Dynamic node critical performance indicator reporting in open radio access network

Abstract

Dynamic node key performance metrics reporting in an open radio access network is implemented (e.g., using computerized tools). For example, a system may include a processor and a memory storing executable instructions that, when executed by the processor, facilitate execution of operations that include determining a resource utilization of a network node; determining a reporting period of a request suitable for a key performance indicator of the network node of an extended application request by the radio access network smart controller; and generating a reporting period suggestion applicable to the network node and the extended application using a reporting model generated by using machine learning on the basis of past resource utilization rates other than the resource utilization rates and past reporting periods other than the requested reporting period.

Description

Related applications

[0001] This application claims priority to U.S. nonprovisional patent application filed June 19, 2023, serial number 18 / 337,149, entitled “DYNAMICNODE KEY PERFORMANCE INDICATOR REPORTING IN OPEN RADIO ACCESS NETWORK”, the entire contents of which are incorporated herein by reference. Background Technology

[0002] Currently, E2 nodes cannot identify the frequency at which they can report data to the controller, potentially leading to subscription rejection and wasted time reconfiguring extended applications (xApps). E2 nodes are susceptible to the cyclical fluctuations in network traffic, which can affect their ability to respond to frequently reported data. xApps are also unaware of these limitations of E2 nodes and therefore cannot adapt to potential network traffic fluctuations. Manual adjustments can be cumbersome, requiring multiple trials and errors, which becomes particularly difficult due to fluctuating network traffic conditions. Even forcing E2 nodes to accept subscriptions would negatively impact their performance regarding user equipment (UE) traffic.

[0003] The above background description concerning telecommunications systems is intended only to provide some background technology on current issues and is not intended to be exhaustive. Further contextual information will become more apparent upon review of the detailed description below. Attached Figure Description

[0004] Figure 1 This is a block diagram of an exemplary system according to one or more embodiments described herein.

[0005] Figure 2 This is a schematic diagram generated based on one or more embodiments described herein, representing an exemplary reporting cycle suggestion.

[0006] Figure 3 This is a schematic diagram of an exemplary flexible xApp subscription according to one or more embodiments described herein.

[0007] Figure 4 This is a schematic diagram illustrating exemplary model training according to one or more embodiments described herein.

[0008] Figure 5 This is a schematic diagram of an exemplary process flow according to one or more embodiments described herein.

[0009] Figure 6This is a flowchart of a process associated with reporting key performance indicators for dynamic nodes in an open radio access network, according to one or more embodiments described herein.

[0010] Figure 7 This is a block flowchart of a process associated with reporting key performance indicators of dynamic nodes in an open radio access network, according to one or more embodiments described herein.

[0011] Figure 8 This is a block flowchart of a process associated with reporting key performance indicators of dynamic nodes in an open radio access network, according to one or more embodiments described herein.

[0012] Figure 9 This is a block flowchart of a process associated with reporting key performance indicators of dynamic nodes in an open radio access network, according to one or more embodiments described herein.

[0013] Figure 10 This is an example non-limiting computing environment in which one or more embodiments described herein can be implemented.

[0014] Figure 11 This is an example, non-limiting network environment in which one or more embodiments described herein can be implemented. Detailed Implementation

[0015] This disclosure will now be described with reference to the accompanying drawings, wherein similar reference numerals are used to denote similar elements. In the following description, numerous specific details are set forth for ease of explanation in order to provide a thorough understanding of this disclosure. However, it will be apparent, however, that this disclosure may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form to facilitate the description of this disclosure.

[0016] As implied above, data reporting in Open Radio Access Networks (ORANs) can be improved in a variety of ways, and various embodiments are described herein for this purpose and / or other purposes. The disclosed subject matter relates to telecommunications systems, and more specifically, to dynamic node key performance indicator reporting in ORANs.

[0017] According to an embodiment, a system may include a processor and a memory storing executable instructions that, when executed by the processor, facilitate the execution of operations including: determining resource utilization of a network node; determining a requested reporting period for key performance indicators applicable to the network node, as requested by an extended application of a radio access network intelligent controller; and generating a reporting period recommendation applicable to the network node and the extended application using a reporting model generated by using machine learning, based on past resource utilization other than the requested resource utilization and past reporting periods other than the requested reporting period.

[0018] In one or more embodiments, the reporting cycle recommendation may include a recommended reporting cycle and / or a minimum reporting cycle. In one or more embodiments, the network node may include an E2 node.

[0019] In one or more embodiments, these operations may further include receiving a subscription request from an extended application, wherein the reporting period recommendation is generated in response to receiving the subscription request. In this regard, the operations may further include determining a priority level applicable to the subscription request based on defined priority criteria, wherein the reporting period recommendation is also generated based on the priority level applicable to the subscription request. Furthermore, in this regard, the operations may further include bypassing the reporting period recommendation in response to determining that the subscription request includes a threshold high priority level based on defined priority criteria.

[0020] In one or more embodiments, the extended application may be among a set of extended applications subscribed to on the network node. In one or more embodiments, the reporting cycle recommendation may be determined as a threshold level for maintaining network performance applicable to that network node, based on defined network performance criteria.

[0021] In one or more embodiments, key performance indicators may include a signal-to-interference-plus-noise ratio (SNR) or a block error rate applicable to a network node. In one or more embodiments, the extended application may include a network optimization extended application. In one or more embodiments, the reporting cycle suggestion may include a reporting frequency applicable to the extended application.

[0022] In another embodiment, a non-transient machine-readable medium may include executable instructions that, when executed by a processor, facilitate the execution of operations including: determining the resource utilization of an E2 network node; determining a requested reporting period for key performance indicators applicable to the E2 network node, as requested by an extended application of the radio access network intelligent controller; and using a reporting model to determine a recommended reporting period applicable to the E2 network node and the extended application, the reporting model being generated by using machine learning, based on past resource utilization other than the resource utilization and past reporting periods other than the requested reporting period.

[0023] In one or more embodiments, the proposed reporting period may include a minimum reporting period. In one or more embodiments, the operation may further include receiving a subscription request from the extension application, wherein the proposed reporting period is generated in response to receiving the subscription request.

