First intent manager entity and method therein

Abstract

A method performed by a first intent manager entity for handling intent decomposition is provided. Upon receiving a first intent comprising one or more expectations, the first intent manager entity decomposes (602) the first intent into one or more decomposition proposals using one or more decomposition rules obtained based on the received intent. Each decomposition proposal comprises one or more target expectations. The first intent manager entity generates (604) one or more final target expectations based on an evaluation (603) of the one or more decomposition proposals. The first intent manager entity sends (605), to one or more second intent manager entities, one or more second intents to be handled by the one or more second intent manager entities. The respective one or more second intents comprise one or more of the generated final target expectations.

Description

[0001] FIRST INTENT MANAGER ENTITY AND METHOD THEREIN

[0002] TECHNICAL FIELD

[0003] Embodiments herein relate to the first intent manager entity for handling intent decomposition and a method therein. In some aspects, they relate to Intent-based management, intent decomposition, closed-loop management, and cognitive networks.

[0004] BACKGROUND

[0005] In a typical wireless communication network, wireless devices, also known as wireless communication devices, mobile stations, stations (ST A) and / or User Equipment (UE), communicate via a Wide Area Network or a Local Area Network such as a Wi-Fi network or a cellular network comprising a Radio Access Network (RAN) part and a Core Network (CN) part. The RAN covers a geographical area which is divided into service areas or cell areas, which may also be referred to as a beam or a beam group, with each service area or cell area being served by a radio network node such as a radio access node e.g., a Wi-Fi access point, a Base Station (BS) or a radio base station (RBS), which in some networks may also be denoted, for example, a Base Station (BS), a NodeB, eNodeB (eNB), or gNodeB (gNB) as denoted in Fifth Generation (5G) telecommunications. A service area or cell area is a geographical area where radio coverage is provided by the radio network node. The radio network node communicates over an air interface operating on a radio frequency with the wireless devices within the range of the radio network node.

[0006] 3rd Generation Partnership Project (3GPP) is the standardization body for specifying the standards for the cellular system evolution, e.g., including 3G, 4G, 5G and the future evolutions. Specifications for Evolved Universal Terrestrial Radio Access (E- UTRA) and Evolved Packet System (EPS) have been completed within the 3GPP. In 4G also called a Fourth Generation (4G) network, EPS is core network and E-UTRA is radio access network. In 5G, 5GC is core network, NR is radio access network. As a continued network evolution, the new release of 3GPP specifies a 5G network also referred to as 5G New Radio (NR) and 5G Core (5GC).

[0007] Frequency bands for 5G NR are being separated into two different frequency ranges, Frequency Range 1 (FR1) and Frequency Range 2 (FR2). FR1 comprises sub-6 GHz frequency bands. Some of these bands are bands traditionally used by legacy standards but have been extended to cover potential new spectrum offerings from 410 MHz to 7125 MHz. FR2 comprises frequency bands from 24.25 GHz to 52.6 GHz. Bands in this millimeter wave range have shorter range but higher available bandwidth than bands in the FR1.

[0008] Multi-antenna techniques may significantly increase the data rates and reliability of a wireless communication system. For a wireless connection between a single user, such as UE, and a base station (BS), the performance is in particular improved if both the transmitter and the receiver are equipped with multiple antennas, which results in a Multiple-Input Multiple-Output (MIMO) communication channel. This may be referred to as Single-User (SU)-MIMO. In the scenario where MIMO techniques is used for the wireless connection between multiple users and the base station, MIMO enables the users to communicate with the base station simultaneously using the same time-frequency resources by spatially separating the users, which increases further the cell capacity. This may be referred to as Multi-User (MU)-MIMO. Note that MU-MIMO may benefit when each UE only has one antenna. The cell capacity can be increased linearly with respect to the number of antennas at the BS side. Due to that, more and more antennas are employed in BS. Such systems and / or related techniques are commonly referred to as massive MIMO.

[0009] An Intent manager (IM), or Intent Management Function (IMF), may provide a zerotouch control for an environment. The IM is controlled by one or more intents and controls one or more environments. Controlling an environment is done by observing the environment, reasoning around the combination of perceived situation and prior knowledge and taking actions on the environment. As illustrated in Figure 1, these steps together form a closed loop. The overall purpose of the IM is to fulfill the intent.

[0010] An intent is defined as “the formal specification of all expectations including requirements, goals, and constraints given to a technical system”. Examples of expectations are “At least 95% of the URLLC users shall experience a latency of maximum 20 msec”, or “At least 80% of the users of the conversational video service shall have a minimum Quality of Experience (QoE) of 4.0”, or “Energy consumption of the system shall be kept to a minimum”.

[0011] The IM described on a high level in Figure 1 may be implemented in a cognitive framework, such as a cognitive layer. The cognitive framework is further described in Figure 2 and outlines such an implementation. One or more intents are sent to the IM. Each expectation in an intent becomes a Key Performance Indicator (KPI) that needs to be met. These are called target KPIs. Raw data is exposed from the environment and processed by data grounding agents. These data grounding agents translate the raw data into measured KPIs. Target and measured KPIs may be compared, and the difference becomes an issue or goal that the intent manager needs to meet. For example, the target KPI is “max 20 msec latency” but the measured KPI is “30 msec latency”. One or more proposal agents are responsible for proposing actions that may solve an issue. Evaluation agents make an assessment if a proposed action is good or not. Finally, actuator agents execute the action on the environment under control.

[0012] When applying the implementation above to a wireless communication network, the environment under control would be the wireless communication network and the operator would be the network operator. Note that, for scalability reasons, the wireless communication network would typically be divided into multiple domains, such as environments. Furthermore, IMs may come in a hierarchy. In general, the environment under control may be a part of the wireless communication network or may be another IM. The intent owner may be the network operator or may be another IM.

[0013] A more elaborate description on intents and the architecture around intents may be found in TM Forum specifications like “TM Forum Introductory Guide: Autonomous Networks - Technical Architecture” (IG1230) or “TM Forum Introductory Guide: Intent in Autonomous Networks” (IG1253). These specifications also include intent managers and the envisioned hierarchy of intent managers.

[0014] Figure 3, coming from IG 1230, illustrates an example of such hierarchy of intent management. Various instances of intent managers are allocated across operational layers and autonomous domains, e.g., Business Support System (BSS) and / or Operations Support System (OSS). Intent may be originated directly from customer input or may be derived automatically by IMs and managed via intent API, see blue arrows. The latter aspect is also known as intent decomposition, or intent refinement.

[0015] Referring to Figure 4, Intent Manager 2 receives an intent and becomes intent handler for "intentl". It then starts operating the intent and planning a solution strategy with actions. In this example, the result of this process is to set requirements for another domain and use the intent mechanism to do so. This means Intent Manager 2 starts the life cycle for another intent "intent2". It, therefore, becomes the owner of this new intent. Here the same intent management function is in the role of handler, of intent 1, and owner, of intent 2, at the same time. SUMMARY

[0016] As a part of developing embodiments herein a problem was identified by the inventors and will first be discussed.

[0017] Two problems may be identified in the example shown in Figure 4. Firstly, how does an IM identify the need for a new intent and hence decompose the incoming requirements, i.e., expectations within intents, in possibly multiple expectations to submit as intents, e.g., using intent APIs, to other IMs. Secondly, how are the newly identified intent(s) formulated and submitted as intent objects.

[0018] Similar topics in the academic literature can be found in goal refinement or intent refinement but these usually only imply one level of translation: from requirements to configuration. For example, in “CLARA: Closed Loop-based Zero-touch Network Management Framework”, Sousa et. al., 2021 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), an end-to-end metric is decomposed into multiple metrics, but the paper does not deal specifically with intent decomposition.

[0019] In US 2022 / 330092 A1, an intent decomposition method is proposed to decompose the intent. However, this disclosure does not specify how the actual decomposition and information for performing it is ingested and used by the intent-based system and has no learning associated with the intent decomposition.

[0020] An object of embodiments herein is to improve intent decomposition efficiency and accuracy and thereby improve the performance in domains, e.g., communication networks managed by intent manager entities.

[0021] According to an aspect of embodiments herein, the object is achieved by a method performed by a first intent manager entity for handling intent decomposition.

[0022] Upon receiving a first intent comprising one or more expectations, the first intent manager entity decomposes the first intent into one or more decomposition proposals using one or more decomposition rules obtained based on the received intent. Each decomposition proposal comprises one or more target expectations.

[0023] The first intent manager entity generates one or more final target expectations based on an evaluation of the one or more decomposition proposals. The first intent manager entity sends, to one or more second intent manager entities, one or more second intents to be handled by the one or more second intent manager entities. The respective one or more second intents comprises one or more of the generated final target expectations.

[0024] According to another aspect of embodiments herein, the object is achieved by a first intent manager entity configured to handle intent decomposition.

[0025] Upon receiving a first intent comprising one or more expectations, the first intent manager entity is configured to decompose the first intent into one or more decomposition proposals using one or more decomposition rules obtained based on the received intent. Each decomposition proposal is adapted to comprise one or more target expectations.

[0026] The first intent manager entity is configured to generate one or more final target expectations based on an evaluation of the one or more decomposition proposals.

[0027] The first intent manager entity is configured to send, to one or more second intent manager entities, one or more second intents adapted to be handled by the one or more second intent manager entities. The respective one or more second intents are adapted to comprise one or more of the generated final target expectations.

[0028] Embodiments herein provide a mechanism for improving intent decomposition efficiency and accuracy. This is enabled by the first network entity decomposing the first intent based on one or more decomposition rules, evaluating the resulting decomposition proposals, and generating final target expectations taking the evaluation into account. This improves the accuracy of the decomposition and allows an efficient intent decomposition.

[0029] Embodiments herein lead to the advantage of improving the performance of domains managed by intent manager entities, such as communication networks. This is achieved by the more accurate and efficient intent decomposition, which enables an improved final target expectations.

[0030] BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Examples of embodiments herein are described in more detail with reference to attached drawings in which:

[0032] Figure 1 is a schematic view of an intent manager according to prior art. Figure 2 is a schematic view of an intent manager according to prior art.

[0033] Figure 3 is a schematic view of a hierarchal intent management according to prior art.

[0034] Figures 4 a and b are a sequence diagram according to prior art.

[0035] Figure 5 is a schematic block diagram illustrating embodiments of a wireless communications network.

[0036] Figure 6 is a flowchart depicting embodiments of a method described herein.

