Sharing e2 data over o1 interface

Abstract

According to some embodiments, a method is performed by a by a non-real time radio intelligent controller (Non-RT RIC) / Service Management and Orchestration (SMO) network node for training a machine learning (ML) model. The method comprises receiving an E2 formatted report from an open radio access network (O-RAN) network function. The E2 formatted report is encapsulated in an O1 message. The method further comprises training a ML model using data in the E2 formatted report as input data.

Description

Sharing E2 Data Over O1 InterfaceTECHNICAL FIELD

[0001] The present disclosure generally relates to communication networks, and more specifically to the open radio access network (O-RAN) architecture and sharing E2 data over 01 interface.BACKGROUND

[0002] Open radio access network (O-RAN) architecture standardization includes work on training and deployment of artificial intelligence (AI) / machine learning (ML) agents or models for improving network performance. Several different scenarios are studied including solutions where the training of an A EML agent is performed by the non-real time RAN intelligent controller (Non- RT RIC) and the inference of the AI / ML agent is performed by the Near-RT RIC or by the O- RAN nodes or network function (e.g., open central unit control plane (O-CU-CP) and open central unit user plane (O-CU-UP)).

[0003] Figure 1 is a block diagram illustrating an example O-RAN architecture. The architecture includes the Service Management and Orchestration Framework, Non-RT RIC, Near- RT RIC, O-CU-CP, O-CU-UP, open distributed unit (O-DU), open radio unit (O-RU) and the interconnecting interfaces.

[0004] Figure 2 is a block diagram illustrating AI / ML training and inference performed by both the Non-RT RIC and the Near-RT RIC. The training and inference steps are numbered 1-8.

[0005] There currently exist certain challenges. For example, the O-RAN working group has discussed a need to ensure that the data collected by the Non-RT RIC for training of the AI / ML model is consistent with the data later used in the Near-RT RIC for the AI / ML model inference. If the data is in different formats, or if some data is only available in one of the RICs, it will not be possible to use the same model in the two RICs, and therefore not possible to train the model in the Non-RT RIC to be used in the Near-RT RIC.

[0006] To solve this problem, one proposal is that the Near-RT RIC should collect the data, pre-process it, and then share it to the Non-RT RIC for training of AI / ML model, as shown in Figure 3.

[0007] Figure 3 is a sequence diagram illustrating processing data in Near-RT RIC before sending to Non-RT RIC fortraining.

[0008] Several problems exist with the proposed solution. One is that it mandates the deployment of a Near-RT RIC collecting the data and running inference host for the AI / ML models. It may be desirable to support Non-RT RIC training also of AI / ML models running in the 0-RAN functions, e.g., in the gNB, or O-CU-CP, O-CU-UP or O-U. It may be desirable to support inferences in Near-RT RIC functions that does not have the data pipeline capabilities.

[0009] Another problem is that the proposed solution may require a new interface or extensions to existing interfaces such as 01, Al or Y1 to be specified for sharing pre-processed data from Near-RT RIC to Non-RT RIC. This may lead to extra standardization, implementation, testing, and integration. It may also duplicate some functionalities in the system, such as data from 0-RAN nodes are reported to the Non-RT RIC in multiple ways, both as 01 reports and reports via the Near-RT RIC. Also, sending data via the Near-RT RIC to the Non-RT RIC increases network processing (decreasing energy efficiency) and delays.SUMMARY

[0010] As described above, certain challenges currently exist with training and deployment of artificial intelligence (AI) / machine learning (ML) agents or models for the open radio access network (0-RAN) architecture. Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. For example, particular embodiments encapsulate E2 reports or data in 01 report messages between the 0-RAN nodes or 0-RAN network functions (e.g., gNB, open central unit control plane (O-CU-CP)) and the non real time radio intelligent controller (Non-RT RIC). The same E2 data may also be sent to the AI / ML agent for inference regardless of whether the agent is deployed in the Near-RT RIC or directly in the 0-RAN nodes, thus making it possible to train an AI / ML agent or model on the same data in the Non-RT RIC as is later used for inference in the Near-RT RIC or 0-RAN nodes ensuring consistency between AI / ML model training and inference.

[0011] According to some embodiments, the AI / ML data trained in the Non-RT RIC may be inferred by any / all 0-RAN nodes or 0-RAN network functions. Although particular embodiments and / or examples focus on encapsulating E2 reports or data in 01 report message, in particular embodiments the encapsulation ofE2 reports or data may be done in Al messages orYl messages.

[0012] In general, a method performed in network functions (e.g., eNB, gNB, gNB-CU-CP, gNB-CU-UP, gNB-DU, O-eNB, O-CU-CP, O-CU-UP, O-DU, O-RU) in a radio access network comprises generating a report on some event, measurement (user equipment (UE) or network related), condition or threshold (that is or is not being met), some preprocessed data such as key performance indicator (KPI) counters, aggregated statistics, etc. where the report uses the format of the E2 interface, the report depends on configuration sent to the Near-RT RIC and / or SMO / Non- RT RIC and / or other network function, or the report is encapsulated in an 01 report when sent to the SMO / Non-RT RIC.

[0013] The network function may be dynamically configured on where to send the reports, facilitating sending the report to the SMO / Non-RT RIC during training of an AI / ML model or agent, and to the Near-RT RIC or other network function during inference of the trained AI / ML model or agent.

[0014] According to some embodiments, a method is performed by a by a Non-RT RIC / SMO network node for training a ML model. The method comprises receiving an E2 formatted report from an open radio access network (0-RAN) network function. The E2 formatted report is encapsulated in an 01 message. The method further comprises training a ML model using data in the E2 formatted report as input data.

[0015] In particular embodiments, the method further comprises receiving fault, configuration, accounting, performance, or security (FCAPS) data from an O-RU encapsulated as an 01 message or an O-FH M-Plane message. Training the ML model is further based on the FCAPS data used as input data.

[0016] In particular embodiments, the method further comprises receiving an E2 formatted report from a Near-RT RIC encapsulated as a Y1 message or an Al message. Training the ML model is further based on the E2 formatted data from the Y1 or Al message as input data.

[0017] In particular embodiments, the method further comprises transmitting the trained ML model to an 0-RAN network function. The trained ML model is encapsulated in an 01 message.

[0018] In particular embodiments, the method further comprises transmitting the trained ML model to an O-RU. The trained ML model is encapsulated in an 01 message or an O-FH M-Plane message.

[0019] In particular embodiments, the method further comprises transmitting the trained ML model to a Near-RT RIC. The trained ML model is encapsulated in a Y1 message or an Al message.

[0020] In particular embodiments, the O-RAN network function comprises an eNB, gNB, gNB-CU-CP, gNB-CU-UP, gNB-DU, O-eNB, O-CU-CP, O-CU-UP, or O-DU network function.

[0021] According to some embodiments, a method is performed by an O-RAN network node for using a ML model. The method comprises generating an E2 formatted report. The report is based on one or more of: occurrence of an event, wireless device measurement, network measurement, condition or threshold, counter, and aggregated statistics. The method further comprises encapsulating the E2 formatted report as an 01 message and transmitting the 01 message to a Non-RT RIC / SMO network node for training a ML model.