[0024] In one or more embodiments, the operation may further include determining a priority level applicable to the subscription request based on a defined priority criterion, wherein the suggested reporting period is also generated based on the priority level applicable to the subscription request. In this regard, the operation may further include bypassing the suggested reporting period in response to determining that the subscription request includes a high priority level based on the defined priority criterion.

[0025] According to another embodiment, a method may include: determining resource utilization of a network node by a radio access network intelligent controller including a processor; determining, by the radio access network intelligent controller, a requested reporting period for key performance indicators applicable to the network node, requested by an extended application associated with the radio access network intelligent controller; and generating, by the radio access network intelligent controller, recommended data representing a suggested reporting period applicable to the network node and the extended application, using a reporting machine learning model, the reporting machine learning model being generated by using artificial intelligence, based on past resource utilization other than the requested resource utilization, and past reporting periods other than the requested reporting period.

[0026] In one or more embodiments, the reporting cycle recommendation may be determined as a threshold level for maintaining network performance applicable to the network node, based on defined network performance criteria. In one or more embodiments, key performance indicators may include ratio data representing the signal-to-interference-plus-noise ratio applicable to the network node or block error rate data representing the block error rate applicable to the network node. In one or more embodiments, the recommended data may include reporting frequency data representing the reporting frequency applicable to the extended application.

[0027] It should be understood that additional performance, configuration, implementation, protocols, etc., may be utilized in combination with the components described herein or as will be known by those skilled in the art.

[0028] Examples of this document may include machine learning (ML)-based rApps to predict minimum and suggested periodicity for parameters used to subscribe to E2 nodes. For example, extended subscription messages with xApp priority may be utilized when sent to E2 nodes. Examples of this document may include an application programming interface (API) to a near real-time (RT) radio access network intelligent controller (RIC) to manage xApp subscriptions.

[0029] The embodiments described herein can dynamically update the suggested and / or minimum reporting periodicity, for example, based on load, utilization, etc. In this regard, the embodiments described herein can use ML-based recommendations to implement periodic and / or event-triggered policy updates. An extended xApp framework can be utilized to manage subscription recommendations with xApp. For example, by recommending appropriate periodicity for a given use case, optimized Quality of Service (QoS) can be enabled. Furthermore, for example, by recommending appropriate periodicity for resource utilization, optimized energy efficiency can be enabled.

[0030] The various embodiments described herein optimize the xApp subscription process by taking into account the time, energy, and / or QoS applicable to the xApp, as well as the resource utilization of the E2 node. The embodiments described herein enable the deployment of ML models as rApps in non-RT RICs, for example, to predict the minimum and recommended reporting periods for xApps using parameters for subscribing to E2 nodes. The ML model can utilize the current state of the E2 node to which a particular xApp is subscribed as input parameters and is then used to predict the minimum reporting period that E2 node will be able to provide (e.g., without affecting the xApp's QoS). The ML model can also be used to update the periodicity of already subscribed xApps, for example, to efficiently support more xApps. These updates can be automatically scheduled or triggered by events, for example, based on defined network requirements. Note that such updates can be performed based on utilization, the load(s) of the E2 node, and / or the number of xApps subscribing to the E2 node. xApps can rely on one or more of a variety of methods to accomplish their tasks and can switch between these methods, for example, based on the available reporting periods determined using the ML model. For example, if the ML model and / or components or elements in this paper predict that the E2 node can support the requested reporting cycle to send its parameters, then xApp can rely on the first approach. However, if the ML model and / or components or elements in this paper suggest a higher reporting cycle, then xApp can be adjusted, for example, by following the second approach, in which it is able to rely on operating with a lower frequency of reporting cycles, even if this results in less reliable performance for xApp. The above approaches can balance the availability of the E2 node at the expense of xApp performance.

[0031] The embodiments described herein can be adapted to the corresponding defined priorities of xApps. This allows higher-priority xApps to bypass model predictions and enables E2 nodes to follow the reporting cycles requested by the xApps. Therefore, the ML model can update the suggested reporting cycles for lower-priority xApps, for example, to accommodate additional resources allocated to higher-priority xApps. This may cause lower-priority xApps to switch to alternative methods utilizing longer reporting cycles.

[0032] In various embodiments, the ML model (e.g., which can be used to predict the minimum reporting cycle for E2 nodes) can be trained using data reflecting usage parameters of the E2 nodes, such as CPU utilization, memory, and / or heat levels. Additionally or alternatively, the ML model can rely on data corresponding to xApp demand and / or historical subscription details (e.g., corresponding to the current demand of the xApp and / or the historical state of previous subscriptions, including modified, failed, and / or accepted requests). Note that the ML model can be used to optimize QoS, for example, by suggesting an appropriate reporting cycle for a given use case, resulting in more reliable reporting from the E2 nodes, which can lead to improved QoS and reduced latency for the xApp. Furthermore, the ML model can be used to optimize energy efficiency, for example, by suggesting an appropriate reporting cycle, resulting in more efficient resource utilization and less wasted power.

[0033] Now go to Figure 1 This document illustrates an example non-limiting system 102 according to one or more embodiments thereof. System 102 may include computerized tools that can be configured to perform various operations related to dynamic E2 node KPI reporting in an ORAN. System 102 may include one or more of various components, such as memory 104, processor 106, bus 108, resource component 110, request determination component 112, suggestion component 114, communication component 116, priority component 118, bypass component 120, multiple models 122, service management and orchestration (SMO) 124, non-RT RIC 126, controller 128 (e.g., near-RT RIC), database 130, multiple xApps 132, xApp framework 134, and / or radio access network (RAN) 136 (e.g., E2 node). In various embodiments, system 102 may be communicatively coupled to one or more user equipment (UE) 138, or may also include one or more user equipment (UE) 138. In various embodiments, one or more of the following components may be communicatively or operatively connected to each other (e.g., via a bus or wireless network): memory 104, processor 106, bus 108, resource component 110, request determination component 112, suggestion component 114, communication component 116, priority component 118, bypass component 120, multiple models 122, SMO 124, non-RT RIC 126, controller 128, database 130, multiple xApps 132, xApp framework 134, RAN 136, and / or UE 138, to perform one or more functions of system 102.