[0037] Figure 7 is a schematic view of an intent management architecture according to examples of embodiments herein.

[0038] Figure 8 is a sequence diagram according to embodiments herein.

[0039] Figure 9 is a sequence diagram according to embodiments herein.

[0040] Figure 10 is a schematic block diagram according to embodiments herein.

[0041] Figure 11 is a sequence diagram according to embodiments herein.

[0042] Figure 12 is a sequence diagram according to embodiments herein.

[0043] Figure 11 is a schematic block diagram according to embodiments herein.

[0044] Figure 13 is a schematic block diagram illustrating embodiments of an intent manager entity.

[0045] Figure 14 shows an example of a communication system QQ100 in accordance with some embodiments.

[0046] Figure 15 shows a UE QQ200 in accordance with some embodiments.

[0047] Figure 16 shows a network node QQ300 in accordance with some embodiments.

[0048] Figure 17 is a block diagram of a host QQ400, which may be an embodiment of the host QQ116 of Fig. 14, in accordance with various aspects described herein.

[0049] Figure 18 is a block diagram illustrating a virtualization environment QQ500 in which functions implemented by some embodiments may be virtualized.

[0050] Figure 19 shows a communication diagram of a host QQ602 communicating via a network node QQ604 with a UE QQ606 over a partially wireless connection in accordance with some embodiments.

[0051] DETAILED DESCRIPTION

[0052] Embodiments provide a domain-specific knowledge-based method for intent decomposition. According to embodiments herein, each decomposition proposal comprises one or more sub-expectations, such as target expectations, to enable the hierarchical intent management approach, where business-level intents can be broken down into services- level intents and further down into network domain intents if necessary.

[0053] Examples of embodiments herein may e.g., provide any one or more following:

[0054] 1. Detection and decomposition: The intent manager is provided by an external actor (e.g., agent, intent owner, IM owner) with a set of decomposition rules for intents that are based on domain-specific knowledge. The rules will reside in the IM’s knowledge base and specify how specific expectations can be decomposed.

[0055] 2. Evaluation: When an intent is decomposed, the intent manager: 1. checks if there are IMs that can accept the generated decomposed intent(s), 2. For each of the generated intents, the intent manager submits a BEST intent request proposal to evaluate what is the best value possible to achieve for the generated intents. The use of BEST API is just an example and in general the investigation can be performed by using different APIs: e.g., PROBE, GUARANTEE.

[0056] 3. Formulation and submission: The decomposed and evaluated intents can now be formulated. Looking at the best values achievable (see step 2) the intents are instantiated accordingly and sent to the IMs responsible via intent APIs.

[0057] 4. Learning: The IM learns over time the decomposition proposals’ effectiveness by monitoring intents’ satisfaction. This knowledge can be updated according to different methods (in this invention, its is exemplified with a tabular-based implementation and a Q- learning implementation). Learning results in fewer calls to downstream intent managers as the IM learns over time which decomposition is most suitable for each intent.

[0058] Examples of embodiments herein may allow for autonomously separating concerns when satisfying intents. This implies autonomous and faster management of requirements, and lower costs via resource optimization.

[0059] Embodiments herein may be implemented in wireless communication networks in general. Figure 5 is a schematic overview depicting a wireless communications network 100. The wireless communications network 100 comprises one or more RANs and one or more CNs. The wireless communications network 100 may use a number of different technologies, such as Wi-Fi, Long Term Evolution (LTE), LTE-Advanced, 5G, New Radio (NR), Wideband Code Division Multiple Access (WCDMA), Global System for Mobile communications / enhanced Data rate for GSM Evolution (GSM / EDGE), Worldwide Interoperability for Microwave Access (WiMax), or Ultra Mobile Broadband (UMB), just to mention a few possible implementations. Embodiments herein relate to recent technology trends that are of particular interest in a 5G context, however, embodiments are also applicable in further development of the existing wireless communication systems such as e.g. WCDMA and LTE, or any future wireless communication systems such as 6G.

[0060] A number of network nodes operate in the wireless communications network 100 such as e.g. a base station 101. The base station 101 provides radio coverage in a number of cells which may also be referred to as a beam or a beam group of beams.

[0061] The base station 101 may be any of a NG-RAN node, a transmission and reception point e.g. a base station, a radio access network node such as a Wireless Local Area Network (WLAN) access point or an Access Point Station (AP STA), an access controller, a base station, e.g. a radio base station such as a NodeB, an evolved Node B (eNB, eNode B), agNB, a base transceiver station, a radio remote unit, an Access Point Base Station, a base station router, a transmission arrangement of a radio base station, a stand-alone access point or any other network unit capable of communicating with a wireless device within the service area served by the base station 101 depending e.g. on the first radio access technology and terminology used. The base station 101 may be referred to as a serving radio network node and communicates with a wireless device with Downlink (DL) transmissions to the wireless device and Uplink (UL) transmissions from the wireless device.

[0062] In the wireless communications network 100, one or more UEs operate, such as e.g. the one or more wireless device 102. The wireless device 102 may also referred to as a UE, a device, an Internet of Things (loT) device, a mobile station, a non-access point (non-AP) STA, a STA, a user equipment and / or a wireless terminal, communicate via one or more Access Networks (AN), e.g. RAN, to one or more core networks (CN). It should be understood by the skilled in the art that “wireless device” is a non-limiting term which means any terminal, wireless communication terminal, user equipment, Machine Type Communication (MTC) device, Device to Device (D2D) terminal, or node e.g. smart phone, laptop, mobile phone, sensor, relay, mobile tablets or even a small base station communicating within a cell.

[0063] The wireless communication network 100 comprises a number of intent manager entities, such as e.g., a first intent manager entity 110 and one or more second intent manager entities 120. An intent manager entity may also be referred to as an intent manager (IM) or an intent manager function (IMF). An intent manager entity is responsible for handling intents and controlling one or more environments, such the wireless communication network 100, or parts thereof. Controlling an environment is done by observing the environment, reasoning around the combination of perceived situation and prior knowledge and taking actions on the environment. The first intent manager entity 110 and / or the one or more second intent manager entities 120 may implemented as standalone network nodes, or they may be part of other nodes, such as the base station 101 , CN nodes or any other node connected to, or part of, the wireless communication network 100.

[0064] Methods herein may be performed by the first intent manager entity 110. As an alternative, a Distributed Node (DN) and functionality, e.g. comprised in a cloud 190 as shown in Figure 5, may be used for performing or partly performing the methods herein.

[0065] A method according to embodiments will now be described from the view of the first intent manger entity 110 together with Figure 6. Figure 6 shows example embodiments of a method performed by the first intent manager entity 110 for handling intent decomposition. The method comprises the following actions, which actions may be taken in any suitable order. Actions that may be optional are presented in dashed boxes in Figure 6.

[0066] Action 601

[0067] In some embodiments, the first intent manager entity 110 obtains a set of decomposition rules for decomposing intents. The set of decomposition rules may e.g., be obtained from another entity, such as another intent manager entity, decomposition rule generation entity, a user, or any other type of entity configured to create decomposition rules.

[0068] Action 602

[0069] In some embodiments, the first intent manager entity 110 obtains, from the set of decomposition rules, one or more decomposition rules for decomposing a first intent, wherein the one or more rules are obtained taking the first intent into account. In other words, the one or more rules are selected from the set of decomposition rules. Which one or more rules that are obtained, or selected, depends on the first intent. Different intents may result in different rules being obtained or selected. The first intent may comprise one or more expectations. An expectation may comprise a requirement to be fulfilled. Thus, obtaining, or selecting, the one or more rules for decomposing the first intent may comprise obtaining, or selecting, one or more rules related to the expectation comprised in the intent. In some embodiments, prior to obtained the one or more rules, the first intent manager entity 110 receives the first intent to be decomposed.

[0070] Action 603

[0071] The first intent manager entity 110, upon receiving the first intent comprising one or more expectations, decomposes the first intent into one or more decomposition proposals using one or more decomposition rules obtained, or selected, based on the first intent. Each decomposition proposal comprises one or more target expectations. As mentioned above, the first intent manager entity 110 may obtain, or select, the one or more decomposition rules before decomposing the intent, e.g., from the set of decomposition rules. Decomposing the intent may comprise decomposing, or splitting, the exception comprised in the intent into the one or more target expectations. As an example, related to data transmission, the first intent may comprise an expectation related to end-to-end latency. The first intent may then e.g., be decomposed into a decomposition proposal comprising two target expectations, one for a latency in the RAN and one for a latency in the transport.

[0072] As mentioned above, an expectation may comprise a requirement to be met. Similarly, a target expectation may comprise a requirement to be met.

[0073] In some embodiments, decomposing the first intent further comprises selecting a respective second intent manager 120 entity for the one or more decomposition proposals. This may mean that the first intent manager entity 110 selects a second intent manager entity 120 for the decomposition proposal. Alternatively, the first intent manager entity 120 may select more than one second intent manager entity 120 for the decomposition proposal. The selected second intent manager entity, or entities, 120 may be selected for the evaluation of the decomposition proposals and / or for handling a second intent comprising one or more final target expectations, as described further below.

[0074] In some embodiments, the one or more decomposition proposals are associated with a respective effectiveness metric. The effectiveness metric as used herein, may mean a metric that indicates how effective the decomposition proposal is at fulfilling the one or more target expectations comprised in the decomposition proposal. The effectiveness metric may e.g., comprise a range from 0 to 1, where a higher value indicates a better effectiveness than a lower value.

[0075] Action 604

[0076] In some embodiments, the first intent manager entity 110 evaluates the one or more decomposition proposals. Evaluating the one or more decomposition proposals may comprise sending the respective one or more decomposition proposals to one or more second intent manager functions 120, and receiving, from the respective one or more second intent manager functions 120, a respective achievability characteristic associated with the one or more target expectations. In other words, in order to evaluate the decomposition proposals, the first intent manager entity 110 receives the respective achievability characteristics from the one or more second intent managers 120. The achievability characteristic may be taken into account when evaluating the one or more decomposition proposals.

[0077] In some embodiments, the achievability characteristic comprises a value achievable by the second intent manager 120 for a target expectation. The value may e.g., comprise any value related to a target expectation. This may mean that the achievability metric comprises one value for each of the one or more target expectations. Continuing the example related to data transmission, the decomposition proposal comprises two target expectations, one for a latency in the RAN and one for a latency in the transport. Then the achievability characteristic may comprise two values, one for a latency in the RAN and one for latency in the transport. The two values may represent the best achievable latency, for RAN and transport respectively, that second intent manager entity 120 may achieve.

