Change-point detection manager for detecting changes of network conditions in a telecommunication network

Abstract

A method performed by a change point detection manager node (130), comprising a change-point detection mechanism (131) operating in several modes, for detecting changes of network conditions in a telecommunication network (100). The method comprises the change point detection manager node (130) performing Action 1: running (31) the change point detection mechanism for a set time (32), receiving measurement data, such as performance data, using initial configurations for pre- and post-change distributions of performance metrics, Action 2: estimating or re-estimating (33), when the set time has expired (32), a pre-change measurement distribution using received newly gathered data of the performance metrics, Action 3: evaluating (34) the confidence of the current pre-change measurement distribution, Action 4: deciding (35) upon a mode, out of a set of modes, in the change-detection mechanism based upon confidence of current pre-change distribution, Action 5: calculating or re-calculating (36) new parameters for the selected mode in the change point detection mechanism; and Action 6: restarting (37) Action 1 with the calculated or recalculated new parameters.

Description

[0001] CHANGE-POINT DETECTION MANAGER FOR DETECTING CHANGES OF NETWORK CONDITIONS IN A TELECOMMUNICATION NETWORK

[0002] TECHNICAL FIELD

[0003] Embodiments herein relate to a change point detection manager node, comprising a change-point detection mechanism operating in several modes, for detecting changes of network conditions in a telecommunication network, and methods therein.

[0004] BACKGROUND

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

[0006] 3rd Generation Partnership Project (3GPP) is the standardization body for specifying the standards for the cellular system evolution, e.g., including 3G, 4G, 5G, 6G and the future evolutions. Specifications for Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Packet System (EPS) have been completed within the 3GPP. In 4G also called a Fourth Generation (4G) network, EPS is core network and E-UTRA is radio access network. In 5G, 5G Core (5GC) is core network, NR is radio access network. As a continued network evolution, the new release of 3GPP specifies a 5G network also referred to as 5G New Radio (NR) and 5GC.

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

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

[0009] Network measurements for dependable networks

[0010] A dependable network is defined as a network where it is possible to quantitatively ascertain the delivery of required service performance for a given network service, belonging to e.g. enhanced mobile broadband, ultra-reliable low latency communication, and massive machine-type communication, according to an agreed service level objective. With respect to such networks, it is paramount to be able to determine what performance level the network segment can guarantee at a given point in time. This is achieved either through intelligent monitoring of the network path, or through predictive models.

[0011] As an example, an end-to-end management agent will require sufficiently fresh network state information in order to make well-informed decisions as illustrated in Figure 1. These can for instance be end-to-end round-trip times (RTTs), or one-way delay (OWD). As such, the agent will maintain contact with one or multiple network intelligent observability functions. These can take active forms, such as measurement probesmeasuring UE performance metric across a network path, or passive forms, such as counters installed in the network logging statistics from passing packets.

[0012] An important role of Al in the future of high-performing networks is the ability to dynamically reconfigure network functions in order to adapt to changing network conditions. The observability function is no exception to this, as measurements are by no means costless. Indeed, probing the network imposes additional overhead, which while small, can impact network traffic if done in abundance. Logging network statistics does not usually impose additional overhead, but polling them and transferring the statistics to the orchestration agent does impose overhead. This requires intelligent solutions in order to handle growing customer demands.

[0013] The importance of dependable networks and mechanisms, such as intelligent observability, has seen increased.

[0014] Change-point detection and theory

[0015] In change point detection, in the frequentist setting, for example detecting that the round-trip time between two entities in the network illustrated in Figure 1 , some system variable is monitored and compared to a pre-change probability density as well as a postchange probability density, in order to quickly raise an alarm when a change is detected, while avoiding false alarms more often than necessary. To do so, the change point detection mechanism constructs a cumulative sum (CUSIIM) statistic, defined as

[0016]

[0017] where f0is the pre-change probability density and

[0018]

[0019] is the post-change probability density.

[0020] The classical change point detection algorithm raises an alarm whenever this statistic exceeds some threshold, as disclosed by Lorden, G., 1971. Procedures for reacting to a change in distribution. The annals of mathematical statistics, pp.1897-1908.

[0021] A well-known issue of the above method is that the probability densities, especially of the post-change distribution, are often not well-known. Instead, several robust alternatives have been proposed. Generally, these alternatives come down to finding a least favorable distribution within a known uncertainty set and using this least favorable distribution in place of the true post-change distribution in the CUSIIM statistic. Lorden himself proposed solutions when the uncertainty set was parameters for a one-parameter exponential family, but more recently more general sets have been treated, as disclosed by Unnikrishnan, J., Veeravalli, V.V. and Meyn, S.P., 2011. Minimax robust quickestchange detection. IEEE Transactions on Information Theory, 57(3), pp.1604-1614, in particular when the set is the set of all densities with mean, or the mean of some statistic, exceeding a certain threshold , as disclosed in publication “Non-parametric quickest mean-change detection”, Liang, Y. and Veeravalli, V.V., 2022. IEEE Transactions on Information Theory, 68(12), pp.8040-8052. When the pre-change distribution is known, the CUSUM statistic can in this case be reduced to the expression

[0022] Sn= max(Sn- 1, 0) + *Xn— K*

[0023] where A* is the solution to the equation

[0024]

[0025] and K* = Ex~o[exp(A*X)]. When the pre-change distribution is less well-known but its mean p and variance a2is known, the same authors proposed the Mean Change Test (MCT) which approximates the above test by replacing the above parameters with

[0026]

[0027] Another problem with classical change point detection approaches is that they do not specify when measurements are taken, and it must therefore be assumed that measurements are taken periodically. This is potentially measurement-inefficient in industrial systems such as telecom networks. This was addressed by both Banerjee and Veeravalli in a slotted time formulation and Lindstahl et al. in a continuous time formulation, as disclosed by Banerjee, T. and Veeravalli, V.V., 2013. Data-efficient quickest change detection in minimax settings. IEEE Transactions on Information Theory, 59(10), pp.6917-6931, and Lindstahl, S., Proutiere, A. and Johnsson, A., 2023. Change point detection with adaptive measurement schedules for network performance verification. Proceedings of the ACM on Measurement and Analysis of Computing Systems, 7(3), pp.1-30. Finally, this was combined with robust approaches to create a simultaneously Robust and Measurement-Efficient Quickest Change Detection algorithm (RoME-QCD) which combined the above robust approaches with a sampling algorithm which sampled less often whenever the CUSIIM statistic was smaller than the stopping threshold but greater than a measurement threshold, as disclosed by, Lindstahl, S., Proutiere, A. and Johnsson, A., 2024, May. RoME-QCD:. Robust and Measurement Efficient Quickest Change Detection in 5G Networks. In 2024 8th Network Traffic Measurement and Analysis Conference (TMA) (pp. 1-11). IEEE.SUMMARY

[0028] As part of developing embodiments herein, some problems have been identified that first will be described.

