile pattern includes a quality of service profile adjustment sequence within a desired time window. Processor, which: Predictive adaptation of radio bearer configuration is determined based on the expected quality of service profile pattern, wherein the predictive adaptation includes remapping of at least one radio bearer to the at least one quality of service flow; and Based on the predictive adaptation, a predictive quality of service (QoS) flow to radio bearer mapping pattern is configured for the expected time window, wherein the predictive QoS flow to radio bearer mapping pattern includes a first QoS flow to radio bearer mapping occurring at a first time and a second QoS flow to radio bearer mapping occurring at a second time; and A transmitter that transmits the predicted quality of service stream to a radio bearer in a mapped pattern to the at least one user equipment.

10. The device of claim 9, wherein the transmitter transmits a request to receive the expected quality of service profile pattern, wherein the request is a one-time request or to receive the expected quality of service profile pattern as a subscription to a service.

11. The device of claim 9, wherein the expected quality of service profile mode includes: Quality of Service Flow Identifier; Session identifier; User equipment identifier; Application identifier; A predictive quality of service remapping pattern, wherein the predictive quality of service remapping pattern includes a list of quality of service profile adjustment sequences within the expected time window, a table of the quality of service profile adjustment sequences within the expected time window, or a combination thereof. Hysteresis tuning threshold; Causes of delayed tuning; The accuracy of the prediction; Predicted time; Effective area; A time range, a time window, or a combination thereof, wherein the time range, the time window, or the combination thereof includes a time instance of each expected quality of service profile adjustment; Enforcement instructions; Upgrade instructions for each Quality of Service profile adjustment for a given time instance; Degradation instructions for each Quality of Service profile for a given time instance; or A certain combination of them.

12. The apparatus of claim 9, wherein the mapping of the predictive quality of service flow to the radio bearer comprises: Conditional radio resource reconfiguration instruction; Reasons for conditional radio resource reallocation; At least one quality of service stream is remapped to the radio bearer; A time instance, which corresponds to each of the at least one remappings of quality of service flow to radio bearer; Reassess the configuration; or A certain combination of them.

13. The apparatus of claim 9, wherein the mapping of the predictive quality of service flow to the radio bearer is configured using a service data adaptation protocol configuration.

14. The device of claim 13, wherein the service data adaptation protocol configuration includes an indication of the mapping of the predictive quality of service flow to the radio bearer.

15. The apparatus of claim 9, wherein the transmitter transmits the mapping of the predictive quality of service flow to the radio bearer to the at least one user equipment, comprising the transmitter transmitting the mapping of the predictive quality of service flow to the radio bearer to the at least one user equipment via radio resource control signaling.

16. The apparatus of claim 9, wherein the transmitter transmits the predictive quality of service flow of the at least one user equipment to a radio bearer mapping pattern to an auxiliary radio access node serving the at least one user equipment.

17. A method comprising: Receive a predictive quality of service (QoS) flow to radio bearer mapping pattern, wherein the predictive QoS flow to radio bearer mapping pattern is determined based on predictive adaptation including remapping to at least one radio bearer of at least one QoS flow, and the predictive adaptation is determined based on an expected QoS profile pattern of the at least one QoS flow, wherein the expected QoS profile pattern includes a QoS profile adaptation sequence within an expected time window, and wherein the predictive QoS flow to radio bearer mapping pattern includes a first QoS flow to radio bearer mapping occurring at a first time and a second QoS flow to radio bearer mapping occurring at a second time; and Radio bearer modification is triggered based on the mapping pattern of the received predictive quality of service flow to the radio bearer.

18. The method of claim 17, wherein receiving the mapping pattern of the predictive quality of service flow to the radio bearer includes receiving the mapping pattern of the predictive quality of service flow to the radio bearer from the master node.

19. The method of claim 17, wherein receiving the mapping pattern of the predictive quality of service flow to the radio bearer includes receiving the mapping pattern of the predictive quality of service flow to the radio bearer from the secondary node.

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