[0078] The achievability characteristic may be taken into account when evaluating the one or more decomposition proposals. Continuing the example related to data transmission above, the end-to-end latency expectation may e.g., be X milliseconds (ms), and the achievability characteristic from a second intent manager entity 120 may indicate RAN latency value of Y ms and a transport latency value of Z ms. According to this example, the first intent manager entity 110 evaluates the decomposition proposal by checking whether the second intent manager entity 120 fulfils the expectation of an end-to-end latency of X ms, i.e., the sum of Y and Z does not exceed X. when the decomposition proposal is sent to more than one second intent manager entity 120, ...

[0079] In some embodiments, evaluating the one or more decomposition proposals comprises taking the effectiveness metric into account. In an example where the decomposing of the first intent has resulted in e.g., two decomposition proposals, the first intent manager entity 110 may look at the respective effectiveness metric of the two decomposition proposals during the evaluation. As mentioned above, a higher value of the effectiveness metric is considered better than a lower value. Thus, if a first of the two decomposition proposals has an effectiveness metric of e.g., 0.9, and a second of the two decomposition proposals has an effectiveness metric of e.g., 0.1, this would indicate to the first intent manager entity 110 that the first decomposition proposal is more effective at fulfilling, or satisfying, the expectation of the first intent and / or the one or more target expectation comprised in the decomposition proposals.

[0080] In some examples, the evaluation may further comprise obtaining the respective effectiveness metric associated with the one or more decomposition proposals.

[0081] Action 605

[0082] The first intent manager entity 110 generates one or more final target expectations based on the evaluation of the one or more decomposition proposals. In other words, the first intent manager entity 110 takes the output of the evaluation and uses this as basis for generating the one or more final target expectations. A final target expectation as used herein, may e.g., mean a target expectation that is generated based on an evaluation of a decomposition proposal. I.e., a final target expectation may be defined as the outcome of a decomposition of an intent. The final target expectation may be sent to another intent manager entity that will be responsible for handling said intent, as explained further below. The one or more final target expectations may further be generated taking the rule used when decomposing the first intent into account.

[0083] In some embodiments, generating the one or more final target expectations further comprises selecting one or more decomposition proposals based on the evaluation. This may mean that the first intent manager entity 110 selects one or more decomposition proposals taking e.g., the effectiveness metric and / or the achievability characteristic into account. E.g., a decomposition proposal with a higher value of the effectiveness metric may be selected before a decomposition proposal with a lower value of the effectiveness metric. Alternatively, or additionally, a decomposition proposal with an achievability characteristic that fulfils, or satisfies, the one or more target expectations and / or expectation in the first intent, may be selected before a decomposition proposal with an achievability characteristic that does not fulfil, or satisfies the one or more target expectations, and / or expectation in the first intent. Alternatively, when more than one decomposition proposals fulfil, or satisfy, the one or more target expectations and / or expectation in the first intent, the decomposition proposal that best fulfils, or satisfies, the one or more target expectations and / or expectation in the first intent. What is considered to be best depends on the expectation to be fulfilled. E.g., referring the example above, a decomposition proposal with an achievability characteristic that indicates a lower latency may considered better than a decomposition proposal with an achievability characteristic that indicates a higher latency. In some embodiments, generating the one or more final target expectations further comprises selecting one or more decomposition proposals and second intent manager entities 120 based on the evaluation. Selecting the one or more second intent manager entities 120 based on the evaluation may e.g., comprise selecting the one or more second intent manager entities 120 based on e.g., the respective achievability characteristics associated with the one or more decomposition proposals. E.g., when a decomposition proposal was sent to two different second intent manager entities 120 during the evaluation, the second intent manager entity 120 that returned the best achievability characteristic may be selected. As mentioned above, What is considered to be best depends on the expectation to be fulfilled. E.g., referring the example above, a decomposition proposal with an achievability characteristic that indicates a lower latency may considered better than a decomposition proposal with an achievability characteristic that indicates a higher latency. For selecting the one or more decomposition proposals, the first intent manager entity 110 may select one or more decomposition proposals taking e.g., the effectiveness metric and / or the achievability characteristic into account. E.g., a decomposition proposal with a higher value of the effectiveness metric may be selected before a decomposition proposal with a lower value of the effectiveness metric. Alternatively, or additionally, a decomposition proposal with an achievability characteristic that fulfils, or satisfies, the one or more target expectations and / or expectation in the first intent, may be selected before a decomposition proposal with an achievability characteristic that does not fulfil, or satisfies the one or more target expectations, and / or expectation in the first intent. Alternatively, when more than one decomposition proposals fulfil, or satisfy, the one or more target expectations and / or expectation in the first intent, the decomposition proposal that best fulfils, or satisfies, the one or more target expectations and / or expectation in the first intent.

[0084] Action 606

[0085] The first intent manager entity 110 sends, to the one or more second intent manager entities 120, one or more second intents to be handled by the one or more second intent manager entities 120. The respective one or more second intents comprises one or more of the generated final target expectations. In other words, the first intent manager entity 110 sends at least one second intent comprising one or more of the generated final target expectations to at least one second intent manager entity 120 to handle. Sending the one or more second intents may comprise generating the one or more second intents such that the respective one or more second intents comprises one or more of the generated final target expectations. In some embodiments, the one or more second intents are sent to the selected one or more second intent manager entities 120. The selected one or more second intent manager entities 120 may the one or more second intent manager entities 120 selected when decomposing the first intent, or the one or more second intent manager entities 120 selected when generating the one or more final target expectations.

[0086] Action 607

[0087] In some embodiments, the first intent manager entity 110 receives feedback from the one or more second intent manager entities 120 handling the one or more second intents. The feedback comprises feedback characteristics associated with the one or more final target expectations of the one or more second intents. The feedback characteristics may e.g., comprise data related to whether or not the respective one or more final target expectations are fulfilled. Thus, for each final target expectation, the feedback characteristics may indicate whether or not the second intent manager has fulfilled said final target expectation.

[0088] Action 608

[0089] In some embodiments, the first intent manager entity 110 updates and effectiveness metric based on the received feedback. This may e.g., mean that when the feedback characteristics indicate that all final target expectations of a second intent are fulfilled, updating the effectiveness metric comprises increasing the effectiveness metric. Similarly, when the feedback characteristics indicate that one more of the final target expectations of a second intent is not fulfilled, updating the effectiveness metric comprises increasing the effectiveness metric. Similarly, the effectiveness metric may be decreased based on the number of the one or more final target expectations of the second intent that is not fulfilled.

[0090] The above embodiments will now be further explained and exemplified below. These below embodiments may be combined with any suitable embodiment as described above.

[0091] Figure 7 is a schematic overview depicting an intent management architecture. The architecture comprises intent manager 1, intent manager 2, being an example of the first intent manager entity 110, and intent manager 3, being an example of the second intent manager entity 120. An intent manager may comprise an intent manager framework comprising a knowledge base e.g., for storing rules, intents, decompositions etc. Upon receiving an intent, such as the first intent, from the intent manager 1, decomposition rule(s), such as the one or more decomposition rules, in the intent manager 2 may propose multiple decompositions for the intent, such as the one or more decomposition proposals. The decomposition proposals may then be evaluated, and the best proposals may result in the formulation and submission of decomposed requirements, such as the one or more second intents comprising the one or more final target expectations.

[0092] Insertion of decomposition rules with domain knowledge

[0093] A decomposition rule may be defined as a procedure that takes as input one or multiple expectations, such as the first intent, and outputs one or multiple decomposed expectations, such as the one or more target expectations. As an example, a slice decomposition rule might be activated by a latency expectation of an intent, for an end-to- end latency requirement, and split the latency expectation into: 1. A latency expectation for transport and, 2. a RAN latency expectation. The identified split is based on domain knowledge, e.g., knowledge of the wireless communications network 100, because the actor designing the decomposition rule knows that, for example, an end-to-end latency always implies sub-latency for different domains. There may be other examples of intent decomposition rules:

[0094] • How to decompose a Maximum Bit Rate (MBR) expectation?

[0095] Here the domain knowledge would say that MBR is set in the CN, so only an expectation for the CN would be generated, nothing for RAN or transport. In this case, a simple refinement may be obtained, not decomposition.

[0096] • How do decompose a Quality of Experience (QoE) expectation?

[0097] The domain knowledge may be “if you get a QoE > x expectation, then this means QoS sent to CN > f(x), and bandwidth allocation at transport should be > g(x)”.

[0098] • How to decompose an availability expectation?

[0099] A high-level expectation on availability x%, e.g., Network Reliability, availability and Resilience (NRAR), may be broken down into the same expectation on availability of (x1 / N)% for all N underlying domains, e.g., RAN, CN, etc.

[0100] How to decompose a packet loss expectation? A high-level expectation on max dropped packets. It doesn’t matter where these packets in sub-domains, e.g., RAN, CN, are lost, as long as the max packet loss is not exceeded.

[0101] Domain knowledge may say that loss depends on packet priority, e.g., a decomposed expectation for the CN, and on data center resources, e.g., a decomposed expectation for the data center.

[0102] In general, an intent may comprise multiple expectations, so for an intent to be fully decomposed, for each of its expectation there must be a rule decomposing such an expectation. This means that intent level decomposition will be the process of collecting the relevant expectations’ decompositions.

[0103] Despite that a common language for specifying decomposition rules does not exist in the standards yet, it may be perceived that this domain knowledge may be provided by using, for example, RuleML, a semantic and interoperable language for specifying rules.

[0104] An example of a very basic rule from the literature may be “The discount for a customer buying a product is 5.0% if the customer is premium and the product is regular”.

[0105] An example of a rule for the wireless communication network 100 may be “When there is a latency expectation for a certain target of user group, decompose the expectation in a transport latency expectation and a RAN latency expectation”. It is easy to understand that, given an ontology available for specifying participants in the rule, this may be implemented similarly as in Figure 8.

[0106] When the intent manager, such as the first intent manager 110, onboards, such as obtains, the rule, it may take the specification and, if necessary, perform code-to-code transformation to make the formal specification of the rule executable in the intent manager. Note that this step might not be necessary if the rule is specified in a language that is directly understood by the intent manager. However, as previously mentioned, this language is not in the standard yet, so a translation step may be necessary.