[0022] In particular embodiments, the method further comprises: generating FCAPS data; encapsulating the FCAPS data as an 01 message or an O-FH M-Plane message, and transmitting the 01 message or M-Plane message to the Non-RT RIC / SMO network node for training the ML model.

[0023] In particular embodiments, the method further comprises encapsulating the E2 formatted report as a Y1 message or an Al message.

[0024] In particular embodiments, the method further comprises receiving a trained ML model from the Non-RT RIC / SMO network node.

[0025] In particular embodiments, the trained ML model is encapsulated in an 01 message, an O-FH M-Plane message, a Y1 message, or an Al message.

[0026] In particular embodiments, the O-RAN network function comprises a eNB, gNB, gNB-CU-CP, gNB-CU-UP, gNB-DU, O-eNB, O-CU-CP, O-CU-UP, O-DU, 0-RU or Near-RT RIC network function.

[0027] According to some embodiments, a network node comprises processing circuitry operable to perform any of the network node methods described above.

[0028] Also disclosed is a computer program product comprising a non-transitory computer readable medium storing computer readable program code, the computer readable program code operable, when executed by processing circuitry to perform any of the methods performed by the network nodes described above.

[0029] Certain embodiments may provide one or more of the following technical advantages. For example, particular embodiments eliminate the need to deploy data collection (or pipeline) and processing functionality in Near-RT RIC to support data sharing with SMO / Non-RT RIC, thus reducing costs, energy consumption and efforts related to standardization, implementation, and testing.

[0030] Another advantage is the option to configure network functions to send 01 encapsulated E2 reports during training, thus avoiding unnecessary transmissions during nontraining times.

[0031] Another advantage is low complexity, e.g., encapsulating E2 reports in 01 messages that are already supported over E2 interface is easy to support in the network functions.BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The present disclosure may be best understood by way of example with reference to the following description and accompanying drawings that are used to illustrate embodiments of the present disclosure. In the drawings:Figure 1 is a block diagram illustrating an example open radio access network (0-RAN) architecture;Figure 2 is a block diagram illustrating artificial intelligence (AI) / machine learning (ML) training and inference performed by both the non-real time radio intelligent controller (Non-RT RIC) and the Near-RT RIC;Figure 3 is a sequence diagram illustrating processing data in Near-RT RIC before sending to Non-RT RIC fortraining;Figure 4 illustrates the flow of data from various 0-RAN network functions to Service Management and Orchestration (SMO)ZNon-RT RIC for AI / ML training;Figure 5 illustrates data flow from Near-RT RIC to SMO / Non-RT RIC through Y 1 interface;Figure 6 shows the deployment of trained AI / ML model from SMO / Non-RT RIC to all / any 0-RAN network functions (except open radio unit (0-RU)) over 01 message;Figure 7 illustrates deployment of trained model over Y 1 message;Figure 8 illustrates examples of sending AI / ML data to SMO / Non-RT RIC;Figure 9 illustrates examples of deploying AI / ML trained model from SMO / Non-RT RIC to 0-RAN network functions;Figure 10 shows an example of a communication system, according to certain embodiments;Figure 11 shows a user equipment (UE), according to certain embodiments;Figure 12 shows a network node, according to certain embodiments;Figure 13 is a block diagram of a host, according to certain embodiments;Figure 14 is a block diagram illustrating a virtualization environment in which functions implemented by some embodiments may be virtualized;Figure 15 shows a communication diagram of a host communicating via a network node with a UE over a partially wireless connection in accordance with some embodiments;Figure 16 is a flowchart illustrating an example method in a network node, according to certain embodiments; andFigure 17 is a flowchart illustrating another example method in a network node, according to certain embodiments.DETAILED DESCRIPTION

[0033] As described above, certain challenges currently exist with training and deployment of artificial intelligence (AI) / machine learning (ML) agents or models for the open radio access network (O-RAN) architecture. Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. For example, particular embodiments encapsulate E2 reports or data in 01 report messages between the O-RAN nodes or O-RAN network functions (e.g., gNB, open central unit control plane (O-CU-CP)) and the non real time radio intelligent controller (Non-RT RIC). The same E2 data may also be sent to the AI / ML agent for inference regardless of whether the agent is deployed in the Near-RT RIC or directly in the O-RAN nodes, thus making it possible to train an AI / ML agent or model on the same data in the Non-RT RIC as is later used for inference in the Near-RT RIC or O-RAN nodes ensuring consistency between AI / ML model training and inference.

[0034] Particular embodiments are described more fully with reference to the accompanying drawings. Embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art.

[0035] Figure 4 illustrates the flow of data from various O-RAN network functions to SMO / Non-RT RIC for AI / ML training. As depicted in Figure 4, the data from any / all O-RAN functions (except for O-RU) is sent to SMO / Non-RT RIC by encapsulating E2 report in 01 message. The fault, configuration, accounting, performance, and security (FCAPS) data from the O-RU, on the other hand, is sent over either OFH (Open Fronthaul) M-Plane or 01 message.

[0036] In an alternative implementation, the Near-RT RIC may encapsulate the E2 report in Y1 message before forwarding it to the SMO / Non-RT RIC, as illustrated in Figure 5.

[0037] Figure 5 illustrates data flow from Near-RT RIC to SMO / Non-RT RIC through Y 1 interface.

[0038] Figure 6 shows the deployment of trained AI / ML model from SMO / Non-RT RIC to all / any O-RAN network functions (except O-RU) over 01 message. Deploying trained AI / ML model from SMO / Non-RT RIC to O-RU, on the other hand, is done over either 01 or OFH M- Plane message.

[0039] In an alternative implementation, the SMO / Non-RT RIC may deploy the trained AI / ML model in Near-RT RIC over Y 1 message, as illustrated in Figure 7.

[0040] Figure 7 illustrates deployment of trained model over Y 1 message.

[0041] Figure 8 illustrates examples of sending AI / ML data to SMO / Non-RT RIC. The four illustrated examples include: E2 report encapsulated in 01 message; E2 report encapsulated in Y1 message; FCAPS data for AI / ML in OFH M-Plane message; and / or FCAPS data for AI / ML in 01 message.

[0042] Figure 9 illustrates examples of deploying AI / ML trained model from SMO / Non-RT RIC to O-RAN network functions. The three examples include: trained AI / ML model over 01 message; trained AI / ML model over Y 1 message; and / or trained AI / ML model over OFH M-Plane message.

[0043] Examples of E2 information that may be encapsulated include reports specified in O- RAN.WG3.E2SM-KPM-R003-v03.00.01, and include the following examples.8.2. 1.2.1 E2SM-KPM Action Definition Format 1.2. 1.2.2 E2SM-KPM Action Definition Format 2.2. 1.2.3 E2SM-KPM Action Definition Format 3.2. 1.2.4 E2SM-KPM Action Definition Format 4.2. 1.2.5 E2SM-KPM Action Definition Format 5

[0044] Figure 10 shows an example of a communication system 100 in accordance with some embodiments. In the example, the communication system 100 includes a telecommunication network 102 that includes an access network 104, such as a radio access network (RAN), and a core network 106, which includes one or more core network nodes 108. The access network 104 includes one or more access network nodes, such as network nodes 110a and 110b (one or more of which may be generally referred to as network nodes 110), or any other similar 3rdGeneration Partnership Project (3GPP) access node or non-3GPP access point. The network nodes 110 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs 112a, 112b, 112c, and 112d (one or more of which may be generally referred to as UEs 112) to the core network 106 over one or more wireless connections.

