Environmental Recognition UE Positioning

Abstract

Methods and systems relating to environmentally aware positioning techniques for user devices and network components are described, employing site-specific information such as the operational status information (MSI) of objects in the environment. Such information can be used to design more robust positioning methods. Furthermore, such information can also be used to detect changes in the wireless environment.

Description

[Technical Field]

[0001] Cross-references to related information This application claims priority to U.S. Priority Application No. 63 / 446,665, “Environmentally Aware UE Positioning,” filed on 17 February 2023.

[0002] This disclosure relates to systems and methods for user equipment used for monitoring environmental changes in general. [Background technology]

[0003] Artificial intelligence (AI) and machine learning (ML) have been studied in both academia and industry as promising tools for optimizing the design of airborne interfaces for wireless communication networks. Examples of use include the use of autoencoders in CSI compression to reduce feedback overhead and improve channel prediction accuracy, the use of deep neural networks in LOS (line of sight) and NLOS (non-line of sight) state classification to improve positioning accuracy, the use of reinforcement learning for beam selection on the network side and / or UE (user equipment) side to reduce signaling overhead and beam tuning delay, and the use of deep reinforcement learning to learn optimal precoding policies for complex MIMO (multiple input multiple output) precoding problems.

[0004] Building an AI / ML model involves multiple development steps, and the actual training of the AI ​​model is only one stage in the training pipeline. A crucial element in AI / ML development is the lifecycle management of the AI / ML model, as illustrated in Figure 1. AI model lifecycle management typically consists of the training pipeline, deployment, and inference pipeline.

[0005] A typical training (retraining) pipeline unfolds as follows: • Data Ingestion: Collect raw data (training data) from data storage. After data ingestion, additional steps may be taken to control the validity of the collected data; Data preprocessing refers to feature engineering applied to collected data. This may include, for example, data normalization or data transformations necessary for AI / ML model input; • The aforementioned actual model training steps; Model evaluation refers to benchmarking performance against some baseline. The iterative steps of model training and model evaluation continue until an acceptable level of performance is achieved, as illustrated above. Model registration refers to the process of registering an AI / ML model. This includes corresponding AI / ML metadata that provides information about how the AI / ML model was developed, and, in some cases, performance results from AI / ML model evaluation.

[0006] Next, in the deployment phase, the trained (or retrained) AI / ML model is incorporated as part of the inference pipeline.

[0007] A typical inference pipeline unfolds as follows: • Data ingestion: The stage of collecting raw (inference) data from data storage. • Data preprocessing stage: Typically the same as the corresponding process performed in the training pipeline. • Model deployment phase: The trained and deployed model is used in operational mode. • Data and model monitoring phase: Verify that the inference data is obtained from a distribution consistent with the training data, and monitor the model's output to detect performance drift and operational drift. • Drift detection phase: Notifies of drift during model operation.

[0008] If the operating environment is determined to have drifted excessively from the training dataset environment, the model will need to be retrained or readjusted to function properly in the new environment.

[0009] UE positioning UE positioning is at the heart of location-based services and has diverse commercial applications ranging from entertainment and healthcare to geo-targeted advertising and smart factories and warehouses. Furthermore, the emergence of augmented reality (XR) has increased the importance of UE positioning. The required positioning accuracy varies depending on the application, with centimeter-level accuracy required for industrial applications and several-meter accuracy required for emergency calls.

[0010] 3GPP® TS36.305 provides a list of position measurement techniques. Some representative methods are described below:

[0011] Extended Cell ID: This technology uses knowledge of the cellular network, such as cell identifiers (IDs) and additional information about the UE's service cell, to determine location.

[0012] Assisted Global Positioning Satellite System (GNSS): The device acquires GNSS information to determine its own position.

[0013] Observed Time of Arrival (OTDOA): This technique estimates the time difference in the arrival of reference signals from different base stations and reports this information to the network for multilattice purposes.

[0014] Uplink Time to Arrive Difference (UTDOA): This technique uses signals received by the UE from multiple known locations (e.g., gNBs) to estimate the relative time to arrive (TOA) at different TRPs. Distance calculations are then performed on the network side to obtain the estimated UE position.

[0015] UE positioning also involves general positioning-related reports. Some representative reports are listed below: • Arrival time (ToA) of DL or UL signals • The time difference (TDoA) between DL signals or UL signals. For example, DL reference time difference (DL RSTD), UL relative time difference (TUL-RTOA), etc. · Timing Advance (TADV); · DL or UL Angle of Departure (AoD); · DL or UL Angle of Arrival (AoA); · Reference Signal Received Power (RSRP). For example, DL PRS Reference Signal Received Power (DL PRS-RSRP) and UL SRS Reference Signal Received Power (UL SRS-RSRP); · Reference Signal Received Path Power (RSRPP). For example, DL PRS Reference Signal Received Path Power (DL PRS-RSRPP) and UL SRS Reference Signal Received Path Power (UL SRS-RSRPP), etc. · Cell ID and TRP related information (e.g., RS resource and / or resource set ID); · Carrier phase difference; · Round-Trip Time (RTT) measurement value. This is obtained by combining the gNB receive-transmit time difference and the UE receive-transmit time difference.

[0016] Two main scenarios related to position positioning are considered. They are network-based and UE-based.

[0017] In the first network-based scenario, it is assumed that reports related to position positioning can be generated by the UE, the base station (BS), or the transmit-receive point (TRP). These reports related to position positioning are reported to a centralized node in the network to determine the position of the UE. For example, the network configures the UE to transmit an uplink (UL) sounding reference symbol (SRS), and sets multiple TRPs to receive the SRS signal. Each TRP processes the received signal and generates a report that can be used by the network to determine the position of the UE. In another example, the UE receives downlink (DL) positioning reference symbols (PRS) from a series of TRPs. The UE processes these received signals and generates a report that can be used by the network to determine the position of the UE.

[0018] In the second UE-based scenario, assume that such positioning-related reports are generated by the UE. These positioning-related reports are further utilized by the UE to determine its own position. For example, the UE receives downlink (DL) positioning reference symbols (PRS) from multiple TRPs. The UE processes these received signals and generates reports that can be further used to determine the UE's own position.

[0019] Current signal processing techniques can generally be applied to generate position-related reports by the UE, BS, or TRP in an operating environment where sufficient line-of-sight (LoS) links exist. However, the transmitted signal may be reflected or scattered by the environment, resulting in multiple non-line-of-sight (NLoS) paths. For example, FIG. 2 shows a multipath radio environment between the UE and two TRPs. For TRP A, there is a LoS path between the UE's transmitter and TRP A's receiver. However, for TRP B, due to the obstacles in the environment, only an NLoS path exists between the UE's transmitter and TRP B's receiver.

[0020] To achieve high-precision positioning in a highly NLoS environment, AI / ML models that overcome the problem of NLoS paths, and combinations of AI / ML models and conventional positioning algorithms have been introduced. Such AI / ML models perform a process also called radio environment fingerprinting. That is, through learning using sufficient data, the correspondence between the received signal and the radio environment is understood. When the radio environment is static or changes only slightly over time, the AI / ML model can continuously generate accurate or sufficiently accurate position estimation values. However, when the radio environment changes excessively over time, the AI / ML model may generate estimation values insufficient for accurate positioning. Therefore, it is important to monitor the performance of the AI / ML model over time and identify whether the AI / ML model needs to be updated or retrained. Summary of the Invention

[0021] One embodiment of the present disclosure includes a method performed by a UE for monitoring environmental changes. This method includes monitoring environmental changes in a deployment area and notifying network nodes of assistive information regarding the environmental changes to assist in the management of machine learning models.

[0022] Another embodiment is a method performed by a UE for monitoring environmental changes. This method includes receiving assistance information from a network node, which is obtained by monitoring environmental changes, and managing a machine learning model present in the UE based on this assistance information.

[0023] Another embodiment is a network-based environmental change monitoring method performed by a network node. This method includes monitoring environmental changes in a deployment area and sending assistive information to the UE for use in model management of machine learning models.

[0024] This summary is provided to introduce some of the concepts described in the detailed explanation below in a simplified form. This summary does not identify any important or essential features of the claimed subject matter, nor is it intended to be used to define the scope of the claimed subject matter. [Brief explanation of the drawing]

[0025] To fully understand this disclosure, refer to the following description while referring to the attached drawings. In the drawings:

[0026] [Figure 1] Figure 1 shows the AI / ML training and inference pipeline.

[0027] [Figure 2] Figure 2 shows a multipath wireless environment.

[0028] [Figure 3]Figure 3 shows an example of the Sensor-ProvideLocationInformation information element in this disclosure.

[0029] [Figure 4] Figure 4 shows an embodiment of the CommonIEsProvideLocationInformation information element in this disclosure.

[0030] [Figure 5] Figure 5 shows a flowchart of an embodiment of the method according to this disclosure.

[0031] [Figure 6] Figure 6 shows a flowchart of an embodiment of the method according to this disclosure.

[0032] [Figure 7] Figure 7 shows a flowchart of an embodiment of the method according to this disclosure.

[0033] [Figure 8] Figure 8 shows a flowchart of an embodiment of the method according to this disclosure.

[0034] [Figure 9] Figure 9 shows a flowchart of an embodiment of the method according to this disclosure.