[0029] An increased set of measurements and observations in the network, in support of e.g. verification of service performance and service assurance, comes with a cost of overhead. The overhead corresponds to network utilization as well as energy consumption. New mechanisms for controlling the measurement overhead are needed.

[0030] In general, with the increased set of ML-functions in the network there is also an increased need for timely observations through well-defined data pipelines. Also in this case, the measurements and observations must be controlled to reduce overhead.

[0031] Change detection methods in networks need to be robust, since network changes can be highly varied in nature. Without robust techniques, any detection method will fall prey to either large detection delays, i.e. false negatives, or high false alarm rates, i.e. false positives. Large detection delays have high costs as it impacts network performance, while high false alarm rates impose unnecessary operations costs due to manual alarm ticket handling.

[0032] While RoME-QCD, as disclosed by Lindstahl, S., Proutiere, A. and Johnsson, A., 2024, May. RoME-QCD: Robust and Measurement Efficient Quickest Change Detection in 5G Networks. In 2024 8th Network Traffic Measurement and Analysis Conference (TMA) (pp. 1-11). IEEE, is both robust to post-change distribution as well as measurement-efficient, it requires good knowledge of the pre-change probability density. Such knowledge is not readily available in an operational network. As such, implementing RoME-QCD and using it in operation could lead to SLA violations due to high detection delay, high maintenance costs due to false alarm or great amounts of measurement overhead imposing unnecessary load on the network.

[0033] The mean change test (MCT), with measurement-efficient methods applied, is more data-efficient compared to RoME-QCD but is potentially inaccurate and can scale very poorly when more data is acquired, since it fails to capture higher moments of the prechange distribution. As such, MCT will perform suboptimally in terms of detection delay (which can lead to SLA violations) as well as impose overly great maintenance and overhead costs.

[0034] An issue that all the above methods have in common is that they assume that the pre-change distribution is fixed while, in a real system, this could vary over a long timeframe (although presumably a shorter timeframe than the time until a change occurs). This could cause them to eventually become inaccurate, which will lead solutions thatwere initially well-tuned to become poor and impose great loads on the network or fail to detect changes, leading to SLA violations.

[0035] An object of embodiments herein is to provide a cost-efficient, robust and fast method for detecting changes in the network performance.

[0036] According to an aspect of embodiments herein, the object is achieved by a method performed by change point detection manager node, comprising a change-point detection mechanism operating in several modes, for detecting changes of network conditions in a telecommunication network.

[0037] The method comprises that the change point detection manager node performs the following actions:

[0038] - Action 1 : running the change point detection mechanism for a set time, receiving measurement data, using initial configurations for pre- and post-change distributions of performance metrics;

[0039] - Action 2: estimating or re-estimating, when the set time has expired, a prechange measurement distribution using received newly gathered data of the performance metrics;

[0040] - Action 3: evaluating the confidence of the current pre-change measurement distribution;

[0041] - Action 4: deciding upon a mode, out of a set of modes, in the change-detection mechanism based upon confidence of current pre-change distribution;

[0042] - Action 5: calculating or re-calculating new parameters for the selected mode in the change point detection mechanism; and

[0043] - Action 6: restarting Action 1 with the calculated or recalculated new parameters. Embodiments herein discloses that the operation of the change point detection mechanism may comprise calculating a cumulative sum measurement statistic that corresponds to the likelihood of a change having occurred within the network based on received measurement data as input, and deciding on the next time of measurement and whether to raise an alarm indicating that a change has occurred within the network.

[0044] It is proposed that the action of deciding upon mode, Action 4, may comprise deciding upon a mode where the change point detection mechanism use the full shape of the estimated or re-estimated pre-change distribution to the next batch e.g. a Robust and Measurement-Efficient Quickest Change Detection, RoME-QCD, mode, if it is found that the current pre-change measurement distribution is confident, and deciding upon a mode where the change point detection mechanism evaluates only certain statistics of the estimated or re-estimated pre-change distribution to the next batch, e.g. a mean changetest, MCT, mode, if it is found that the current pre-change measurement distribution is not confident.

[0045] Proposed embodiments disclose that the detection management node may be implemented within a dedicated measurement probe equipment or within a radio access network, RAN.

[0046] According to another aspect of embodiments herein, the object is achieved by a change point detection manager node, comprising a change-point detection mechanism operating in several modes, the detection manager node being adapted to detect changes of network conditions in a telecommunication network, wherein the detection manager node is adapted to perform the above disclosed method.

[0047] It is disclosed that the detection manager node may be implemented within a dedicated measurement probe equipment or within a radio access network, RAN.

[0048] According to another aspect of embodiments herein, the object is achieved by a computer program comprising instructions, which when executed by a processor, causes the processor to perform actions according to the above disclosed method.

[0049] It is disclosed that the object is also achieved by a carrier comprising the above mentioned computer program, wherein the carrier is one of an electronic signal, an optical signal, an electromagnetic signal, a magnetic signal, an electric signal, a radio signal, a microwave signal, or a computer-readable storage medium.

[0050] Thanks to that a cost-efficient and fast method for detecting changes in the network performance is provided.

[0051] Embodiments herein may provide one or more of the following advantages:

[0052] Proposed embodiments provides a cost-efficient and fast method for detecting changes in network performance, e.g. through analysis of active probing of RTT and OWD, for example measurement data. Other examples of measurement data include counters, number of passed packets, corrupt packets, amount of used energy, and signal quality, such as RSSI. That is, measurement data is numerical data that develops over time. There is overhead associated with taking measurements, both using active and passive means. The overhead in terms of measurements is reduced by disclosed embodiments.

[0053] The solution is robust to many different pre- and post-change distributions, including non-stationary ones. The robustness to post-change distributions is inherent to the formulations of both RoME-QCD and MCT. This allows our solution to handle a large variety of network events and failures, making it more suitable for a real-world network implementation.The method can be used for supporting service assurance by quickly providing feedback whenever the performance has changed. Importantly, it is able to detect a wide variety of changes with very little information of previous performance.