[0107] As an illustrative example, the intent manager translates the above decomposition rule as in the code below.

[0108] The code above is just an example of the feasibility of implementing a decomposition rule in an intent manager. In the example, the decomposition rule decomposes a single expectation into two expectations. Note that the above rule only shows the logic for decomposing the intent and not how to set the concrete values of v:bestTransport and v:bestRAN once retrieved. The latter part may be realized, see the example below, by checking that the sum of the best possible achievable latencies is less than the one required and then going into defining the required split via “assert”.

[0109] Note that a decomposition rule may also be designed in a way that such rule comprises multiple sub-rules decomposing, on their behalf, multiple expectations.

[0110] Each IM in a hierarchy may have many of these rules, each possibly submitted by different actors. Hence, an intent manager is provided by an external actor, e.g., agent, intent owner, intent manager owner, with one or multiple decomposition rules that are based on domain-specific knowledge. Figure 8 shows an example of a flow for domain knowledge insertion. The intent manager framework, such as the intent manager framework of the first intent manager entity 110, obtains one or more decomposition rules, at step 1, and stores the decomposition rules, e.g., in a knowledge base in the first intent manager entity 110, such as a memory, at step 2. At step 3 and step 4 the actor providing the decomposition rules is acknowledged that the decomposition rules have been stored. Detection and decomposition

[0111] The decomposition rules, such as the one or more decomposition rules, are triggered when the input they are matching, e.g., single expectations or multiple expectations of an intent, is present in the intent manager, such as the first intent manager 110. This results in the generation of decomposition proposals.

[0112] Figure 9 shows an example of an intent decomposition flow. When an intent is inserted, at step 1, the intent manager framework, such as the intent manager framework of the first intent manager entity 110, retrieves from the knowledge base, such as the intent manager framework of the first intent manager entity 110, e.g., a knowledge base in the first intent manager entity 110, the decomposition rules that operate on the intent’s expectations, at step 2 and step 3, and such rules are triggered at step 4.

[0113] Each rule will output a decomposition proposal at step 5. In the example above, given an end-to-end latency expectation, the proposal will be 1. A latency expectation for transport and, 2 a RAN latency expectation.

[0114] The decomposition proposals will reside in the knowledge base of the intent manager, such as the first intent manager entity 110, pointing back to the original expectations for which the proposals originated at step 6. This allows the method to track when the intent, submitted at step 1, will be fully decomposed. This happens when for each expectation, there is at least one decomposition proposal available.

[0115] Note: The intent manager, e.g., the first intent manager entity 110, may have built-in decomposition rules. This means that, even if an actor has not provided decomposition rules for all expectations in a certain intent, the intent may still be fully decomposable.

[0116] Note that steps 4 and 5 may lead to the generation of multiple decomposition proposals. This means that at the end of step 6 the knowledge base may contain multiple and different ways of fully decomposing an intent.

[0117] Decomposition evaluation

[0118] Decomposition evaluation may be triggered when decomposition proposals are registered in the knowledge base and the intent is fully decomposed. This may happen according to a phasing approach, where first all the decomposition proposals are gathered, and then, in a separate phase, the evaluation is performed.

[0119] For each decomposition proposal, the decomposition evaluation identifies the availability of an intent manager able to satisfy all the decomposed expectations.

[0120] Figure 10 shows an example of how the discovery of an available intent manager, e.g., the second intent manager entity 120, is realized. In the decomposition evaluation phase, the idea is to discover available intent managers from an intent manager capability profile registry.

[0121] If it is not possible to find an intent manager able to satisfy all expectations in a decomposition proposal, the full decomposition proposal is mapped as “impossible to satisfy”. If all decomposition proposals for an intent, as suggested by the rules, are marked as “impossible to satisfy”, the intent will not be fulfilled.

[0122] If all the decomposed expectations of a decomposition proposal have an IM able to satisfy them, the decomposition evaluation phase proceeds to identify the best values achievable for such decomposed expectation.

[0123] For example, the BEST operation may be used. The difference from a usual intent submission is that some requirements, i.e. , expectations, may be marked by the owner, such as the first intent manager entity 110. The owner wants a proposal from the intent handler, such as the second intent manager entity 120, about the best value achievable for the marked expectation given that all the other requirements also apply. The intent handler may add its proposals to an intent report. Such intent report may be used in the next step of the method.

[0124] In the example below, an intent owner, such as the first intent manager entity 110, asks the IM retrieved from the capability profile registry, such as the second intent manager entity 120, what is the best QoE that is possible to obtain for 80% of a certain group of user group of a video application. The intent owner specifies that it wants a response within 2 minutes with a minimum accuracy level of 95%.

[0125] Note that in case that multiple IMs are retrieved during the discovery procedure, the IM with the best reported “BEST value” may be selected. The intent manager submitting the requests, such as the first intent manager entity 110, may optionally cache the intent reports received to avoid continuous calls to downstream intent managers, such as the one or more second intent manager entities 120.

[0126] Formulation and submission When intent reports are received, the decompositions may be fully formulated. In particular, the decompositions were already identified, but only after the BEST values for such decomposition proposals are reported the concrete expectation targets, such as the one or more target expectations may be set before realizing intent submission.

[0127] According to example of embodiments herein, the decomposition rules have additional domain knowledge that may allow formulating in a precise way the expectation target given a BEST value reported.

[0128] As an example:

[0129] 1. The original intent, such as the first intent, specifies an end-to-end latency expectation of 200ms.

[0130] 2. A rule decomposed the latency expectation into two expectations, one RAN latency expectation and one transport latency expectation. The original intent is then fully decomposed.

[0131] 3. The BEST operation returned the best value of 120ms for transport and 20ms for RAN.

[0132] 4. A formulation rule decides to do the split in 150ms transport and 50ms RAN. Note that this information comes from domain knowledge, i.e., how, from the BEST values, the decomposed expectations need to be formulated, i.e., how to set the expectation target, and is part of the domain knowledge given as input via decomposition rules.

[0133] Once the expectations, such as the one or more target expectations, are formulated, they are submitted with a normal intent submission procedure, such as sending the one or more second intents to the one or more second intent manager entities 120. The intent manager decomposing the original expectation, such as the first intent manager entity 110, may then be referred to as the intent owner of the one or more second intents.

[0134] Learning

[0135] In this section, an approach to optimize decompositions without asking the downstream intent managers for evaluating all the decomposition proposals is introduced. The main idea is that the intent manager, such as the first intent manager entity 110, shall learn the decomposition proposals’ effectiveness over time, and store this metric, such as the effectiveness metric, in the knowledge base. The effectiveness metric may be updated and used according to different methods. Below, a tabular-based implementation and a Q- learning implementation are exemplified. To keep track of the effectiveness of decompositions the intent manager, such as the first intent manager entity 110, may need to monitor intent satisfaction. This may be realized by leveraging intent reports coming from lower-level intent managers, such the one or more second intent manager entities 120, indicating whether the intents submitted are satisfied, and to which level or not.

[0136] Figure 11 shows the sequence diagram for the management of the effectiveness metric. In the sequence, a decomposition rule generates a decomposition proposal, at step 1, which is retrieved by the intent manager framework, such as the first intent manager entity 110 at step 2 and 3. Step 4 is described above in the examples of evaluation, formulation and submission of the intent, such as the one or more second intents.

[0137] An important aspect is handling fluctuation of intent satisfaction, i.e. , met / unmet intents over time. To this end, the intent manager, such as the first intent manager entity 110, keeps, for each decomposition, multiple time series representing the satisfaction of the target expectations over time. Intent reports, received at step 5, from intent managers, such as the second intent manager entity 120, contribute to updating such time series at step 6.

[0138] A function may be used to map these time series to an effectiveness metric’s update, that is stored in the knowledge base at step 7. The function may be implemented in different ways:

[0139] - If after “x” seconds all the decomposed expectations are satisfied for at least “y” seconds boost the effectiveness of the proposal, otherwise, decrease the effectiveness of the proposal.

[0140] - Over a period of “x” seconds calculate how many times intent reports state that the target expectations are met. If for more than 90% of time target expectations are met, increase the effectiveness metric of the proposal, otherwise, decrease the effectiveness metric.

[0141] Note that increasing and decreasing of the effectiveness metric may be realized in different ways depending on the implementation. In some examples, this update may fixed, e.g., increase or decrease the effectiveness metric by 10% if target expectations are met or unmet within a time window respectively. In other examples, the update may be proportional to a reward, such as a feedback or feedback signal, obtained after a decomposition proposal is invoked. In a tabular-based implementation, the effectiveness metric may be associated with a decomposition proposal and is represented with a simple scalar value in (0,1], or in another range that is anyway normalized to allow all the effectiveness values being in the same range.

[0142] The intent manager, such as the first intent manager entity 110, may start with zero knowledge, i.e., all the effectiveness metrics equal to 1, and the decomposition proposals that results in expectations, such as an expectation of the first intent, that are satisfied increase the effectiveness metric, while decomposition proposals that are experienced to behave badly decrease the effectiveness metric. Over time, a learning table may look like the following:

[0143] The effectiveness metric may then be used as a probability to invoke the decomposition proposal for certain expectations. In the table above, decomposition proposal 1 is most likely always the only one invoked for Expectation 1, while decomposition proposal 4 is most likely always the only one invoked for Expectation 2.

[0144] Hence, in the example above, if an intent contains Expectation 1 and Expectation 2, the IM knows that it needs to fully decompose the intent by invoking Decomposition proposal 1 and Decomposition proposal 4.

[0145] This approach may result in less calls to downstream intent managers, such as the one or more intent manager entities 120, as the IM implementing it, such as the first intent manager entity 110, learns over time which decompositions are most suitable for each intent. To keep a certain level of exploration, necessary in dynamic environments, this tabular-based implementation may not allow the effectiveness metric to go below a certain threshold “t”.

[0146] Q-learning can be applied to learn an optimal policy, i.e., invoking a right decomposition proposal, for certain expectations. Also, for the Q-learning implementation, the goal is to enhance the efficiency of intent handling via less invocation to downstream intent managers, such as the one or more second intent manager entities 120, by learning decompositions that are highly applicable for fully decomposing a given intent.

[0147] A Q-learning implementation may entail three important definitions that describe the learning process, these are:

[0148] State: The state should represent enough information about the intent manger that receives the intent. Hence, in Q-learning, the state is basically the expectations that needs to be decomposed. Referring to the above tabular based implementation example, the state corresponds to intent’s expectations.