[0035] [Figure 10] Figure 10 is a schematic diagram showing an embodiment of the communication system according to this disclosure.

[0036] [Figure 11] Figure 11 is a schematic diagram showing an embodiment of the user device according to this disclosure.

[0037] [Figure 12] Figure 12 is a schematic diagram showing an embodiment of a network node according to this disclosure.

[0038] [Figure 13] Figure 13 is a schematic diagram of an embodiment of the host according to this disclosure.

[0039] [Figure 14] Figure 14 is a schematic diagram of an embodiment of the virtualization environment according to this disclosure.

[0040] [Figure 15] Figure 15 shows a schematic diagram of an embodiment of communication between a node, a host, and user equipment according to this disclosure. [Modes for carrying out the invention]

[0041] Before describing in detail the various embodiments of this disclosure, it should be understood that this disclosure is not limited to the parameters of the systems, methods, apparatus, products, processes, and / or kits specifically exemplified, and these may naturally vary. Therefore, although specific embodiments of this disclosure are described in detail with reference to particular configurations, parameters, components, elements, etc., these descriptions are illustrative and should not be construed as limiting the scope of the embodiments described in the claims. Furthermore, the terms used herein are for the purpose of describing embodiments and are not necessarily intended to limit the scope of the embodiments described in the claims.

[0042] As described above, there are currently specific challenges in UE positioning in the AI / ML field. Current positioning algorithms rely on location-related reports provided by wireless network nodes or UEs, and determine the UE's location using conventional signal processing algorithms and / or advanced machine learning models. Furthermore, these methods cannot detect changes in the wireless environment. For example, fingerprinting techniques are highly dependent on the wireless environment, and their performance degrades significantly with changes in the environment. Meanwhile, the main objects that affect positional performance within a deployment area may be stationary or moving. Moreover, new objects usually intrude into the environment from the boundaries. Therefore, relying solely on location-related reports from the network or UEs and ignoring important elements and objects in the environment can result in a positioning method that is highly sensitive to environmental changes.

[0043] Certain aspects and embodiments of this disclosure may provide solutions to these or other problems. This disclosure includes environment-aware positioning techniques that employ site-specific information, such as operational status information (MSI) of objects in the environment. This information can be used to design more robust positioning methods. Furthermore, it can also be used to detect changes in the radio environment.

[0044] A particular embodiment provides one or more of the following technical advantages: The described embodiment can monitor potential causes of model performance degradation. The information obtained from this monitoring enables more robust application of the AI / ML model.

[0045] Some of the embodiments described herein will be described in more detail with reference to the accompanying drawings. The embodiments are provided as examples to convey the scope of the subject to those skilled in the art. Specific embodiments can generally be classified into network-based and UE-based embodiments.

[0046] Network-based environmental change monitoring In certain embodiments, the network function monitors environmental changes in the AI / ML deployment area. If a significant change is detected that is expected to cause a performance degradation of the fingerprinting-type AI / ML positioning method, the network function notifies the UE as assist information.

[0047] Detection of environmental changes In one embodiment, environmental changes are derived based on movement information of objects in the environment that are expected to significantly alter the radio wave environment. Such objects include large clutter such as large metal objects, large machinery with irregular structures, objects with reflective metal surfaces, and moving vehicles. By attaching sensors to these objects, a location server can collect their location and velocity information. To distinguish the potential impact of the objects, multiple characteristics of the clutter object and sensor operation information reports can be reported together. Characteristics include: • Clutter object size: For example, "Small" (e.g., 2m or less), "Medium" (e.g., 2m to 10m), "Large" (e.g., over 10m) • The height of the clutter object, e.g., "low" (e.g., less than 1.5m), "medium" (e.g., 1.5m to 5m), "high" (e.g., more than 5m); • Material and shape of the clutter object. For example, "irregular metal surface," "regular metal surface," "irregular nonmetal," or "regular nonmetal."

[0048] This kind of information allows location servers to track changes in the wireless environment (including the degree and speed of change). This detection information can be used to assist machine learning models in determining how much they degrade over time and whether there are patterns (seasonality) in the changes (e.g., day and night, weekdays and weekends).

[0049] In another embodiment, multiple sensor operation information reports are aggregated to calculate an aggregated environmental change index indicating the degree of environmental change. In one non-limiting embodiment, multiple sensor operation information reports are summed or averaged to obtain an aggregated environmental change index. In another non-limiting embodiment, different sensor operation information reports are weighted differently, and then summed or averaged to obtain an aggregated environmental change index.

[0050] These different weights for different sensor operation reports may depend, for example, on vertical dimensions, horizontal dimensions, or surface material type. For instance, a tall clutter object may be weighted more heavily than a short clutter object. A wide object may be weighted more heavily than a narrow clutter object. A metallic clutter object may be weighted more heavily than an object with a less reflective surface.

[0051] Examples of different weighting scenarios include, for example: Different weights for different sensor operation reports are assigned based on domain knowledge regarding the dimensions and material composition of the clutter object in question. • Different weights for different sensor operation reports are determined by the performance differences of positioning AI / ML models in relation to the movement of clutter objects in the environment. Different weights are assigned to different sensor operation information reports by the AI / ML model. Different weights for different sensor operation reports are determined by the relative positions of clutter objects within the environment. For example, clutter objects closer to the edge of the environment may be weighted more heavily than clutter objects in the center of the environment. Another example is that clutter objects closer to the center of the environment may be weighted more heavily than clutter objects closer to the edge of the environment.

[0052] The aggregated environmental change indicators can be provided to other nodes as assisting information for model management.

[0053] For example, the IE (Information Element) "Sensor-ProvideLocationInformation" can be extended to report characteristic information about the object to which the sensor is attached. Figure 3 shows an example of the IE "Sensor-ProvideLocationInformation". Movement information (i.e., Sensor-Motioninformation) is reported as before, including orientation, horizontal / vertical direction, and distance within the reporting time frame. The IE "Sensor-ProvideLocationInformation" can be used for the target device to provide location information to a location server for sensor-based solutions. It can also be used to provide sensor-specific reasons for malfunction. As is clear from the figure, the IE "Sensor-ProvideLocationInformation" can provide object information such as size, height, and material information.

[0054] In another embodiment, location information obtained from network-based position measurement can be used to determine the actual movement of objects and to help determine how much the ML model degrades over time.

[0055] IE "CommonIEsProvideLocationInformation" is used for providing location information to a location server using a cellular-based location measurement method performed by the target device. It can also be extended to provide object information. An example of IE "CommonIEsProvideLocationInformation" is shown in Figure 4. As can be seen from the figure, IE "CommonIEsProvideLocationInformation" can provide object information such as size, height, and material information.

[0056] In yet another embodiment, location information from sensors and location information from a cellular system can be combined to determine the actual movement of an object and help determine how much the ML model degrades over time.

[0057] Model management decisions In some embodiments, one model (Model A) may serve as a good reference point for another model (Model B) operating in the same environment. Therefore, model management decisions for Model A can be used to assist model management decisions for Model B in the same service area. This is particularly true if Model A is a more powerful model than Model B, or if Model A has more information about the environment than Model B.

[0058] As an example, information regarding significant environmental changes is derived based on the operation of an AI / ML model deployed on the network side for UE positioning. This information can be used to assist the UE-side model for positioning. For target UEs within the service area, positioning may be performed by both the network-side AI / ML model and the UE-side AI / ML model, depending on the positioning method. The network-side AI / ML model may be, for example, a model on NG-RAN (e.g., gNB) or a model on a location server (e.g., a Location Management Function (LMF)). Illustrated scenarios include: Case 1: UE-based positioning using a UE-side model, direct AI / ML positioning, or AI / ML-assisted positioning Case 2: UE Assisted a. Case 2a: UE-assisted / LMF-based positioning and AI / ML-assisted positioning using a UE-side model b. Case 2b: UE-assisted / LMF-based positioning using an LMF-side model, direct AI / ML positioning Case 3: NG-RAN Assisted a. Case 3a: NG-RAN node-assisted positioning and AI / ML-assisted positioning using the gNB side model. b. Case 3b: NG-RAN node-assisted positioning using LMF-side model, direct AI / ML positioning

[0059] Compared to UE-side AI / ML models, network-side AI / ML models are expected to possess superior knowledge of network deployment (e.g., complete knowledge of TRP locations) and deployment environments (e.g., office environments or indoor factories). Furthermore, unlike UE-side models, network-side models are not subject to processing power limitations such as hardware size, power consumption, and memory capacity.

[0060] Therefore, management actions for AI / ML models on the network side can provide useful information for UE-side model management. For example: If the AI / ML model on the network side detects model drift due to environmental changes, it is highly likely that the UEs within that service area will also need to update their models. • If the network-side AI / ML model detects a change in the environment from a moderate NLOS environment to a severe NLOS environment, the UE-side AI / ML model is likely to have experienced a similar change. Here, the NLOS environment condition is the probability (P) that UEs within the service area do not have sufficient LOS links. NLOS This is shown by P. NLOS =20% are low NLOS type, P NLOS =90% means that it belongs to the high NLOS type.

[0061] In this way, the network / location server can provide assistance information to the UE to help update the UE-side model. This assistance information allows the UE to monitor its own model performance and decide whether or not to perform a model update.