[0045] Example wireless communications over a wireless connection include transmitting and / or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and / or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system 100 may include any number of wired or wireless networks, network nodes, UEs, and / or any other components or systems that may facilitate or participate in the communication of data and / or signals whether via wired or wireless connections. The communication system 100 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0046] The UEs 112 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and / or operable to communicate wirelessly with the network nodes 110 and other communication devices. Similarly, the network nodes 110 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs 112 and / or with other network nodes or equipment in the telecommunication network 102 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in the telecommunication network 102.

[0047] In the depicted example, the core network 106 connects the network nodes 110 to one or more hosts, such as host 116. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network 106 includes one more core network nodes (e.g., core network node 108)that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 108. Example core network nodes include functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier De-concealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).

[0048] The host 116 may be under the ownership or control of a service provider other than an operator or provider of the access network 104 and / or the telecommunication network 102, and may be operated by the service provider or on behalf of the service provider. The host 116 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio / video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.

[0049] As a whole, the communication system 100 of Figure 10 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM); Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and / or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standard (e.g., 6G); wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (WiFi); and / or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, LiFi, and / or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox.

[0050] In some examples, the telecommunication network 102 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network 102 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 102. For example, the telecommunications network102 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and / or Massive Machine Type Communication (mMTC)ZMassive loT services to yet further UEs.

[0051] In some examples, the UEs 112 are configured to transmit and / or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network 104 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 104. Additionally, a UE may be configured for operating in single- or multi-RAT or multi-standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e. being configured for multi -radio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio - Dual Connectivity (EN-DC).

[0052] In the example, the hub 114 communicates with the access network 104 to facilitate indirect communication between one or more UEs (e.g., UE 112c and / or 112d) and network nodes (e.g., network node 110b). In some examples, the hub 114 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 114 may be a broadband router enabling access to the core network 106 for the UEs. As another example, the hub 114 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes 110, or by executable code, script, process, or other instructions in the hub 114. As another example, the hub 114 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub 114 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 114 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 114 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 114 acts as a proxy server or orchestrator for the UEs, in particular in if one or more of the UEs are low energy loT devices.

[0053] The hub 114 may have a constant / persistent or intermittent connection to the network node 110b. The hub 114 may also allow for a different communication scheme and / or schedule between the hub 114 and UEs (e.g., UE 112c and / or 112d), and between the hub 114 and the core network 106. In other examples, the hub 114 is connected to the core network 106 and / or one or more UEs via a wired connection. Moreover, the hub 114 may be configured to connect to anM2M service provider over the access network 104 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 110 while still connected via the hub 114 via a wired or wireless connection. In some embodiments, the hub 114 may be a dedicated hub - that is, a hub whose primary function is to route communications to / from the UEs from / to the network node 110b. In other embodiments, the hub 114 may be a nondedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 110b, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0054] Figure 11 shows a UE 200 in accordance with some embodiments. As used herein, a UE refers to a device capable, configured, arranged and / or operable to communicate wirelessly with network nodes and / or other UEs. Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), smart device, wireless customer-premise equipment (CPE), vehicle-mounted or vehicle embedded / integrated wireless device, etc. Other examples include any UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-IoT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0055] A UE may support device-to-device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle-to- everything (V2X). In other examples, a UE may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, a UE may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter).

[0056] The UE 200 includes processing circuitry 202 that is operatively coupled via a bus 204 to an input / output interface 206, a power source 208, a memory 210, a communication interface 212, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Figure 2. The level of integration between the components mayvary from one UE to another UE. Further, certain UEs may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.

[0057] The processing circuitry 202 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs in the memory 210. The processing circuitry 202 may be implemented as one or more hardware -implemented state machines (e.g., in discrete logic, field- programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs, general-purpose processors, such as a microprocessor or digital signal processor (DSP), together with appropriate software; or any combination of the above. For example, the processing circuitry 202 may include multiple central processing units (CPUs).

[0058] In the example, the input / output interface 206 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and / or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into the UE 200. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device.

[0059] In some embodiments, the power source 208 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used. The power source 208 may further include power circuitry for delivering power from the power source 208 itself, and / or an external power source, to the various parts of the UE 200 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 208. Power circuitry may perform any formatting, converting, or other modification to the power from thepower source 208 to make the power suitable for the respective components of the UE 200 to which power is supplied.

[0060] The memory 210 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory 210 includes one or more application programs 214, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 216. The memory 210 may store, for use by the UE 200, any of a variety of various operating systems or combinations of operating systems.

[0061] The memory 210 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and / or ISIM, other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory 210 may allow the UE 200 to access instructions, application programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory 210, which may be or comprise a device -readable storage medium.

[0062] The processing circuitry 202 may be configured to communicate with an access network or other network using the communication interface 212. The communication interface 212 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 222. The communication interface 212 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another UE or a network node in an access network). Each transceiver may include a transmitter 218 and / or a receiver 220 appropriate to provide network communications (e.g., optical, electrical, frequency allocations,and so forth). Moreover, the transmitter 218 and receiver 220 may be coupled to one or more antennas (e.g., antenna 222) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0063] In the illustrated embodiment, communication functions of the communication interface 212 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short- range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented in according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol / intemet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0064] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 212, via a wireless connection to a network node. Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE. The output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature), random (e.g., to even out the load from reporting from several sensors), in response to a triggering event (e.g., when moisture is detected an alert is sent), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient).

[0065] As another example, a UE comprises an actuator, a motor, or a switch, related to a communication interface configured to receive wireless input from a network node via a wireless connection. In response to the received wireless input the states of the actuator, the motor, or the switch may change. For example, the UE may comprise a motor that adjusts the control surfaces or rotors of a drone in flight according to the received input or to a robotic arm performing a medical procedure according to the received input.

[0066] A UE, when in the form of an Internet of Things (loT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application and healthcare. Non-limiting examples of such an loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smartspeaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / window sensor, a flood / moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a head-mounted display for Augmented Reality (AR) or Virtual Reality (VR), a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or itemtracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an loT device comprises circuitry and / or software in dependence of the intended application of the loT device in addition to other components as described in relation to the UE 200 shown in Figure 2.

[0067] As yet another specific example, in an loT scenario, a UE may represent a machine or other device that performs monitoring and / or measurements, and transmits the results of such monitoring and / or measurements to another UE and / or a network node. The UE may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, the UE may implement the 3GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation.

[0068] In practice, any number of UEs may be used together with respect to a single use case. For example, a first UE might be or be integrated in a drone and provide the drone’s speed information (obtained through a speed sensor) to a second UE that is a remote controller operating the drone. When the user makes changes from the remote controller, the first UE may adjust the throttle on the drone (e.g. by controlling an actuator) to increase or decrease the drone’s speed. The first and / or the second UE can also include more than one of the functionalities described above. For example, a UE might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators.