[0054] The method can also be used for security purposes, as it can detect threats such as DDOS attacks by detecting changing network traffic.

[0055] The proposed solution can scale well with more data while still performing decently with small amounts of data. This is because, whenever the pre-change distribution stays similar for a long time, the solution adapts the scaling properties of RoME-QCD, while it adapts the simple form of MCT whenever the pre-change distribution is changing quickly (or before much data has been collected). It can also adapt to changing day-to-day conditions (the pre-change distribution). As such, it will continuously update and adjust itself to perform optimally in terms of detection delay, maintenance costs and overhead costs.

[0056] Because of the simple forms of RoME-QCD and MCT, the solution is very fast on a step-by-step basis, only requiring extra computation time at the end of each batch. This allows most components of our solution to be implemented on cheap hardware and allows the detection mechanism to respond quickly to events, imposing minimal overhead.

[0057] BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Examples of embodiments herein are described in more detail with reference to attached drawings in which:

[0059] Figure 1 is a flowchart illustrating a dependable network and the need for intelligent observability to guarantee service performance.

[0060] Figure 2 is a schematic block diagram illustrating embodiments of a communications network.

[0061] Figure 3 is a flowchart illustrating a core essence of an embodiment.

[0062] Figure 4 is a flowchart illustrating an example embodiment of a method herein.

[0063] Figure 5 is a flowchart illustrating an example embodiment of a method using active measurement.

[0064] Figure 6 is a flowchart illustrating an example embodiment of a method using passive measurement.

[0065] Figure 7 is a schematic block diagram illustrating an example embodiment of a detection manager.

[0066] Figure 8 is a schematic block diagram illustrating embodiments of a change point detection mechanism.Figure 9 is a schematic block diagram illustrating embodiments of a time controller. Figure 10 is a schematic block diagram illustrating embodiments of a data aggregator and filter.

[0067] Figure 11 is a signal diagram illustrating an embodiment with active measurements. Figure 12 is a signal diagram illustrating an embodiment with passive measurements.

[0068] Figure 13 is a generalized block diagram of embodiments of a change point detection manager node.

[0069] Figure 14 is a generalized block diagram of embodiments of a network node.

[0070] Figure 15 is a generalized block diagram of embodiments of a host.

[0071] Figure 16 is a generalized block diagram of embodiments of a virtualization environment.

[0072] Figure 17 is a generalized block diagram of embodiments of a communication diagram of a host.

[0073] DETAILED DESCRIPTION

[0074] The key idea of disclosed embodiments is to detect when a performance indicator, such as RTT or OWD, changes from a conforming state to a performance-violating state. The disclosed embodiments balance the time to detection with the number of measurements needed, i.e. cost of overhead.

[0075] More specifically, it is proposed that a change point detection mechanism is adapted by updating the estimate and confidence in the pre-change distribution.

[0076] Maintaining the estimate of pre-change distribution, the update is done in batches. Then, it is determined whether there is sufficient knowledge of the current pre-change distribution. If so, a change point detection mechanism using the full shape of the prechange distribution is applied to the next batch. If not, a change point detection mechanism evaluating only the mean of the pre-change distribution is applied to the next batch, increasing measurement frequency. To ensure that the density estimate is accurate, the estimated pre-change distribution is only updated with data which is not believed to be part of abnormal cases.

[0077] In such scenarios, measurement overhead caused by monitoring will be minimized, while sudden changes can be detected adaptively even as slow changes to regular network operations occur.

[0078] Figure 2 is a schematic overview depicting a wireless communications network 100 wherein embodiments herein may be implemented. The wireless communications network 100 comprises one or more RANs, and one or more CNs. The communicationsnetwork 100 may use 5G NR but may further use a number of other different technologies, such as, 6G, Wi-Fi, Long Term Evolution (LTE), LTE-Advanced, Wideband Code Division Multiple Access (WCDMA), Global System for Mobile communications / enhanced Data rate for GSM Evolution (GSM / EDGE), Worldwide Interoperability for Microwave Access (WiMax), or Ultra Mobile Broadband (UMB), just to mention a few possible implementations.

[0079] Base stations, such as a first base station 111 and a second base station 112, operate in the RAN the communications network 100. The base stations 111, 112, may each be a transmission and reception point e.g. a radio access network node such as a base station, e.g. a radio base station such as a NodeB, an evolved Node B (eNB, eNode B), an NR Node B (gNB), a base transceiver station, a radio remote unit, an Access Point Base Station, a base station router, a transmission arrangement of a radio base station, a stand-alone access point, a Wireless Local Area Network (WLAN) access point or an Access Point Station (AP STA), an access controller, or any other network unit capable of communicating with UEs, such as a UE 121, within a cell, served by the respective base station 111, 112. The respective base station 111, 112 may be referred to as a serving radio network node and may communicate with the UE 121 with Downlink (DL) transmissions to the UE 121 and Uplink (UL) transmissions from the UE 121.

[0080] A change point detection manager node 130 may operate in the wireless communications network 100. This node may be Distributed Nodes (DN)s and functionality, e.g. comprised in a cloud.

[0081] The core essence of disclosed embodiments will be described with reference to the flowchart outlined in Figure 3.

[0082] In a first step or action the change point detection manager node 130 run 31 a change point detection mechanism 131 for a set time, using initial configurations for the pre- and post-change distributions of the performance metrics. While doing so, it alerts the network operator whenever a change is detected.

[0083] When the set time has expired 32, the newly gathered data of the performance metrics is used to estimate or re-estimate 33 the pre-change measurement distribution.

[0084] Depending on the confidence 34 in the pre-change distribution, a mode is chosen 35 where either the full shape of the distribution or only the mean of the distribution will be used.

[0085] New parameters for a more effective change point detection mechanism are calculated or re-calculated 36 and fed 37 into the first step or action 31, which repeats the loop.A number of embodiments will now be described, some of which may be seen as alternatives, while some may be used in combination.

[0086] A method according to embodiments will first be described as seen from the view of the change point detection manager node 130 together with Figure 4.

[0087] Figure 4 shows exemplary embodiments of a method performed by the change point detection manager node 130, comprising a change-point detection mechanism 131 operating in several modes, for detecting changes of network conditions in a telecommunication network 100.