[0034] In various embodiments, SMO 124 may include a management and orchestration layer that controls the configuration and automation aspects of RIC and / or RAN elements. In this regard, SMO 124 may mount xApp and / or rApp onto the RIC components. In various embodiments, controller 128 may include a near-RT RIC.

[0035] In various embodiments, controller 128 (e.g., near-RT RIC) may include database 130, one or more xApps 132, and / or xApp framework 134. Database 130 may store KPIs collected from E2 nodes (e.g., RAN 136) (e.g., via resource component 110) and / or store subscription details (e.g., requested KPIs, accepted / failed requests). In various embodiments, xApp 132 may subscribe to KPIs of E2 nodes. xApp framework 134 may include APIs for xApps (e.g., xApp 132) used to subscribe to newly registered E2 nodes (e.g., RAN 136) and / or enable configuration updates applicable to E2 nodes (e.g., RAN 136) and / or xApps (e.g., xApp 132).

[0036] According to an embodiment, resource component 110 can determine the resource utilization of a network node (e.g., RAN 136). In one or more embodiments, the network node (e.g., RAN 136) may include an E2 node. In this regard, resource component 110 may store KPIs and / or subscription details (e.g., requested KPIs, accepted / failed requests) collected from the E2 node (e.g., RAN 136) in database 130.

[0037] According to embodiments, the request determination component 112 can determine the reporting period for a requested KPI (e.g., applicable to RAN 136) by xApp 132 requested by a near-RT RIC (e.g., controller 128). In one or more embodiments, xApp 132 may include a network optimization xApp 132. In various embodiments, xApp 132 may be among a set of xApps (e.g., xApp 132) subscribed to on RAN 136 (e.g., E2 node). In one or more embodiments, the KPI may include a signal-to-interference-plus-noise ratio (SINR) or a block error rate (BER) for a network node (e.g., RAN 136).

[0038] According to embodiments, the recommendation component 114 may use (e.g., via the recommendation component 114) a reporting model (e.g., ML model 204) (e.g., one of (a plurality of) models 122) to generate reporting cycle recommendations for RAN 136 and / or xApp 132, which are generated by using machine learning, based on past resource utilization other than the current resource utilization, and past reporting cycles other than the currently requested reporting cycle. In various embodiments, the reporting cycle recommendations may include a recommended reporting cycle and / or a minimum reporting cycle. In one or more embodiments, the reporting cycle recommendations may be determined (e.g., via the recommendation component 114) as a threshold level for maintaining defined network performance applicable to network nodes (e.g., RAN 136) according to defined network performance criteria. Such defined network performance criteria may include one or more of the following: such as packet loss, overheating of components of system 102, near-maximum utilization of CPU and / or RAM of RAN 136 or another component of system 102, bandwidth, throughput, latency, jitter, or another suitable network performance criterion. In some embodiments, the reporting cycle recommendations may include a reporting frequency applicable to xApp 132. In the various embodiments described herein, the recommendation component 114 may generate a reporting cycle recommendation, for example, by analyzing CPU utilization and radio access memory (RAM) data from E2 nodes (e.g., RAN 136), analyzing xApp 132 requirements (e.g., the requested reporting cycle), and historical subscription request details from near-RT RICs (e.g., controller 128) and / or updating the configuration on E2 nodes (e.g., RAN 136) as an O1 message.

[0039] According to an embodiment, communication component 116 can receive subscription requests from xApp 132. In this regard, a report cycle recommendation can be generated in response to receiving a subscription request from xApp 132 (e.g., by recommendation component 114). Note that communication component 116 may include the hardware required to implement various communication protocols (e.g., infrared (“IR”), shortwave transmission, near field communication (“NFC”), Bluetooth, Wi-Fi, Long Term Evolution (“LTE”), 3G, 4G, 5G, 6G, Global System for Mobile Communications (“GSM”), Code Division Multiple Access (“CDMA”), satellite, visual cues, radio waves, etc.).

[0040] According to an embodiment, priority component 118 can determine the priority level applicable to a subscription request from xApp 132 based on defined priority criteria. In this regard, reporting cycle recommendations can also be generated (e.g., via recommendation component 114) based on the priority level applicable to the subscription request. In various embodiments, such priority ID / level can be included in configuration data applicable to the corresponding xApp 132. Note that priority as used herein can include one or more of normal priority, high priority, low priority, or other appropriately defined priority levels or IDs. According to an embodiment, in response to (e.g., via priority component 118) determining that a subscription request (e.g., from xApp 132) includes a threshold high priority level, and based on defined priority criteria, bypass component 120 can bypass the reporting cycle recommendations. In this regard, the corresponding E2 node (e.g., RAN 136) can accept the subscription from xApp 132, regardless of whether the performance of the E2 node will be negatively affected.

[0041] The various embodiments described herein may employ artificial intelligence or machine learning systems and techniques to facilitate learning of user behavior, context-based scenarios, preferences, etc., in order to promote automated actions taken with high confidence. Utility-based analytics can be used to weigh the benefits of taking action against the costs of taking incorrect action. Probability-based or statistical analytics may be employed in combination with the above and / or the following.

[0042] Note that the system and / or associated controllers, servers or machine learning components described herein may include (multiple) artificial intelligence components that may employ artificial intelligence (AI) models and / or ML or ML models that can learn to perform the functions described above or below (e.g., by training using historical training data and / or feedback data).

[0043] In some embodiments, the proposal component 114 may include an AI and / or ML model that can be trained (e.g., via supervised and / or unsupervised techniques) to perform the functions described above or below using historical training data, including various contextual conditions corresponding to various augmentation network optimization operations. In this example, such an AI and / or ML model can also be learned (e.g., via supervised and / or unsupervised techniques) to perform the functions described above or below using training data including feedback data, which can be collected and / or stored (e.g., in memory) by the proposal component 114. In this example, such feedback data may include various instructions described above / below, which, for example, can be input into the system herein over time in response to observed / stored context-based information.