[0062] Taking the AI / ML model in the NG-RAN as an example, this method is shown in Embodiment 900 of FIG. 5. FIG. 5 shows an example where the location server 960 provides assistance information to the UE 915 within the service area. This assistance information can be used by the UE 915 for decisions regarding UE-side AI / ML model management (e.g., UE-side models 920, 930, 950). The location server 960 derives the assistance information for a predetermined service area based on the model update status report on the NG-RAN side in step 988. Thereafter, in step 989, the location server 960 updates (or transmits assistance information) to the UE-side models M UE1 920, M UE2 930, M UEn 950.

[0063] In another example, model A is an AI / ML model on the location server. When the model on the location server needs to be updated due to environmental changes, this becomes useful information for the UE-side model and the NG-RAN-side model. The UE and the NG-RAN should monitor their own model performance and determine whether to perform model updates. This is shown in Example 1100 of FIG. 6. FIG. 6 shows an example where the location server 1160 provides assistance information to the NG-RAN 1110 and the UE 1115 within the service area. This assistance information can be used by the NG-RAN 1110 and the UE 1115 to make decisions regarding NG-RAN-side and UE-side AI / ML model management, respectively. The location server derives the assistance information for a predetermined service area based on the update status of the location server model Mloc that it executes. Thereafter, in step 1188, an update is sent to the NG-RAN 1110, and in step 1189, an update is sent to the UE-side models M UE1 1120, M UE2 1,130, M UEn 1150.

[0064] As another example, the decision to update the UE-side model can be used as assisting information for updating the network-side model. UE-side AI / ML models are likely to have stricter hardware constraints than network-side models, but there are likely to be many UE-side AI / ML models within the service area. Therefore, aggregated model management information obtained from many UE-side models can be used to assist the network-side model. For example, N within the service area UE,thresh Units or more UE (or percentage P) UE,thresh When the above-mentioned UE (User Environment) detects an environmental change, it provides useful information indicating that a significant environmental change may have occurred. Therefore, the network-side model also needs to monitor for potential updates.

[0065] Figure 7 shows an embodiment of the method according to the present disclosure. Method 1300 constitutes a method performed by user equipment for monitoring environmental changes. Step 1310 monitors environmental changes in the deployment area. Step 1320 notifies network nodes of assistive information regarding environmental changes to assist in the management of machine learning models. Method 1300 may include various options, alternatives, and / or additional steps.

[0066] Figure 8 shows an embodiment of a possible method based on this disclosure. Method 1500 is a method performed by user equipment for monitoring environmental changes. Step 1510 receives assist information obtained from monitoring environmental changes from a network node. Step 1520 manages a machine learning model present in the UE based on the assist information. Method 1500 may include various options, alternatives, and / or additional steps.

[0067] Figure 9 shows an embodiment of a method based on the present disclosure. Method 1700 is a method performed by a network node for network-based environmental change monitoring. Step 1710 monitors environmental changes in the deployment area. Step 1720 sends assist information to the UE for use in model management of a machine learning model. Method 1700 may include various options, alternatives, and / or additional steps.

[0068] Additional Embodiments Figure 10 shows an example of a communication system 2100 according to several embodiments. In this example, the communication system 2100 includes a telecommunications network 2102 which includes an access network 2104 such as a RAN and a core network 2106 which includes one or more core network nodes 2108. The access network 2104 includes one or more access network nodes such as network nodes 2110a and 2110b (one or more of which may generally be referred to as network nodes 2110), or some other similar 3GPP access node or non-3GPP access point. The network nodes 2110 facilitate direct or indirect connections of user equipment (UEs) by connecting UEs 2112a, 2112b, 2112c and 2112d (one or more of which may generally be referred to as UE2112) to the core network 2106 over one or more wireless connections.

[0069] Exemplary wireless communications on 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 carrying information without using wires, cables, or other physical conductors. Furthermore, in various embodiments, the communication system 2100 may include any number of wired or wireless networks, network nodes, UEs, and / or any other components or systems that can facilitate or participate in the communication of data and / or signals, whether wired or wireless. The communication system 2100 may include and / or interface with any type of communication, telecommunications, data, cellular, wireless network, and / or other similar types of systems.

[0070] UE2112 may be any of a broad range of communication devices, including wireless devices that are arranged, configured, and / or operable to communicate wirelessly with network node 2110 and other communication devices. Similarly, network node 2110 is arranged, can communicate, is configured, and / or operable to communicate directly or indirectly with UE2112 and / or other network nodes or devices in telecommunications network 2102 in order to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as management within telecommunications network 2102.

[0071] In the illustrated example, the core network 2106 connects network node 2110 to one or more hosts, such as host 2116. These connections may be direct or indirect, via one or more intermediate networks or devices. In other examples, network nodes may be directly connected to hosts. The core network 2106 includes one or more core network nodes (e.g., core network node 2108) structured with hardware and software components. The functions of these components may be substantially the same as those described for the UE, network nodes, and / or hosts, and therefore those descriptions are generally applicable to the corresponding components of core network node 2108. An exemplary core network node includes one or more of the following functions: 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 Decryption Function (SIDF), Unified Data Management (UDM), Security Edge Protected Proxy (SEPP), Network Exposure Function (NEF), and / or User Plane Function (UPF).

[0072] Host 2116 may be owned by or under the control of a service provider other than the operator or provider of the access network 2104 and / or the telecommunications network 2102, and may be operated by or on behalf of such service provider. Host 2116 may host a variety of applications and provide one or more services. Examples of such applications include live and pre-recorded audio / video content, data collection services such as acquisition and editing of data on various ambient conditions detected by multiple UEs, analytical functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for alarm and monitoring centers, or any other such functions performed by a server.

[0073] Overall, the communication system 2100 in Figure 10 enables connectivity between the UE, network nodes, and hosts. In this sense, the communication system may be configured to operate according to predefined rules or procedures, such as certain standards, including, but not limited to, the following: GSM (Global System for Mobile Communications), UMTS (Universal Mobile Telecommunications System), LTE (Long Term Evolution), and / or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standards (e.g., 6G), WLAN (wireless local area network) standards such as the IEEE (Institute of Electrical and Electronics Engineers) 802.11 standard (WiFi), and / or any other suitable wireless communication standards such as WiMax (Worldwide Interoperability for Microwave Access), Bluetooth, Z-Wave, NFC (Near Field Communication), ZigBee, LiFi, and / or any LPWAN (low-power wide-area network) standards such as LoRa and Sigfox.

[0074] In some examples, the telecommunications network 2102 is a cellular network implementing functions standardized by 3GPP. Therefore, the telecommunications network 2102 may support network slicing to provide various logical networks to various devices connected to the telecommunications network 2102. For example, the telecommunications network 2102 may provide ultra-high reliability low latency communication (URLLC) services to some UEs while providing extended mobile broadband (eMBB) services to other UEs, and / or provide massive machine type communication (mMTC) / massive IoT services to further UEs.

[0075] In some examples, UE2112 is configured to transmit and / or receive information without direct human interaction. For example, the UE may be designed to transmit information to access network 2104 on a predetermined schedule, triggered by internal or external events, or in response to a request from access network 2104. Additionally, the UE may be configured to operate in single or multi-RAT, or multi-standards mode. For example, the UE may be configured to operate in any one or a combination of Wi-Fi, NR (New Radio), and LTE, i.e., for multi-radio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio-Dual Connectivity (EN-DC).

[0076] In the above example, hub 2114 communicates with access network 2104 to facilitate indirect communication between one or more UEs (e.g., UE2112c and / or 2112d) and a network node (e.g., network node 2110b). In some examples, hub 2114 may be a controller, router, content source and analytics, or any other communication device described herein with respect to the UE. For example, hub 2114 may be a broadband router that enables the UE to access core network 2106. In another example, hub 2114 may be a controller that sends commands or instructions to one or more actuators within the UE. Commands or instructions may be received from the UE or network node 2110, or accepted by executable code, scripts, processes, or other instructions within hub 2114. In yet another example, hub 2114 may be a data collector acting as temporary storage for the UE's data, which in some embodiments may perform analysis or other processing of that data. In yet another example, hub 2114 may be a content source. For example, with respect to a UE that is a VR headset, display, loudspeaker, or other media delivery device, the hub 2114 may acquire media or data related to VR assets, video, audio, or other sensory information via network nodes, in which case the hub 2114 provides it to the UE either directly, after performing local processing, and / or after adding additional local content. In another example, the hub 2114 acts as a proxy server or orchestrator for the UE, in particular when one or more of the UEs are low-energy IoT devices.

[0077] Hub 2114 may have a steady / persistent or intermittent connection to network node 2110b. Furthermore, hub 2114 may enable different communication methods and / or schedules between hub 2114 and UEs (e.g., UE 2112c and / or 2112d), and between hub 2114 and the core network 2106. In another example, hub 2114 is connected to the core network 2106 and / or one or more UEs via a wired connection. Additionally, hub 2114 may be configured to connect to an M2M service provider on the access network 2104 and / or to other UEs via a direct connection. In some scenarios, a UE may establish a wireless connection with network node 2110 while still being connected via hub 2114 via a wired or wireless connection. In some embodiments, hub 2114 may be a dedicated hub, i.e., a hub whose primary function is to route communication between UEs and network node 2110b. In other embodiments, the hub 2114 may be a non-dedicated hub, i.e., a device capable of routing communication between the UE and the network node 2110b, but also capable of acting as the source and / or destination of communication for some data channel.