[0088] The method comprises the change point detection manager node 130 performing the following actions, which actions may be taken in any suitable order. Optional actions are referred to as dashed boxes in Figure 4.

[0089] - Action 1: running 31 the change point detection mechanism 131 for a set time 32, receiving measurement data, using initial configurations for pre- and post-change distributions of performance metrics, i.e. measurement data such as RTT and OWD;

[0090] - Action 2: estimating or re-estimating 33, when the set time 32 has expired, a prechange measurement distribution using received newly gathered data of the performance metrics;

[0091] - Action 3: evaluating 34 the confidence of the current pre-change measurement distribution;

[0092] - Action 4: deciding 35 upon a mode, out of a set of modes, in the change-detection mechanism based upon confidence of current pre-change distribution;

[0093] - Action 5: calculating or re-calculating 36 new parameters for the selected mode in the change point detection mechanism; and

[0094] - Action 6: restarting 37 Action 1 31 with the calculated or recalculated new parameters.

[0095] Disclosed embodiments teaches that the operation of the change point detection mechanism 131 may comprise calculating 41 a cumulative sum measurement statistic that corresponds to the likelihood of a change having occurred within the network based on received measurement data as input, and deciding 42 on the next time of measurement and whether to raise an alarm indicating that a change has occurred within the network in order to let the network operator or autonomous manager know.

[0096] It is disclosed that the action of deciding upon mode, Action 4, may comprise deciding upon a mode 43 where the change point detection mechanism use the full shape 44 of the estimated or re-estimated pre-change distribution to the next batch e.g. a Robustand Measurement-Efficient Quickest Change Detection, RoME-QCD, mode, if it was found that the current pre-change measurement distribution is confident, and deciding upon a mode where the change point detection mechanism evaluates only certain statistics 45 of the estimated or re-estimated pre-change distribution to the next batch, e.g. a mean change test, MCT, mode, if it was found that the current pre-change measurement distribution is not confident.

[0097] It is proposed that the detection management node 130 may be implemented within a dedicated measurement probe equipment 51, as shown in Figure 5. The detection management node 130 may also be implemented within a radio access network 61, RAN, in order to quickly reach a non-dedicated measurement probe, such as a UE 121, as shown in Figure 6.

[0098] In this way by using the methods above, the UE 121 will be affected by and perceive fewer and shorter performance drops and outages. This is because network changes are detected faster and more efficiently.

[0099] Embodiments herein such as the embodiments mentioned above will now be further described and exemplified. The text below is applicable to and may be combined with any suitable embodiment described above.

[0100] System overview

[0101] The key component of the proposed solution is a change point detection manager node 130 will now be described with reference to Figure 5.

[0102] It should be understood that the change point detection manager node 130 may be a function, a detection manager, which may be distributed over several nodes or components, but it is exemplified here as a node for the sake of simplicity.

[0103] The change point detection manager node 130 is communicating with one or several measurement probes 51. These probes 51 then may send a ping package or use other standardized protocols for network measurements such as TWAMP or STAMP, to a measurement reflector 52 located at another position in the network to obtain an end-to-end metric, such as RTT. Sending this metric back to the detection manager node 130, the detection manager 130 will then wait for some time before configuring the measurement probe’s 51 inter-measurement time or raising an alarm for change detection.

[0104] It is proposed that the change point detection manager node 130 works on two loops, one fast loop and one slow loop, which will be described in more detail further down. However, it can be mentioned that in the fast loop, each data point is sent to thechange point detection mechanism 131 in order to recalculate the cumulative sum measurement statistic at each iteration of the fast loop. Then, the data is aggregated and, when the slow loop begins, filtered and sent to a density estimator and mode selector in the slow loop.

[0105] An alternative to using a measurement probe is proposed in a disclosed embodiment illustrated in Figure 6, where there is a counter 61 within the network passively gathering usage information. When a measurement is required, the change detection mechanism 131 sends a push notification to the counter 61.

[0106] As mentioned before, the change detection manager node 130, or detection manager, may be a piece of software that, in theory, could be implemented anywhere within the network 100. However, to avoid unnecessarily large delays and communication overhead, it is recommended to implement it either within a dedicated measurement probe 51 user equipment, or within the RAN in order to quickly reach a non-dedicated measurement probe, such as a UE 121.

[0107] Change detection manager node overview

[0108] As previously mentioned, the change detection manager node 130, or detection manager, is divided into a fast and a slow control loop, as will be illustrated in Figure 7.

[0109] The fast control loop 7a takes measurement results from the measurement probe 701 as input, the change detection mechanism 131 calculates a cumulative sum measurement statistic that corresponds to the likelihood of a change having occurred within the network 100, and then a time controller 702 decides on either the next time of measurement or to raise an alarm in order to let the network operator, or autonomous manager, know that a change has occurred.

[0110] A data aggregator and filter 703 in the slow control loop 7b gathers and filters data from the fast control loop 7a in, which is communicated to a density estimator 704, which update the density estimate of the pre-change measurement distribution.

[0111] Gathered and filtered data is also communicated to a mode selector 705 which select the mode of operations to be used by the change point detection mechanism 131 in the fast control loop 7a. There are different possible modes to choose from, and according to one proposed embodiment the mode may be the more exact RoME-QCD, and according to another proposed embodiment the mode may be a more approximate MCT.

[0112] This slow loop 7b update is done periodically as the slow loop is repeated and triggered by the fast loop 7a, as will be described in more detail below.The different components of the change detection manager node 130 will now be described in more detail.

[0113] Density estimator 704: The density estimator 704 may keep a fixed number of data points in memory, or fewer, if not yet collected, and, after each batch, updates the density estimate, for instance through use of Gaussian Kernel Density Estimation (Gaussian KDE) through the data in memory. The memory should not be too large, in order to adapt better to changes in the environment. It should, however, be large enough to sufficiently capture the shape of the pre-change distribution.

[0114] Mode selector 705: The mode selector compares the most recent batch of data to the previous density of the estimate, not the one just received through the density estimator. Through a hypothesis test, it checks whether the most recent batch of data was reasonably generated by the density estimate. If so, it maintains a high degree of confidence in its density estimate and may select a more exact mode, such as mode “RoME-QCD”. Otherwise, it is likely that the underlying pre-change distribution has changed, in which case it may select a more approximate mode, such as mode “MCT”.