[0044] The AI / ML components of this paper can initiate associated operations based on a defined confidence level determined by usage information (e.g., feedback data). For example, based on learning to perform the function described above using feedback data, performance information, and / or past performance information of this paper, the recommendation component 114 of this paper can initiate operations associated with determining various thresholds of this paper (e.g., motion pattern threshold, input pattern threshold, similarity threshold, authentication signal threshold, audio frequency threshold, or other suitable thresholds).

[0045] In one embodiment, the recommendation component 114 may perform a utility-based analysis that weighs the costs and benefits of initiating the operations described above. In this embodiment, the recommendation component 114 may use one or more additional contextual conditions to determine various thresholds for this document.

[0046] To facilitate the functionality described above, the proposed component 114 of this paper can perform classification, correlation, inference, and / or expression related to the principles of artificial intelligence. For example, the proposed component 114 can employ an automatic classification system and / or automatic classification. In one example, the proposed component 114 can employ probability-based and / or statistical analysis (e.g., incorporating utility and cost into the analysis) to learn and / or generate inferences. The proposed component 114 can employ any suitable machine learning-based, statistical, and / or probability-based techniques. For example, the proposed component 114 can employ expert systems, fuzzy logic, support vector machines (SVM), hidden Markov models (HMM), greedy search algorithms, rule-based systems, Bayesian models (e.g., Bayesian networks), neural networks, other nonlinear training techniques, data fusion, utility-based analysis systems, systems employing Bayesian models, and so on. In another example, the proposed component 114 can perform a collection of machine learning computations. For example, it is suggested that component 114 may perform a set of clustering machine learning computations, a set of logistic regression machine learning computations, a set of decision tree machine learning computations, a set of random forest machine learning computations, a set of regression tree machine learning computations, a set of least squares machine learning computations, a set of instance-based machine learning computations, a set of regression machine learning computations, a set of support vector regression machine learning computations, a set of k-means machine learning computations, a set of spectral clustering machine learning computations, a set of rule learning machine learning computations, a set of Bayesian machine learning computations, a set of deep Boltzmann machine computations, a set of deep belief network computations, and / or a set of different machine learning computations.

[0047] Now go to Figure 2This illustrates a schematic diagram 200 of exemplary reporting cycle suggestion generation (e.g., via suggestion component 114) according to one or more embodiments described herein. In various embodiments, suggestion component 114 may predict a minimum supported reporting cycle (e.g., in output 206) and / or a suggested reporting cycle (e.g., in output 206), for example, for a registered E2 node (e.g., RAN 136). Input 202 to ML model 204 may include, for example, resource utilization data (e.g., applicable to RAN 136) and / or xApp 132 demand data. The minimum reporting cycle may indicate the highest granularity at which an E2 node (e.g., RAN 136) can report data. The suggested reporting cycle may indicate the most energy-efficient granularity for the corresponding E2 node. In one or more embodiments, suggestion component 114 may trigger, for example, an O1 message from SMO 124 to configure the E2 node (e.g., RAN 136) (e.g., via suggestion component 114) using updated reporting cycle suggestions (e.g., using resource utilization data and / or xApp demand data). Such recommendations can be generated periodically (e.g., via recommendation component 114), for example, to update the recommendation cycle of E2 nodes (e.g., RAN 136).

[0048] Figure 3 This is a schematic diagram 300 illustrating an exemplary flexible xApp 132 subscription according to one or more embodiments described herein. Note that xApp 132 may support different reporting periods on requested KPIs. When a reporting period update is configured on an E2 node (e.g., RAN 136): (1) the configuration update is sent via E2 (e.g., via communication component 116) to a near-RT RIC (e.g., controller 128), (2) the xApp framework 134 manages the subscribed xApp 132 to trigger subscription changes for xApp 132s that no longer support the requested period, and (3) RAN 136 may utilize the recommended and minimum available reporting period. Longer reporting periods may impact the performance of xApp 132. However, this trade-off optimizes the subscription to ensure that the E2 node (e.g., RAN 136) can continue to support multiple xApp 132 subscriptions.

[0049] Figure 4This is a schematic diagram 400 of an exemplary ML model training 402 (e.g., ML model 204) according to one or more embodiments described herein. ML model 204 can be trained, for example, by utilizing data collected from an E2 node (e.g., RAN 136) and subscribing to xApp 132. In this regard, ML model 204 can be trained using: (1) CPU utilization on the E2 node (e.g., RAN 136), (2) available RAM on the E2 node (e.g., RAN 136), (3) xApp 132 requirements (e.g., requested reporting cycles), and / or (4) historical subscription data (e.g., accepted and / or revised subscription requests). In various embodiments, ML model training 402 can be performed on a non-RT RIC 126 (e.g., in a corresponding dedicated rApp). Note that the above process can utilize data collected by the host SMO 124. Alternatively, ML model training 402 can be performed offline, for example, using data collected from multiple networks, and then deployed to the rApp for prediction.

[0050] Figure 5This is a schematic diagram of an exemplary process flow 500 according to one or more embodiments described herein. At 502, SMO 124 may configure controller 128 and / or E2 nodes (e.g., RAN 136). This may include (e.g., via SMO 124) setting an initial recommended reporting period for E2 node KPIs, and onboard xApp 132 on controller 128. At 504, the E2 node (e.g., RAN 136) may utilize controller 128 to register, thereby identifying all KPIs that controller 128 can subscribe to, as well as the recommended and minimum reporting period for each, and connect to controller 128. The xApp 132 deployed on controller 128 may determine the recommended reporting period and adjust accordingly, e.g., before subscribing to KPIs. At 506, SMO 124 may subscribe to resource KPIs of the E2 node (e.g., RAN 136), which may track one or more elements, such as CPU and / or RAM usage applicable to RAN 136. At 508, SMO 124 can host one or more xApps 132. At 510, one or more xApps can subscribe to periodic data for a specific KPI. At 512, RAN 136 can determine whether the periodicity and / or KPI is acceptable. In this regard, RAN 136 can determine whether the periodicity falls within a defined acceptable range. At 514, RAN 136 can send a subscription success message to controller 128 (e.g., if within the defined acceptable range), and KPI data can begin to flow periodically. At 516, if the load on an E2 node (e.g., RAN 136) fluctuates drastically, ML model 204 in SMO 124 can adjust the recommended and / or minimum reporting period for the KPI in the E2 node (e.g., RAN 136). For example, during peak traffic periods, the corresponding network traffic may surge. At 518, updated resource KPIs can be sent from RAN 136 to SMO 124. At 520, it is suggested that component 114 (e.g., via ML model 204) can calculate the reporting period applicable to xApp 132. At 522, SMO 124 can send the adjusted reporting period to RAN 136. At 524, RAN 136 can send configuration updates to controller 128, for example, to adjust the corresponding subscription accordingly. At 526, controller 128 and / or xApp 132 can send the adjusted subscription to RAN 136.