[0149] Actions: For the Q-learning setup, the set of actions that may be taken by the intent manager should be defined. Here, the set of actions that may be taken in each state are the decomposition proposals for given expectations. For instance, referring to the above section, the set of actions for Expectation 1, which is a state, are decomposition proposal 1 and decomposition proposal 2. Similarly, decomposition proposal 3 and 4 are the actions that can be taken from the state Expectation 2.

[0150] Note: since a decomposition rule may target multiple expectations, in general the state may be a set of expectations.

[0151] Reward: It is the feedback signal that shows the level of success of an action. Here, the action is the decomposition proposal that is invoked. The reward should be engineered in such a way that a decomposition proposal that results in high intent satisfaction score should be highly rewarded. Unlike other reinforcement learning settings, here the feedback is not immediate. As shown in Figure 12, there is a need to monitor whether the target expectations, submitted as intents such as the one or more second intents, that comes from a decomposition proposal are met or unmet through intent report time series. Hence, the level of intent satisfaction for sub-intents, such as second intents, within a certain ‘X’ seconds time-window may be monitored. An example definition for a reward signal can be as follows: are met less than 20% of the time are met more than 80% of the time 0.5, otherwise

[0152] The Q-learning implementation starts with zero knowledge, hence the Q values for each state-action pair are initialized with same values. Hence initially, the Q-learning starts by setting each Q(S, A) values to zero. Optionally, such Q values can be pre-trained offline and adjusted at runtime.

[0153] Through interaction, i.e. , trial and error, the algorithm updates its knowledge, or Q values, via the update rule as follows: maixQ(^4.i\ ®) — ?{$s- )

[0154] In some situations, a good decomposition may result in a low reward signal due to low intent satisfaction level. This may be due to, for example, limited resources. However, most of the time, a good decomposition results in a good intent satisfaction level. Hence, Q-learning may approximate the expected long-term reward of a good decomposition even though low immediate rewards may be obtained during the learning process.

[0155] Once the intent manager, such as the first intent manager entity 110, learns, i.e. , the Q values are updated sufficiently, it may select the most successful decomposition proposals for an intent. That is, for a given intent, the actions, or the decomposition proposals, with highest Q-values are selected. Since the environment is dynamic, some level of exploration may be ensured by selecting decomposition proposals randomly, i.e., via e-greedy approach.

[0156] To perform the method actions above, the first intent manager entity 110 is configured to handle intent decomposition, e.g., in the wireless communications network 100. The first intent manager entity 110 may comprise an arrangement depicted in Figure 13.

[0157] The first intent manager entity 110 may comprise an input and output interface 1300 configured to communicate with each other. The input and output interface 1300 may comprise a receiver, e.g. wired and / or wireless, (not shown) and a transmitter, e.g. wired and / or wireless, (not shown).

[0158] The embodiments herein may be implemented through a respective processor or one or more processors, such as at least one processor 1360 of a processing circuitry in the first intent manager entity 110 depicted in Figure 13, together with computer program code for performing the functions and actions of the embodiments herein. The program code mentioned above may also be provided as a computer program product, for instance in the form of a data carrier carrying computer program code for performing the embodiments herein when being loaded into the first intent manager entity 110. One such carrier may be in the form of a CD ROM disc. It is however feasible with other data carriers such as a memory stick. The computer program code may furthermore be provided as pure program code on a server and downloaded to the first intent manager entity 110.

[0159] The first intent manager entity 110 and / or the processor 1360 is configured to handle intent decomposition, e.g., in the wireless communication network 100. Upon receiving a first intent comprising one or more expectations, the first intent manager entity 110 and / or the processor 1360 decomposes the first intent into one or more decomposition proposals using one or more decomposition rules obtained based on the received intent. Each decomposition proposal is adapted to comprise one or more target expectations.

[0160] The first intent manager entity 110 and / or the processor 1360 generates one or more final target expectations based on an evaluation of the one or more decomposition proposals.

[0161] The first intent manager entity 110 and / or the processor 1360 sends, to one or more second intent manager entities 120, one or more second intents adapted to be handled by the one or more second intent manager entities 120. The respective one or more second intents are adapted to comprise one or more of the generated final target expectations.

[0162] In some embodiments, the first intent manager entity 110 and / or processor 1360 is configured to evaluate the one or more decomposition proposals by further being configured to send the respective one or more decomposition proposals to one or more second intent manager functions 120. The first intent manager entity 110 and / or the processor 1360 is further configured to receive, from the respective one or more second intent manager functions 120, a respective achievability characteristic associated with the one or more target expectations.

[0163] In some embodiments, the achievability characteristic comprises a value achievable by the second intent manager 120 for a target expectation.

[0164] In some embodiments, first intent manager entity 110 and / or the processor 1360 is configured to generate the one or more final target expectations by further being configured to select one or more decomposition proposals and second intent manager entities 120 based on the evaluation. The first intent manager entity 110 and / or the processor 1360 is further configured to send the one or more second intents to the selected one or more second intent manager entities 120.

[0165] In some embodiments, the first intent manager entity 110 and / or the processor 1360 is configured to decompose the first intent by further being configured to select a respective second intent manager entity 120 for the one or more decomposition proposals. The first intent manager entity 110 and / or the processor 1360 is configured to generate the one or more final target expectations by further being configured to select one or more decomposition proposals based on the evaluation. The first intent manager entity 110 and / or the processor 1360 is further configured to send the one or more second intents to the selected one or more second intent manager entities 120. In some embodiments, the one or more decomposition proposals are adapted to be associated with a respective effectiveness metric. The first intent manager entity 110 and / or the processor 1360 is further configured to receive feedback from the one or more second intent manager entities 120 handling the one or more second intents. The feedback is adapted to comprise feedback characteristics associated with the one or more final target expectations of the one or more second intents. The first intent manager entity 110 and / or the processor 1360 is further configured to update the respective effectiveness metrics based on the received feedback.

[0166] In some embodiments, to evaluate the one or more decomposition proposals is adapted to comprise to take the effectiveness metric into account.

[0167] In some embodiments, the first intent manager entity 110 and / or the processor 1360 is further configured to obtain the one or more decomposition rules for decomposing the intent. The one or more decomposition rules are obtained taking the received intent into account.

[0168] In some embodiments, an expectation is adapted to comprises a requirement to be met.

[0169] In some embodiments, the first intent manager entity 110 and / or the processor 1360 is further configured to obtain the set of decomposition rules for decomposing intents.

[0170] The first intent manager entity 110 may further comprise a memory 1370 comprising one or more memory units. The memory 1370 comprises instructions executable by the processor 1360 in the first intent manager entity 110. The memory 1370 is arranged to be used to store e.g. information, indications, decomposition proposals, intents, expectations, target expectations, decomposition rules, effectiveness metrics, achievability values, data, configurations, and applications to perform the methods herein when being executed in the first intent manager entity 110.

[0171] In some embodiments, a computer program 1380 comprises instructions, which when executed by the respective at least one processor 1360, cause the at least one processor 1360 of the first intent manager entity 110 to perform the actions above.

[0172] In some embodiments, a respective carrier 1390 comprises the respective computer program 1380, wherein the carrier 1390 is one of an electronic signal, an optical signal, an electromagnetic signal, a magnetic signal, an electric signal, a radio signal, a microwave signal, or a computer-readable storage medium.

[0173] Thus, embodiments herein may disclose the first intent manager entity 110 configured to handle intent decomposition. The first intent manager entity 110 comprises the processor 1360 and the memory 1370, said memory 1370 comprising instructions executable by said processor 1360 whereby said first intent manager entity 110 is operative to perform any of the methods herein.

[0174] As will be readily understood by those familiar with communications design, that functions means or modules may be implemented using digital logic and / or one or more microcontrollers, microprocessors, or other digital hardware. In some embodiments, several or all of the various functions may be implemented together, such as in a single application-specific integrated circuit (ASIC), or in two or more separate devices with appropriate hardware and / or software interfaces between them. Several of the functions may be implemented on a processor shared with other functional components of a base station, for example.

[0175] Alternatively, several of the functional elements of the processing means discussed may be provided through the use of dedicated hardware, while others are provided with hardware for executing software, in association with the appropriate software or firmware. Thus, the term “processor” or “controller” as used herein does not exclusively refer to hardware capable of executing software and may implicitly include, without limitation, digital signal processor (DSP) hardware, read-only memory (ROM) for storing software, random-access memory for storing software and / or program or application data, and nonvolatile memory. Other hardware, conventional and / or custom, may also be included. Designers of communications receivers will appreciate the cost, performance, and maintenance trade-offs inherent in these design choices.

[0176] Any appropriate steps, methods, features, functions, or benefits disclosed herein may be performed through one or more functional units or modules of one or more virtual apparatuses. Each virtual apparatus may comprise a number of these functional units. These functional units may be implemented via processing circuitry, which may include one or more microprocessor or microcontrollers, as well as other digital hardware, which may include digital signal processors (DSPs), special-purpose digital logic, and the like. The processing circuitry may be configured to execute program code stored in memory, which may include one or several types of memory such as read-only memory (ROM), random-access memory (RAM), cache memory, flash memory devices, optical storage devices, etc. Program code stored in memory includes program instructions for executing one or more telecommunications and / or data communications protocols as well as instructions for carrying out one or more of the techniques described herein. In some implementations, the processing circuitry may be used to cause the respective functional unit to perform corresponding functions according one or more embodiments of the present disclosure. ADDITIONAL EXPLANATION

[0177] Some of the embodiments contemplated herein will now be described more fully with reference to the accompanying drawings. Embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art.

[0178] Figure 14 shows an example of a communication system QQ100 in accordance with some embodiments.

[0179] In the example, the communication system QQ100 includes a telecommunication network QQ102 that includes an access network QQ104, such as a radio access network (RAN), and a core network QQ106, which includes one or more core network nodes QQ108 (being examples of the base station 110). The access network QQ104 includes one or more access network nodes, such as network nodes QQ110a and QQ110b (one or more of which may be generally referred to as network nodes QQ110 being examples of the base station 110), or any other similar 3rd Generation Partnership Project (3GPP) access nodes or non-3GPP access points. Moreover, as will be appreciated by those of skill in the art, a network node is not necessarily limited to an implementation in which a radio portion and a baseband portion are supplied and integrated by a single vendor. Thus, it will be understood that network nodes include disaggregated implementations or portions thereof. For example, in some embodiments, the telecommunication network QQ102 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a node in the telecommunication network QQ102 that supports an ORAN specification (e.g., a specification published by the O-RAN Alliance, or any similar organization) and may operate alone or together with other nodes to implement one or more functionalities of any node in the telecommunication network QQ102, including one or more network nodes QQ110 and / or core network nodes QQ108.

