Assistance information among call drop prediction tools

Abstract

A method in a network function (NF) includes determining that an abnormal release (AR) is occurring in the network at a time t1. Responsive to determining that the AR is affecting a specific type of UE and / or UE identity, determining that the AR is an issue with the specific type of UE and / or UE identity at a UE side. Responsive to determining that the AR was caused by a software bug, determining that the AR is an issue at a network side. Responsive to determining that the AR was caused by bad radio conditions, determining that the AR could be caused by radio conditions at the network side or at the UE side. The NF transmits information to the UE about whether the AR was caused at the network side, at the UE side, or at the network and / or UE sides.

Description

ASSISTANCE INFORMATION AMONG CALL DROP PREDICTION TOOLSTECHNICAL FIELD

[0001] The present disclosure relates generally to communications, and more particularly to communication methods and related devices and nodes supporting wireless communications .BACKGROUND

[0002] In every wireless network, the occurrence of a call abnormal release, also called a drop call, is unavoidable.

[0003] There are different reasons for that. In one example, the subscriber that is on call moves to an area out of radio coverage and the call is dropped. In another example, the operator staff might cause the abnormal release of some calls, e.g., by taking some actions on the equipment, e.g., by restarting a Radio Base Station.

[0004] All the different known causes of abnormal call release are listed in the standards, e.g., in 3rd Generation Partnership Project (3GPP) Technical Specification (TS) 23.502 (V16.11.0) (section 4.2.6) and 3GPP TS 38.413 (vl6.8.0) (section 9.3.1) for 5G and in 3GPP TS 36.413 (section 9.2.1) for 4G, each of which is incorporated by reference herein for all purposes. An example of existing causes for 5G, as listed in the latest release of 3GPP TS 23.502 are:Radio Access Network (RAN)-initiated with cause e.g., O&M (operation and maintenance) Intervention, Unspecified Failure, Inter-System Redirection, request for establishment of QoS (quality of service) Flow for IMS Voice, Release due to User Equipment (UE) generated signalling connection release, mobility restriction, Release Assistance Information (RAI) from the UE, etc.; orAMF (access and mobility management function) initiated with cause, e.g., Unspecified Failure, etc.

[0005] Note that in 5G, either the NR (New Radio) or AMF (Access and Mobility Management Function) triggers the abnormal release message. If the NR has triggered the abnormal release, it will send a signalling message to the AMF that contains the cause of the release, and vice versa if the AMF has triggered the abnormal release, it will send a signallingmessage to the NR that contains the cause of the release.

[0006] The exchange of release messages and causes are not limited to NR and AMF only, as they are also exchanged between a UE (User Equipment) and a Radio Node, NR (5G) and an eNodeB (eNB) (4G). In such scenario, the release messages and causes are described in 3GPP RRC (Radio Resource Control) specifications: 3GPP TS 38.331 (vl6.7.0) for 5G and 3GPP TS 36.331 (vl6.7.0) for 4G, each of which is incorporated by reference herein for all purposes.

[0007] Predicting the occurrence of an abnormal call release would be very beneficial as a network might take some precautions to avoid or minimize the effect of the expected drop call, e.g., by handing over a UE from the network (e.g., 5G) where the call drop is experienced towards another radio technology (e.g., 4G) before reaching the location of an expected drop call. A prevention is more relevant for a sensitive application running on the UE, as the sensitive application could take in advance some additional precautions. For example, suppose that a smart entity, denoted as network_prediction, that handles the abnormal call release at the network side, has detected, e.g., based on previous reported events by different UEs, that at one particular location, location X, in a 5G network, the calls are being dropped. One potential outcome of network_prediction is to broadcast location X to all UEs in the cell or only to some UEs running sensitive applications, e.g., autonomous vehicles, so that when such vehicles approach location X, the vehicles might slow down because brakes’ reaction under a 4G network is slower than that under a 5G network due to the difference in network latency between 4G and 5G.

[0008] There are many different tools that could predict an abnormal call release or e.g., a drop call. Such prediction tools might be implemented at the network side or at the UE side.

[0009] Many companies already provide drop calls prediction tools that are located at the network side. This might be done after the occurrence of one or more drop calls, of the same or of different UE(s), at one location X, and based on input that is either:■ reported by the UE, e.g., UE location or UE radio conditions etc., or detected at the network side, e.g., the triggering of some particular signalling message like the receipt of a measurement report from the UE, or by tracking UE location and movement, e.g., via network location procedures, etc.

[0010] The network, based on machine learning algorithms located at the network, could use this information to predict the occurrence of the next drop call.

[0011] There are many prediction tools implemented at the UE side that could predict the occurrence of a UE next drop call. This is done based on different factors, e.g., actual UEradio conditions like the value of RSRP (Reference Signal Received Power) or based on actual UE location, e.g., UE is moving towards a known location X, etc.SUMMARY

[0012] There currently exist certain challenges. A prediction tool implemented at the network can do the investigation of the root cause of previous drop call(s) at the network side and then can give the result to one or more UEs, e.g., the UE is informed that after a period of time or at a particular location, it might experience a drop call. In other words, this is about a communication between one call drop investigation tool (at the network side) and a UE application or procedure that needs to know about the occurrence of the next drop call. Alternatively, a prediction tool implemented at the UE side does the investigation of the root cause of previous drop call(s) at the UE side and then could predict that it might experience a drop call after a period of time or at a particular location. In other words, this is about a communication between one call drop investigation tool (at the UE side) and a UE application or procedure that needs to know about the occurrence of the next drop call.

[0013] The results of the investigation of the root cause of drop call(s) collected at a first tool located at a first side, e.g., the network side, are not communicated as an additional input for the investigation of the root cause of drop call(s) of a second tool located at the second side, e.g., the UE side. In other words, there is no communication between one call drop investigation tool (at the network side) and another call drop investigation tool (at the UE side). The absence of such communication leads to problems.

[0014] The first problem is that in the same network, there may be a call drop prediction tool at the network side but there is no call prediction tool at the UE side. For example, where the cause of abnormal call failure is Unspecified, then having only one prediction tool implemented at the network side is not sufficient and may not lead to the prediction of a UE next drop call.

[0015] The second problem is when a first prediction tool exists at the network side and a second prediction tool exist at the UE side, there is no communication between the tools.

[0016] Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. According to some embodiments, a method in a network function (e.g., a call drop function tool) at a network node includes determining that an abnormal release is occurring in the network at a time tl. The method includes responsive to determining that the abnormal release is affecting a specific type of user equipment, UE,and / or UE identity, determining that the abnormal release is not a network issue but is an issue with the specific type of UE and / or UE identity of a UE at a UE side. The method includes responsive to determining that the abnormal release was caused by a software bug, determining that the abnormal release is a network issue at a network side. The method includes responsive to determining that the abnormal release was caused by bad radio conditions, determining that the abnormal release could be caused by an issue of radio conditions at the network side or at the UE side. The method includes transmitting information to the UE about whether the abnormal release was caused by an issue at the network side, at the UE side, or was caused at the network side and / or the UE side.