[0078] Figure 11 shows UE2200 according to several embodiments. As used herein, UE refers to a device that is capable of, configured, deployed, and / or operating wirelessly with network nodes and / or other UEs. Examples of UEs include, but are not limited to, smartphones, mobile phones, cell phones, VoIP (Voice over IP) phones, wireless local loop phones, desktop computers, personal digital assistants (PDAs), wireless cameras, game consoles or devices, music storage devices, playback appliances, wearable terminal devices, wireless endpoints, mobile stations, tablets, laptops, laptop embedded devices (LEEs), laptop-mounted devices (LMEs), smart devices, wireless customer premises equipment (CPEs), vehicles, in-vehicle or vehicle-embedded / integrated wireless devices, etc. Other examples include any UE identified by the Third Generation Partnership Project (3GPP), including Narrowband Internet of Things (NB-IoT) UEs, Machine Type Communications (MTC) UEs, and / or Enhanced MTC (eMTC) UEs.

[0079] A UE may support device-to-device (D2D) communication, for example, by implementing 3GPP standards for side-link communication, dedicated short-range communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle-to-everything (V2X). In other examples, a UE does not necessarily have a user in the sense of a person who owns and / or operates the device in question. Instead, a UE may represent a device (e.g., a smart sprinkler controller) that is intended to be sold to or operated by a human user, but may not be associated with a particular human user, at least initially. Alternatively, a UE may represent a device (e.g., a smart power meter) that is not intended to be sold to or operated by an end user, but may be associated with a user or operated for the benefit of a user.

[0080] The UE2200 includes an input / output interface 2206, a power supply 2208, memory 2210, a communication interface 2212, and / or any other components, or any combination thereof, and processing circuitry 2202 operably connected via bus 2204. A certain UE may utilize all or a subset of the components shown in Figure 11. The level of integration between components may vary between one UE and another. Furthermore, a certain UE may include multiple instances of a component, such as multiple processors, memory, transceivers, transmitters, receivers, etc.

[0081] The processing circuit 2202 is configured to process instruction sets and data, and may implement some sequential state machine capable of operating to execute instruction sets stored in memory 2210 as machine-readable computer programs. The processing circuit 2202 may be implemented as one or more hardware-implemented state machines (e.g., discrete logic, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), etc.), programmable logic with appropriate firmware, one or more stored computer programs, a general-purpose processor such as a microprocessor or digital signal processor (DSP) with appropriate software, or any combination of the above. For example, the processing circuit 2202 may include multiple central processing units (CPUs).

[0082] In the above example, the input / output interface 2206 may be configured to provide input devices, output devices, or one or more interfaces to one or more input and / or output devices. Examples of output devices include speakers, sound cards, video cards, displays, monitors, printers, actuators, emitters, smart cards, other output devices, or any combination thereof. Input devices may allow a user to capture information to the UE2200. Examples of input devices include touch-sensitive or presence-sensitive displays, cameras (e.g., digital cameras, digital video cameras, webcams, etc.), microphones, sensors, mice, trackballs, directional pads, trackpads, scroll wheels, and smart cards. Presence-sensitive displays may include capacitive or resistive touch sensors for sensing user input. Sensors may include, for example, accelerometers, gyroscopes, tilt sensors, force sensors, magnetic sensors, optical sensors, proximity sensors, biosensors, or any combination thereof. Output devices may use the same type of interface port as input devices. For example, a Universal Serial Bus (USB) port may be used to provide input and output devices.

[0083] In some embodiments, the power supply 2208 is structured as a battery or battery pack. Other types of power sources may be used, such as an external power source (e.g., an electrical outlet), a solar power device, or a battery. The power supply 2208 may further include power circuits for transmitting power from the power supply 2208 itself and / or an external power source to various parts of the UE2200 via interfaces such as input circuits or power cables. Power transmission may be, for example, for charging the power supply 2208. The power circuits may perform some shaping, conversion, or other modification on the power from the power supply 2208 to suit the power of each component of the UE2200 to which it is being powered.

[0084] Memory 2210 may be, or may be configured to include, 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, and flash drives. In one example, memory 2210 includes one or more application programs 2214, such as an operating system, a web browser application, a widget, a gadget engine, or other application, and corresponding data 2216. Memory 2210 may store any of a wide variety of operating systems or combinations of multiple operating systems for use by UE2200.

[0085] Memory 2210 may be configured to include multiple physical drive units such as RAID (Redundant Array of Independent Disks), flash memory, USB flash drives, external hard disk drives, thumb drives, pen drives, key drives, HD-DVD (High-Density Digital Versatile Disc), optical disc drives, internal hard disk drives, Blu-ray optical disc drives, HDDS (Holographic Digital Data Storage) optical disc drives, external miniDIMM (Dual In-Line Memory Module), SDRAM (Synchronous Dynamic Random Access Memory), external microDIMM SDRAM, tamper-resistant modules in the form of UICC (universal integrated circuit card) including one or more SIMs (subscriber identity modules) such as USIM and / or ISIM, other memories, or any combination thereof. The UICC may be, for example, an embedded UICC (eUICC), an integrated UICC (iUICC), or a removable UICC commonly known as a "SIM card". Memory 2210 may enable UE2200 to access instruction sets and application programs stored in temporary or non-temporary storage media to offload or upload data. Product items, such as those utilizing communication systems, may be tangibly embodied as or within memory 2210, which is a device-readable storage medium or may contain one.

[0086] The processing circuit 2202 may be configured to communicate with an access network or other network using a communication interface 2212. The communication interface 2212 may include one or more communication subsystems, and may include or be communicatively connected to an antenna 2222. The communication interface 2212 may include one or more transceivers used to perform communication, such as by communicating with one or more remote transceivers of other wirelessly communicable devices (e.g., other UEs or network nodes in the access network). Each transceiver may include a transmitter 2218 and / or receiver 2220 appropriate for providing network communication (e.g., optical, electrical, frequency-allocated, etc.). Furthermore, the transmitter 2218 and receiver 2220 may be connected to one or more antennas (e.g., antenna 2222), and they may share circuit components, software, or firmware, or alternatively, be implemented separately.

[0087] In the illustrated embodiment, the communication functions of the communication interface 2212 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, near-field communication such as Bluetooth, location-based communication such as the use of GPS (Global Positioning System) for location determination, other similar communication functions, or any combination thereof. The communication may be implemented in accordance with one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA®), GSM, LTE, New Radio (NR), UMTS, WiMAX, Ethernet, Transmission Control Protocol / Internet Protocol (TCP / IP), Synchronous Optical Networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, and Hypertext Transfer Protocol (HTTP).

[0088] Regardless of the sensor type, the UE may provide an output of data captured by its sensor to a network node via a wireless connection through its communication interface 2212. The data captured by the UE's sensor may be communicated to a network node via another UE through a wireless connection. The output may be periodic (e.g., once every 15 minutes if reporting sensed temperature), random (e.g., to equalize the load from notifications from multiple sensors), in response to a triggering event (e.g., moisture is detected and an alert is sent), in response to a request (e.g., a user-initiated request), or as a continuous stream (e.g., a live video feed of a patient).

[0089] Other examples include actuators, motors, or switches associated with a communication interface configured to receive wireless input from a network node via a wireless connection. The state of the actuator, motor, or switch may change in response to the received wireless input. For example, the UE may include a motor that adjusts the control surface or rotor of a drone in flight according to the received input, or a robotic arm that performs a medical procedure according to the received input.

[0090] If a UE is in the form of an IoT (Internet of Things) device, it may be a device for use in one or more application domains, which include, but are not limited to, wearable technology in urban environments, augmented industrial applications, and healthcare. Non-exclusive examples of such IoT devices include, or are incorporated into, devices such as, connected refrigerators or freezers, TVs, connected lighting fixtures, electric meters, robotic vacuum cleaners, voice-controlled smart speakers, home security cameras, motion detectors, thermostats, smoke detectors, door / window sensors, moisture detectors (flood / moisture sensors), electric door locks, connected doorbells, air conditioning systems such as heat pumps, autonomous vehicles, surveillance systems, weather monitoring devices, vehicle parking monitoring devices, electric vehicle charging stations, smartwatches, fitness trackers, head-mounted displays for augmented reality (AR) or virtual reality (VR), wearables for haptic enhancement or sensory improvement, water sprinklers, animal or object tracking devices, sensors for monitoring plants or animals, industrial robots, unmanned aerial vehicles (UAVs), and any type of medical device such as heart rate monitors or remotely controlled surgical robots. The UE in the form of an IoT device comprises, in addition to circuitry and / or software that depends on the intended application of the IoT device, other components such as those described in relation to the UE2200 shown in Figure 11.

[0091] In another specific example, in an IoT scenario, the UE may represent a machine or other device that performs monitoring and / or measurement and transmits the results of such monitoring and / or measurement to other UEs and / or network nodes. In this case, the UE may be an M2M device and may be referred to as an MTC device in the context of 3GPP. In one particular example, the UE may implement the 3GPP NB-IoT standard. In other scenarios, the UE may represent a vehicle such as a passenger car, bus, truck, ship or aircraft, or other equipment capable of monitoring and / or reporting on its operational status or other functions associated with its operation.