[0069] Figure 12 shows a network node 300 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment, in a telecommunication network. Examples of network nodes include, but are not limited to, accesspoints (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NRNodeBs (gNBs)).

[0070] Base stations may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. A base station may be a relay node or a relay donor node controlling a relay. A network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units and / or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).

[0071] Other examples of network nodes include multiple transmission point (multi-TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi-cell / multicast coordination entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and / or Minimization of Drive Tests (MDTs).

[0072] The network node 300 includes a processing circuitry 302, a memory 304, a communication interface 306, and a power source 308. The network node 300 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the network node 300 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node 300 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory 304 for different RATs) and some components may be reused (e.g., a same antenna 310 may be shared by different RATs). The network node 300 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 300, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave,LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node 300.

[0073] The processing circuitry 302 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and / or encoded logic operable to provide, either alone or in conjunction with other network node 300 components, such as the memory 304, to provide network node 300 functionality.

[0074] In some embodiments, the processing circuitry 302 includes a system on a chip (SOC). In some embodiments, the processing circuitry 302 includes one or more of radio frequency (RF) transceiver circuitry 312 and baseband processing circuitry 314. In some embodiments, the radio frequency (RF) transceiver circuitry 312 and the baseband processing circuitry 314 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry 312 and baseband processing circuitry 314 may be on the same chip or set of chips, boards, or units.

[0075] The memory 304 may comprise any form of volatile or non-volatile computer-readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and / or any other volatile or non-volatile, non-transitory device-readable and / or computer-executable memory devices that store information, data, and / or instructions that may be used by the processing circuitry 302. The memory 304 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and / or other instructions capable of being executed by the processing circuitry 302 and utilized by the network node 300. The memory 304 may be used to store any calculations made by the processing circuitry 302 and / or any data received via the communication interface 306. In some embodiments, the processing circuitry 302 and memory 304 is integrated.

[0076] The communication interface 306 is used in wired or wireless communication of signaling and / or data between a network node, access network, and / or UE. As illustrated, the communication interface 306 comprises port(s) / terminal(s) 316 to send and receive data, forexample to and from a network over a wired connection. The communication interface 306 also includes radio front-end circuitry 318 that may be coupled to, or in certain embodiments a part of, the antenna 310. Radio front-end circuitry 318 comprises fdters 320 and amplifiers 322. The radio front-end circuitry 318 may be connected to an antenna 310 and processing circuitry 302. The radio front-end circuitry may be configured to condition signals communicated between antenna 310 and processing circuitry 302. The radio front-end circuitry 318 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry 318 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 320 and / or amplifiers 322. The radio signal may then be transmitted via the antenna 310. Similarly, when receiving data, the antenna 310 may collect radio signals which are then converted into digital data by the radio front-end circuitry 318. The digital data may be passed to the processing circuitry 302. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0077] In certain alternative embodiments, the network node 300 does not include separate radio front-end circuitry 318, instead, the processing circuitry 302 includes radio front-end circuitry and is connected to the antenna 310. Similarly, in some embodiments, all or some of the RF transceiver circuitry 312 is part of the communication interface 306. In still other embodiments, the communication interface 306 includes one or more ports or terminals 316, the radio front-end circuitry 318, and the RF transceiver circuitry 312, as part of a radio unit (not shown), and the communication interface 306 communicates with the baseband processing circuitry 314, which is part of a digital unit (not shown).

[0078] The antenna 310 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 310 may be coupled to the radio front-end circuitry 318 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 310 is separate from the network node 300 and connectable to the network node 300 through an interface or port.

[0079] The antenna 310, communication interface 306, and / or the processing circuitry 302 may be configured to perform any receiving operations and / or certain obtaining operations described herein as being performed by the network node. Any information, data and / or signals may be received from a UE, another network node and / or any other network equipment. Similarly, the antenna 310, the communication interface 306, and / or the processing circuitry 302 may beconfigured to perform any transmitting operations described herein as being performed by the network node. Any information, data and / or signals may be transmitted to a UE, another network node and / or any other network equipment.

[0080] The power source 308 provides power to the various components of network node 300 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 308 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 300 with power for performing the functionality described herein. For example, the network node 300 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source 308. As a further example, the power source 308 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail.

[0081] Embodiments of the network node 300 may include additional components beyond those shown in Figure 12 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and / or any functionality necessary to support the subject matter described herein. For example, the network node 300 may include user interface equipment to allow input of information into the network node 300 and to allow output of information from the network node 300. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 300.

[0082] Figure 13 is a block diagram of a host 400, which may be an embodiment of the host 116 of Figure 1, in accordance with various aspects described herein. As used herein, the host 400 may be or comprise various combinations hardware and / or software, including a standalone server, a blade server, a cloud-implemented server, a distributed server, a virtual machine, container, or processing resources in a server farm. The host 400 may provide one or more services to one or more UEs.

[0083] The host 400 includes processing circuitry 402 that is operatively coupled via a bus 404 to an input / output interface 406, a network interface 408, a power source 410, and a memory 412. Other components may be included in other embodiments. Features of these components may be substantially similar to those described with respect to the devices of previous figures, such asFigures 10 and 3, such that the descriptions thereof are generally applicable to the corresponding components of host 400.

[0084] The memory 412 may include one or more computer programs including one or more host application programs 414 and data 416, which may include user data, e.g., data generated by a UE for the host 400 or data generated by the host 400 for a UE. Embodiments of the host 400 may utilize only a subset or all of the components shown. The host application programs 414 may be implemented in a container-based architecture and may provide support for video codecs (e.g., Versatile Video Coding (VVC), High Efficiency Video Coding (HEVC), Advanced Video Coding (AVC), MPEG, VP9) and audio codecs (e.g., FLAC, Advanced Audio Coding (AAC), MPEG, G.711), including transcoding for multiple different classes, types, or implementations of UEs (e.g., handsets, desktop computers, wearable display systems, heads-up display systems). The host application programs 414 may also provide for user authentication and licensing checks and may periodically report health, routes, and content availability to a central node, such as a device in or on the edge of a core network. Accordingly, the host 400 may select and / or indicate a different host for over-the-top services for a UE. The host application programs 414 may support various protocols, such as the HTTP Live Streaming (HLS) protocol, Real-Time Messaging Protocol (RTMP), Real-Time Streaming Protocol (RTSP), Dynamic Adaptive Streaming over HTTP (MPEG-DASH), etc.

[0085] Figure 14 is a block diagram illustrating a virtualization environment 500 in which functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments 500 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, UE, core network node, or host. Further, in embodiments in which the virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized.

[0086] Applications 502 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in thevirtualization environment Q400 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0087] Hardware 504 includes processing circuitry, memory that stores software and / or instructions executable by hardware processing circuitry, and / or other hardware devices as described herein, such as a network interface, input / output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers 506 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs 508a and 508b (one or more of which may be generally referred to as VMs 508), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 506 may present a virtual operating platform that appears like networking hardware to the VMs 508.

[0088] The VMs 508 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 506. Different embodiments of the instance of a virtual appliance 502 may be implemented on one or more of VMs 508, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV). NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers, and customer premise equipment.