[0017] Certain embodiments may provide one or more of the following technical advantage(s). When a call drop prediction tool is running at the UE side, it is triggered only when necessary. Especially, it is not triggered when the root cause of the abnormal call release is known to be at the network side. Such feature prevents the UE from consuming unnecessary processing and battery life. Additionally, it will be possible for a call drop prediction tool at the UE to predict the occurrence of some cases of abnormal call release which are not possible to predict with existing solutions.BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this application, illustrate certain non-limiting embodiments of inventive concepts of the present disclosure. In the drawings:

[0019] Figures 1 A- IB are a flowchart illustrating operations of network entities according to some embodiments of the present disclosure;

[0020] Figures 2-5 are flowcharts illustrating operations of a network node having a network function according to some embodiments of the present disclosure;

[0021] Figure 6 is a diagram illustrating an example of inputs and outputs of a network prediction tool according to some embodiments of the present disclosure;

[0022] Figures 7 is a flowchart illustrating operations of a network node having a network function according to some embodiments of the present disclosure;

[0023] Figure 8 is a flowchart illustrating operations of network entities according to some embodiments of the present disclosure;

[0024] Figures 9-12 are flowcharts illustrating operations of a network node having anetwork function according to some embodiments of the present disclosure;

[0025] Figures 13-16 are flowcharts illustrating operations of a user equipment according to some embodiments of the present disclosure;

[0026] Figure 17 is a block diagram of a communication system in accordance with some embodiments;

[0027] Figure 18 is a block diagram of a user equipment in accordance with some embodiments;

[0028] Figure 19 is a block diagram of a network node in accordance with some embodiments;

[0029] Figure 20 is a block diagram of a host computer communicating with a user equipment in accordance with some embodiments;

[0030] Figure 21 is a block diagram of a virtualization environment in accordance with some embodiments; and

[0031] Figure 22 is a block diagram of a host computer communicating via a network node with a user equipment over a partially wireless connection in accordance with some embodiments.DETAILED DESCRIPTION

[0032] 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, in which examples of embodiments of inventive concepts are shown. Inventive concepts may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of present inventive concepts to those skilled in the art. It should also be noted that these embodiments are not mutually exclusive. Components from one embodiment may be tacitly assumed to be present / used in another embodiment.

[0033] As previously indicated, in one problem, in the same network, there is a call drop prediction tool at the network side but there is no call prediction tool at the UE side. There are many different tools that could be implemented at the UE side in order to predict the occurrence of the next abnormal call release (drop call). However, many UE vendors do not implement such tools - instead they count on existing tools implemented at the network sidethat handles and predicts the occurrence of the abnormal call release. The question that arises is: If there is already a tool at the network side, denoted here network_prediction, that could inform the UE in advance, about the occurrence of a drop call at location X, then why do UE vendors need to have another tool to be installed at the UE side, denoted here UE_prediction, that has the role of handling & predicting the abnormal call release?

[0034] In some scenarios, e.g., when the cause of the abnormal call release is for example Radio Link Failure, having only one tool network_prediction at the network side could help the UE predict or know in advance a location X where a drop call is possible. In such scenario, there is no need for having UE_prediction at the UE side. However, in some other scenarios, e.g., where the cause of abnormal call failure is Unspecified, then having only one prediction tool implemented at the network side is not sufficient and could not lead the prediction of the UE’s next drop call. Take, for example, where the abnormal call release is caused by a specific event at the UE side, e.g., a buffer, e.g., bufferl, is reset when RSRP is below a certain threshold. In such example, because no prediction tool at the network side has visibility at the buffer level at the UE side, the prediction tool at the network side could not find the root cause of the issue. Consequently, it could not predict a next abnormal call release that is caused by bufferl reset. Only when a second tool that handles abnormal call release is implemented at the UE side would there be a possibility of detecting and predicting the next bufferl reset.

[0035] The second problem mentioned above is when a first prediction tool exists at the network side and a second prediction tool exists at the UE side, but there is no communication between both tools. The 3GPP standards, whether the release was an abnormal or a normal release, provides a call release message. In 5G, the release message is denoted RRCRelease and an extract of the RRCRelease message is reproduced below: RRCRelease-SEQUENCE { redirectedCarrierlnfo Redir ectedCarrierlnfo OPTIONAL,Need N cellReselectionPriorities CellReselectionPriorities OPTIONAL,Need R suspendConfig SuspendConfig OPTIONAL,Need R deprioritisationReq SEQUENCE { deprioritisationType ENUMERATED {frequency, nr}, deprioritisationTimer ENUMERATED {min5, minlO, minl5, min30}} OPTIONAL, -Need N lateNonCriticalExtension OCTET STRING OPTIONAL, nonCriticalExtension RRCRelease-vl540-IEs OPTIONAL}

[0036] In 4G, the call release is accompanied by a message, denoted RRCConnectionRelease and is reproduced below RRCConnectionRelease-r8-IEs ::= SEQUENCE { releaseCause ReleaseCause, redirectedCarrierlnfo RedirectedCarrierlnfo OPTIONAL,— NeedON idleModeMobilityControlInfo IdleModeMobilityControlInfo OPTIONAL, — NeedOP nonCriticalExtension RRCConnectionRelease-v890-IEs OPTIONAL }

[0037] An extract of the release cause of the RRCConnectionRelease is reproduced below ReleaseCause ::= ENUMERATED {loadBalancingTAUrequired, other, cs-FallbackHighPriority-vl020, rrc-Suspend-V1320}

[0038] In both cases, 4G and 5G, the network does not specify whether the release was due to an issue at the UE side or at the network side. In fact, in 5G, there is no release cause sent from the network to the UE in the RRCRelease message. Whereas in 4G, even though a release cause is sent from the network to the UE in the RRCConnectionRelease message, none of the four causes mentioned in the release cause (loadbalancingTAUrequired, other, cs- FallbackHighPriority-vl020 and rrc-suspend) informs the UE whether the release was caused by an issue at the UE side or at the network side.

[0039] Without the network, in particular the network_prediction, informing the UE, in particular the UE_prediction, that the root cause of the abnormal call release is an issue at the network or at the UE side, a problem is experienced by the UE, and it could be illustrated with the following example. Suppose that, at time tl, an abnormal call release was caused by a software bug at the network side. The UE_prediction tool, without any assisted information from the network, could not determine whether the issue is at its side or at the network side. Consequently, it will trigger all the predefined actions of UE_prediction in order toinvestigate the root cause of the call drop and then predict the occurrence of a future drop call when similar conditions to the ones gathered at tl are met at a future time t2. In such scenario, the problem in all the actions and activities performed at the UE side, by UE_prediction, from tl until t2 (and worse in case future occasions having similar conditions to the one of tl are encountered) are a waste of processing and UE energy consumption, because the issue is caused by a software bug at the network side and hence, the occurrence of the next UE drop call could never be predicted by the UE despite whatever actions or investigations the UE_prediction performs.

[0040] Prior to describing the various embodiments in detail that provide solutions to the above problems, a brief summary of the solutions will first be discussed.

[0041] In a first embodiment, a first tool, network_prediction, that handles abnormal call release at the network side, predicts whether an abnormal call release was caused by an issue: (1) at the network side only, e.g., a software bug at the network side; (2) at the UE side only, e.g., a software bug on a certain type / version of a particular terminal identity; or (3) at both sides (network and UE), e.g., when the UE has bad radio conditions in the downlink or uplink.

[0042] Such prediction is then communicated, from the network to the UE, in particular to a second prediction tool that handles abnormal call release at the UE side, denoted UE_prediction. Such communication can be done via a new parameter added to existing standards.