[0051] Figure 6This is a flowchart of process 600 associated with reporting dynamic node critical performance indicators in ORAN according to one or more embodiments described herein. Note that the priority ID / level of xApp 132 may be assigned to xApp 132 onboard in SMO 124 (e.g., via system 102). In this regard, some xApps may require strict reporting cycles, for example, to maintain critical functionality. At 602, a new xApp 132 subscription request is received by RAN 136 (e.g., via communication component 116). xApp 132 may send an E2 message with the subscription request and / or xApp priority ID / level. At 604, the priority ID / level may be determined (e.g., via priority component 118). In various embodiments, this priority ID / level may be included in the configuration data applicable to the corresponding xApp 132. If at 604, the priority level / ID (e.g., via priority component 118) is determined to be excluding the defined high priority (e.g., no at 604), the subscription request can be supported using the recommendations configured at the E2 node (e.g., RAN 136) (e.g., at 608). If at 604, the priority level / ID (e.g., via priority component 118) is determined to include the defined high priority (e.g., yes at 604), then the E2 node (e.g., RAN 136) can accept the subscription request regardless and can (e.g., via bypass component 120) bypass the recommended reporting cycle (e.g., at 606). At 610, resource overflow (e.g., network degradation, such as packet loss, system overheating, near-maximum CPU and / or RAM utilization, etc.) can be determined (e.g., via resource component 110). Note that such performance degradation can lead to performance degradation across the entire corresponding network. If the E2 node (e.g., RAN 136) can support the new xApp 132 without any degradation in the xApp (e.g., no at 610), there is no need to update the reporting cycle to accommodate additional resource allocation for the priority xApp. If the E2 node (e.g., RAN 136) cannot support the new xApp without any degradation in the xApp (e.g., yes at 610), an update is triggered at 612 to (e.g., via recommendation component 114) update the frequency of the reporting cycle for the xApp (e.g., with lower priority). The updated (e.g., lower priority) xApp will follow another appropriate approach if necessary.

[0052] Figure 7This is a block flowchart of process 700 associated with dynamic node key performance indicator reporting in an ORAN according to one or more embodiments described herein. At 702, process 700 may include (e.g., via resource component 110) determining the resource utilization of a network node (e.g., RAN 136). At 704, process 700 may include (e.g., via request determination component 112) determining a requested reporting period for key performance indicators applicable to the network node (e.g., RAN 136) requested by xApp 132 of Radio Access Network Intelligent Controller 128. At 706, process 700 may include generating a reporting period recommendation for the network node (e.g., RAN 136) and extended application (xApp 132) using a reporting model (e.g., ML model 204), (e.g., via recommendation component 114), which is generated by using machine learning, based on past resource utilization other than the requested resource utilization and past reporting periods other than the requested reporting period.

[0053] Figure 8 This is a block flowchart of process 800 associated with reporting dynamic node key performance indicators in an ORAN according to one or more embodiments described herein. At 802, process 800 may include (e.g., via resource component 110) determining the resource utilization of an E2 network node (e.g., RAN 136). At 804, process 800 may include (e.g., via request determination component 112) determining a requested reporting period for key performance indicators applicable to the E2 network node (e.g., RAN 136) requested by an extended application (e.g., xApp 132) of the Radio Access Network Intelligent Controller 128. At 806, process 800 may include using a reporting model (e.g., ML model 204), (e.g., via recommendation component 114) to determine a recommended reporting period for the E2 network node (e.g., RAN 136) and the extended application (e.g., xApp 132), the reporting model being generated using machine learning, based on past resource utilization other than the requested resource utilization and past reporting periods other than the requested reporting period.

[0054] Figure 9This is a block flowchart of process 900 associated with dynamic node key performance indicator reporting in an ORAN according to one or more embodiments described herein. At 902, process 900 may include determining the resource utilization of a network node (e.g., RAN 136) by a radio access network intelligent controller (e.g., via resource component 110) including a processor. At 904, process 900 may include determining the reporting period for requested key performance indicators applicable to the network node (e.g., RAN 136) by a radio access network intelligent controller (e.g., via request determination component 112) requested by an extended application (e.g., xApp 132) associated with radio access network intelligent controller 128. At 906, process 900 may include generating recommendation data representing reporting cycle recommendations for network nodes (e.g., RAN 136) and extended applications (e.g., xApp 132) by a radio access network intelligent controller (e.g., via recommendation component 114) using a reporting machine learning model (e.g., ML model 204), which is generated by using artificial intelligence, based on past resource utilization other than the resource utilization and past reporting cycles other than the requested reporting cycle.

[0055] To provide additional context for the various embodiments described herein, Figure 10 The following discussion is intended to provide a brief general description of a suitable computing environment 1000 in which various embodiments of the embodiments described herein can be implemented. While the embodiments have been described above in the general context of computer-executable instructions that can run on one or more computers, those skilled in the art will recognize that these embodiments can also be used in combination with other program modules and / or implemented as a combination of hardware and software.

[0056] Typically, program modules include routines, programs, components, data structures, etc., that perform specific tasks or implement specific abstract data types. Furthermore, those skilled in the art will appreciate that various methods can be practiced in conjunction with other computer system configurations, including single-processor or multi-processor computer systems, minicomputers, mainframes, Internet of Things (IoT) devices, distributed computing systems, and personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, etc., each of which can be operatively coupled to one or more associated devices.

[0057] The illustrative embodiments described herein can also be practiced in a distributed computing environment, where certain tasks are performed by remote processing devices linked via a communication network. In a distributed computing environment, program modules can reside on both local and remote memory storage devices.