[0089] In the context of NFV, a VM 508 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine. Each of the VMs 508, and that part of hardware 504 that executes that VM, be it hardware dedicated to that VM and / or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more VMs 508 on top of the hardware 504 and corresponds to the application 502.

[0090] Hardware 504 may be implemented in a standalone network node with generic or specific components. Hardware 504 may implement some functions via virtualization. Alternatively, hardware 504 may be part of a larger cluster of hardware (e.g. such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration 510, which, among others, oversees lifecycle management of applications 502. In some embodiments, hardware 504 is coupled to one or more radio units that each include one ormore transmiters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system 512 which may alternatively be used for communication between hardware nodes and radio units.

[0091] Figure 15 shows a communication diagram of a host 602 communicating via a network node 604 with a UE 606 over a partially wireless connection in accordance with some embodiments. Example implementations, in accordance with various embodiments, of the UE (such as a UE 112a of Figure 10 and / or UE 200 of Figure 2), network node (such as network node 110a of Figure 10 and / or network node 300 of Figure 3), and host (such as host 116 of Figure 10 and / or host 400 of Figure 4) discussed in the preceding paragraphs will now be described with reference to Figure 6.

[0092] Like host 400, embodiments of host 602 include hardware, such as a communication interface, processing circuitry, and memory. The host 602 also includes software, which is stored in or accessible by the host 602 and executable by the processing circuitry. The software includes a host application that may be operable to provide a service to a remote user, such as the UE 606 connecting via an over-the-top (OTT) connection 650 extending between the UE 606 and host 602. In providing the service to the remote user, a host application may provide user data which is transmited using the OTT connection 650.

[0093] The network node 604 includes hardware enabling it to communicate with the host 602 and UE 606. The connection 660 may be direct or pass through a core network (like core network 106 of Figure 1) and / or one or more other intermediate networks, such as one or more public, private, or hosted networks. For example, an intermediate network may be a backbone network or the Internet.

[0094] The UE 606 includes hardware and software, which is stored in or accessible by UE 606 and executable by the UE’s processing circuitry. The software includes a client application, such as a web browser or operator-specific “app” that may be operable to provide a service to a human or non-human user via UE 606 with the support of the host 602. In the host 602, an executing host application may communicate with the executing client application via the OTT connection 650 terminating at the UE 606 and host 602. In providing the service to the user, the UE's client application may receive request data from the host's host application and provide userdata in response to the request data. The OTT connection 650 may transfer both the request data and the user data. The UE's client application may interact with the user to generate the user data that it provides to the host application through the OTT connection 650.

[0095] The OTT connection 650 may extend via a connection 660 between the host 602 and the network node 604 and via a wireless connection 670 between the network node 604 and the UE 606 to provide the connection between the host 602 and the UE 606. The connection 660 and wireless connection 670, over which the OTT connection 650 may be provided, have been drawn abstractly to illustrate the communication between the host 602 and the UE 606 via the network node 604, without explicit reference to any intermediary devices and the precise routing of messages via these devices.

[0096] As an example of transmitting data via the OTT connection 650, in step 608, the host 602 provides user data, which may be performed by executing a host application. In some embodiments, the user data is associated with a particular human user interacting with the UE 606. In other embodiments, the user data is associated with a UE 606 that shares data with the host 602 without explicit human interaction. In step 610, the host 602 initiates a transmission carrying the user data towards the UE 606. The host 602 may initiate the transmission responsive to a request transmitted by the UE 606. The request may be caused by human interaction with the UE 606 or by operation of the client application executing on the UE 606. The transmission may pass via the network node 604, in accordance with the teachings of the embodiments described throughout this disclosure. Accordingly, in step 612, the network node 604 transmits to the UE 606 the user data that was carried in the transmission that the host 602 initiated, in accordance with the teachings of the embodiments described throughout this disclosure. In step 614, the UE 606 receives the user data carried in the transmission, which may be performed by a client application executed on the UE 606 associated with the host application executed by the host 602.

[0097] In some examples, the UE 606 executes a client application which provides user data to the host 602. The user data may be provided in reaction or response to the data received from the host 602. Accordingly, in step 616, the UE 606 may provide user data, which may be performed by executing the client application. In providing the user data, the client application may further consider user input received from the user via an input / output interface of the UE 606. Regardless of the specific manner in which the user data was provided, the UE 606 initiates, in step 618, transmission of the user data towards the host 602 via the network node 604. In step 620, in accordance with the teachings of the embodiments described throughout this disclosure, thenetwork node 604 receives user data from the UE 606 and initiates transmission of the received user data towards the host 602. In step 622, the host 602 receives the user data carried in the transmission initiated by the UE 606.

[0098] One or more of the various embodiments improve the performance of OTT services provided to the UE 606 using the OTT connection 650, in which the wireless connection 670 forms the last segment. More precisely, the teachings of these embodiments may improve the data rate and latency and thereby provide benefits such as reduced user waiting time, better responsiveness, and better QoE.

[0099] In an example scenario, factory status information may be collected and analyzed by the host 602. As another example, the host 602 may process audio and video data which may have been retrieved from a UE for use in creating maps. As another example, the host 602 may collect and analyze real-time data to assist in controlling vehicle congestion (e.g., controlling traffic lights). As another example, the host 602 may store surveillance video uploaded by a UE. As another example, the host 602 may store or control access to media content such as video, audio, VR or AR which it can broadcast, multicast or unicast to UEs. As other examples, the host 602 may be used for energy pricing, remote control of non-time critical electrical load to balance power generation needs, location services, presentation services (such as compiling diagrams etc. from data collected from remote devices), or any other function of collecting, retrieving, storing, analyzing and / or transmitting data.

[0100] In some examples, a measurement procedure may be provided for the purpose of monitoring data rate, latency and other factors on which the one or more embodiments improve. There may further be an optional network functionality for reconfiguring the OTT connection 650 between the host 602 and UE 606, in response to variations in the measurement results. The measurement procedure and / or the network functionality for reconfiguring the OTT connection may be implemented in software and hardware of the host 602 and / or UE 606. In some embodiments, sensors (not shown) may be deployed in or in association with other devices through which the OTT connection 650 passes; the sensors may participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or supplying values of other physical quantities from which software may compute or estimate the monitored quantities. The reconfiguring of the OTT connection 650 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not directly alter the operation of the network node 604. Such procedures and functionalities may be known and practiced in the art. In certainembodiments, measurements may involve proprietary UE signaling that facilitates measurements of throughput, propagation times, latency and the like, by the host 602. The measurements may be implemented in that software causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection 650 while monitoring propagation times, errors, etc.

[0101] Although the computing devices described herein (e.g., UEs, network nodes, hosts) may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and / or software needed to perform the tasks, features, functions and methods disclosed herein. Determining, calculating, obtaining or similar operations described herein may be performed by processing circuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and / or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination. Moreover, while components are depicted as single boxes located within a larger box, or nested within multiple boxes, in practice, computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitioned between separate components. For example, a communication interface may be configured to include any of the components described herein, and / or the functionality of the components may be partitioned between the processing circuitry and the communication interface. In another example, non-computationally intensive functions of any of such components may be implemented in software or firmware and computationally intensive functions may be implemented in hardware.