[0043] In a second embodiment, after the UE_prediction receives the new parameter described in the first embodiment, the UE_prediction could then decide as follows. If the new parameter is indicating that the abnormal call release was caused by an issue at the network side, the UE might not trigger UE_prediction, and as a result, the UE could avoid unnecessary processing and energy consumption at its side. If the new parameter indicates that the abnormal call release was caused by an issue at the UE side, e.g., an issue that is affecting one particular terminal identity, e.g., Vendor model Y, the UE in such case would trigger UE_prediction in order to collect logs and investigate in order to find the root cause of the abnormal call release and hence be able to predict the occurrence of an abnormal call release in the future.

[0044] In a third embodiment, the second embodiment is extended to apply not only between a call drop prediction tool located at the network side and another call drop prediction tool located at the UE side but between any two call prediction tools located at any two different entities, e.g., located at two different cells or at two different Radio Nodes orbetween a Radio Node and an AMF etc.

[0045] In the description that follows, a UE and a network node that implement tools will be used to describe the various embodiments. The UE and network node each have a network interface, a processor (also referred to as processing circuitry), and memory (also referred to as memory circuitry). However, any of the UEs and network nodes described in Figures 17-22 may be used. In the description that follows, while any of these UEs and network nodes can use the embodiments described herein, the UE 1800 of Figure 18 and the network node 1900 of Figure 19 shall be used in the description.

[0046] Turning to Figure 1A, in the initial state, at least two tools for handling and predicting abnormal releases are implemented. A first tool is on the network side and is denoted as network_prediction. The first tool can be implemented (e.g., instantiated) at a network node 1900 or at an OSS (Operations Support System) node 1900. The second tool is implemented (e.g., instantiated) at the UE 1800 and is denoted UE_prediction.

[0047] In step 1 , the network_prediction tool can determine whether the cause of an abnormal call release was something related to a network issue or to a UE issue. The network_prediction tool is equipped with Al (Artificial Intelligence), e.g., ML (Machine learning), receives information from different inputs (e.g., alarms on OSS; UE historical KPI (Key Performance Indicator); UE actual location and velocity; UE radio conditions; etc.), and can determine whether an abnormal call release was caused by an issue at the network side only, at the UE side only, or at either or both sides. This could be done as follows and illustrated in Figure 2.

[0048] Turning to Figure 2, the network function (i.e., the network_prediction tool) determines that an abnormal release is occurring in the network at a time tl (Block 201).

[0049] When an abnormal release occurs in the network, at any time, e.g., at time tl, the network_prediction tool checks whether there was input received from one of its many diversified inputs. For example, if the drop calls are affecting a specific type and / or identity of terminal, then the network_prediction tool might conclude that the abnormal release is not a network issue but rather it is an issue with that particular terminal identity and / or specific type of terminal. This is illustrated in block 203 of Figure 2 where the network function, responsive to determining that the abnormal release is affecting a specific type of user equipment, UE, and / or UE identity, determines that the abnormal release is not a network issue but is an issue with the specific type of UE and / or UE identity of a UE 1800 at a UE side.

[0050] Figure 3 illustrates an example of determining that the abnormal release is anissue with that particular terminal identity. Turning to Figure 3, in block 301, the network function determines that the abnormal release is not a network issue but is an issue with the specific type of UE and / or UE identity at a UE side by determining that the abnormal releases are occurring at the UE 1800 associated with the UE identity or at specific types of UEs.

[0051] If the call drop was caused by a software bug, then the network_prediction tool might conclude that the abnormal release is an issue with the network only and it has no relation with the UE. This is illustrated in block 205 of Figure 2 where the network function, responsive to determining that the abnormal release was caused by a software bug, determines that the abnormal release is a network issue at a network side.

[0052] Figure 4 illustrates an example of determining there is a software bug. Turning to Figure 4, in block 401, the network function determines that the abnormal release was caused by a software bug by determining that an alarm is raised at a network node or at a cell level of the network.

[0053] In one example, the network_prediction tool checks whether at time tl there was any activity at the OSS (e.g., an operator action, like restarting a Radio Node, or an alarm, etc.). The network_prediction tool can use all its different inputs, and the network_prediction tool could make a conclusion on whether an abnormal release was caused by the network only or by the UE only.

[0054] In case the abnormal release was caused by bad radio conditions, in downlink or in uplink, then the network_prediction tool might conclude that this could be caused by an issue of radio conditions at the network side (e.g., uplink interference) or at the UE side, e.g., UE passes by an area with a radio coverage hole. This is illustrated in block 207 of Figure 2 where the network function, responsive to determining that the abnormal release was caused by bad radio conditions (e.g., in downlink or in uplink) determines that the abnormal release could be caused by an issue of radio conditions at the network side or at the UE side.

[0055] Figure 5 illustrates the network tool concluding that the abnormal release could be caused by an issue of radio conditions at the network side or at the UE side. Turning to Figure 5, in block 501, the network tool determines that the abnormal release could be a network issue, a UE issue, or either a network issue or a UE issue by determining that the abnormal releases are not determined as being a network issue only or a UE issue only.

[0056] The network function in block 209 transmits information to the UE 1800 about whether the abnormal release was caused by an issue at the network side, at the UE side, or was caused at the network side and / or the UE side. The information in one embodiment is transmitted via a mobile application. The protocols that may be used are described below.

[0057] Figure 6 illustrates an example of inputs and outputs of the network_prediction tool. The network_prediction tool receives one or more of input 1 to input 6 and predicts one or more of outcome 1 to outcome Z with artificial intelligence (e.g., machine learning) dedicated to handling abnormal releases such as calls that were abnormally released. Input 1 is information including a timestamp, tl, of the abnormal release message from the NR or from the AMF, the cause of the abnormal release, the cell identity where the abnormal release has occurred, the PEI (permanent equipment identity) of the UE 1800, and the permanent identity of the UE 1800 (e.g., IMSI (international mobile subscriber identity)). Input 2 indicates whether there is a software bug incidence. Input 3 indicates whether there is an OSS alarm. Input 4 provides historical KPI data. Input 5 is UE location and velocity of the UE 1800. Input 6 is input for other sources.

[0058] Outcome 1 is an indication of whether (i) the issue was caused by the network node only; (ii) the issue was caused by the UE only; or (iii) the issue was caused by either or both the network node and the UE.

[0059] Outcome 2 is a prediction of a drop call at a next location X or after a time T period. Outcome Z is other output related to a drop call or other abnormal release.

[0060] Returning to Figure 1A, step 2 illustrates procedures that can be used to communicate the outcome(s) of the network_prediction tool to the UE_prediction tool.

[0061] The information about whether the abnormal release was caused by an issue at the "network side only," at the "UE side only," or at either or both sides, could be communicated to the UE 1800 in one of the following two procedures: (1) via dedicated RRC signaling protocol (described hereinbelow); or (2) via a UE application (described hereinbelow).

[0062] Procedure via RRC signaling protocol

[0063] Once the network_prediction tool determines whether the abnormal call release was caused by an issue that is at the UE side or at the network side, the network_prediction tool communicates such information to the UE 1800.

[0064] In one example, such communication could be performed by adding a new parameter to existing standards, in particular to the signaling protocol used between the Radio Node and the UE, that is via RRC protocol. As shown below, a new parameter denoted here "UE_or_network" and coded in one or more bits (e.g., 2 bits in the description below) is added to the RRCRelease message (in 5G). Similarly, a same parameter could be added to RRCConnectionRelease message (in 4G).If UE_or_network is equal to 11 that means the abnormal release was caused by an issue at the network side only, e.g., when the abnormal release was caused by a software bug or an alarm at the network side.If UE_or_network is equal to 00 that means the abnormal release was caused by an issue at the UE side only, e.g., when the abnormal release was caused by a software bug at the UE side.If UE_or_network is equal to 10 that means the abnormal release might be caused by an issue at the network side or at the UE side, e.g., when the abnormal release is caused by radio coverage issue.The fourth value 01 is left for future use.