[0058] Computing devices typically include various media, which may include computer-readable storage media, machine-readable storage media, and / or communication media, these two terms being used interchangeably herein as follows. A computer-readable storage medium or a machine-readable storage medium can be any storage medium accessible by a computer and includes both volatile and non-volatile media, removable media, and non-removable media. By way of example, and not limitation, a computer-readable storage medium or a machine-readable storage medium can be implemented in conjunction with any method or technique for storing information, such as computer-readable or machine-readable instructions, program modules, structured data, or unstructured data.

[0059] Computer-readable storage media may include, but are not limited to: random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, optical disc read-only memory (CDROM), digital versatile disc (DVD), Blu-ray disc (BD) or other optical disc storage devices, magnetic cartridges, magnetic tapes, disk storage devices or other magnetic storage devices, solid-state drives or other solid-state storage devices, or other tangible and / or non-transient media that can be used to store desired information. In this regard, the terms “tangible” or “non-transient” as used herein for storage devices, memories, or computer-readable media shall be understood to exclude only the propagation of transient signals themselves, without waiving the rights to all standard storage devices, memories, or computer-readable media that do not only propagate transient signals themselves.

[0060] A computer-readable storage medium can be accessed by one or more local or remote computing devices, for example via access requests, queries or other data retrieval protocols, for various operations concerning the information stored on the medium.

[0061] Communication media typically embody computer-readable instructions, data structures, program modules, or other structured or unstructured data in data signals such as modulated data signals, like carrier waves or other transmission mechanisms, and include any medium for delivering or transmitting information. The term "modulated data signal" or signal refers to a signal having one or more of its characteristics, which are set or altered in such a way as to encode information in one or more signals. By way of example, and not limitation, communication media include wired media (such as wired networks or direct wired connections) and wireless media (such as acoustic waves, RF, infrared, and other wireless media).

[0062] Refer again Figure 10 An example environment 1000 for implementing various embodiments of the various aspects described herein includes a computer 1002, which includes a processing unit 1004, a system memory 1006, and a system bus 1008. The system bus 1008 couples system components, including but not limited to the system memory 1006, to the processing unit 1004. The processing unit 1004 can be any processor from a variety of commercially available processors. Dual microprocessors and other multiprocessor architectures can also be used as the processing unit 1004.

[0063] System bus 1008 can be any of several types of bus architectures, and it can also use any of a variety of commercially available bus architectures to interconnect to the memory bus (with or without a memory controller), peripheral bus, and local bus. System memory 1006 includes ROM 1010 and RAM 1012. The Basic Input / Output System (BIOS) can be stored in non-volatile memory such as ROM, erasable programmable read-only memory (EPROM), or EEPROM, containing basic routines that facilitate the transfer of information between components within computer 1002, such as during startup. RAM 1012 may also include high-speed RAM, such as static RAM for caching data.

[0064] Computer 1002 also includes an internal hard disk drive (HDD) 1014 (e.g., EIDE, SATA), one or more external storage devices 1016 (e.g., floppy disk drive (FDD) 1016, memory stick or flash drive reader, memory card reader, etc.), and an optical disc drive 1020 (e.g., capable of reading from or writing to discs 1022 such as CD-ROMs, DVDs, BDs, etc.). Although the internal HDD 1014 is shown as being located inside computer 1002, it can also be configured for external use in a suitable chassis (not shown). Furthermore, although not shown in environment 1000, a solid-state drive (SSD) may be used in addition to or in place of the HDD 1014. The HDD 1014, the multiple external storage devices 1016, and the optical disc drive 1020 can be connected to the system bus 1008 via the HDD interface 1024, the external storage interface 1026, and the optical drive interface 1028, respectively. The interface 1024 for external driver implementation may include at least one or both of Universal Serial Bus (USB) and Institute of Electrical and Electronics Engineers (IEEE) 1394 interface technologies. Other external driver connectivity technologies are within the scope of the embodiments described herein.

[0065] The drive and its associated computer-readable storage medium provide non-volatile storage of data, data structures, computer-executable instructions, etc. For computer 1002, the drive and storage medium are adapted to store any data in a suitable digital format. Although the above description of computer-readable storage media refers to corresponding types of storage devices, it should be understood by those skilled in the art that other types of computer-readable storage media (whether currently existing or to be developed in the future) may also be used in the example operating environment, and furthermore, any such storage medium may contain computer-executable instructions for performing the methods described herein.

[0066] Multiple program modules can be stored in the drive and RAM 1012, including an operating system 1030, one or more application programs 1032, other program modules 1034, and program data 1036. All or part of the operating system, applications, modules, and / or data can also be cached in RAM 1012. The systems and methods described herein can be implemented using various commercially available operating systems or combinations of operating systems.

[0067] Computer 1002 may optionally include emulation technology. For example, a hypervisor (not shown) or other middleware may emulate the hardware environment for operating system 1030, and the emulated hardware may be compatible with... Figure 10 The hardware shown is different. In this embodiment, the operating system 1030 may include one of a plurality of virtual machines (VMs) hosted at the computer 1002. Furthermore, the operating system 1030 may provide a runtime environment for the application 1032, such as the Java Runtime Environment or the .NET Framework. A runtime environment is a consistent execution environment that allows the application 1032 to run on any operating system that includes that runtime environment. Similarly, the operating system 1030 may support containers, and the application 1032 may be in the form of a container, which is a lightweight, standalone, executable software package that includes, for example, code, runtime, system tools, system libraries, and application-specific settings.

[0068] Furthermore, computer 1002 can be implemented using security modules, such as a Trusted Processing Module (TPM). For example, using a TPM, the startup component hashes the next startup component in time and waits for the result to match a security value before loading the next startup component. This process can be performed at any layer of the computer 1002's code execution stack, for example, at the application execution level or at the operating system (OS) kernel level, thereby achieving security at any level of code execution.