[0102] In certain embodiments, some or all of the functionality described herein may be provided by processing circuitry executing instructions stored on in memory, which in certain embodiments may be a computer program product in the form of a non-transitory computer- readable storage medium. In alternative embodiments, some or all of the functionality may be provided by the processing circuitry without executing instructions stored on a separate or discrete device-readable storage medium, such as in a hard-wired manner. In any of those particular embodiments, whether executing instructions stored on a non-transitory computer-readable storage medium or not, the processing circuitry can be configured to perform the described functionality. The benefits provided by such functionality are not limited to the processingcircuitry alone or to other components of the computing device, but are enjoyed by the computing device as a whole, and / or by end users and a wireless network generally.

[0103] FIGURE 16 is a flowchart illustrating an example method in a network node, according to certain embodiments. In particular embodiments, one or more steps of FIGURE 16 may be performed by network node 300 described with respect to FIGURE 12. The network node may comprise a Non-RT RIC / SMO network node for training a ML model

[0104] The method begins at step 1612, where the network node (e.g., network node 300) receives an E2 formatted report from an open radio access network (O-RAN) network function. The E2 formatted report is encapsulated in an 01 message. \In particular embodiments, the O- RAN network function comprises an eNB, gNB, gNB-CU-CP, gNB-CU-UP, gNB-DU, O-eNB, O-CU-CP, O-CU-UP, or O-DU network function. An example is illustrated in Figure 4.

[0105] At step 1614, the network node may receive fault, configuration, accounting, performance, or security (FCAPS) data from an O-RU encapsulated as an 01 message or an O- FH M-Plane message. An example is illustrated in Figure 4.

[0106] At step 1616, the network node may receive an E2 formatted report from a near real time radio intelligent controller (Near-RT RIC) encapsulated as a Y1 message or an Al message. An example is illustrated in Figure 5.

[0107] At step 1618, the network node trains a ML model using data in the E2 formatted report and / or FCAPS data as input data. The network node may train the ML model according to any of the embodiments and examples described herein. In some embodiments, the network node may transmit the data in the E2 formatted report and / or FCAPS data to a remote application (rApp) over an R1 interface fortraining the model and receive the trained ML model from the rApp.

[0108] At step 1620, the network node may transmit the trained ML model to an O-RAN network function. The trained ML model may be encapsulated in an 01 message. An example is illustrated in Figure 6.

[0109] At step 1622, the network node may transmit the trained ML model to an O-RU. The trained ML model may be encapsulated in an 01 message or an O-FH M-Plane message. An example is illustrated in Figure 6.

[0110] At step 1624, the network node may transmit the trained ML model to a Near-RT RIC. The trained ML model may be encapsulated in a Y1 message or an Al message. An example is illustrated in Figure 7.

[0111] Modifications, additions, or omissions may be made to method 1600 of FIGURE 16. Additionally, one or more steps in the method of FIGURE 16 may be performed in parallel or in any suitable order.

[0112] FIGURE 17 is a flowchart illustrating another example method in a network node, according to certain embodiments. In particular embodiments, one or more steps of FIGURE 17 may be performed by network node 300 described with respect to FIGURE 12. The network node may comprise an O-RAN network node capable of using a ML model. In particular embodiments, the O-RAN network function comprises a eNB, gNB, gNB-CU-CP, gNB-CU-UP, gNB-DU, O- eNB, O-CU-CP, O-CU-UP, O-DU, O-RU or Near-RT RIC network function.

[0113] The method begins at step 1712, where the O-RAN network node (e.g., network node 300) generates an E2 formatted report. The report is based on one or more of: occurrence of an event, wireless device measurement, network measurement, condition or threshold, counter, and aggregated statistics.

[0114] At step 1714, the network node encapsulates the E2 formatted report as an 01 message.

[0115] At step 1716, the network node (e.g., O-RU) may generate FCAPS data and at step 1718 may encapsulate the FCAPS data as an 01 message or an O-FH M-Plane message.

[0116] At step 1720, the network node (e.g., Near-RT RIC) may encapsulate the E2 formatted report as a Y1 message or an Al message.

[0117] At step 1722, the network node transmits the 01 message to a Non-RT RIC / SMO network node fortraining a ML model. An example is illustrated in Figure 4.

[0118] At step 1724, the network node (e.g., O-RU) may transmit the 01 message or M-Plane message to the Non-RT RIC / SMO network node for training the ML model. An example is illustrated in Figure 4.

[0119] At step 1726, the network node (e.g., Near-RT RIC) may transmit the Y1 message or Al message to the Non-RT RIC / SMO network node for training the ML model. An example is illustrated in Figure 5.

[0120] At step 1728, the network node may receive a trained ML model from the Non-RT RIC / SMO network node. In particular embodiments, the trained ML model is encapsulated in an 01 message, an O-FH M-Plane message, a Y1 message, or an Al message. Examples are illustrated in Figures 6 and 7.

[0121] Modifications, additions, or omissions may be made to method 1700 of FIGURE 17. Additionally, one or more steps in the method of FIGURE 17 may be performed in parallel or in any suitable order.

[0122] The foregoing description sets forth numerous specific details. It is understood, however, that embodiments may be practiced without these specific details. In other instances, well-known circuits, structures and techniques have not been shown in detail in order not to obscure the understanding of this description. Those of ordinary skill in the art, with the included descriptions, will be able to implement appropriate functionality without undue experimentation.

[0123] References in the specification to “one embodiment,” “an embodiment,” “an example embodiment,” etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to implement such feature, structure, or characteristic in connection with other embodiments, whether or not explicitly described.

[0124] Although this disclosure has been described in terms of certain embodiments, alterations and permutations of the embodiments will be apparent to those skilled in the art. Accordingly, the above description of the embodiments does not constrain this disclosure. Other changes, substitutions, and alterations are possible without departing from the scope of this disclosure, as defined by the claims below.