[0065] Figure 7 illustrates an embodiment of the network tool coding the UE_or_network parameter. Turing to Figure 7, in block 701, the network function codes a multi-bit parameter (e.g., UE_or_network) in a signaling message where a first combination of the bits indicates the abnormal release is an issue at the network side, a second combination of the bits indicates the abnormal release is an issue at the UE side, and a third combination of the bits indicates the abnormal release is an issue at either or both the network side and the UE side.

[0066] An example of the new parameter, e.g., "UE_or_network," is illustrated in theRRCRelease-IEs below:RRCRelease-IEs ::= SEQUENCE { redirectedCarrierlnfo RedirectedCarrierlnfo OPTIONAL, - NeedN cellReselectionPriorities CellReselectionPriorities OPTIONAL, — NeedR suspendConfig SuspendConfig OPTIONAL, -Need R deprioritisationReq SEQUENCE { deprioritisationType ENUMERATED {frequency, nr}, deprioritisationTimer ENUMERATED {min5, minlO, minl5, min30}UE_or_network 2 bits} OPTIONAL, - NeedN lateNonCriticalExtension OCTET STRINGOPTIONAL, nonCriticalExtension RRCRelease-v 1540-IEsOPTIONAL}

[0067] In other embodiments, the parameter (e.g., UE_or_network) could be communicated to the UE via a mobile application, e.g., a server accessible by the UE vendor, which receives the value of the parameter from the wireless network via a third server or viaan internal server. Such procedure might be used, e.g., in case the network needs to communicate to the UE other information in addition to the value of the parameter, e.g., a location X where a drop call is occurring etc.

[0068] Although these other embodiments are an alternative solution, the better solution might be using the procedure of adding the parameter (e.g., UE_or_network) to existing release messages as described above, as it does not require any additional security procedure, between the operator network and an external server, in order to communicate the values of network_prediction with any external server that is accessible by the UEs in the network.

[0069] Step 3 in Figure IB illustrates the UE 1800 reaction when the UE receives the parameter (e.g., UE_or_network).

[0070] When the UE 1800 receives the parameter (e.g., UE_or_network), it will act as follows:If the value of the parameter is equal to 11 , which means the issue is at the network side only, then the UE_prediction tool will not take any further action. In other words, it will not trigger any investigation in order to find the root cause of the abnormal call release that was experienced by the UE 1800.However, if the value of bit of the parameter is equal to 00, which means the abnormal release was caused by an issue at the UE side only, then some actions, that could help predict the occurrence of a next abnormal call release, are executed by the UE 1800, e.g., (i) check whether there was an error or fault message inside the UE 1800 at time tl when the abnormal release was triggered; and / or (2) store the values of some elements inside the UE 1800 as they were just before the abnormal call release (e.g., buffer 1 was at 95% of its maximum value, parameter Y was at value 0.65, the value of error bit rate was Z etc.). Such elements could be defined by the UE_prediction tool developers whose objective is to know the conditions at the UE side that has triggered the abnormal call release.If the parameter is equal to 10, that means the abnormal release might be caused by an issue at the network side or at the UE side. In such case the UE 1800 will trigger some internal investigations that are predefined for such situation, e.g., collect logs related to UE radio conditions and UE location.If the parameter is equal to 01, then no further action is taken by the UE_prediction tool.

[0071] Expansion to two call prediction tools located at two different nodes

[0072] With the method of Figures 1A-1B described above, a first tool thathandles / predicts abnormal releases and located at the network side provides data assistance for a second tool that handles / predicts abnormal releases located at the UE side.

[0073] The procedures of the method of Figures 1 A-1B could apply between any two different nodes in the network, e.g., a cell or a Radio Node or AMF or any other node in the network. Thus, a first node equipped with Al (e.g., ME) that handles and predicts a certain task, e.g., abnormal release, provides data assistance to a second node also equipped with Al (e.g., ML) that handles the same task, e.g., abnormal release as described herein. This could be translated with the following steps.

[0074] Turning to Figure 8, in the initial state, a tool is implemented at each node. Two tools for handling and predicting any specific task, e.g., abnormal release, are implemented as follows:A first tool, denoted tool_nodel, is implemented at a first node, e.g., NR.A second tool, denoted tool_node2, is implemented at a second node, e.g., AMF.

[0075] The specific task could be handling any issue in the network, e.g., a handover failure, a call setup failure, a specific alarm, etc. The method applies between any two types of nodes (equipped with Al (e.g., ML) and having the objective of finding and predicting a specific issue or task). The nodes might be specific to a wireless network, e.g., AMF and NR, or the nodes are not specific to a wireless network, e.g., the methods described herein might apply between two IP (Internet Protocol) routers or between two smart machines in a factory.

[0076] In step 1, the tool should have as many diversified inputs as possible. Thus, each tool equipped with Al receives inputs, e.g., alarms from OSS; historical data; software bug detection etc. An example of such inputs is shown in Figure 6 above. Based on these inputs, the tool can determine which of the following node side was behind the cause of triggering of the specified task, e.g., abnormal release: (1) nodel only, e.g., the operator has lock / unlock an entity at nodel;(2) node2 only, e.g., a software bug at node2; or node 1 and / or node 2 (and hence, requiring investigation at both sides (nodel & node2)).

[0077] In step 2, the two tools notify each other only when a specific event occurs. The information about which side is behind the abnormal call release is sent via a new parameter, e.g., denoted my_node_only, which can be coded in 1 bit and that works as follows:

[0078] If a node, e.g., nodel, detects that the root cause of the issue is at the nodel side, the nodel sends a notification to node2 with my_node_only = 1. Then the tool at node2 does not have to take any further action.

[0079] Otherwise, if a first node, nodel or node2, is not sure whether the root cause ofthe issue is at its side or at the other side, then the first node does not send any notification to the second node, and in such scenario, both tools (e.g., tool_nodel & tool_node2) will both work to find the root cause of the issue.

[0080] By sending a notification to the second node only when one condition is validated (when my_node_only = 1) results in the second value of my_node_only (value = 0) not being used.

[0081] The main benefit of the new parameter (e.g., my_node_only) in this embodiment is not only to relieve the tool at the second node from useless processing but also it could prevent the tool at the second node from including wrong information in its Al algorithms.

[0082] Figure 9 illustrates operations from the perspective of one of nodel or node2. Turning to Figure 9, in block 901, the first tool at the network side connected to one or more inputs, predicts whether a network node 1900 or a UE 1800 is a cause of the abnormal release.

[0083] In block 903, the first tool at the network side transmits an indication to the UE 1800 indicating the network side is the cause of the abnormal release when the network side is determined to be the cause of the abnormal release.

[0084] In block 905, the first tool at the network side does not transmit (e.g., abstains from transmitting) an indication to the UE 1800 when the first tool determines that it is not sure if the cause of the abnormal release is at the network side.

[0085] Thus, the first tool will not receive an indication from the second tool if the second tool is not sure if the cause of the abnormal release is at the second tool node (e.g., UE 1800, or AMF, or second network node, etc.). Thus, as illustrated in block 1001 of Figure 10, the first tool receives an indication from a second tool at the UE 1800 that the UE side is the cause of the abnormal release.