[0069] Users can input commands and information into computer 1002 using one or more wired / wireless input devices, such as keyboard 1038, touchscreen 1040, and pointing devices (such as mouse 1042). Other input devices (not shown) may include microphones, infrared (IR) remote controls, radio frequency (RF) remote controls or other remote controls, joysticks, virtual reality controllers and / or virtual reality headsets, game controllers, styluses, image input devices (such as cameras), gesture sensor input devices, visual motion sensor input devices, emotion or face detection devices, biometric input devices (such as fingerprint or iris scanners), and so on. These and other input devices are typically connected to processing unit 1004 via input device interface 1044, which may be coupled to system bus 1008, but may also be connected via other interfaces such as parallel ports, IEEE 1394 serial ports, game ports, USB ports, IR interfaces, Bluetooth® interfaces, and so on.

[0070] Monitor 1046 or other types of display devices can also be connected to system bus 1008 via an interface such as video adapter 1048. In addition to monitor 1046, computers typically include other peripheral output devices (not shown), such as speakers, printers, etc.

[0071] Computer 1002 can operate in a network environment using logical connections to one or more remote computers (such as (multiple) remote computers 1050) via wired and / or wireless communications. The (multiple) remote computers 1050 can be workstations, server computers, routers, personal computers, laptops, microprocessor-based entertainment devices, peer-to-peer devices, or other common network nodes, and typically include many or all of the elements associated with computer 1002; however, for simplicity, only memory / storage device 1052 is shown. The depicted logical connections include wired / wireless connectivity to a local area network (LAN) 1054 and / or a larger network (e.g., a wide area network (WAN) 1056). Such LAN and WAN network environments are common in offices and companies and facilitate enterprise-wide computer networks, such as intranets, all of which can connect to global communication networks, such as the Internet.

[0072] When used in a LAN network environment, computer 1002 can connect to local network 1054 via a wired and / or wireless communication network interface or adapter 1058. Adapter 1058 can facilitate wired or wireless communication with LAN 1054, which may also include a wireless access point (AP) configured thereon for wireless communication with adapter 1058.

[0073] When used in a WAN network environment, computer 1002 may include modem 1060, or may be connected to a communication server on WAN 1056 via other means (such as via the Internet) for establishing communication over WAN 1056. Modem 1060 may be a built-in or external, wired or wireless device, which may be connected to system bus 1008 via input device interface 1044. In a network environment, program modules or portions thereof depicted in relation to computer 1002 may be stored in remote memory / storage device 1052. It will be understood that the network connections shown are illustrative, and other means of establishing communication links between computers may be used.

[0074] When used in a LAN or WAN network environment, computer 1002 can access cloud storage systems or other network-based storage systems, in addition to or replacing external storage device 1016 as described above. Typically, the connection between computer 1002 and the cloud storage system can be established via LAN 1054 or WAN 1056, for example, via adapter 1058 or modem 1060, respectively. After connecting computer 1002 to the associated cloud storage system, external storage interface 1026, with the assistance of adapter 1058 and / or modem 1060, manages the storage devices provided by the cloud storage system just as it manages other types of external storage devices. For example, external storage interface 1026 can be configured to provide access to cloud storage resources as if these resources were physically connected to computer 1002.

[0075] Computer 1002 is operable to communicate with any wireless device or entity operably configured for wireless communication, such as a printer, scanner, desktop and / or laptop computer, portable data assistant, communications satellite, any device or location associated with a wirelessly detectable tag (e.g., an information kiosk, newsstand, store shelf, etc.), and telephone. This can include Wi-Fi and Bluetooth® wireless technologies. Therefore, communication can be a predefined structure, such as having a traditional network, or simply ad hoc communication between at least two devices.

[0076] Now for reference Figure 11 This diagram illustrates a schematic block diagram of a computing environment 1100 according to this specification. System 1100 includes one or more clients 1102 (e.g., computers, smartphones, tablets, cameras, PDAs). Clients 1102 may be hardware and / or software (e.g., threads, processes, computing devices). For example, clients 1102 may use this specification to contain cookies and / or associated contextual information.

[0077] System 1100 also includes one or more servers 1104. The servers 1104 may also be hardware or a combination of hardware and software (e.g., threads, processes, computing devices). For example, server 1104 may house threads for performing conversions of media items by employing various aspects of this disclosure. One possible communication between client 1102 and server 1104 is in the form of data packets adapted for transmission between two or more computer processes, wherein the data packets may include encoded analysis header space and / or input. For example, data packets may include cookies and / or associated contextual information. System 1100 includes a communication framework 1106 (e.g., a global communication network, such as the Internet) that can be used to facilitate communication between client 1102 and server 1104.

[0078] Communication can be facilitated via wired (including fiber optic) and / or wireless technologies. Multiple clients 1102 are operatively connected to one or more client data repositories 1108, which can be used to store information local to the multiple clients 1102 (e.g., multiple cookies and / or associated context information). Similarly, multiple servers 1104 are operatively connected to one or more server data repositories 1110, which can be used to store information local to the server 1104.

[0079] In one exemplary embodiment, client 1102 may transmit an encoded file (e.g., an encoded media item) to server 1104. Server 1104 may store the file, decode the file, or transmit the file to another client 1102. Note that client 1102 may also transmit an uncompressed file to server 1104, and server 1104 may compress and / or convert files according to the present disclosure. Similarly, server 1104 may encode information and transmit it to one or more clients 1102 via communication frame 1106.

[0080] The various aspects illustrated in this disclosure can also be implemented in a distributed computing environment, where certain tasks are performed by remote processing devices linked via a communication network. In a distributed computing environment, program modules can reside on both local and remote memory storage devices.

[0081] The foregoing description includes non-limiting examples of various embodiments. Of course, in order to describe the disclosed subject matter, it is impossible to describe every conceivable combination of components or methods, and those skilled in the art will recognize that further combinations and arrangements of the various embodiments are possible. The content of the disclosed subject matter is intended to cover all such changes, modifications, and variations that fall within the spirit and scope of the appended claims.

[0082] Regarding the various functions performed by the components, devices, circuits, systems, etc., described above, unless otherwise indicated, the terminology used to describe such components (including references to "device") is intended to also include any (or more) structures (e.g., functional equivalents) that perform the specified functions of the described components, even if they are not structurally equivalent to the disclosed structures. Furthermore, while specific features of the disclosed subject matter may be disclosed only with respect to one of several implementations, such features may be combined with one or more other features of other implementations that may be desirable and advantageous for any given or particular application.