[0125] Some example embodiments are described below.Group A Embodiments1. A method performed by a wireless device, the method comprising:- any of the wireless device steps, features, or functions described above, either alone or in combination with other steps, features, or functions described above.2. The method of the previous embodiment, further comprising one or more additional wireless device steps, features or functions described above.3. The method of any of the previous embodiments, further comprising:- providing user data; and- forwarding the user data to a host computer via the transmission to the base station.Group B Embodiments4. A method performed by a base station (e.g., eNB, gNB, gNB-CU-CP, gNB-CU-UP, gNB- DU, O-eNB, O-CU-CP, O-CU-UP, O-DU, O-RU), the method comprising: a. generating an E2 formatted report, wherein the report is based on one or more of: occurrence of an event, wireless device measurement, network measurement, condition or threshold, counter, and aggregated statistics; b. encapsulating the E2 formatted report as an Ol, Al orYl formatted report; and c. transmitting the 01, Al, or Y1 formatted report to a network node.5. The method of the previous embodiment, wherein the network node comprises one of a non-real time radio access network intelligent controller (non-RT RIC), near-RT RIC, and a network function.6. The method of any one of the previous embodiments, further comprising receiving a configuration of which network node to transmit the 01, Al, or Y1 formatted report.7. The method of any one of the previous embodiments, further comprising receiving a trained artificial intelligence (AI) / machine learning (ML) model over an 01, Al, or Yl interface.8. A method performed by a base station, the method comprising:- any of the steps, features, or functions described above with respect to base station, either alone or in combination with other steps, features, or functions described above.9. The method of the previous embodiment, further comprising one or more additional base station steps, features or functions described above.10. The method of any of the previous embodiments, further comprising:- obtaining user data; and- forwarding the user data to a host computer or a wireless device.Group C Embodiments11. A mobile terminal comprising:- processing circuitry configured to perform any of the steps of any of the Group A embodiments; and- power supply circuitry configured to supply power to the wireless device.12. A base station comprising:- processing circuitry configured to perform any of the steps of any of the Group B embodiments;- power supply circuitry configured to supply power to the wireless device.13. A user equipment (UE) comprising:- an antenna configured to send and receive wireless signals;- radio front-end circuitry connected to the antenna and to processing circuitry, and configured to condition signals communicated between the antenna and the processing circuitry;- the processing circuitry being configured to perform any of the steps of any of the Group A embodiments;- an input interface connected to the processing circuitry and configured to allow input of information into the UE to be processed by the processing circuitry;- an output interface connected to the processing circuitry and configured to output information from the UE that has been processed by the processing circuitry; and- a battery connected to the processing circuitry and configured to supply power to the UE.14. A communication system including a host computer comprising:- processing circuitry configured to provide user data; and- a communication interface configured to forward the user data to a cellular network for transmission to a user equipment (UE),- wherein the cellular network comprises a base station having a radio interface and processing circuitry, the base station’s processing circuitry configured to perform any of the steps of any of the Group B embodiments. The communication system of the pervious embodiment further including the base station. The communication system of the previous 2 embodiments, further including the UE, wherein the UE is configured to communicate with the base station. The communication system of the previous 3 embodiments, wherein:- the processing circuitry of the host computer is configured to execute a host application, thereby providing the user data; and- the UE comprises processing circuitry configured to execute a client application associated with the host application. A method implemented in a communication system including a host computer, a base station and a user equipment (UE), the method comprising:- at the host computer, providing user data; and- at the host computer, initiating a transmission carrying the user data to the UE via a cellular network comprising the base station, wherein the base station performs any of the steps of any of the Group B embodiments. The method of the previous embodiment, further comprising, at the base station, transmitting the user data. The method of the previous 2 embodiments, wherein the user data is provided at the host computer by executing a host application, the method further comprising, at the UE, executing a client application associated with the host application. A user equipment (UE) configured to communicate with a base station, the UE comprising a radio interface and processing circuitry configured to performs any of the previous 3 embodiments.A communication system including a host computer comprising:- processing circuitry configured to provide user data; and- a communication interface configured to forward user data to a cellular network for transmission to a user equipment (UE),- wherein the UE comprises a radio interface and processing circuitry, the UE’s components configured to perform any of the steps of any of the Group A embodiments. The communication system of the previous embodiment, wherein the cellular network further includes a base station configured to communicate with the UE. The communication system of the previous 2 embodiments, wherein:- the processing circuitry of the host computer is configured to execute a host application, thereby providing the user data; and- the UE’s processing circuitry is configured to execute a client application associated with the host application. A method implemented in a communication system including a host computer, a base station and a user equipment (UE), the method comprising:- at the host computer, providing user data; and- at the host computer, initiating a transmission carrying the user data to the UE via a cellular network comprising the base station, wherein the UE performs any of the steps of any of the Group A embodiments. The method of the previous embodiment, further comprising at the UE, receiving the user data from the base station. A communication system including a host computer comprising:- communication interface configured to receive user data originating from a transmission from a user equipment (UE) to a base station,- wherein the UE comprises a radio interface and processing circuitry, the UE’sprocessing circuitry configured to perform any of the steps of any of the Group A embodiments. The communication system of the previous embodiment, further including the UE. The communication system of the previous 2 embodiments, further including the base station, wherein the base station comprises a radio interface configured to communicate with the UE and a communication interface configured to forward to the host computer the user data carried by a transmission from the UE to the base station. The communication system of the previous 3 embodiments, wherein:- the processing circuitry of the host computer is configured to execute a host application; and- the UE’s processing circuitry is configured to execute a client application associated with the host application, thereby providing the user data. The communication system of the previous 4 embodiments, wherein:- the processing circuitry of the host computer is configured to execute a host application, thereby providing request data; and- the UE’s processing circuitry is configured to execute a client application associated with the host application, thereby providing the user data in response to the request data. A method implemented in a communication system including a host computer, a base station and a user equipment (UE), the method comprising:- at the host computer, receiving user data transmitted to the base station from the UE, wherein the UE performs any of the steps of any of the Group A embodiments. The method of the previous embodiment, further comprising, at the UE, providing the user data to the base station. The method of the previous 2 embodiments, further comprising:- at the UE, executing a client application, thereby providing the user data to be transmitted; and- at the host computer, executing a host application associated with the client application. The method of the previous 3 embodiments, further comprising:- at the UE, executing a client application; and- at the UE, receiving input data to the client application, the input data being provided at the host computer by executing a host application associated with the client application,- wherein the user data to be transmitted is provided by the client application in response to the input data. A communication system including a host computer comprising a communication interface configured to receive user data originating from a transmission from a user equipment (UE) to a base station, wherein the base station comprises a radio interface and processing circuitry, the base station’s processing circuitry configured to perform any of the steps of any of the Group B embodiments. The communication system of the previous embodiment further including the base station. The communication system of the previous 2 embodiments, further including the UE, wherein the UE is configured to communicate with the base station. The communication system of the previous 3 embodiments, wherein:- the processing circuitry of the host computer is configured to execute a host application;- the UE is configured to execute a client application associated with the host application, thereby providing the user data to be received by the host computer. A method implemented in a communication system including a host computer, a base station and a user equipment (UE), the method comprising:- at the host computer, receiving, from the base station, user data originating from a transmission which the base station has received from the UE, wherein the UE performs any of the steps of any of the Group A embodiments. The method of the previous embodiment, further comprising at the base station, receiving the user data from the UE. The method of the previous 2 embodiments, further comprising at the base station, initiating a transmission of the received user data to the host computer.

Claims

Claims1. A method performed by a non -real time radio intelligent controller (Non-RT RIC) / Service Management and Orchestration (SMO) network node for training a machine learning (ML) model, the method comprising: receiving (1612) an E2 formatted report from an open radio access network (O-RAN) network function, wherein the E2 formatted report is encapsulated in an 01 message; and training (1618) a ML model using data in the E2 formatted report as input data.

2. The method of claim 1, further comprising receiving (1614) fault, configuration, accounting, performance, or security (FCAPS) data from an open radio unit (0-RU) encapsulated as an 01 message or an open front haul (0-FH) M-Plane message, and wherein training the ML model is further based on the FCAPS data used as input data.