[0086] The case where the UE 1800 does not have a prediction tool shall now be discussed. Learning activity consumes lots of processing and results in UE battery consumption. The extra processing might be acceptable when the learning tools are applied between two nodes in the network, e.g., between two NR or between NR and AMF etc. In such case, the method of Figures 1A-1B and Figure 8 are valid methods and could bring all the advantages listed above.

[0087] However, when implementing a learning tool at the UE side, this might not be a valid case as this could consume UE battery and increase UE processing, and due to the continuous change in the parameters and the configuration of the network, the UE 1800 has to re-train constantly. As a consequence of not implementing a prediction / learning tool at theUE 1800, the first problem described above will be a valid case, and as a result, there will be some scenarios of drop calls that could not be predicted by the UE. To mitigate such a scenario, the application of the method of Figures 1A-1B, for example, may be executed as follows:

[0088] The network still sends to the UE 1800 the parameter UE_or_network.

[0089] The UE 1800, not having any learning / prediction tool, will behave as follows: If the issue is at the network side (value of UE_or_network = 11), the UE 1800 will not trigger any action.If the issue is at the network and / or UE side (value of UE_or_network = 10), the UE 1800 might collect some internal logs, e.g., predefined by the designers of UE vendor. If the issue is at the UE side (value of UE_or_network = 00), the UE 1800 might collect some internal logs, e.g., predefined by the designers of the UE 1800.

[0090] In other words, the UE behavior just collects some predefined logs and does not do any learning activity.

[0091] The collected logs by the UE 1800 can be reported to the network depending on an agreement between the operator and UE vendor: the logs are reported immediately after each abnormal call release so that any reaction could be done in real time or the soonest; or the logs are not reported in real time, but at a later time, e.g., with other UE logs collected for other reasons.

[0092] When these logs are collected at the network side, then the logs are forwarded to the UE designer, which will try to find the root cause of that abnormal release and try to solve the issue, e.g., via a software patch, as soon as possible so that next time the abnormal release for the same cause is avoided.

[0093] The log collection is illustrated in Figure 11. Turning to Figure 11, in block 1101, the network function receives one or more logs from the UE 1800. In block 1103, the network function forwards the one or more logs to designers of the UE 1800.

[0094] Note that if the method of Figure 8 is not applied, then no internal UE logs are reported quickly and the operator reaction might come late, e.g., when the abnormal releases have increased largely in the network and after a deep and long investigation where many traces collected by automatic tools, e.g., located at the OSS, and / or by the operator staff, are analyzed. Hence, without implementing a learning tool at the UE side, the method of Figure 8 brings an advantage over prior art solutions as it could accelerate the finding of the root cause of abnormal call release that are caused by UE only.

[0095] Figure 12 illustrates the operations of the first tool and the second tool when the tools are at a first node and a second node. Turning to Figure 12, in block 1201, the first tool at the first node predicts whether the first node is the cause of the abnormal release.Responsive to predicting the first node is a cause of the abnormal release, the first tool transmits an indication to the second node that the first node is the cause of the abnormal release in block 1203. In block 1205, the first tool does not transmit an indication to the second node when the first tool determines that it is not sure if the first node is the cause of the abnormal release. In some embodiments, a determination that the first node is not sure if it is the cause of the abnormal release is made by determining that a value of the prediction that the first node is the cause of the abnormal release is below a threshold (e.g., a predefined or dynamically set threshold). In block 1207, the first tool receives an indication from the second tool that the second node is the cause of the abnormal release.

[0096] Figures 13-16 illustrate operations from the perspective of the UE 1800 in the above embodiments. Turning to Figure 13, in block 1301, the UE 1800 receives from a network function of a network node of the network, information about whether an abnormal release was caused by an issue at the network side, at the UE side, or at the network side and / or the UE side.

[0097] In block 1301, responsive to the UE 1800 having a predictive tool and the abnormal release was caused at the UE side, the UE 1800 performs an action by the predictive tool to help in predicting an occurrence of a next abnormal release.

[0098] Figure 14 illustrates an embodiment of such an action. Turning to Figure 14, in block 1401, the UE 1800 checks whether there was an error or fault message inside the UE 1800 at time tl when the abnormal release was triggered. In block 1403, the UE 1800 stores values of one or more elements of the UE 1800 as the one or more values were just before the abnormal release.

[0099] Figure 15 illustrates operations the UE 1800 may perform when the abnormal release was caused at the network side and / or the UE side and at the network side. Turning to Figure 15, in block 1501, the UE 1800, responsive to the UE 1800 having a predictive tool and the abnormal release was caused at the network side and / or the UE side, triggers an internal investigation. In some embodiments, the UE 1800 triggers the internal investigation by collecting logs related to UE radio conditions and to a UE location at least within a predefined time range of time tl.

[0100] In block 1503, the UE 1800, responsive to the UE 1800 having a predictive tool and the abnormal release was caused at the network side only, does not trigger any action atthe UE side.

[0101] Figure 16 illustrates operations the UE 1800 performs when the UE 1800 does not have a prediction tool. Turning to Figure 16, the UE 1800, responsive to the UE 1800 not having a predictive tool, collects one or more internal logs when the abnormal release was caused by an issue at the UE side, or was caused at the network side and / or the UE side.

[0102] Figure 17 shows an example of a communication system 1700 in accordance with some embodiments.

[0103] In the example, the communication system 1700 includes a telecommunication network 1702 that includes an access network 1704, such as a radio access network (RAN), and a core network 1706, which includes one or more core network nodes 1708. The access network 1704 includes one or more access network nodes, such as network nodes 1710A and 1710B (one or more of which may be generally referred to as network nodes 1710), or any other similar 3rdGeneration Partnership Project (3GPP) access node or non-3GPP access point. The network nodes 1710 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs 1712A, 1712B, 1712C, and 1712D (one or more of which may be generally referred to as UEs 1712) to the core network 1706 over one or more wireless connections.

[0104] 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 1700 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 1700 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0105] The UEs 1712 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 1710 and other communication devices. Similarly, the network nodes 1710 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs 1712 and / or with other network nodes or equipment in the telecommunication network 1702 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 1702.

[0106] In the depicted example, the core network 1706 connects the network nodes 1710to one or more hosts, such as host 1716. 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 1706 includes one more core network nodes (e.g., core network node 1708) 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 1708. Example core network nodes include functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier De-concealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).

[0107] The host 1716 may be under the ownership or control of a service provider other than an operator or provider of the access network 1704 and / or the telecommunication network 1702, and may be operated by the service provider or on behalf of the service provider. The host 1716 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.

[0108] As a whole, the communication system 1700 of Figure 17 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 (Light Fidelity), and / or any low-power wide-area network (LPWAN) standards such as LoRa (Long Range) and Sigfox.

[0109] In some examples, the telecommunication network 1702 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network 1702 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 1702. For example, the telecommunications network 1702 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 (Internet of Things) services to yet further UEs.

[0110] In some examples, the UEs 1712 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 1704 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 1704.Additionally, a UE may be configured for operating in single- or multi-RAT or multistandard 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).

[0111] In the example, the hub 1714 communicates with the access network 1704 to facilitate indirect communication between one or more UEs (e.g., UE 1712C and / or 1712D) and network nodes (e.g., network node 1710B). In some examples, the hub 1714 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 1714 may be a broadband router enabling access to the core network 1706 for the UEs. As another example, the hub 1714 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 1710, or by executable code, script, process, or other instructions in the hub 1714. As another example, the hub 1714 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 1714 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 1714 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 1714 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 1714 acts as a proxy server or orchestrator for the UEs, in particular in if one or more of the UEs are low energyloT devices.