[0092] In practice, any number of UEs may be used together for a single use case. For example, the first UE may be a drone or integrated into a drone and provide speed information of the drone (obtained through a speed sensor) to a second UE, which is a remote controller operating the drone. When the user makes a change from the remote controller, the first UE may adjust the drone's throttle (for example, by controlling an actuator) to increase or decrease the drone's speed. The first and / or second UE may include more than one of the functionalities described above. For example, the UE may include sensors and actuators and handle data communication for both the speed sensor and the actuator.

[0093] Figure 12 shows network node 3300 according to several embodiments. As used herein, network node means equipment that is capable of communicating directly or indirectly with the UE and / or other network nodes or equipment in the telecommunications network, and is configured, positioned and / or operational in such a manner. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points) and base stations (BSs) (e.g., radio base stations, node B, evolved node B (eNB), and NR node B (gNB)).

[0094] Base stations may be categorized based on the amount of coverage they provide (or, in other words, their transmit power level), and therefore may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations, depending on the amount of coverage they provide. A base station may also be a relay node or a relay donor node controlling a relay device. Network nodes may also include one or all of the parts of a distributed radio base station, such as a centralized digital unit and / or a remote radio unit (RRU), sometimes called a remote radio head (RRH). Such remote radio units may or may not be integrated with an antenna, such as an antenna-integrated radio. Some parts of a distributed radio base station may also be referred to as nodes within a distributed antenna system (DAS).

[0095] Other examples of network nodes include multi-transmitting point (multi-TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSR BS, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base station transceivers (BTSs), transmit points, transmit nodes, multi-cell / multicast cooperative entities (MCEs), operation and maintenance (O&M) nodes, operation support system (OSS) nodes, self-organizing network (SON) nodes, and positioning nodes (including, for example, evolved serving mobile location centers (E-SMLCs) and / or drive test minimization (MDTs)).

[0096] Network node 3300 includes a processing circuit 3302, memory 3304, a communication interface 3306, and a power supply 3308. Network node 3300 may consist of multiple physically separate components (e.g., node B component and RNC component, or BTS component and BSC component), each of which may have its own respective components. In a scenario in which network node 3300 has multiple separate components (e.g., BTS and BSC components), one or more of these separate components may be shared among several network nodes. For example, a single RNC may control multiple node Bs. In such a scenario, each unique pair of node B and RNC may, in some examples, be considered a single separate network node. In some embodiments, network node 3300 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be redundant (e.g., separate memory 3304 for different RATs), and some components may be reused (e.g., the same antenna 3310 may be shared by multiple different RATs). Furthermore, the network node 3300 may include multiple sets of diverse exemplary components for various wireless technologies to be integrated into the network node 3300, such as GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, LoRaWAN, RFID (Radio Frequency Identification), or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chips or sets of chips and other components within the network node 3300.

[0097] The processing circuit 3302 may include one or more combinations of microprocessors, controllers, microcontrollers, central processing units, digital signal processors, application-specific integrated circuits, field-programmable gate arrays, or other suitable computing devices, resources, or hardware, software, and / or coding logic, which can operate independently or in conjunction with other network node 3300 components such as memory 3304 to provide the functionality of the network node 3300.

[0098] In some embodiments, the processing circuit 3302 includes a system-on-a-chip (SOC). In some embodiments, the processing circuit 3302 includes one or more of the radio frequency (RF) transceiver circuit 3312 and the baseband processing circuit 3314. In some embodiments, the RF transceiver circuit 3312 and the baseband processing circuit 3314 may be on separate chips (or sets of chips), substrates, or units, such as a radio unit and a digital unit. In alternative embodiments, some or all of the RF transceiver circuit 3312 and the baseband processing circuit 3314 may be on the same chip or set of chips, substrate, or unit.

[0099] Memory 3304 may include, but is not limited to, any form of volatile or non-volatile computer-readable memory, including persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random-access memory (RAM), read-only memory (ROM), large storage media (e.g., hard disk), removable storage media (e.g., flash drive, compact disc (CD) or digital video disc (DVD)), and / or any other volatile or non-volatile non-temporary device-readable and / or computer-executable memory device, for storing information, data and / or instructions that can be used by the processing circuit 3302. Memory 3304 may store any suitable instructions, data or information, including applications, and / or other instructions, which can be executed by the processing circuit 3302 and are available to the network node 3300, including one or more computer programs, software, logic, rules, code, and tables. The memory 3304 may be used to store any calculation results generated by the processing circuit 3302 and / or any data received via the communication interface 3306. In some embodiments, the processing circuit 3302 and the memory 3304 are integrated.

[0100] The communication interface 3306 is used in wired or wireless signaling and / or data between network nodes, access networks, and / or UEs. As illustrated, the communication interface 3306 includes, for example, a port / terminal 3316 for sending and receiving data to and from the network over a wired connection. The communication interface 3306 also includes a wireless front-end circuit 3318, which is connected to or, in some embodiments, part of the antenna 3310. The wireless front-end circuit 3318 includes a filter 3320 and an amplifier 3322. The wireless front-end circuit 3318 may be connected to the antenna 3310 and the processing circuit 3302. The wireless front-end circuit may be configured to adjust signals communicated between the antenna 3310 and the processing circuit 3302. The wireless front-end circuit 3318 may receive digital data to be sent to other network nodes or UEs via the wireless connection. The wireless front-end circuit 3318 can convert its digital data into a radio signal with appropriate channel and bandwidth parameters using a combination of filter 3320 and / or amplifier 3322. The radio signal can then be transmitted via antenna 3310. Similarly, when data is received, antenna 3310 collects the radio signal, which can then be converted into digital data by the wireless front-end circuit 3318. The digital data can then be passed to processing circuit 3302. In other embodiments, the communication interface may include different components and / or different combinations of components.

[0101] In one alternative embodiment, the network node 3300 does not include a separate radio front-end circuit 3318; rather, the processing circuit 3302 includes the radio front-end circuit and is connected to the antenna 3310. Similarly, in some embodiments, all or some of the RF transceiver circuits 3312 are part of the communication interface 3306. In yet another embodiment, the communication interface 3306, as part of a radio unit (not shown), includes one or more ports or terminals 3316, a radio front-end circuit 3318, and an RF transceiver circuit 3312, and the communication interface 3306 communicates with a baseband processing circuit 3314, which is part of a digital unit (not shown).

[0102] Antenna 3310 may include one or more antennas or antenna arrays configured to transmit and / or receive wireless signals. Antenna 3310 may be connected to the wireless front-end circuit 3318 and may be any type of antenna capable of wirelessly transmitting and receiving data and / or signals. In one embodiment, antenna 3310 is separate from the network node 3300 and can be connected to the network node 3300 through an interface or port.

[0103] The antenna 3310, communication interface 3306, and / or processing circuit 3302 may be configured to perform any receiving operations and / or acquisition operations described herein as being performed by a network node. Any information, data, and / or signals may be received from the UE, other network nodes, and / or any other network equipment. Similarly, the antenna 3310, communication interface 3306, and / or processing circuit 3302 may be configured to perform any transmitting operations described herein as being performed by a network node. Any information, data, and / or signals may be transmitted to the UE, other network nodes, and / or any other network equipment.

[0104] Power supply 3308 provides power to the various components of network node 3300 in a format suitable for each component (for example, at the voltage and current levels required for each component). Power supply 3308 may further include, or be connected to, a power management circuit for supplying power to the components of network node 3300 to perform the functions described herein. For example, network node 3300 may be connectable to an external power source (e.g., a power grid, an electrical outlet) via an input circuit or interface such as an electrical cable, thereby allowing the external power source to supply power to the power circuit of power supply 3308. As a further example, power supply 3308 may include a power source in the form of a battery or battery pack connected to or integrated into the power circuit. The battery may provide backup power in case of failure of the external power source.

[0105] Embodiments of network node 3300 may include additional components other than those shown in Figure 12 to provide a functional view of the network node, including any functionality described herein and / or any functionality necessary to support the subject matter described herein. For example, network node 3300 may include user interface equipment that enables input of information to and output of information from network node 3300. This may enable users to perform diagnostic, maintenance, repair, and other management functions on network node 3300.

[0106] Figure 13 is a block diagram of a host 4400 that may be an embodiment of host 2116 in Figure 10, relating to the various perspectives described herein. Where used herein, host 4400 may be, or include, a variety of hardware and / or software, including standalone servers, blade servers, cloud-implemented servers, distributed servers, virtual machines, containers, or processing resources within a server farm. Host 4400 may provide one or more services to one or more UEs.

[0107] The host 4400 includes an input / output interface 4406, a network interface 4408, a power supply 4410, and a processing circuit 4402 operably connected via a bus 4404 to a memory 4412. In other embodiments, other components may be included. The functions of these components may be substantially the same as those described for the devices in previous drawings such as Figures 11 and 12, and thus those descriptions are generally applicable to the corresponding components of the host 4400.