3. The method of any one of claims 1-2, further comprising receiving (1616) an E2 formatted report from a near real time radio intelligent controller (Near-RT RIC) encapsulated as a Y1 message or an Al message, and wherein training the ML model is further based on the E2 formatted data as input data.

4. The method of any one of claims 1-3, further comprising transmitting (1620) the trained ML model to an O-RAN network function, wherein the trained ML model is encapsulated in an 01 message.

5. The method of any one of claims 1-4, further comprising transmitting (1622) the trained ML model to an open radio unit (0-RU), wherein the trained ML model is encapsulated in an 01 message or an open front haul (0-FH) M-Plane message.

6. The method of any one of claims 1-5, further comprising transmitting (1624) the trained ML model to a near real time radio intelligent controller (Near-RT RIC), wherein the trained ML model is encapsulated in a Y1 message or an Al message.

7. The method of any one of claims 1-6, wherein the 0-RAN network function comprisesa eNB, gNB, gNB central unit control plane (gNB-CU-CP), gNB central unit user plane (gNB- CU-UP), gNB distributed unit (gNB-DU), open eNB (O-eNB), open central unit control plane (O- CU-CP), open central unit user plane (O-CU-UP), or open distributed unit (O-DU) network function.

8. A non-real time radio intelligent controller (Non-RT RIC) / Service Management and Orchestration (SMO) network node (300) capable of 1 training a machine learning (ML) model, the network node comprising processing circuitry (302) operable to: receive an E2 formatted report from an open radio access network (O-RAN) network function, wherein the E2 formatted report is encapsulated in an 01 message; and train a ML model using data in the E2 formatted report as input data.

9. The network node of claim 8, the processing circuitry further operable to receive fault, configuration, accounting, performance, or security (FCAPS) data from an open radio unit (0-RU) encapsulated as an 01 message or an open front haul (O-FH) M-Plane message, and wherein the processing circuitry is operable to train the ML model further based on the FCAPS data used as input data.

10. The network node of any one of claims 8-9, the processing circuitry further operable to receive an E2 formatted report from a near real time radio intelligent controller (Near-RT RIC) encapsulated as a Y1 message or an Al message, and wherein the processing circuitry is operable to train the ML model further based on the E2 formatted data as input data.

11. The network node of any one of claims 8-10, the processing circuitry further operable to transmit the trained ML model to an O-RAN network function, wherein the trained ML model is encapsulated in an 01 message.

12. The network node of any one of claims 8-11, the processing circuitry further operable to transmit the trained ML model to an open radio unit (0-RU), wherein the trained ML model is encapsulated in an 01 message or an open front haul (O-FH) M-Plane message.

13. The network node of any one of claims 8-12, the processing circuitry further operableto transmit the trained ML model to a near real time radio intelligent controller (Near-RT RIC), wherein the trained ML model is encapsulated in a Y1 message or an Al message.

14. The network node of any one of claims 8-13, wherein the O-RAN network function comprises a eNB, gNB, gNB central unit control plane (gNB-CU-CP), gNB central unit user plane (gNB-CU-UP), gNB distributed unit (gNB-DU), open eNB (O-eNB), open central unit control plane (O-CU-CP), open central unit user plane (O-CU-UP), or open distributed unit (O-DU) network function.

15. A method performed by an open radio access network (O-RAN) network node for using a machine learning (ML) model, the method comprising: generating (1712) an E2 formatted report, wherein the report is based on one or more of: occurrence of an event, wireless device measurement, network measurement, condition or threshold, counter, and aggregated statistics; encapsulating (1714) the E2 formatted report as an 01 message; and transmitting (1722) the 01 message to anon-real time radio intelligent controller (Non-RT RIQ / Service Management and Orchestration (SMO) network node for training a ML model.

16. The method of claim 15, further comprising: generating (1716) fault, configuration, accounting, performance, or security (FCAPS) data; encapsulating (1718) the FCAPS data as an 01 message or an open front haul (0-FH) M- Plane message, and transmitting ( 1724) the 01 message or M-Plane message to the Non-RT RIC / SMO network node fortraining the ML model.

17. The method of any one of claims 16-17, further comprising: encapsulating (1720) the E2 formatted report as a Y1 message or an Al message; and transmitting ( 1726) the Y 1 message or A 1 message to the Non-RT RIC / SMO network node fortraining the ML model.

18. The method of any one of claims 15-17, further comprising receiving (1728) a trained ML model from the Non-RT RIC / SMO network node.

19. The method of claim 18, wherein the trained ML model is encapsulated in an 01 message.

20. The method of claim 18, wherein the trained ML model is encapsulated in an open front haul (O-FH) M-Plane message.

21. The method of claim 18, wherein the trained ML model is encapsulated in a Y1 message.

22. The method of claim 18, wherein the trained ML model is encapsulated in an Al message.

23. The method of any one of claims 15-22, wherein the 0-RAN network function comprises a eNB, gNB, gNB central unit control plane (gNB-CU-CP), gNB central unit user plane (gNB-CU-UP), gNB distributed unit (gNB-DU), open eNB (O-eNB), open central unit control plane (O-CU-CP), open central unit user plane (O-CU-UP), open distributed unit (0-DU), open radio unit (0-RU) or near real time radio intelligent network controller (Near-RT RIC) network function.

24. An open radio access network (0-RAN) network node (300) capable of using a machine learning (ML) model, the network node comprising processing circuitry (302) operable to: generate an E2 formatted report, wherein the report is based on one or more of: occurrence of an event, wireless device measurement, network measurement, condition or threshold, counter, and aggregated statistics; encapsulate the E2 formatted report as an 01 message; and transmit the 01 message to a non-real time radio intelligent controller (Non-RT RIC) / Service Management and Orchestration (SMO) network node for training a ML model.

25. The network node of claim 24, the processing circuitry further operable to: generate fault, configuration, accounting, performance, or security (FCAPS) data;encapsulate the FCAPS data as an 01 message or an open front haul (0-FH) M-Plane message, and transmit the 01 message or M-Plane message to the Non-RT RIC / SMO network node for training the ML model.

26. The network node of any one of claims 24-25, the processing circuitry further operable to: encapsulate the E2 formatted report as a Y1 message or an Al message; and transmit the Y1 message or Al message to the Non-RT RIC / SMO network node for training the ML model.

27. The network node of any one of claims 24-26, the processing circuitry further operable to receive a trained ML model from the Non-RT RIC / SMO network node.

28. The network node of claim 27, wherein the trained ML model is encapsulated in an 01 message.

29. The network node of claim 27, wherein the trained ML model is encapsulated in an open front haul (0-FH) M-Plane message.

30. The network node of claim 27, wherein the trained ML model is encapsulated in a Y1 message.

31. The network node of claim 27, wherein the trained ML model is encapsulated in an Al message.

32. The network node of any one of claims 24-31, wherein the 0-RAN network function comprises a eNB, gNB, gNB central unit control plane (gNB-CU-CP), gNB central unit user plane (gNB-CU-UP), gNB distributed unit (gNB-DU), open eNB (0-eNB), open central unit control plane (O-CU-CP), open central unit user plane (0-CU-UP), open distributed unit (0-DU), open radio unit (0-RU) or near real time radio intelligent network controller (Near-RT RIC) network function.

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