[0112] The hub 1714 may have a constant / persistent or intermittent connection to the network node 1710B. The hub 1714 may also allow for a different communication scheme and / or schedule between the hub 1714 and UEs (e.g., UE 1712C and / or 1712D), and between the hub 1714 and the core network 1706. In other examples, the hub 1714 is connected to the core network 1706 and / or one or more UEs via a wired connection. Moreover, the hub 1714 may be configured to connect to an M2M service provider over the access network 1704 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 1710 while still connected via the hub 1714 via a wired or wireless connection. In some embodiments, the hub 1714 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 1710B. In other embodiments, the hub 1714 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 1710B, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0113] Figure 18 shows a UE 1800 in accordance with some embodiments. As used herein, a UE refers to a device capable, configured, arranged and / or operable to communicate wirelessly with network nodes and / or other UEs. Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), smart device, wireless customer-premise equipment (CPE), vehicle-mounted or vehicle embedded / integrated wireless device, etc. Other examples include any UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-IoT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0114] 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 smartsprinkler 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).

[0115] The UE 1800 includes processing circuitry 1802 that is operatively coupled via a bus 1804 to an input / output interface 1806, a power source 1808, a memory 1810, a communication interface 1812, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Figure 18. 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.

[0116] The processing circuitry 1802 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 1810. The processing circuitry 1802 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 1802 may include multiple central processing units (CPUs).

[0117] In the example, the input / output interface 1806 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 1800. 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.

[0118] In some embodiments, the power source 1808 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 1808 may further include power circuitry for delivering power from the power source 1808 itself, and / or an external power source, to the various parts of the UE 1800 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 1808. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 1808 to make the power suitable for the respective components of the UE 1800 to which power is supplied.

[0119] The memory 1810 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 1810 includes one or more application programs 1814, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 1816. The memory 1810 may store, for use by the UE 1800, any of a variety of various operating systems or combinations of operating systems.

[0120] The memory 1810 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and / or ISIM, other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory 1810 may allow the UE 1800 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 1810, which may be or comprise a device -readable storage medium.

[0121] The processing circuitry 1802 may be configured to communicate with an accessnetwork or other network using the communication interface 1812. The communication interface 1812 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 1822. The communication interface 1812 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 1818 and / or a receiver 1820 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 1818 and receiver 1820 may be coupled to one or more antennas (e.g., antenna 1822) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0122] In the illustrated embodiment, communication functions of the communication interface 1812 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.

[0123] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 1812, 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).

[0124] 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 thecontrol 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.

[0125] 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 1800 shown in Figure 18.

[0126] As yet another specific example, in an loT scenario, a UE may represent a machine or other device that performs monitoring and / or measurements, and transmits the results of such monitoring and / or measurements to another UE and / or a network node. The UE may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, the UE may implement the 3GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation.

[0127] 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 theactuator, and handle communication of data for both the speed sensor and the actuators.

[0128] Figure 19 shows a network node 1900 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment, in a telecommunication network. Examples of network nodes include, but are not limited to, 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)).

[0129] Base stations may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. A base station may be a relay node or a relay donor node controlling a relay. A network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units and / or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).

[0130] 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).

[0131] The network node 1900 includes a processing circuitry 1902, a memory 1904, a communication interface 1906, and a power source 1908. The network node 1900 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 1900 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 1900 may be configured to support multiple radio access technologies (RATs). In suchembodiments, some components may be duplicated (e.g., separate memory 1904 for different RATs) and some components may be reused (e.g., a same antenna 1910 may be shared by different RATs). The network node 1900 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 1900, 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 1900.

[0132] The processing circuitry 1902 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 1900 components, such as the memory 1904, to provide network node 1900 functionality.

[0133] In some embodiments, the processing circuitry 1902 includes a system on a chip (SOC). In some embodiments, the processing circuitry 1902 includes one or more of radio frequency (RF) transceiver circuitry 1912 and baseband processing circuitry 1914. In some embodiments, the radio frequency (RF) transceiver circuitry 1912 and the baseband processing circuitry 1914 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 1912 and baseband processing circuitry 1914 may be on the same chip or set of chips, boards, or units.

[0134] The memory 1904 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 computerexecutable memory devices that store information, data, and / or instructions that may be used by the processing circuitry 1902. The memory 1904 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 1902 and utilized by the network node 1900. The memory 1904 may be used to store any calculations made by the processing circuitry 1902 and / or any data receivedvia the communication interface 1906. In some embodiments, the processing circuitry 1902 and memory 1904 is integrated.

[0135] The communication interface 1906 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 1906 comprises port(s) / terminal(s) 1916 to send and receive data, for example to and from a network over a wired connection. The communication interface 1906 also includes radio front-end circuitry 1918 that may be coupled to, or in certain embodiments a part of, the antenna 1910. Radio front-end circuitry 1918 comprises filters 1920 and amplifiers 1922. The radio front-end circuitry 1918 may be connected to an antenna 1910 and processing circuitry 1902. The radio front-end circuitry may be configured to condition signals communicated between antenna 1910 and processing circuitry 1902. The radio front-end circuitry 1918 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 1918 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 1920 and / or amplifiers 1922. The radio signal may then be transmitted via the antenna 1910. Similarly, when receiving data, the antenna 1910 may collect radio signals which are then converted into digital data by the radio front-end circuitry 1918. The digital data may be passed to the processing circuitry 1902. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0136] In certain alternative embodiments, the network node 1900 does not include separate radio front-end circuitry 1918, instead, the processing circuitry 1902 includes radio front-end circuitry and is connected to the antenna 1910. Similarly, in some embodiments, all or some of the RF transceiver circuitry 1912 is part of the communication interface 1906. In still other embodiments, the communication interface 1906 includes one or more ports or terminals 1916, the radio front-end circuitry 1918, and the RF transceiver circuitry 1912, as part of a radio unit (not shown), and the communication interface 1906 communicates with the baseband processing circuitry 1914, which is part of a digital unit (not shown).

[0137] The antenna 1910 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 1910 may be coupled to the radio front-end circuitry 1918 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 1910 is separate from the network node 1900 and connectable to the network node 1900 through an interface or port.

[0138] The antenna 1910, communication interface 1906, and / or the processing circuitry 1902 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 1910, the communication interface 1906, and / or the processing circuitry 1902 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.

[0139] The power source 1908 provides power to the various components of network node 1900 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 1908 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 1900 with power for performing the functionality described herein. For example, the network node 1900 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 1908. As a further example, the power source 1908 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.

[0140] Embodiments of the network node 1900 may include additional components beyond those shown in Figure 19 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 1900 may include user interface equipment to allow input of information into the network node 1900 and to allow output of information from the network node 1900. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 1900.

[0141] Figure 20 is a block diagram of a host 2000, which may be an embodiment of the host 1716 of Figure 17, in accordance with various aspects described herein. As used herein, the host 2000 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 2000 may provide one or more services to one or more UEs.

[0142] The host 2000 includes processing circuitry 2002 that is operatively coupled via a bus 2004 to an input / output interface 2006, a network interface 2008, a power source 2010, and a memory 2012. 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 18 and 19, such that the descriptions thereof are generally applicable to the corresponding components of host 2000.