[0115] Notably, a good choice of batch time and testing algorithm is necessary for this component to work well.

[0116] Parameter calculator 706: This component uses the working mode from the mode selector 705 and the probability density estimate from the density estimator 704 in order to set the scaling parameters, stopping threshold and waiting time function to be used by the change point detection mechanism 131. This completes the slow loop 7b and updates the working condition of the fast loop 7a.

[0117] Change point detection mechanism 131: The information flow of the detection algorithm 81 as used by the detection mechanism 131 is depicted in Figure 8. Using the parameters 8a from the parameter calculator 706, this component takes measurement values 8b from the measurement probe 501 or counter 601 and rescales them into a loglikelihood ratio. It then maintains a cumulative sum statistic 8c using these log-likelihood ratios. Then, depending on which mode is applied by the parameter calculator 706, a decreasing time function 8d is applied to the statistic, and the statistic is compared to the stopping threshold. If the statistic is greater than said threshold, the detection mechanism 131 sends a “Change detected” signal 8e to the time controller 702. Otherwise, it sends the waiting time 8f generated by the time function 8g to the time controller 702.

[0118] Time controller 702: The time controller 702 is outlined in Figure 9. Taking control input 8e, 8f from the change point detection mechanism 131, this component waits for the time interval given by the detector if no change is detected. If a change is detected, itsends the “Change detected” signal 9a to the data aggregator and filter 703, and then waits for a long pre-specified time in order to allow the operator time to fix any arisen problems. In either case, once it has waited, it sends a push signal 9b to the measurement probe 501 or counter 601 in order to observe the network and provide a new measurement input to the change point detection algorithm 81. Then, it checks if the pre-specified time “Batch time” has elapsed since it last sent a “Batch over” signal. If so, it sends a “Batch over” signal 9c to the data aggregator and filter.

[0119] Measurement probe 501: This component, possibly not co-located with the detection manager 130, probes the network for the desired statistic when pushed to do so by the time controller 702. Then, it sends the result to the change point detection mechanism 131.

[0120] Data aggregator and filter 703: The data aggregator and filter 703 is outlined in Figure 10. As measurement and control data 10a are collected, they are stored 10b in the data aggregator. When the time controller 702 sends the “Change detected” signal 9a, the filter performs a maximum-likelihood estimator 10c on the change time and discards all data that is believed to have occurred after the change. This prevents the moving estimation of the pre-change distribution from being polluted with post-change data. When the time controller 702 sends the “Batch over” signal 9c, this component forwards 10e the aggregated data to the density estimator and mode selector and discards all aggregated data.

[0121] Signal diagrams

[0122] To see how the different components of disclosed embodiments communicate over time, we present signal diagrams of disclosed embodiments.

[0123] A signal diagram representing the embodiment with active measurements, where the detection mechanism configures a measurement probe, is illustrated in Figure 11. A signal diagram representing the embodiment with passive measurements, where the detection mechanism sends push notifications in order to receive a measurement report from the counter, is illustrated in Figure 12.

[0124] To perform the method actions above, the change detection manager node 130, comprising a change-point detection mechanism 31 operating in several modes, the detection manager node 130 being adapted to detect changes of network conditions in a telecommunication network 100, wherein the change detection manager node 130 is adapted to perform the previously described method.It is disclosed that the change point detection manager node 130 may be implemented within a dedicated measurement probe equipment 501. It is also disclosed that the change point detection manager node 130 may be implemented within a radio access network, RAN, in order to quickly reach a non-dedicated measurement probe UE.

[0125] Embodiments herein may be implemented through a processor or one or more processors, such as the processor 1310 of a processing circuitry in the change point detection manager node 130 depicted in Figure 13 together with computer program code for performing the functions and actions of the embodiments herein. The program code mentioned above may also be provided as a computer program product, for instance in the form of a data carrier carrying computer program code for performing the embodiments herein when being loaded into the change point detection manager node 130. One such carrier may be in the form of a CD ROM disc. It is however feasible with other data carriers such as a memory stick. The computer program code may furthermore be provided as pure program code on a server and downloaded to the change point detection manager node 130.

[0126] The change point detection manager node 130 may further comprise a memory 1320 comprising one or more memory units. The respective memory 1320 comprises instructions executable by the processor in the change point detection manager node 130. The memory 1320 is arranged to be used to store e.g., media functions, indications, tags, information, data, configurations, communication data, and applications to perform the methods herein when being executed in the change point detection manager node 130.

[0127] In some embodiments, a computer program 1330 comprises instructions, which when executed by the at least one processor 1310 cause the at least one processor of the change point detection manager node 130 to perform the actions above.

[0128] In some embodiments, a carrier 1340 comprises the computer program 1330, wherein the carrier 1340 is one of an electronic signal, an optical signal, an electromagnetic signal, a magnetic signal, an electric signal, a radio signal, a microwave signal, or a computer-readable storage medium.

[0129] Those skilled in the art will appreciate that units in the change point detection manager node 130 described above may refer to a combination of analog and digital circuits, and / or one or more processors configured with software and / or firmware, e.g. stored in the change point detection manager node 130, that when executed by the one or more processors such as the processors described above. One or more of these processors, as well as the other digital hardware, may be included in a single Application-Specific Integrated Circuitry ASIC, or several processors and various digital hardwaremay be distributed among several separate components, whether individually packaged or assembled into a System-on-a-Chip (SoC).

[0130] It should be understood that as disclosed embodiments aims at controlling existing measurement and observability functions, e.g. STAMP, ICMP, TWAMP, NETCONF, with respect to frequency, these embodiments may be implemented as part of the O-RAN Service Management and Orchestration Framework. The exact location of the change point detection manager is irrelevant to the implementation.

[0131] ADDITIONAL EXPLANATION

[0132] 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.

[0133] Figure 14 shows an example of a communication system QQ100 in accordance with some embodiments.