[0180] Examples of an ORAN network node include an open radio unit (O-RU), an open distributed unit (O-DU), an open central unit (O-CU), including an O-CU control plane (O- CU-CP) or an O-CU user plane (O-CU-UP), a RAN intelligent controller (near-real time or non-real time) hosting software or software plug-ins, such as a near-real time control application (e.g., xApp) or a non-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification). The network node may support a specification by, for example, supporting an interface defined by the ORAN specification, such as an A1, F1, W1 , E1 , E2, X2, Xn interface, an open fronthaul user plane interface, or an open fronthaul management plane interface. Moreover, an ORAN access node may be a logical node in a physical node. Furthermore, an ORAN network node may be implemented in a virtualization environment (described further below) in which one or more network functions are virtualized. For example, the virtualization environment may include an O-Cloud computing platform orchestrated by a Service Management and Orchestration Framework via an 0-2 interface defined by the O-RAN Alliance or comparable technologies. The network nodes QQ110 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs QQ112a, QQ112b, QQ112c, and QQ112d (one or more of which may be generally referred to as UEs QQ112 being examples of the wireless device 121) to the core network QQ106 over one or more wireless connections.

[0181] Example wireless communications over a wireless connection include transmitting and / or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and / or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system QQ100 may include any number of wired or wireless networks, network nodes, UEs, and / or any other components or systems that may facilitate or participate in the communication of data and / or signals whether via wired or wireless connections. The communication system QQ100 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0182] The UEs QQ112 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and / or operable to communicate wirelessly with the network nodes QQ110 and other communication devices. Similarly, the network nodes QQ110 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs QQ112 and / or with other network nodes or equipment in the telecommunication network QQ102 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in the telecommunication network QQ102.

[0183] In the depicted example, the core network QQ106 connects the network nodes QQ110 to one or more hosts, such as host QQ116. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network QQ106 includes one more core network nodes (e.g., core network node QQ108) that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node QQ108. Example core network nodes include functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (ALISF), Subscription Identifier Deconcealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).

[0184] The host QQ116 may be under the ownership or control of a service provider other than an operator or provider of the access network QQ104 and / or the telecommunication network QQ102, and may be operated by the service provider or on behalf of the service provider. The host QQ116 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio / video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.

[0185] As a whole, the communication system QQ100 of Figure 14 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM); Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and / or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standard (e.g., 6G); wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (WiFi); and / or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, LiFi, and / or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox.

[0186] In some examples, the telecommunication network QQ102 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network QQ102 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network QQ102. For example, the telecommunications network QQ102 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and / or Massive Machine Type Communication (mMTC) / Massive loT services to yet further UEs. In some examples, the UEs QQ112 are configured to transmit and / or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network QQ104 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network QQ104. Additionally, a UE may be configured for operating in single- or multi- RAT or multi-standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e. being configured for multi-radio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio - Dual Connectivity (EN-DC).

[0187] In the example, the hub QQ114 communicates with the access network QQ104 to facilitate indirect communication between one or more UEs (e.g., UE QQ112c and / or QQ112d) and network nodes (e.g., network node QQ110b). In some examples, the hub QQ114 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub QQ114 may be a broadband router enabling access to the core network QQ106 for the UEs. As another example, the hub QQ114 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes QQ110, or by executable code, script, process, or other instructions in the hub QQ114. As another example, the hub QQ114 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub QQ114 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub QQ114 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub QQ114 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub QQ114 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy loT devices.

[0188] The hub QQ114 may have a constant / persistent or intermittent connection to the network node QQ110b. The hub QQ114 may also allow for a different communication scheme and / or schedule between the hub QQ114 and UEs (e.g., UE QQ112c and / or QQ112d), and between the hub QQ114 and the core network QQ106. In other examples, the hub QQ114 is connected to the core network QQ106 and / or one or more UEs via a wired connection. Moreover, the hub QQ114 may be configured to connect to an M2M service provider over the access network QQ104 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes QQ110 while still connected via the hub QQ114 via a wired or wireless connection. In some embodiments, the hub QQ114 may be a dedicated hub - that is, a hub whose primary function is to route communications to / from the UEs from / to the network node QQ110b. In other embodiments, the hub QQ114 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node QQ110b, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0189] Figure 15 shows a UE QQ200 in accordance with some embodiments. As used herein, a UE refers to a device capable, configured, arranged and / or operable to communicate wirelessly with network nodes and / or other UEs. Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop- embedded equipment (LEE), laptop-mounted equipment (LME), smart device, wireless customer-premise equipment (CPE), vehicle, vehicle-mounted or vehicle embedded / integrated wireless device, etc. Other examples include any UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-loT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0190] A UE may support device-to-device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle-to-everything (V2X). In other examples, a UE may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, a UE may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter).

[0191] The UE QQ200 includes processing circuitry QQ202 that is operatively coupled via a bus QQ204 to an input / output interface QQ206, a power source QQ208, a memory QQ210, a communication interface QQ212, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Figure QQ2. The level of integration between the components may vary from one UE to another UE. Further, certain UEs may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.

[0192] The processing circuitry QQ202 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs in the memory QQ210. The processing circuitry QQ202 may be implemented as one or more hardware-implemented state machines (e.g., in discrete logic, field-programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs, general-purpose processors, such as a microprocessor or digital signal processor (DSP), together with appropriate software; or any combination of the above. For example, the processing circuitry QQ202 may include multiple central processing units (CPUs).

[0193] In the example, the input / output interface QQ206 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and / or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into the UE QQ200. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device.

[0194] In some embodiments, the power source QQ208 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used. The power source QQ208 may further include power circuitry for delivering power from the power source QQ208 itself, and / or an external power source, to the various parts of the UE QQ200 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source QQ208. Power circuitry may perform any formatting, converting, or other modification to the power from the power source QQ208 to make the power suitable for the respective components of the UE QQ200 to which power is supplied.

[0195] The memory QQ210 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory QQ210 includes one or more application programs QQ214, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data QQ216. The memory QQ210 may store, for use by the UE QQ200, any of a variety of various operating systems or combinations of operating systems.

[0196] The memory QQ210 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and / or ISIM, other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUlCC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory QQ210 may allow the UE QQ200 to access instructions, application programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory QQ210, which may be or comprise a device-readable storage medium.

[0197] The processing circuitry QQ202 may be configured to communicate with an access network or other network using the communication interface QQ212. The communication interface QQ212 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna QQ222. The communication interface QQ212 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another UE or a network node in an access network). Each transceiver may include a transmitter QQ218 and / or a receiver QQ220 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter QQ218 and receiver QQ220 may be coupled to one or more antennas (e.g., antenna QQ222) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0198] In the illustrated embodiment, communication functions of the communication interface QQ212 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented in according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol / internet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0199] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface QQ212, via a wireless connection to a network node. Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE. The output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature), random (e.g., to even out the load from reporting from several sensors), in response to a triggering event (e.g., when moisture is detected an alert is sent), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient).

[0200] As another example, a UE comprises an actuator, a motor, or a switch, related to a communication interface configured to receive wireless input from a network node via a wireless connection. In response to the received wireless input the states of the actuator, the motor, or the switch may change. For example, the UE may comprise a motor that adjusts the control surfaces or rotors of a drone in flight according to the received input or to a robotic arm performing a medical procedure according to the received input.

[0201] A UE, when in the form of an Internet of Things (loT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application and healthcare. Non-limiting examples of such an loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / window sensor, a flood / moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a head-mounted display for Augmented Reality (AR) or Virtual Reality (VR), a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item-tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an loT device comprises circuitry and / or software in dependence of the intended application of the loT device in addition to other components as described in relation to the UE QQ200 shown in Figure QQ2.

[0202] As yet another specific example, in an loT scenario, a UE may represent a machine or other device that performs monitoring and / or measurements, and transmits the results of such monitoring and / or measurements to another UE and / or a network node. The UE may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, the UE may implement the 3GPP NB-loT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation.

[0203] In practice, any number of UEs may be used together with respect to a single use case. For example, a first UE might be or be integrated in a drone and provide the drone’s speed information (obtained through a speed sensor) to a second UE that is a remote controller operating the drone. When the user makes changes from the remote controller, the first UE may adjust the throttle on the drone (e.g. by controlling an actuator) to increase or decrease the drone’s speed. The first and / or the second UE can also include more than one of the functionalities described above. For example, a UE might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators.

[0204] Figure 16 shows a network node QQ300 in accordance with some embodiments.

[0205] As used herein, network node refers to equipment capable, configured, arranged and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment, in a telecommunication network. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NR NodeBs (gNBs)), O- RAN nodes or components of an O-RAN node (e.g., 0-Rll, 0-Dll, O-CU).

[0206] Base stations may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. A base station may be a relay node or a relay donor node controlling a relay. A network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units, distributed units (e.g., in an O-RAN access node) and / or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).

[0207] Other examples of network nodes include multiple transmission point (multi-TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi- cell / multicast coordination entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and / or Minimization of Drive Tests (MDTs).

[0208] The network node QQ300 includes a processing circuitry QQ302, a memory QQ304, a communication interface QQ306, and a power source QQ308. The network node QQ300 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the network node QQ300 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node QQ300 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory QQ304 for different RATs) and some components may be reused (e.g., a same antenna QQ310 may be shared by different RATs). The network node QQ300 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node QQ300, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node QQ300.

[0209] The processing circuitry QQ302 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and / or encoded logic operable to provide, either alone or in conjunction with other network node QQ300 components, such as the memory QQ304, to provide network node QQ300 functionality.

[0210] In some embodiments, the processing circuitry QQ302 includes a system on a chip (SOC). In some embodiments, the processing circuitry QQ302 includes one or more of radio frequency (RF) transceiver circuitry QQ312 and baseband processing circuitry QQ314. In some embodiments, the radio frequency (RF) transceiver circuitry QQ312 and the baseband processing circuitry QQ314 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry QQ312 and baseband processing circuitry QQ314 may be on the same chip or set of chips, boards, or units.