[0143] The memory 2012 may include one or more computer programs including one or more host application programs 2014 and data 2016, which may include user data, e.g., data generated by a UE for the host 2000 or data generated by the host 2000 for a UE.Embodiments of the host 2000 may utilize only a subset or all of the components shown. The host application programs 2014 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 (A VC), MPEG, VP9) and audio codecs (e.g., FLAC, Advanced Audio Coding (AAC), MPEG, G.711), including transcoding for multiple different classes, types, or implementations of UEs (e.g., handsets, desktop computers, wearable display systems, heads-up display systems). The host application programs 2014 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 2000 may select and / or indicate a different host for over-the-top services for a UE. The host application programs 2014 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.

[0144] Figure 21 is a block diagram illustrating a virtualization environment 2100 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 2100 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 notrequire radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized.

[0145] Applications 2102 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment 2100 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0146] Hardware 2104 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 2106 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs 2108A and 2108B (one or more of which may be generally referred to as VMs 2108), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 2106 may present a virtual operating platform that appears like networking hardware to the VMs 2108.

[0147] The VMs 2108 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 2106. Different embodiments of the instance of a virtual appliance 2102 may be implemented on one or more of VMs 2108, 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.

[0148] In the context of NFV, a VM 2108 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 2108, and that part of hardware 2104 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 2108 on top of the hardware 2104 and corresponds to the application 2102.

[0149] Hardware 2104 may be implemented in a standalone network node with generic or specific components. Hardware 2104 may implement some functions via virtualization. Alternatively, hardware 2104 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 viamanagement and orchestration 2110, which, among others, oversees lifecycle management of applications 2102. In some embodiments, hardware 2104 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 2112 which may alternatively be used for communication between hardware nodes and radio units.

[0150] Figure 22 shows a communication diagram of a host 2202 communicating via a network node 2204 with a UE 2206 over a partially wireless connection in accordance with some embodiments. Example implementations, in accordance with various embodiments, of the UE (such as a UE 1712A of Figure 17 and / or UE 1800 of Figure 18), network node (such as network node 1710A of Figure 17 and / or network node 1900 of Figure 19), and host (such as host 1716 of Figure 17 and / or host 2000 of Figure 20) discussed in the preceding paragraphs will now be described with reference to Figure 22.

[0151] Like host 2000, embodiments of host 2202 include hardware, such as a communication interface, processing circuitry, and memory. The host 2202 also includes software, which is stored in or accessible by the host 2202 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 2206 connecting via an over-the-top (OTT) connection 2250 extending between the UE 2206 and host 2202. In providing the service to the remote user, a host application may provide user data which is transmitted using the OTT connection 2250.

[0152] The network node 2204 includes hardware enabling it to communicate with the host 2202 and UE 2206. The connection 2260 may be direct or pass through a core network (like core network 1706 of Figure 17) 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.

[0153] The UE 2206 includes hardware and software, which is stored in or accessible by UE 2206 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 2206 with the support of the host 2202. In the host 2202, an executing host application may communicate with the executing client application via the OTT connection 2250 terminating at the UE 2206 and host 2202. Inproviding 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 2250 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 2250.

[0154] The OTT connection 2250 may extend via a connection 2260 between the host 2202 and the network node 2204 and via a wireless connection 2270 between the network node 2204 and the UE 2206 to provide the connection between the host 2202 and the UE 2206. The connection 2260 and wireless connection 2270, over which the OTT connection 2250 may be provided, have been drawn abstractly to illustrate the communication between the host 2202 and the UE 2206 via the network node 2204, without explicit reference to any intermediary devices and the precise routing of messages via these devices.

[0155] As an example of transmitting data via the OTT connection 2250, in step 2208, the host 2202 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 2206. In other embodiments, the user data is associated with a UE 2206 that shares data with the host 2202 without explicit human interaction. In step 2210, the host 2202 initiates a transmission carrying the user data towards the UE 2206. The host 2202 may initiate the transmission responsive to a request transmitted by the UE 2206. The request may be caused by human interaction with the UE 2206 or by operation of the client application executing on the UE 2206. The transmission may pass via the network node 2204, in accordance with the teachings of the embodiments described throughout this disclosure.Accordingly, in step 2212, the network node 2204 transmits to the UE 2206 the user data that was carried in the transmission that the host 2202 initiated, in accordance with the teachings of the embodiments described throughout this disclosure. In step 2214, the UE 2206 receives the user data carried in the transmission, which may be performed by a client application executed on the UE 2206 associated with the host application executed by the host 2202.

[0156] In some examples, the UE 2206 executes a client application which provides user data to the host 2202. The user data may be provided in reaction or response to the data received from the host 2202. Accordingly, in step 2216, the UE 2206 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 2206. Regardless of the specific manner in which the user data was provided, the UE 2206 initiates, in step 2218, transmission of the user data towards the host2202 via the network node 2204. In step 2220, in accordance with the teachings of the embodiments described throughout this disclosure, the network node 2204 receives user data from the UE 2206 and initiates transmission of the received user data towards the host 2202. In step 2222, the host 2202 receives the user data carried in the transmission initiated by the UE 2206.

[0157] In an example scenario, factory status information may be collected and analyzed by the host 2202. As another example, the host 2202 may process audio and video data which may have been retrieved from a UE for use in creating maps. As another example, the host 2202 may collect and analyze real-time data to assist in controlling vehicle congestion (e.g., controlling traffic lights). As another example, the host 2202 may store surveillance video uploaded by a UE. As another example, the host 2202 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 2202 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.

[0158] 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 2250 between the host 2202 and UE 2206, 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 2202 and / or UE 2206. In some embodiments, sensors (not shown) may be deployed in or in association with other devices through which the OTT connection 2250 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 2250 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not directly alter the operation of the network node 2204. 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 2202. The measurements may be implemented in that software causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection 2250 while monitoringpropagation times, errors, etc.

[0159] 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.

[0160] 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.

Claims

CLAIMSWhat is claimed is:

1. A method in a network function at a network node (1710A-1710B, 1900, 2102, 2204) of a network, the method comprising: determining (201) that an abnormal release is occurring in the network at a time tl; responsive to determining that the abnormal release is affecting a specific type of user equipment, UE, and / or UE identity, determining (203) that the abnormal release is not a network issue but is an issue with the specific type of UE and / or UE identity of a UE (1712A- 1712D, 1800, 2206) at a UE side; responsive to determining that the abnormal release was caused by a software bug, determining (205) that the abnormal release is a network issue at a network side; responsive to determining that the abnormal release was caused by bad radio conditions, determining (207) that the abnormal release could be caused by an issue of radio conditions at the network side or at the UE side; and transmitting (209) information to the UE (1712A-1712D, 1800, 2206) about whether the abnormal release was caused by an issue at the network side, at the UE side, or was caused at the network side and / or the UE side.

2. The method of Claim 1, further comprising determining that the abnormal release is not a network issue but is an issue with the specific type of UE and / or UE identity at a UE side by determining (301) that the abnormal releases are occurring at the UE (1712A-1712D, 1800, 2206) associated with the UE identity or at specific types of UEs.

3. The method of any of Claims 1-2, wherein determining that the abnormal release was caused by a software bug by determining (401) that an alarm is raised at a network node or at a cell level of the network.

4. The method of any of Claims 1-3, further comprising determining that the abnormal release could be a network issue, a UE issue, or a network issue and / or a UE issue by determining (501) that the abnormal releases are not determined as being a network issue only or a UE issue only.

5. The method of Claim 1 wherein transmitting the information to the UE (1712A-1712D, 1800, 2206) comprises coding (701) a multi-bit parameter in a signaling message where a first combination of the bits indicates the abnormal release is an issue at the network side, asecond combination of the bits indicates the abnormal release is an issue at the UE side, and a third combination of the bits indicates the abnormal release is an issue at either or both the network side and the UE side.