[0134] In the example, the communication system QQ100 includes a telecommunication network QQ102 that includes an access network QQ104, such as a radio access network (RAN), and a core network QQ106, which includes one or more core network nodes QQ108. The access network QQ104 includes one or more access network nodes, such as network nodes QQ110a and QQ110b (one or more of which may be generally referred to as network nodes QQ110), or any other similar 3rdGeneration Partnership Project (3GPP) access nodes or non-3GPP access points. Moreover, as will be appreciated by those of skill in the art, a network node is not necessarily limited to an implementation in which a radio portion and a baseband portion are supplied and integrated by a single vendor. Thus, it will be understood that network nodes include disaggregated implementations or portions thereof. For example, in some embodiments, the telecommunication network QQ102 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a node in the telecommunication network QQ102 that supports an ORAN specification (e.g., a specification published by the O-RAN Alliance, or any similar organization) and may operate alone or together with other nodes to implement one or more functionalities of any node in the telecommunication network QQ102, including one or more network nodes QQ110 and / or core network nodes QQ108.

[0135] Examples of an ORAN network node include an open radio unit (O-RU), an open distributed unit (O-DU), an open central unit (O-CU), including an O-CU control plane (O-CU-CP) or an O-CU user plane (O-CU-UP), a RAN intelligent controller (near-real time or non-real time) hosting software or software plug-ins, such as a near-real time controlapplication (e.g., xApp) or a non-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification). The network node may support a specification by, for example, supporting an interface defined by the ORAN specification, such as an A1, F1, W1, E1, E2, X2, Xn interface, an open fronthaul user plane interface, or an open fronthaul management plane interface. Moreover, an ORAN access node may be a logical node in a physical node. Furthermore, an ORAN network node may be implemented in a virtualization environment (described further below) in which one or more network functions are virtualized. For example, the virtualization environment may include an O-Cloud computing platform orchestrated by a Service Management and Orchestration Framework via an 0-2 interface defined by the O-RAN Alliance or comparable technologies. The network nodes QQ110 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs QQ112a, QQ112b, QQ112c, and QQ112d (one or more of which may be generally referred to as UEs QQ112) to the core network QQ106 over one or more wireless connections.

[0136] Example wireless communications over a wireless connection include transmitting and / or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and / or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system QQ100 may include any number of wired or wireless networks, network nodes, UEs, and / or any other components or systems that may facilitate or participate in the communication of data and / or signals whether via wired or wireless connections. The communication system QQ100 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0137] The UEs QQ112 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and / or operable to communicate wirelessly with the network nodes QQ110 and other communication devices. Similarly, the network nodes QQ110 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs QQ112 and / or with other network nodes or equipment in the telecommunication network QQ102 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in the telecommunication network QQ102.

[0138] In the depicted example, the core network QQ106 connects the network nodes QQ110 to one or more host computing systems, such as host QQ116. These connections may be direct or indirect via one or more intermediary networks or devices. In otherexamples, network nodes may be directly coupled to hosts. The core network QQ106 includes one more core network nodes (e.g., core network node QQ108) that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node QQ108. Example core network nodes include functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (ALISF), Subscription Identifier De-concealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).

[0139] The host QQ116 may be under the ownership or control of a service provider other than an operator or provider of the access network QQ104 and / or the telecommunication network QQ102. The host QQ116 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio / video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.

[0140] As a whole, the communication system QQ100 of Figure 14 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM); Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and / or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standard (e.g., 6G); wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (WiFi); and / or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, LiFi, and / or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox.

[0141] In some examples, the telecommunication network QQ102 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network QQ102 may support network slicing to provide different logical networks to differentdevices that are connected to the telecommunication network QQ102. For example, the telecommunications network QQ102 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and / or Massive Machine Type Communication (mMTC) / Massive loT services to yet further UEs.

[0142] In some examples, the UEs QQ112 are configured to transmit and / or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network QQ104 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network QQ104. Additionally, a UE may be configured for operating in single- or multi-RAT or multi-standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e. being configured for multi-radio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio - Dual Connectivity (EN-DC).

[0143] In the example, the hub QQ114 communicates with the access network QQ104 to facilitate indirect communication between one or more UEs (e.g., UE QQ112c and / or QQ112d) and network nodes (e.g., network node QQ110b). In some examples, the hub QQ114 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub QQ114 may be a broadband router enabling access to the core network QQ106 for the UEs. As another example, the hub QQ114 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes QQ110, or by executable code, script, process, or other instructions in the hub QQ114. As another example, the hub QQ114 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub QQ114 may be a content source. For example, for a UE that is a VR device, display, loudspeaker, or other media delivery device, the hub QQ114 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub QQ114 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub QQ114 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy loT devices.

[0144] The hub QQ114 may have a constant / persistent or intermittent connection to the network node QQ110b. The hub QQ114 may also allow for a different communicationscheme and / or schedule between the hub QQ114 and UEs (e.g., UE QQ112c and / or QQ112d), and between the hub QQ114 and the core network QQ106. In other examples, the hub QQ114 is connected to the core network QQ106 and / or one or more UEs via a wired connection. Moreover, the hub QQ114 may be configured to connect to an M2M service provider over the access network QQ104 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes QQ110 while still connected via the hub QQ114 via a wired or wireless connection. In some embodiments, the hub QQ114 may be a dedicated hub - that is, a hub whose primary function is to route communications to / from the UEs from / to the network node QQ110b. In other embodiments, the hub QQ114 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node QQ110b, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0145] Figure 15 shows a UE QQ200 in accordance with some embodiments. The UE QQ200 presents additional details of some embodiments of the UE QQ112 of Figure 1. 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 / playback device, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), an Augmented Reality (AR) or Virtual Reality (VR) device, wireless customer-premise equipment (CPE), vehicle, vehiclemounted or vehicle embedded / integrated wireless device, etc. Other examples include any UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-loT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0146] A UE may support device-to-device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle-to-everything (V2X). In other examples, a UE may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, a UE may represent a device that is notintended 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).

[0147] The UE QQ200 includes processing circuitry QQ202 that is operatively coupled via a bus QQ204 to an input / output interface QQ206, a power source QQ208, a memory QQ210, a communication interface QQ212, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Figure 15. 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.

[0148] The processing circuitry QQ202 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs in the memory QQ210. The processing circuitry QQ202 may be implemented as one or more hardware-implemented state machines (e.g., in discrete logic, field-programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs, general-purpose processors, such as a microprocessor or digital signal processor (DSP), together with appropriate software; or any combination of the above. For example, the processing circuitry QQ202 may include multiple central processing units (CPUs).