[0211] The memory QQ304 may comprise any form of volatile or non-volatile computer- readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and / or any other volatile or non-volatile, non-transitory device- readable and / or computer-executable memory devices that store information, data, and / or instructions that may be used by the processing circuitry QQ302. The memory QQ304 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and / or other instructions capable of being executed by the processing circuitry QQ302 and utilized by the network node QQ300. The memory QQ304 may be used to store any calculations made by the processing circuitry QQ302 and / or any data received via the communication interface QQ306. In some embodiments, the processing circuitry QQ302 and memory QQ304 is integrated.

[0212] The communication interface QQ306 is used in wired or wireless communication of signaling and / or data between a network node, access network, and / or UE. As illustrated, the communication interface QQ306 comprises port(s) / terminal(s) QQ316 to send and receive data, for example to and from a network over a wired connection. The communication interface QQ306 also includes radio front-end circuitry QQ318 that may be coupled to, or in certain embodiments a part of, the antenna QQ310. Radio front-end circuitry QQ318 comprises filters QQ320 and amplifiers QQ322. The radio front-end circuitry QQ318 may be connected to an antenna QQ310 and processing circuitry QQ302. The radio front-end circuitry may be configured to condition signals communicated between antenna QQ310 and processing circuitry QQ302. The radio front-end circuitry QQ318 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry QQ318 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters QQ320 and / or amplifiers QQ322. The radio signal may then be transmitted via the antenna QQ310. Similarly, when receiving data, the antenna QQ310 may collect radio signals which are then converted into digital data by the radio front-end circuitry QQ318. The digital data may be passed to the processing circuitry QQ302. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0213] In certain alternative embodiments, the network node QQ300 does not include separate radio front-end circuitry QQ318, instead, the processing circuitry QQ302 includes radio front-end circuitry and is connected to the antenna QQ310. Similarly, in some embodiments, all or some of the RF transceiver circuitry QQ312 is part of the communication interface QQ306. In still other embodiments, the communication interface QQ306 includes one or more ports or terminals QQ316, the radio front-end circuitry QQ318, and the RF transceiver circuitry QQ312, as part of a radio unit (not shown), and the communication interface QQ306 communicates with the baseband processing circuitry QQ314, which is part of a digital unit (not shown).

[0214] The antenna QQ310 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna QQ310 may be coupled to the radio front-end circuitry QQ318 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna QQ310 is separate from the network node QQ300 and connectable to the network node QQ300 through an interface or port.

[0215] The antenna QQ310, communication interface QQ306, and / or the processing circuitry QQ302 may be configured to perform any receiving operations and / or certain obtaining operations described herein as being performed by the network node. Any information, data and / or signals may be received from a UE, another network node and / or any other network equipment. Similarly, the antenna QQ310, the communication interface QQ306, and / or the processing circuitry QQ302 may be configured to perform any transmitting operations described herein as being performed by the network node. Any information, data and / or signals may be transmitted to a UE, another network node and / or any other network equipment.

[0216] The power source QQ308 provides power to the various components of network node QQ300 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source QQ308 may further comprise, or be coupled to, power management circuitry to supply the components of the network node QQ300 with power for performing the functionality described herein. For example, the network node QQ300 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source QQ308. As a further example, the power source QQ308 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail.

[0217] Embodiments of the network node QQ300 may include additional components beyond those shown in Figure 16 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and / or any functionality necessary to support the subject matter described herein. For example, the network node QQ300 may include user interface equipment to allow input of information into the network node QQ300 and to allow output of information from the network node QQ300. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node QQ300.

[0218] Figure 17 is a block diagram of a host QQ400, which may be an embodiment of the host QQ116 of Figure QQ1, in accordance with various aspects described herein. As used herein, the host QQ400 may be or comprise various combinations hardware and / or software, including a standalone server, a blade server, a cloud-implemented server, a distributed server, a virtual machine, container, or processing resources in a server farm. The host QQ400 may provide one or more services to one or more UEs.

[0219] The host QQ400 includes processing circuitry QQ402 that is operatively coupled via a bus QQ404 to an input / output interface QQ406, a network interface QQ408, a power source QQ410, and a memory QQ412. Other components may be included in other embodiments. Features of these components may be substantially similar to those described with respect to the devices of previous figures, such as Figures 14 and QQ3, such that the descriptions thereof are generally applicable to the corresponding components of host QQ400.

[0220] The memory QQ412 may include one or more computer programs including one or more host application programs QQ414 and data QQ416, which may include user data, e.g., data generated by a UE for the host QQ400 or data generated by the host QQ400 for a UE. Embodiments of the host QQ400 may utilize only a subset or all of the components shown. The host application programs QQ414 may be implemented in a container-based architecture and may provide support for video codecs (e.g., Versatile Video Coding (VVC), High Efficiency Video Coding (HEVC), Advanced Video Coding (AVC), MPEG, VP9) and audio codecs (e.g., FLAG, Advanced Audio Coding (AAC), MPEG, G.711), including transcoding for multiple different classes, types, or implementations of UEs (e.g., handsets, desktop computers, wearable display systems, heads-up display systems). The host application programs QQ414 may also provide for user authentication and licensing checks and may periodically report health, routes, and content availability to a central node, such as a device in or on the edge of a core network. Accordingly, the host QQ400 may select and / or indicate a different host for over-the-top services for a UE. The host application programs QQ414 may support various protocols, such as the HTTP Live Streaming (HLS) protocol, Real-Time Messaging Protocol (RTMP), Real-Time Streaming Protocol (RTSP), Dynamic Adaptive Streaming over HTTP (MPEG-DASH), etc.

[0221] Figure 18 is a block diagram illustrating a virtualization environment QQ500 in which functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments QQ500 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, UE, core network node, or host. Further, in embodiments in which the virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized. In some embodiments, the virtualization environment QQ500 includes components defined by the O-RAN Alliance, such as an O- Cloud environment orchestrated by a Service Management and Orchestration Framework via an 0-2 interface.

[0222] Applications QQ502 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment Q400 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0223] Hardware QQ504 includes processing circuitry, memory that stores software and / or instructions executable by hardware processing circuitry, and / or other hardware devices as described herein, such as a network interface, input / output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers QQ506 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs QQ508a and QQ508b (one or more of which may be generally referred to as VMs QQ508), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer QQ506 may present a virtual operating platform that appears like networking hardware to the VMs QQ508.

[0224] The VMs QQ508 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer QQ506. Different embodiments of the instance of a virtual appliance QQ502 may be implemented on one or more of VMs QQ508, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV). NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers, and customer premise equipment.

[0225] In the context of NFV, a VM QQ508 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine. Each of the VMs QQ508, and that part of hardware QQ504 that executes that VM, be it hardware dedicated to that VM and / or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more VMs QQ508 on top of the hardware QQ504 and corresponds to the application QQ502.

[0226] Hardware QQ504 may be implemented in a standalone network node with generic or specific components. Hardware QQ504 may implement some functions via virtualization. Alternatively, hardware QQ504 may be part of a larger cluster of hardware (e.g. such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration QQ510, which, among others, oversees lifecycle management of applications QQ502. In some embodiments, hardware QQ504 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system QQ512 which may alternatively be used for communication between hardware nodes and radio units.

[0227] Figure 19 shows a communication diagram of a host QQ602 communicating via a network node QQ604 with a UE QQ606 over a partially wireless connection in accordance with some embodiments. Example implementations, in accordance with various embodiments, of the UE (such as a UE QQ112a of Figure 14 and / or UE QQ200 of Figure QQ2), network node (such as network node QQ110a of Figure 14 and / or network node QQ300 of Figure QQ3), and host (such as host QQ116 of Figure 14 and / or host QQ400 of Figure QQ4) discussed in the preceding paragraphs will now be described with reference to Figure QQ6.

[0228] Like host QQ400, embodiments of host QQ602 include hardware, such as a communication interface, processing circuitry, and memory. The host QQ602 also includes software, which is stored in or accessible by the host QQ602 and executable by the processing circuitry. The software includes a host application that may be operable to provide a service to a remote user, such as the UE QQ606 connecting via an over-the-top (OTT) connection QQ650 extending between the UE QQ606 and host QQ602. In providing the service to the remote user, a host application may provide user data which is transmitted using the OTT connection QQ650. The network node QQ604 includes hardware enabling it to communicate with the host QQ602 and UE QQ606. The connection QQ660 may be direct or pass through a core network (like core network QQ106 of Figure QQ1) and / or one or more other intermediate networks, such as one or more public, private, or hosted networks. For example, an intermediate network may be a backbone network or the Internet.

[0229] The UE QQ606 includes hardware and software, which is stored in or accessible by UE QQ606 and executable by the UE’s processing circuitry. The software includes a client application, such as a web browser or operator-specific “app” that may be operable to provide a service to a human or non-human user via UE QQ606 with the support of the host QQ602. In the host QQ602, an executing host application may communicate with the executing client application via the OTT connection QQ650 terminating at the UE QQ606 and host QQ602. In providing the service to the user, the UE's client application may receive request data from the host's host application and provide user data in response to the request data. The OTT connection QQ650 may transfer both the request data and the user data. The UE's client application may interact with the user to generate the user data that it provides to the host application through the OTT connection QQ650.

[0230] The OTT connection QQ650 may extend via a connection QQ660 between the host QQ602 and the network node QQ604 and via a wireless connection QQ670 between the network node QQ604 and the UE QQ606 to provide the connection between the host QQ602 and the UE QQ606. The connection QQ660 and wireless connection QQ670, over which the OTT connection QQ650 may be provided, have been drawn abstractly to illustrate the communication between the host QQ602 and the UE QQ606 via the network node QQ604, without explicit reference to any intermediary devices and the precise routing of messages via these devices.

[0231] As an example of transmitting data via the OTT connection QQ650, in step QQ608, the host QQ602 provides user data, which may be performed by executing a host application. In some embodiments, the user data is associated with a particular human user interacting with the UE QQ606. In other embodiments, the user data is associated with a UE QQ606 that shares data with the host QQ602 without explicit human interaction. In step QQ610, the host QQ602 initiates a transmission carrying the user data towards the UE QQ606. The host QQ602 may initiate the transmission responsive to a request transmitted by the UE QQ606. The request may be caused by human interaction with the UE QQ606 or by operation of the client application executing on the UE QQ606. The transmission may pass via the network node QQ604, in accordance with the teachings of the embodiments described throughout this disclosure. Accordingly, in step QQ612, the network node QQ604 transmits to the UE QQ606 the user data that was carried in the transmission that the host QQ602 initiated, in accordance with the teachings of the embodiments described throughout this disclosure. In step QQ614, the UE QQ606 receives the user data carried in the transmission, which may be performed by a client application executed on the UE QQ606 associated with the host application executed by the host QQ602.