6. The method of Claim 1 wherein transmitting the information comprises transmitting the information via a mobile application.

7. The method of any of Claims 1-6, further comprising: predicting (901), by a first tool at the network side connected to one or more inputs, whether a network node or a UE is a cause of the abnormal release; transmitting (903) an indication to the UE (1712A-1712D, 1800, 2206) indicating the network side is the cause of the abnormal release when the network side is determined to be the cause of the abnormal release; and not transmitting (905) an indication to the UE (1712A-1712D, 1800, 2206) when the first tool determines that it is not sure if the cause of the abnormal release is at the network side.

8. The method of Claim 7, further comprising: receiving (1001) an indication from a second tool at the UE (1712A-1712D, 1800, 2206) that the UE side is the cause of the abnormal release.

9. The method of any of Claims 7-8, further comprising: receiving (1101) one or more logs from the UE (1712A-1712D, 1800, 2206).

10. The method of Claim 9, further comprising: forwarding (1103) the one or more logs to designers of the UE (1712A-1712D, 1800, 2206).

11. The method of any of Claims 1-5 wherein the network function of a first node has a first tool connected to one or more inputs, and a second node has a second tool connected to one or more inputs, the method further comprising: predicting (1201), by the first tool at the first node, whether the first node is a cause of the abnormal release; and responsive to predicting the first node is a cause of the abnormal release, transmitting (1203) an indication to the second node that the first node is the cause of the abnormal release; andnot transmitting (1205) an indication to the second node when the first tool determines that it is not sure if the first node is the cause of the abnormal release.

12. The method of Claim 11, further comprising: receiving (1207) an indication from the second tool that the second node is the cause of the abnormal release.

13. A method in a user equipment, UE, (1712A-1712D, 1800, 2206) operating in a network, the method comprising: receiving (1301), from a network function of a network node of the network, information about whether an abnormal release was caused by an issue at the network side, at the UE side, or at the network side and / or the UE side; and responsive to the UE (1712A-1712D, 1800, 2206) having a predictive tool and the abnormal release was caused at the UE side, performing (1303) an action by the predictive tool to help in predicting an occurrence of a next abnormal release.

14. The method of Claim 13, wherein performing the action comprises: checking (1401) whether there was an error or fault message inside the UE (1712A- 1712D, 1800, 2206) at time tl when the abnormal release was triggered; and storing (1403) values of one or more elements of the UE (1712A-1712D, 1800, 2206) as the one or more values were just before the abnormal release.

15. The method of any of Claims 13-14, further comprising: responsive to the UE (1712A-1712D, 1800, 2206) having a predictive tool and the abnormal release was caused at the network side and / or the UE side, triggering (1501) an internal investigation.

16. The method of Claim 15, wherein triggering the internal investigation comprises collecting logs related to UE radio conditions and to a UE location at least within a predefined time range of time tl.

17. The method of any of Claims 13-16, further comprising: responsive to the UE (1712A-1712D, 1800, 2206) having a predictive tool and the abnormal release was caused at the network side only, not triggering (1503) any action at the UE side.

18. The method of any of Claims 13-17, further comprising: responsive to the UE (1712A-1712D, 1800, 2206) not having a predictive tool, collecting (1601) one or more internal logs when the abnormal release was caused by an issue at the UE side, or was caused at the network side and / or the UE side.

19. A network node (1710A-1710B, 1900, 2102, 2204) in a network, the network node comprising: processing circuitry (1902); and memory (1904) coupled with the processing circuitry, wherein the memory includes instructions that when executed by the processing circuitry causes the network node to perform operations comprising: determining (201) that an abnormal release is occurring in the network at a time tl; responsive to determining that the abnormal release is affecting a specific type of user equipment, UE, and / or UE identity, determining (203) that the abnormal release is not a network issue but is an issue with the specific type of UE and / or UE identity of a UE (1712A-1712D, 1800, 2206) at a UE side; responsive to determining that the abnormal release was caused by a software bug, determining (205) that the abnormal release is a network issue at a network side; responsive to determining that the abnormal release was caused by bad radio conditions, determining (207) that the abnormal release could be caused by an issue of radio conditions at the network side or at the UE side; and transmitting (209) information to the UE (1712A-1712D, 1800, 2206) about whether the abnormal release was caused by an issue at the network side, at the UE side, or was caused at the network side and / or the UE side.

20. The network node (1710A-1710B, 1900, 2102, 2204) of Claim 19, wherein the memory includes further instructions that when executed by the processing circuitry causes the network node to perform operations according to Claims 2-12.

21. A computer program product comprising a non-transitory storage medium including program code to be executed by processing circuitry (1902) of a network node (1710A- 1710B, 1900, 2102, 2204), whereby execution of the program code causes the network node (1710A-1710B, 1900, 2102, 2204) to perform operations comprising: determining (201) that an abnormal release is occurring in the network at a time tl;responsive to determining that the abnormal release is affecting a specific type of user equipment, UE, and / or UE identity, determining (203) that the abnormal release is not a network issue but is an issue with the specific type of UE and / or UE identity of a UE (1712A- 1712D, 1800, 2206) at a UE side; responsive to determining that the abnormal release was caused by a software bug, determining (205) that the abnormal release is a network issue at a network side; responsive to determining that the abnormal release was caused by bad radio conditions , determining (207) that the abnormal release could be caused by an issue of radio conditions at the network side or at the UE side; and transmitting (209) information to the UE (1712A-1712D, 1800, 2206) about whether the abnormal release was caused by an issue at the network side, at the UE side, or was caused at the network side and / or the UE side.

22. The computer program product of Claim 21 wherein non-transitory storage medium includes further program code whereby execution of the further program code causes the network node (1710A-1710B, 1900, 2102, 2204) to perform operations according to any of Claims 2-12.

23. A user equipment, UE, (1712A-1712D, 1800, 2206) comprising: processing circuitry (1802); and memory (1810) coupled with the processing circuitry, wherein the memory includes instructions that when executed by the processing circuitry causes the network node to perform operations comprising: receiving (1301), from a network function of a network node of the network, information about whether an abnormal release was caused by an issue at the network side, at the UE side, or at the network side and / or the UE side; and responsive to the UE (1712A-1712D, 1800, 2206) having a predictive tool and the abnormal release was caused at the UE side, performing (1303) an action by the predictive tool to help in predicting an occurrence of a next abnormal release.

24. The UE (1712A-1712D, 1800, 2206) of Claim 23, wherein the memory includes further instructions that when executed by the processing circuitry causes the network node to perform operations in accordance with Claim 14-18.

25. A computer program product comprising a non-transitory storage medium includingprogram code to be executed by processing circuitry (1802) of a UE (1712A-1712D, 1800, 2206), whereby execution of the program code causes the UE (1712A-1712D, 1800, 2206) to perform operations comprising: receiving (1301), from a network function of a network node of the network, information about whether an abnormal release was caused by an issue at the network side, at the UE side, or at the network side and / or the UE side; and responsive to the UE (1712A-1712D, 1800, 2206) having a predictive tool and the abnormal release was caused at the UE side, performing (1303) an action by the predictive tool to help in predicting an occurrence of a next abnormal release.

26. The computer program product of Claim 25, wherein non-transitory storage medium includes further program code whereby execution of the further program code causes the UE (1712A-1712D, 1800, 2206) to perform operations according to any of Claims 14-18.

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