[0149] In the example, the input / output interface QQ206 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and / or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into the UE QQ200. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device.In some embodiments, the power source QQ208 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used. The power source QQ208 may further include power circuitry for delivering power from the power source QQ208 itself, and / or an external power source, to the various parts of the UE QQ200 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source QQ208. Power circuitry may perform any formatting, converting, or other modification to the power from the power source QQ208 to make the power suitable for the respective components of the UE QQ200 to which power is supplied.

[0150] The memory QQ210 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory QQ210 includes one or more application programs QQ214, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data QQ216. The memory QQ210 may store, for use by the UE QQ200, any of a variety of various operating systems or combinations of operating systems.

[0151] The memory QQ210 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and / or ISIM, other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUlCC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory QQ210 may allow the UE QQ200 to access instructions, application programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory QQ210, which may be or comprise a device-readable storage medium.The processing circuitry QQ202 may be configured to communicate with an access network or other network using the communication interface QQ212. The communication interface QQ212 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna QQ222. The communication interface QQ212 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another UE or a network node in an access network). Each transceiver may include a transmitter QQ218 and / or a receiver QQ220 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter QQ218 and receiver QQ220 may be coupled to one or more antennas (e.g., antenna QQ222) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0152] In the illustrated embodiment, communication functions of the communication interface QQ212 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof.

[0153] 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.

[0154] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface QQ212, via a wireless connection to a network node. Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE. The output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature), random (e.g., to even out the load from reporting from several sensors), in response to a triggering event (e.g., when moisture is detected an alert is sent), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient).

[0155] As another example, a UE comprises an actuator, a motor, or a switch, related to a communication interface configured to receive wireless input from a network node via a wireless connection. In response to the received wireless input the states of the actuator,the motor, or the switch may change. For example, the UE may comprise a motor that adjusts the control surfaces or rotors of a drone in flight according to the received input or to a robotic arm performing a medical procedure according to the received input.

[0156] 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 smartwatch, a fitness tracker, a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item-tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an loT device comprises circuitry and / or software in dependence of the intended application of the loT device in addition to other components as described in relation to the UE QQ200 shown in Figure 15.

[0157] As yet another specific example, in an loT scenario, a UE may represent a machine or other device that performs monitoring and / or measurements, and transmits the results of such monitoring and / or measurements to another UE and / or a network node. The UE may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, the UE may implement the 3GPP NB-loT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation.

[0158] 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 comprisethe sensor and the actuator, and handle communication of data for both the speed sensor and the actuators.

[0159] Figure 16 shows a network node QQ300 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)), O-RAN nodes or components of an O-RAN node (e.g., 0-Rll, 0-Dll, O-CU).

[0160] Base stations may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. A base station may be a relay node or a relay donor node controlling a relay. A network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units, distributed units (e.g., in an O-RAN access node) and / or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).

[0161] 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).

[0162] The network node QQ300 includes a processing circuitry QQ302, a memory QQ304, a communication interface QQ306, and a power source QQ308. The network node QQ300 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the network node QQ300 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In sucha scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node QQ300 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory QQ304 for different RATs) and some components may be reused (e.g., a same antenna QQ310 may be shared by different RATs). The network node QQ300 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node QQ300, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node QQ300.

[0163] The processing circuitry QQ302 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and / or encoded logic operable to provide, either alone or in conjunction with other network node QQ300 components, such as the memory QQ304, to provide network node QQ300 functionality.

[0164] In some embodiments, the processing circuitry QQ302 includes a system on a chip (SOC). In some embodiments, the processing circuitry QQ302 includes one or more of radio frequency (RF) transceiver circuitry QQ312 and baseband processing circuitry QQ314. In some embodiments, the radio frequency (RF) transceiver circuitry QQ312 and the baseband processing circuitry QQ314 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry QQ312 and baseband processing circuitry QQ314 may be on the same chip or set of chips, boards, or units.

[0165] The memory QQ304 may comprise any form of volatile or non-volatile computer-readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and / or any other volatile or non-volatile, non-transitory device-readable and / or computer-executable memory devices that store information, data, and / or instructions that may be used by the processing circuitry QQ302. The memory QQ304 may store any suitable instructions, data, or information, including a computer program,software, an application including one or more of logic, rules, code, tables, and / or other instructions capable of being executed by the processing circuitry QQ302 and utilized by the network node QQ300. The memory QQ304 may be used to store any calculations made by the processing circuitry QQ302 and / or any data received via the communication interface QQ306. In some embodiments, the processing circuitry QQ302 and memory QQ304 is integrated.

[0166] The communication interface QQ306 is used in wired or wireless communication of signaling and / or data between a network node, access network, and / or UE. As illustrated, the communication interface QQ306 comprises port(s) / terminal(s) QQ316 to send and receive data, for example to and from a network over a wired connection. The communication interface QQ306 also includes radio front-end circuitry QQ318 that may be coupled to, or in certain embodiments a part of, the antenna QQ310. Radio front-end circuitry QQ318 comprises filters QQ320 and amplifiers QQ322. The radio front-end circuitry QQ318 may be connected to an antenna QQ310 and processing circuitry QQ302. The radio front-end circuitry may be configured to condition signals communicated between antenna QQ310 and processing circuitry QQ302. The radio front-end circuitry QQ318 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry QQ318 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters QQ320 and / or amplifiers QQ322. The radio signal may then be transmitted via the antenna QQ310. Similarly, when receiving data, the antenna QQ310 may collect radio signals which are then converted into digital data by the radio front-end circuitry QQ318. The digital data may be passed to the processing circuitry QQ302. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0167] In certain alternative embodiments, the network node QQ300 does not include separate radio front-end circuitry QQ318, instead, the processing circuitry QQ302 includes radio front-end circuitry and is connected to the antenna QQ310. Similarly, in some embodiments, all or some of the RF transceiver circuitry QQ312 is part of the communication interface QQ306. In still other embodiments, the communication interface QQ306 includes one or more ports or terminals QQ316, the radio front-end circuitry QQ318, and the RF transceiver circuitry QQ312, as part of a radio unit (not shown), and the communication interface QQ306 communicates with the baseband processing circuitry QQ314, which is part of a digital unit (not shown).The antenna QQ310 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna QQ310 may be coupled to the radio front-end circuitry QQ318 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna QQ310 is separate from the network node QQ300 and connectable to the network node QQ300 through an interface or port.