[0108] Memory 4412 may include one or more computer programs, including one or more host application programs 4414, and data 4416, which may include user data, such as data generated by the UE for the host 4400 or data generated by the host 4400 for the UE. Embodiments of the host 4400 may utilize only a subset or all of the illustrated components. The host application program 4414 may be implemented in a container-based architecture and may provide support for video codecs (e.g., VVC (Versatile Video Coding), HEVC (High Efficiency Video Coding), AVC (Advanced Video Coding), MPEG, VP9) and audio codecs (e.g., FLAC, AAC (Advanced Audio Coding), MPEG, G.711), including transcoding for multiple different classes, types, or implementations of UEs (e.g., handsets, desktop computers, wearable display systems, head-up display systems). Furthermore, the host application program 4414 may provide user authentication and license checks, and may periodically report health, route, and content availability to central nodes such as devices within or at the edge of the core network. Thus, host 4400 may select and / or point to different hosts for over-the-top services for the UE. The host application program 4414 may support a variety of protocols, such as HLS (HTTP Live Streaming), RTMP (Real-Time Messaging Protocol), RTSP (Real-Time Streaming Protocol), and MPEG-DASH (Dynamic Adaptive Streaming over HTTP).

[0109] Figure 14 is a block diagram showing a virtualization environment 5500 in which functions implemented by several embodiments can be virtualized. In this context, virtualization means for generating a virtual version of a device or apparatus may include a virtualization hardware platform, storage devices, and networking resources. As used herein, virtualization can be applied to any of the devices or components thereof described herein and relates to implementation examples in which at least a portion of its 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 within one or more virtual environments 5500 hosted by one or more hardware nodes, such as network nodes, UEs, core network nodes, or hardware computing devices acting as hosts. Furthermore, in embodiments in which the virtual node does not require radio connectivity (e.g., a core network node or a host), the node as a whole may be virtualized.

[0110] Application 5502 (which may alternatively be called a software instance, virtual appliance, network function, virtual node, virtual network function, etc.) runs in a virtualized environment 5500 to implement some of the features, functions and / or benefits of some of the embodiments disclosed herein.

[0111] Hardware 5504 includes a processing circuit, memory for storing software and / or instruction sets executable by the hardware processing circuit, and / or hardware devices as described herein, such as network interfaces and input / output interfaces. The software is executed by the processing circuit to instantiate one or more virtualization layers 5506 (also referred to as a hypervisor or virtual machine monitor (VMM)), provide VM5508a and VM5508b (one or more of which may generally be referred to as VM5508), and / or perform any of the functions, features and / or benefits described herein in relation to some of the embodiments described herein. The virtualization layer 5506 may present a virtual operating platform that appears to the VM5508 as networking hardware.

[0112] VM5508 includes virtual processing, virtual memory, virtual networking or interfaces, and virtual storage, and may be executed by the corresponding virtualization layer 5506. Various embodiments of instances of the virtual appliance 5502 may be implemented in one or more of the VM5508, and such implementation may be carried out in various ways. Hardware virtualization is referred to as network function virtualization (NFV) in several contexts. NFV can be used to consolidate many types of network equipment into industry-standard, high-capacity server hardware, physical switches, and physical storage that can reside in data centers and customer premises equipment.

[0113] In the context of NFV, VM5508 may be a software implementation of a physical machine that runs a program as if it were running on a physical, non-virtualized machine. Each VM5508, and the portion of hardware 5504 on which the VM runs, whether dedicated hardware for that VM and / or hardware shared by that VM with other VMs, forms a separate virtual network element. Again in the context of NFV, the virtual network function is responsible for handling the specific network functions running in one or more VM5508s at the top level of hardware 5504 and corresponds to application 5502.

[0114] Hardware 5504 may be implemented in a standalone network node with general-purpose or specific components. Hardware 5504 may implement some functions through virtualization. Alternatively, hardware 5504 may be part of a larger hardware cluster (such as one in a data center or CPE) in which multiple hardware nodes cooperate and are managed via management and orchestration 5510, which oversees, among other things, the lifecycle management of application 5502. In some embodiments, hardware 5504 is coupled to one or more radio units, each including one or more transmitters and one or more receivers, which can be coupled to one or more antennas. The radio units may communicate directly with other hardware nodes via one or more suitable network interfaces, or they may be used in combination with virtual components to provide radio capabilities to virtual nodes, such as radio access nodes or base stations. In some embodiments, some signaling can be provided in conjunction with the use of a control system 5512, which may alternatively be used for communication between hardware nodes and radio units.

[0115] Figure 15 shows a communication diagram of host 6602 communicating with UE6606 via network node 6604 over a connection including a wireless portion, according to several embodiments. Exemplary implementations of various embodiments of the UEs (UE2112a in Figure 10 and / or UE2200 in Figure 11), network nodes (network node 2110a in Figure 10 and / or network node 3300 in Figure 12), and hosts (host 2116 in Figure 10 and / or host 4400 in Figure 13) discussed in the preceding paragraphs will now be described with reference to Figure 15.

[0116] Similar to host 4400, embodiments of host 6602 include hardware such as a communication interface, processing circuitry, and memory. Host 6602 also includes software stored within or accessible by host 6602, which is executable by the processing circuitry. This software may include a host application that can operate to provide services to remote users, such as UE6606 connected via an over-the-top (OTT) connection 6650 extending between UE6606 and host computer 6602. While providing services to remote users, the host application may provide user data transmitted using the OTT connection 6650.

[0117] Network node 6604 includes hardware that enables communication with host 6602 and UE6606. Connection 6660 can be direct or traverse one or more other intermediate networks, such as a core network (like core network 2106 in Figure 10) and / or one or more public, private, or hosted networks. For example, the intermediate network may be a backbone network or the internet.

[0118] UE6606 includes software stored within or accessible by UE6606, which is executable by the UE's processing circuitry. This software includes client applications, such as a web browser or a service provider-specific “app,” which may operate to provide services to human or non-human users via UE6606, with the support of host 6602. On host 6602, the host application being executed may communicate with the client application being executed via an OTT connection 6650 terminating at UE6606 and host 6602. While providing services to a user, the UE's client application may receive request data from the host's host application and provide user data in response to that request data. The OTT connection 6650 may transport both the request data and the user data. The UE's client application may interact with the user to generate user data that it provides to the host application via the OTT connection 6650.

[0119] The OTT connection 6650 extends via connection 6660 between host 6602 and network node 6604, and via wireless connection 6670 between network node 6604 and UE6606, and may provide connectivity between host 6602 and UE6606. To illustrate the communication between host 6602 and UE6606 via network node 6604 without any explicit reference to any intermediate devices and the precise routing of messages through those devices, the connections 6660 and wireless connection 6670, which may be provided by the OTT connection 6650, are depicted abstractly.

[0120] As an example of transmitting data via the OTT connection 6650, in step 6608, host 6602 provides user data, which may be done by running a host application. In some embodiments, the user data is associated with a specific human user interacting with UE6606. In other embodiments, the user data is associated with UE6606 sharing data with host 6602 without explicit human interaction. In step 6610, host 6602 initiates a transmission to UE6606 carrying the user data. Host 6602 may initiate such a transmission in response to a request sent by UE6606. Such a request may be triggered by human interaction with UE6606 or by the operation of a client application running on UE6606. Such a transmission may pass through network node 6604 in accordance with the teachings of the embodiments described through this disclosure. Accordingly, in step 6612, network node 6604 transmits the user data carried in the transmission initiated by host 6602 to UE 6606 in accordance with the teachings of the embodiments described through this disclosure. In step 6614, UE 6606 receives the user data carried in the transmission, which may be done by a client application running on UE 6606 associated with a host application running on host 6602.

[0121] In some examples, UE6606 runs a client application, thereby providing user data destined for host 6602. User data may be provided in reaction to or in response to receiving data from host 6602. Accordingly, in step 6616, UE6606 may provide user data, which may be done by running a client application. While providing user data, the client application may further consider user input received from the user via the input / output interface of UE6606. Regardless of the specific way in which the user data is provided, in step 6618, UE6606 initiates transmission of the user data to host 6602 via network node 6604. In step 6620, in accordance with the teachings of the embodiments described through this disclosure, network node 6604 receives user data from UE6606 and initiates transmission of the received user data to host 6602. In step 6622, host 6602 receives the user data carried in the transmission initiated by UE6606.

[0122] One or more of the various embodiments improve the performance of OTT services provided to UE6606 using OTT connectivity 6650, with wireless connectivity 6670 forming the final segment. More precisely, the teachings of these embodiments can improve, for example, data rate, latency, and power consumption, thereby providing benefits such as reduced user latency, relaxed constraints on file size, improved content resolution, better responsiveness, and / or longer battery life.

[0123] In an exemplary scenario, Host 6602 may collect and analyze factory status information. In another example, Host 6602 may process audio and video data, which may be acquired from the UE, for use in generating maps. In yet another example, Host 6602 may collect and analyze real-time data to assist in controlling vehicle congestion (e.g., traffic light control). In yet another example, Host 6602 may store surveillance video uploaded by the UE. In yet another example, Host 6602 may store or control access to media content such as video, audio, VR, or AR that can be broadcast, multicast, or unicast to the UE. In yet another example, Host 6602 may be used for energy pricing, remote control of non-time-critical power loads for balancing power generation needs, location services, presentation services (such as editing diagrams from data collected from remote devices), or any other function of collecting, acquiring, storing, analyzing, and / or transmitting data.