[0083] As used herein, the terms “exemplary” and / or “illustrator” are intended to mean as an example, instance, or illustration. For the avoidance of ambiguity, the subject matter disclosed herein is not limited to such examples. Furthermore, any aspect or design described herein as “exemplary” and / or “illustrator” is not necessarily to be construed as superior to or advantageous to other aspects or designs, nor does it exclude equivalent structures and techniques known to those skilled in the art. Additionally, where the terms “comprising,” “having,” “including,” and other similar words are used in the embellishments or claims, such terms are intended to be inclusive—in a manner similar to the term “comprising” as an open-ended transitional term—without excluding any additional or other elements.

[0084] As used herein, the term “or” is intended to mean inclusive “or” rather than exclusive “or.” For example, the phrase “A or B” is intended to include instances of A, B, and both A and B. Furthermore, unless otherwise stated or clearly indicated from the context to refer to the singular form, the words “a” and “an” as used in this application and the appended claims should generally be interpreted as meaning “one or more”.

[0085] As used herein, the term "set" does not include an empty set, i.e., a set containing no elements. Therefore, "set" in this disclosure includes one or more elements or entities. Similarly, as used herein, the term "group" refers to a collection of one or more entities.

[0086] The description of the embodiments shown in this disclosure (including those described in the abstract) provided herein is not intended to be exhaustive or to limit the disclosed embodiments to the precise forms disclosed. While specific embodiments and examples have been described herein for illustrative purposes, various modifications that are considered to be within the scope of such embodiments and examples will be recognized by those skilled in the art. Therefore, although the subject matter has been described herein in conjunction with various embodiments and corresponding drawings (where applicable), it will be understood that other similar embodiments may be used, or modifications and additions may be made to the described embodiments to perform the same, similar, alternative, or substituted functions as the disclosed subject matter without departing from it. Consequently, the disclosed subject matter should not be limited to any single embodiment described herein, but should be interpreted in accordance with the breadth and scope of the appended claims.

Claims

1. A system, including: processor; as well as A memory storing executable instructions that, when executed by the processor, facilitate the execution of operations, including: Determine the resource utilization rate of network nodes; The reporting period for requests to key performance indicators applicable to the network node, determined by the extended application requests from the radio access network intelligent controller; and Using a reporting model, a reporting cycle recommendation is generated for the network node and the extended application. The reporting model is generated by using machine learning and based on past resource utilization other than the resource utilization rate and past reporting cycles other than the requested reporting cycle.

2. The system of claim 1, wherein the reporting cycle recommendation includes a recommended reporting cycle and a minimum reporting cycle.

3. The system according to claim 1, wherein the network node includes an E2 node.

4. The system according to claim 1, wherein the operation further includes: The extension application receives a subscription request, wherein the reporting period suggestion is generated in response to receiving the subscription request.

5. The system according to claim 4, wherein the operation further comprises: The priority level applicable to the subscription request is determined according to the defined priority criteria, wherein the reporting period recommendation is also generated based on the priority level applicable to the subscription request.

6. The system of claim 5, wherein the operation further comprises: In response to determining that the subscription request includes a high priority level based on the defined priority criteria, the reporting cycle recommendation is bypassed.

7. The system of claim 1, wherein the extended application is among a set of extended applications subscribed to on the network node.

8. The system of claim 1, wherein the reporting period is recommended to be determined as a threshold level for maintaining network performance applicable to the network nodes, based on defined network performance criteria.

9. The system of claim 1, wherein the key performance indicators include the signal-to-interference-plus-noise ratio applicable to the network node or the block error rate applicable to the network node.

10. The system of claim 1, wherein the extended application includes a network optimization extended application.

11. The system of claim 1, wherein the recommended reporting cycle includes a reporting frequency suitable for the extended application.

12. A non-transient machine-readable medium, the non-transient machine-readable medium comprising executable instructions that, when executed by a processor, facilitate the execution of operations, the operations including: Determine the resource utilization rate of E2 network nodes; The reporting cycle for requests to key performance indicators applicable to the E2 network node are determined by the extended application requests from the radio access network intelligent controller. as well as A reporting model is used to determine a recommended reporting period applicable to the E2 network node and the extended application. The reporting model is generated by using machine learning and based on past resource utilization other than the resource utilization rate and past reporting periods other than the requested reporting period.

13. The non-transient machine-readable medium of claim 12, wherein the proposed reporting period includes a minimum reporting period.

14. The non-transient machine-readable medium of claim 12, wherein the operation further comprises: A subscription request is received from the extended application, wherein the proposed reporting period is generated in response to receiving the subscription request.

15. The non-transient machine-readable medium of claim 14, wherein the operation further comprises: The priority level applicable to the subscription request is determined according to the defined priority criteria, and the recommended reporting period is also generated based on the priority level applicable to the subscription request.

16. The non-transient machine-readable medium of claim 15, wherein the operation further comprises: In response to determining that the subscription request includes a high priority level based on the defined priority criteria, the recommended reporting cycle is bypassed.

17. A method comprising: The resource utilization of network nodes is determined by a radio access network intelligent controller that includes a processor; The reporting period for requests for key performance indicators applicable to the network node, as requested by an extended application associated with the radio access network intelligent controller, is determined by the radio access network intelligent controller. as well as Using a reporting machine learning model, the radio access network intelligent controller generates recommended data representing reporting cycle recommendations applicable to the network nodes and the extended applications. The reporting machine learning model is generated using artificial intelligence, based on past resource utilization other than the requested resource utilization, and past reporting cycles other than the requested reporting cycle.

18. The method of claim 17, wherein the reporting period recommendation is determined as a threshold level for maintaining network performance applicable to the network node, based on a defined network performance standard.

19. The method of claim 17, wherein the key performance indicator includes ratio data representing the signal-to-interference-plus-noise ratio applicable to the network node or block error rate data representing the block error rate applicable to the network node.

20. The method of claim 17, wherein the recommended data includes report frequency data representing the report frequency applicable to the extended application.

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