[0168] The antenna QQ310, communication interface QQ306, and / or the processing circuitry QQ302 may be configured to perform any receiving operations and / or certain obtaining operations described herein as being performed by the network node. Any information, data and / or signals may be received from a UE, another network node and / or any other network equipment. Similarly, the antenna QQ310, the communication interface QQ306, and / or the processing circuitry QQ302 may be configured to perform any transmitting operations described herein as being performed by the network node. Any information, data and / or signals may be transmitted to a UE, another network node and / or any other network equipment.

[0169] The power source QQ308 provides power to the various components of network node QQ300 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source QQ308 may further comprise, or be coupled to, power management circuitry to supply the components of the network node QQ300 with power for performing the functionality described herein. For example, the network node QQ300 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source QQ308. As a further example, the power source QQ308 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail.

[0170] Embodiments of the network node QQ300 may include additional components beyond those shown in Figure 16 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and / or any functionality necessary to support the subject matter described herein. For example, the network node QQ300 may include user interface equipment to allow input of information into the network node QQ300 and to allow output of information from the network node QQ300. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node QQ300. In some embodiments providing acore network node, such as core network node 108 of FIG. 14, some components, such as the radio front-end circuitry QQ318 and the RF transceiver circuitry QQ312 may be omitted.

[0171] Figure 17 is a block diagram illustrating a virtualization environment QQ400 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 QQ400 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, UE, core network node, or host. Further, in embodiments in which the virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized. In some embodiments, the virtualization environment QQ400 includes components defined by the O-RAN Alliance, such as an O-Cloud environment orchestrated by a Service Management and Orchestration Framework via an 0-2 interface. Virtualization may facilitate distributed implementations of a network node, UE, core network node, or host.

[0172] Applications QQ402 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment Q400 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0173] Hardware QQ404 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 QQ406 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs QQ408a and QQ408b (one or more of which may be generally referred to as VMs QQ408), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer QQ406 may present a virtual operating platform that appears like networking hardware to the VMs QQ408.The VMs QQ408 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer QQ406. Different embodiments of the instance of a virtual appliance QQ402 may be implemented on one or more of VMs QQ408, 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.

[0174] In the context of NFV, a VM QQ408 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 QQ408, and that part of hardware QQ404 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 QQ408 on top of the hardware QQ404 and corresponds to the application QQ402.

[0175] Hardware QQ404 may be implemented in a standalone network node with generic or specific components. Hardware QQ404 may implement some functions via virtualization. Alternatively, hardware QQ404 may be part of a larger cluster of hardware (e.g. such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration QQ410, which, among others, oversees lifecycle management of applications QQ402. In some embodiments, hardware QQ404 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 QQ412 which may alternatively be used for communication between hardware nodes and radio units.

[0176] Although the computing devices described herein (e.g., UEs, network nodes) 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 methodsdisclosed 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.

[0177] 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.

[0178] When using the word "comprise" or “comprising” it shall be interpreted as nonlimiting, i.e. meaning "consist at least of".

[0179] The embodiments herein are not limited to the preferred embodiments described above. Various alternatives, modifications and equivalents may be used.

[0180] Abbreviations

[0181] LTE Long Term Evolution

[0182] 5G NR 5G New RadioEPS Evolved Packet System

[0183] RoME-QCD Robust and Measurement-Efficient Quickest Change Detection MCT Mean Change Test

[0184] NETCONF Network Configuration Protocol

[0185] UE User equipment

[0186] KDE Kernel density estimation

[0187] CUSUM Cumulative Sum (statistic for change point detection) RTT Round Trip Time

[0188] OWD One-Way Delay

[0189] TWAMP Two-way Active Measurement Protocol

[0190] STAMP Simple TWAMP

Claims

CLAIMS1. A method performed by a change point detection manager node (130), comprising a change-point detection mechanism (131) operating in several modes, for detecting changes of network conditions in a telecommunication network (100), the method comprising:- Action 1 : running (31) the change point detection mechanism for a set time, receiving measurement data, using initial configurations for pre- and post-change distributions of performance metrics;- Action 2: estimating or re-estimating (33), when the set time has expired, a prechange measurement distribution using received newly gathered data of the performance metrics;- Action 3: evaluating (34) the confidence of the current pre-change measurement distribution;- Action 4: deciding (35) upon a mode, out of a set of modes, in the changedetection mechanism based upon confidence of current pre-change distribution;- Action 5: calculating or re-calculating (36) new parameters for the selected mode in the change point detection mechanism; and- Action 6: restarting (37) Action 1 with the calculated or recalculated new parameters.

2. The method according to claim 1, wherein the operation of the change point detection mechanism (131) comprises:calculating (41) a cumulative sum measurement statistic that corresponds to the likelihood of a change having occurred within the network based on received measurement data as input, anddeciding (42) on the next time of measurement and whether to raise an alarm indicating that a change has occurred within the network.

3. The method according to claim 1 or 2, wherein the action of deciding upon mode, Action 4, comprises:deciding upon a mode where the change point detection mechanism use the full shape (44) of the estimated or re-estimated pre-change distribution to the next batch e.g. a Robust and Measurement-Efficient Quickest Change Detection, RoME-QCD, mode, if confident, anddeciding upon a mode where the change point detection mechanism evaluates only certain statistics (45) of the estimated or re-estimated pre-change distribution to the next batch, e.g. a mean change test, MCT, mode, if not confident.

4. The method according to claim 1, 2 or 3, wherein the detection management node (130) is implemented within a dedicated measurement probe equipment ().

5. The method according to claim 1, 2 or 3, wherein the detection management node is implemented within a radio access network, RAN.

6. A change point detection manager node (130), comprising a change-point detection mechanism (131) operating in several modes, the detection manager node (130) being adapted to detect changes of network conditions in a telecommunication network (100), wherein the detection manager node is adapted to perform the method of claim 1 , 2 or 3.

7. The change point detection manager node (130) according to claim 6, wherein the detection manager node (130) is implemented within a dedicated measurement probe equipment (51).

8. The change point detection manager node (130) according to claim 6, wherein the detection manager node (130) is implemented within a radio access network (61), RAN.

9. A computer program (1330) comprising instructions, which when executed by a processor (1310), causes the processor to perform actions according to any of the claims 1-5.

10. A carrier (1340) comprising the computer program (1330) of claim 9, wherein the carrier (1340) is one of an electronic signal, an optical signal, an electromagnetic signal, a magnetic signal, an electric signal, a radio signal, a microwave signal, or a computer-readable storage medium.

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