[0124] In some examples, measurement procedures may be provided for the purpose of monitoring data rate, latency, and other factors that may be improved by one or more embodiments. Further network functionality may exist as an option for reconfiguring the OTT connection 6650 between host 6602 and UE6606 in response to variations in the measurement results. The above measurement procedures and / or network functionality for reconfiguring the OTT connection may be implemented in the software and hardware of host 6602 and / or UE6606. In some embodiments, sensors (not shown) through which the OTT connection 6650 passes may be deployed in or associated with other devices, and these sensors may participate in the measurement procedures by supplying values ​​of the monitored quantities exemplified above or values ​​of other physical quantities, from which the monitored quantities may be calculated or estimated by software. Reconfiguration of the OTT connection 6650 may include message formatting, retransmission settings, preferred routing, etc., and such reconfiguration does not need to directly change the operation of network node 6604. Such procedures and functionality may be known or in practice in the art. In one embodiment, the measurement may include proprietary UE signaling that facilitates the measurement of throughput, propagation time, and latency by the host 6602. The measurement may be implemented by the software monitoring propagation time, errors, etc., while sending messages that are specifically empty or "dummy" messages using the OTT connection 6650.

[0125] While the computing devices described herein (e.g., UEs, network nodes, hosts) may include combinations of illustrated hardware components, other embodiments may include computing devices with different combinations of components. It should be understood that these computing devices may include any suitable combination of hardware and / or software required to perform the tasks, features, functions, and methods disclosed herein. The decisions, calculations, acquisitions, or similar operations described herein may be performed by processing circuits, which may process information by, for example, converting acquired information to other information, comparing acquired or converted information with information stored in the network node, and / or performing one or more operations based on the acquired or converted information, and making decisions as a result of the processing. Furthermore, while components are depicted as single boxes located within larger boxes or nested within multiple boxes, in practice, computing devices may include multiple different physical components that make up the illustrated single component, and functionality may be separated between distinct components. For example, a communication interface may be configured to include any of the components described herein, and the functionality of those components may be separated between the processing circuit and the communication interface. In other examples, computationally intensive functions of any of these components may be implemented in software or firmware, while computationally intensive functions may be implemented in hardware.

[0126] In some embodiments, some or all of the functionalities described herein may be provided by a processing circuit executing a set of instructions stored in memory, which may be a computer program product in the form of a non-temporary computer-readable storage medium. In alternative embodiments, some or all of the functionalities may be provided by the processing circuit, such as in a hardwired manner, without executing instructions stored in separate or discrete device-readable storage mediums. In any of these specific embodiments, the processing circuit can be configured to perform the functionalities described, whether or not it executes instructions stored in a non-temporary computer-readable storage medium. The benefits provided by such functionalities are not limited to the processing circuit alone or other components of the computing device, but are enjoyed by the computing device as a whole, and / or by the end user and the wireless network in general.

[0127] It will be understood that computer systems are taking on an increasingly diverse range of forms. In this specification and in the claims, the terms “controller,” “computer system,” or “computing system” are defined broadly as any device or system (or combination thereof) that includes at least one physical and tangible processing unit and a physical and tangible storage device capable of holding computer executable instructions that can be executed by the processing unit. Exemplary, but not limited to, the terms “computer system” or “computing system” as used herein include personal computers, desktop computers, laptop computers, tablets, portable devices (e.g., mobile phones, PDAs, pagers), microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, multiprocessor systems, network PCs, distributed computing systems, data centers, message processors, routers, switches, and even devices that have not traditionally been considered computing systems (e.g., wearable devices (such as eyeglasses)).

[0128] This computing system also contains several structures, often referred to as “executable components.” For example, the memory of the computing system may contain executable components. The term “executable component” refers to a structure known to those skilled in the art of computing. This structure can be software, hardware, or a combination thereof. For example, when implemented in software, those skilled in the art understand that the structure of an executable component may include software objects, routines, methods, etc., regardless of whether such executable components, which can be executed by one or more processors on the computing system, reside on the heap of the computing system or on a computer-readable storage medium. The structure of an executable component resides on a computer-readable medium in a form that, when executed by one or more processors of the computing system, causes the computing system to perform one or more functions (e.g., functions and methods described herein). Such a structure may be directly computer-readable by the processor, as is the case when the executable component is a binary. Alternatively, the structure may be configured to be interpretable and / or compileable in one or more steps to generate a binary that can be directly interpreted by the processor.

[0129] In this specification, terms such as “component,” “service,” “engine,” “module,” “control,” and “generator” may also be used. In this specification and this application, these terms, with or without modifying clauses, are intended to be synonymous with “executable component” and therefore intended to have a structure familiar to those skilled in the field of computing technology.

[0130] In terms of computer implementations, a computer is generally understood to consist of one or more processors or one or more controllers, and the terms computer, processor, and controller are used interchangeably. Where provided by a computer, processor, or controller, the functionality may be provided by a single dedicated computer, processor, or controller, a single shared computer, processor, or controller, or by multiple individual computers, processors, or controllers (some of which may be shared or distributed). Furthermore, the terms “processor” or “controller” also refer to other hardware capable of performing and / or running such functionality, as exemplified above.

[0131] In general, various embodiments can be implemented in hardware or dedicated chips, circuits, software, logic, or any combination thereof. For example, some features may be implemented in hardware, while others may be implemented in firmware or software executable by a controller, microprocessor, or other computing device, but this disclosure is not limited thereto. Features of embodiments of this disclosure may be described using block diagrams, flowcharts, or other illustrative representations, but it is well known that these blocks, devices, systems, techniques, or methods may be implemented in hardware, software, firmware, dedicated circuits or logic, general-purpose hardware or controllers or other computing devices, or a combination thereof, in non-limiting examples.

[0132] Not all computing systems require a user interface, but in some embodiments, the computing system includes a user interface used to communicate information with the user. The user interface may include not only input mechanisms but also output mechanisms. The principles described herein are not limited to specific forms of output or input mechanisms, and these mechanisms depend on the nature of the device. However, examples of output mechanisms include, for example, speakers, displays, haptic outputs, projections, and holograms. Examples of input mechanisms include, for example, microphones, touchscreens, projections, holograms, cameras, keyboards, styluses, mice, other pointer inputs, and sensors of any kind.

[0133] Conclusion and Terminology To help you understand the scope and content of this specification and the appended claims, some terms are defined below. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by those skilled in the art relating to this disclosure.

[0134] As used herein, the terms “approximately,” “about,” and “substantially” refer to a quantity or state that is close to a specific quantity or state described and that performs the desired function or achieves the desired result. For example, the terms “approximately,” “about,” and “substantially” may refer to a quantity or state that deviates from the specifically described quantity or state by less than 10%, less than 5%, less than 1%, less than 0.1%, or less than 0.01%.

[0135] Various aspects of this disclosure, namely devices, systems, methods, etc., may be described by reference to one or more embodiments or examples that are in the nature of an example. The term “example” as used herein means “serving as an example, case, or illustration,” and should not necessarily be construed as being preferable or advantageous to other embodiments disclosed herein. Furthermore, references to “examples” in this disclosure or embodiments include references to one or more specific examples thereof, and vice versa, and are intended to provide explanatory examples without limiting the scope of this disclosure, the scope of which is indicated not herein but by the appended claims.

[0136] In this specification, unless the context clearly indicates otherwise, a singular noun includes its plural form, and similarly, a plural noun includes its singular form. Therefore, note that in this specification and the attached claims, singular "a," "an," and "the" include plural references unless the context clearly indicates otherwise. For example, a singular reference (e.g., "a widget") includes one, two, or more references unless the context clearly indicates otherwise. Similarly, references to multiple references should be interpreted as including one and / or multiple references unless the content and / or context clearly indicates otherwise. For example, a plural reference (e.g., "widgets") does not necessarily require multiple such references. Instead, it should be understood that, independently of the estimated number of references, one or more references are assumed in this specification unless otherwise specified.

[0137] In this specification, expressions such as “one embodiment,” “embodiment,” and “exemplary embodiment” indicate that the described embodiments may include a particular function, structure, or feature, but not all embodiments are required to include such a particular function, structure, or feature. Furthermore, these expressions do not necessarily refer to the same embodiment. Also, if a particular function, structure, or characteristic is described in relation to an embodiment, it is considered to be within the knowledge of a person skilled in the art that it will affect that function, structure, or characteristic in relation to other embodiments, whether or not it is explicitly described.

[0138] In this specification, terms such as “first” and “second” may be used to describe various elements, but it should be understood that these elements are not limited by these terms. These terms are simply used to distinguish one element from another. For example, without departing from the scope of the exemplary embodiments, the first element may be referred to as the second element, and similarly, the second element may be referred to as the first element. In this specification, the term “and / or” includes any combination of one or more of the terms described herein.

[0139] In this specification, the terms “comprises,” “comprising,” “has,” “having,” “includes,” and / or “including” identify the features, elements, and / or components described, but do not exclude the presence or addition of one or more other features, elements, components, and / or combinations thereof.