[0232] In some examples, the UE QQ606 executes a client application which provides user data to the host QQ602. The user data may be provided in reaction or response to the data received from the host QQ602. Accordingly, in step QQ616, the UE QQ606 may provide user data, which may be performed by executing the client application. In providing the user data, the client application may further consider user input received from the user via an input / output interface of the UE QQ606. Regardless of the specific manner in which the user data was provided, the UE QQ606 initiates, in step QQ618, transmission of the user data towards the host QQ602 via the network node QQ604. In step QQ620, in accordance with the teachings of the embodiments described throughout this disclosure, the network node QQ604 receives user data from the UE QQ606 and initiates transmission of the received user data towards the host QQ602. In step QQ622, the host QQ602 receives the user data carried in the transmission initiated by the UE QQ606.

[0233] One or more of the various embodiments improve the performance of OTT services provided to the UE QQ606 using the OTT connection QQ650, in which the wireless connection QQ670 forms the last segment.

[0234] In an example scenario, factory status information may be collected and analyzed by the host QQ602. As another example, the host QQ602 may process audio and video data which may have been retrieved from a UE for use in creating maps. As another example, the host QQ602 may collect and analyze real-time data to assist in controlling vehicle congestion (e.g., controlling traffic lights). As another example, the host QQ602 may store surveillance video uploaded by a UE. As another example, the host QQ602 may store or control access to media content such as video, audio, VR or AR which it can broadcast, multicast or unicast to UEs. As other examples, the host QQ602 may be used for energy pricing, remote control of non-time critical electrical load to balance power generation needs, location services, presentation services (such as compiling diagrams etc. from data collected from remote devices), or any other function of collecting, retrieving, storing, analyzing and / or transmitting data. In some examples, a measurement procedure may be provided for the purpose of monitoring data rate, latency and other factors on which the one or more embodiments improve. There may further be an optional network functionality for reconfiguring the OTT connection QQ650 between the host QQ602 and UE QQ606, in response to variations in the measurement results. The measurement procedure and / or the network functionality for reconfiguring the OTT connection may be implemented in software and hardware of the host QQ602 and / or UE QQ606. In some embodiments, sensors (not shown) may be deployed in or in association with other devices through which the OTT connection QQ650 passes; the sensors may participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or supplying values of other physical quantities from which software may compute or estimate the monitored quantities. The reconfiguring of the OTT connection QQ650 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not directly alter the operation of the network node QQ604. Such procedures and functionalities may be known and practiced in the art. In certain embodiments, measurements may involve proprietary UE signaling that facilitates measurements of throughput, propagation times, latency and the like, by the host QQ602. The measurements may be implemented in that software causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection QQ650 while monitoring propagation times, errors, etc.

[0235] Although the computing devices described herein (e.g., UEs, network nodes, hosts) may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and / or software needed to perform the tasks, features, functions and methods disclosed herein. Determining, calculating, obtaining or similar operations described herein may be performed by processing circuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and / or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination. Moreover, while components are depicted as single boxes located within a larger box, or nested within multiple boxes, in practice, computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitioned between separate components. For example, a communication interface may be configured to include any of the components described herein, and / or the functionality of the components may be partitioned between the processing circuitry and the communication interface. In another example, non-computationally intensive functions of any of such components may be implemented in software or firmware and computationally intensive functions may be implemented in hardware.

[0236] In certain embodiments, some or all of the functionality described herein may be provided by processing circuitry executing instructions stored on in memory, which in certain embodiments may be a computer program product in the form of a non-transitory computer-readable storage medium. In alternative embodiments, some or all of the functionality may be provided by the processing circuitry without executing instructions stored on a separate or discrete device-readable storage medium, such as in a hard-wired manner. In any of those particular embodiments, whether executing instructions stored on a non-transitory computer-readable storage medium or not, the processing circuitry can be configured to perform the described functionality. The benefits provided by such functionality are not limited to the processing circuitry alone or to other components of the computing device, but are enjoyed by the computing device as a whole, and / or by end users and a wireless network generally.

[0237] When using the word "comprise" or “comprising” it shall be interpreted as nonlimiting, i.e. meaning "consist at least of".

[0238] The embodiments herein are not limited to the preferred embodiments described above. Various alternatives, modifications and equivalents may be used.

Claims

CLAIMS1. A method performed by a first intent manager entity (110) for handling intent decomposition, the method comprising: upon receiving a first intent comprising one or more expectations, decomposing (603) the first intent into one or more decomposition proposals using one or more decomposition rules obtained based on the first intent, wherein each decomposition proposal comprises one or more target expectations, generating (605) one or more final target expectations based on an evaluation (604) of the one or more decomposition proposals, and sending (606), to one or more second intent manager entities (120), one or more second intents to be handled by the one or more second intent manager entities (120), wherein the respective one or more second intents comprises one or more of the generated final target expectations.

2. The method according to claim 1, wherein evaluating (604) the one or more decomposition proposals comprises sending the respective one or more decomposition proposals to one or more second intent manager functions (120), and receiving, from the respective one or more second intent manager functions (120), a respective achievability characteristic associated with the one or more target expectations.

3. The method according to claim 2, wherein the achievability characteristic comprises a value achievable by the second intent manager (120) for a target expectation.

4. The method according to any of claims 1-3, wherein generating (605) the one or more final target expectations further comprises selecting one or more decomposition proposals and second intent manager entities (120) based on the evaluation (604), and wherein the one or more second intents are sent (606) to the selected one or more second intent manager entities (120).

5. The method according to any of claims 1-4, wherein decomposing (603) the first intent further comprises selecting a respective second intent manager entity (120) for the one or more decomposition proposals, and wherein generating (605) the one or more final target expectations further comprises selecting one or more decomposition proposalsbased on the evaluation (604), and wherein the one or more second intents are sent (606) to the selected one or more second intent manager entities (120).

6. The method according to any of claims 1-5, wherein the one or more decomposition proposals are associated with a respective effectiveness metric, the method further comprises: receiving (607) feedback from the one or more second intent manager entities (120) handling the one or more second intents, which feedback comprises feedback characteristics associated with the one or more final target expectations of one or more the second intents, and updating (608) the respective effectiveness metrics based on the received feedback.

7. The method according to claim 6, wherein evaluating (604) the one or more decomposition proposals comprises taking the effectiveness metric into account.

8. The method according to any of claim 1-7, wherein the method further comprises: obtaining (602) the one or more decomposition rules for decomposing the first intent, wherein the one or more decomposition rules are obtained taking the first intent into account.

9. The method according to any of claims 1-8, wherein a target expectation comprises a requirement to be met.

10. The method according to any of claims 1-9, wherein the method further comprises: obtaining (601) a set of decomposition rules for decomposing intents.

11. A computer program (1380) comprising instructions, which when executed by a processor (1360), causes the processor (1360) to perform actions according to any of the claims 1-10.

12. A carrier (1390) comprising the computer program (1380) of claim 11, wherein the carrier (1390) is one of an electronic signal, an optical signal, an electromagnetic signal, a magnetic signal, an electric signal, a radio signal, a microwave signal, or a computer- readable storage medium.

13. A first intent manager entity (110) configured to handle intent decomposition, the first intent manager entity (110) further being configured to: upon receiving a first intent comprising one or more expectations, decompose the first intent into one or more decomposition proposals using one or more decomposition rules obtained based on the received intent, wherein each decomposition proposal is adapted to comprise one or more target expectations, generate one or more final target expectations based on an evaluation of the one or more decomposition proposals, and send, to one or more second intent manager entities (120), one or more second intents to be handled by the one or more second intent manager entities (120), wherein the respective one or more second intents are adapted to comprise one or more of the generated final target expectations.

14. The first intent manager entity (110) according to claim 13, wherein first intent manager entity (110) is configured to evaluate the one or more decomposition proposals by further being configured to send the respective one or more decomposition proposals to one or more second intent manager functions (120), and receive, from the respective one or more second intent manager functions (120), a respective achievability characteristic associated with the one or more target expectations.

15. The first intent manager entity (110) according to claim 14, wherein the achievability characteristic is adapted to comprise a value achievable by the second intent manager (120) for a target expectation.

16. The first intent manager entity (110) according to any of claims 13-15, wherein the first intent manager entity (110) is configured to generate the one or more final target expectations by further being configured to select one or more decomposition proposals and second intent manager entities (120) based on the evaluation, and wherein the first intent manager entity (110) is further configured to send the one or more second intents to the selected one or more second intent manager entities (120).

17. The first intent manager entity (110) according to any of claims 13-16, wherein the first intent manager entity (110) is configured to decompose the first intent by further being configured to select a respective second intent manager entity (120) for the one or more decomposition proposals, and wherein the first intent manager entity (110) is configured togenerate the one or more final target expectations by further being configured to select one or more decomposition proposals based on the evaluation (603), and wherein the first intent manager entity (110) is further configured to send the one or more second intents to the selected one or more second intent manager entities (120).

18. The first intent manager entity (110) according to any of claims 13-17, wherein the one or more decomposition proposals are adapted to be associated with a respective effectiveness metric, the first intent manager entity (110) further being configured to: receive feedback from the one or more second intent manager entities (120) handling the one or more second intents, which feedback is adapted to comprise feedback characteristics associated with the one or more final target expectations of one or more the second intents, and update the respective effectiveness metric based on the received feedback.

19. The first intent manager entity (110) according to claim 18, wherein to evaluate the one or more decomposition proposals is adapted to comprise to take the effectiveness metric into account.

20. The first intent manager entity (110) according to any of claim 13-19, wherein the first intent manager entity (110) is further configured to: obtain, from a set of decomposition rules, the one or more decomposition rules for decomposing the first intent, wherein the one or more decomposition rules are obtained taking the received intent into account.

21. The first intent manager entity (110) according to any of claims 13-20, wherein an expectation is adapted to comprise a requirement to be met.

22. The first intent manager entity (110) according to any of claims 13-21, wherein the first intent manager entity (110) is further configured to: obtain a set of decomposition rules for decomposing intents.

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