[0140] This disclosure includes novel features or combinations of features explicitly disclosed, or generalizations thereof. Various modifications and adaptations to the embodiments described herein will be apparent to those skilled in the art by reading the foregoing description in conjunction with the accompanying drawings. However, any modifications remain within the scope of the non-limiting and exemplary embodiments of this disclosure.

[0141] With respect to any component or embodiment described herein, the possible candidates or alternatives listed for such component may generally be used alone or in combination with each other, unless otherwise understood or stated expressly or implicitly. Furthermore, the list of such candidates or alternatives is merely illustrative and not limiting, unless otherwise understood or stated expressly or implicitly.

[0142] Furthermore, unless otherwise stated, numerical values ​​representing quantities, components, distances, and other measurements used in the specification and claims are understood to be modified by the term “approximately” as defined herein. Thus, unless otherwise stated, numerical parameters described in the specification and accompanying claims are approximations that may vary depending on the desired characteristics intended to be obtained by the subject matter presented herein. At least, without the intention of limiting the scope of the doctrine of equivalents, each numerical parameter should be interpreted using normal rounding based on the reported number of significant figures. Despite the numerical ranges and parameters representing the broad scope of the subject matter presented herein being approximations, numerical values ​​described in specific examples are reported as accurately as possible. However, any numerical value inherently contains a certain degree of error that inevitably arises from the standard deviation inherent in its measurement.

[0143] The headings and subheadings used herein are for organizational purposes only and are not intended to be used to limit the scope of the specification or claims. The terms and expressions used herein are for illustrative purposes only, not limitation, and the use of these terms and expressions is not intended to exclude any equivalents of the features or parts thereof shown or described, although it should be recognized that various modifications are possible within the scope of this disclosure. Thus, although this disclosure is partially specific by particular embodiments and any features, those skilled in the art should understand that modifications and variations of the concepts disclosed herein can be utilized, and such modifications and variations are deemed to be within the scope of this disclosure.

[0144] Furthermore, it will be understood that systems, devices, products, kits, methods, and / or processes according to specific embodiments of this disclosure may include, incorporate, or otherwise constitute characteristics or features (e.g., components, members, elements, parts, and / or parts) described in other embodiments disclosed and / or described herein. Accordingly, various features of a particular embodiment may be compatible with, combined with, included in, and / or incorporated into other embodiments of this disclosure. Therefore, the disclosure of a particular feature relating to a particular embodiment should not be construed as limiting the application or inclusion of such feature to that particular embodiment. Rather, it will be understood that other embodiments may also include such features, members, elements, parts, and / or parts without necessarily departing from the scope of this disclosure.

[0145] Furthermore, unless explicitly stated otherwise, any feature described herein can be combined with other features of the same or different embodiments disclosed herein. Also, various well-known aspects, such as exemplary systems, methods, and apparatus, are not described in particular detail herein so as not to obscure the aspects of the exemplary embodiments. However, such aspects are also assumed herein.

[0146] As will be apparent to engineers in the art, methods, devices, device elements, materials, procedures, and techniques other than those specifically described herein are also applicable to the embodiments broadly disclosed herein without requiring excessive experimentation. All functional equivalents of the methods, devices, device elements, materials, procedures, and techniques specifically described herein that are well known in the art are intended to be included in this disclosure.

[0147] Where a group of materials, compositions, components, or compounds is disclosed herein, it is understood that all individual members of that group and all subgroups thereof are disclosed separately. Where a group of Markush or other groups is used herein, it is intended that all individual members of that group and all possible combinations and subcombinations thereof are included in the separate disclosure.

[0148] The embodiments described above are merely examples. Those skilled in the art can modify, alter, and change specific embodiments without departing from the scope of this specification as defined solely by the appended claims.

Claims

1. A method for monitoring environmental changes, performed by a user device (UE) (2200), Receiving assistance information (1510) from a network node (3300), wherein the assistance information is obtained from monitoring environmental changes, and the receiving (1510), Based on the aforementioned assistance information, the machine learning model (920) present in the UE is managed (1520), Methods that include...

2. The aforementioned management includes one or more of the following: training, retraining, and updating. The method according to claim 1.

3. The aforementioned assistance information includes location assistance information. The method according to claim 1 or 2.

4. The aforementioned environmental changes were detected by the second machine learning model (910). The method according to any one of claims 1 to 3.

5. When the aforementioned environmental change is detected to exceed a threshold that is expected to degrade the performance of the machine learning model, assistance information is generated. The method according to any one of claims 1 to 4.

6. The aforementioned machine learning model includes fingerprinting positioning. The method according to any one of claims 1 to 5.

7. The network node includes one or more of the following: a location server (960), a gNB (3300), and a location management function (LMF) (3300). The method according to any one of claims 1 to 6.

8. This further includes using a second machine learning model management decision to assist in the management of the aforementioned machine learning model. The method according to any one of claims 1 to 7.

9. The second machine learning model is deployed on at least one of the following: the network side, the network node, the UE, the gNB (3300), and the location management function (LMF) (3300). The method according to claim 8.

10. The model update status report further includes updating the location server (960). The method according to any one of claims 1 to 9.

11. The further includes receiving second assistance information from a location server for managing the machine learning model. The method according to any one of claims 1 to 10.

12. The aforementioned UE includes a sensor (2200). The method according to any one of claims 1 to 11.

13. A method for monitoring environmental changes, performed by a user device (UE) (2200), Monitoring environmental changes within the deployment area (1310), To assist in the management of the machine learning model (920), assist information regarding environmental changes is notified to the network node (3300) (1320), Methods that include...

14. The aforementioned monitoring includes monitoring the deployment area using a machine learning model (920). The method according to claim 13.

15. Changes within the aforementioned deployment area are counted as environmental changes if they exceed a threshold. The method according to claim 13 or 14.

16. The threshold is selected based on the expected degradation of the machine learning model. The method according to claim 15.

17. The aforementioned machine learning model includes fingerprinting positioning. The method according to any one of claims 13 to 16.

18. The network node includes one or more of the following: a location server (960), a gNB (3300), and a location management function (LMF) (3300). The method according to any one of claims 13 to 17.

19. This further includes using a second machine learning model management decision to assist in the management of the aforementioned machine learning model. The method according to any one of claims 13 to 18.

20. The second machine learning model is deployed on at least one of the following: the network side, the network node, the UE, the gNB (3300), and the location management function (LMF) (3300). The method according to claim 19.

21. The model update status report further includes updating the location server (960). The method according to any one of claims 13 to 20.

22. The system further includes receiving second assistance information from a location server (960) for managing the machine learning model. The method according to any one of claims 13 to 21.

23. The aforementioned UE includes a sensor (2200). The method according to any one of claims 13 to 22.

24. A method for monitoring environmental changes, performed by a network node (3300), Monitoring environmental changes within the deployment area (1710), To transmit assist information for use in model management of machine learning models to the user device (UE) (2200) (1720), Methods that include...

25. The aforementioned monitoring is performed using a second machine learning model. The method according to claim 24.

26. Changes detected within the aforementioned deployment area include environmental changes that exceed a certain threshold expected to degrade the machine learning model. The method according to claim 24 or 25.

27. The aforementioned model management includes at least one of training, retraining, and updating. The method according to any one of claims 24 to 26.

28. The aforementioned assistance information includes location assistance information. The method according to any one of claims 24 to 27.

29. The machine learning model or the second machine learning model includes a fingerprinting positioning method. The method according to any one of claims 24 to 28.

30. The aforementioned environmental changes are derived based on the movement information of one or more objects within the deployment area that are expected to significantly alter the wireless environment. The method according to any one of claims 24 to 29.

31. One or more sensors are attached to one or more objects, and the one or more sensors are operable to transmit position and / or velocity information to a position server (960). The method according to claim 30.

32. The network node includes the location server. The method according to claim 31.

33. The position and / or velocity information is aggregated to determine an aggregated environmental change metric that indicates the degree of environmental change. The method according to claim 31 or 32.

34. The aforementioned position and / or velocity information is summed or averaged to obtain an aggregated environmental change metric. The method according to claim 31 or 32.

35. Several location and / or velocity data points are weighted differently before being summed or averaged to obtain an aggregated environmental change metric. The method according to any one of claims 31 to 34.

36. The position and / or velocity information is received within the Sensor-ProvideLocationInformation information element from one or more sensors. The method according to any one of claims 31 to 35.

37. The position and / or velocity information may be used to determine the movement of one or more substantial objects and to help determine how much the machine learning model or a second machine learning model degrades over time. The method according to any one of claims 31 to 36.

38. The position and / or velocity information is received within the CommonIEsProvideLocationInformation information element. The method according to any one of claims 31 to 37.

39. This further includes using a second machine learning model's model management decision to assist in the management decisions of the aforementioned machine learning model. The method according to any one of claims 24 to 38.

40. The first machine learning model is deployed to the UE, and the second machine learning model is deployed to the network side. The method according to claim 39.

41. User equipment (2200) for monitoring environmental changes, A processing circuit (2202) configured to perform any of the steps of the method described in any one of claims 1 to 23, A power supply circuit (2208) configured to supply power to the processing circuit, User equipment (2200), including...

42. A network node (3300) for monitoring environmental changes, A processing circuit (3302) configured to perform any of the steps of the method described in any one of claims 24 to 40, A power supply circuit (3308) configured to supply power to the processing circuit, Network nodes (3300), including the following.

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