Enabling Prediction of Future Operational Condition for Sites

It is provided a method for enabling prediction of a future operational condition for at least one site, each site comprising at least one radio network node of a radio access technology, RAT, of a cellular network. The method comprises the steps of: obtaining input properties of the at least one site; selecting a plurality of machine learning models based on the input properties; and activating the selected plurality of machine learning models in an inference engine, such that all of the selected plurality of machine learning models are collectively applicable to enable prediction of a future operational condition of the at least one site.

TECHNICAL FIELD

The invention relates to a method, an operational condition predictors, a computer program and a computer program product for enabling prediction of future operational condition for sites, each site comprising at least one radio network node.

BACKGROUND

In cellular networks, an operator controls a number of sites, where each site is provided with one or more network nodes for providing connectivity to instances of user equipment, UEs. A single site can have several radio network nodes supporting different radio access technologies (RATs), i.e. different types of cellular networks.

A Network Operations Centre (NOC) is used to monitor and control the cellular networks of the operator. When an alarm is raised in a NOC, it is typically associated with a certain site and this is vital to the process of troubleshooting.

A number of different operating conditions can happen to sites. For instance, grid power may fail and secondary power, such as batteries or generators, may eventually run out. Another operating condition is a sleeping cell, where the radio network node broadcasts its presence to UEs, but the radio network node is unable to set up any traffic channels.

SUMMARY

It would be of great benefit if operating conditions of sites of radio network nodes could be predicted better.

According to a first aspect, it is provided a method for enabling prediction of a future operational condition for at least one site, each site comprising at least one radio network node of a radio access technology, RAT, of a cellular network. The method comprises the steps of: obtaining input properties of the at least one site; selecting a plurality of machine learning models based on the input properties; and activating the selected plurality of machine learning models in an inference engine, such that all of the selected plurality of machine learning models are collectively applicable to enable prediction of a future operational condition of the at least one site.

The method may further comprise the step of: obtaining a specific future operational condition to be predicted. In such a case, the step of selecting a plurality of machine learning models is also based on the specific future operational condition; and the step of activating the selected plurality of machine learning models enables prediction of the specific future operational condition.

In the step of selecting a plurality of machine learning models, at least one machine learning model may have been filtered to omit data according to a configuration by the source entity of each of the at least one machine learning model.

The method may further comprise the step of: determining weights of each one of the selected plurality of machine learning models. In such a case, in the step of activating the selected plurality of machine learning models, the weights are provided for the collective application of the selected plurality of machine learning models.

The method may further comprise the steps of: receiving feedback from at least one user equipment device, UE, relating to accuracy of the collectively applied machine learning models; and adjusting the weights based on the feedback.

The input properties may comprise keywords and/or key-value pairs.

The input properties may relate to at least one of: supported RATs, power source(s), geographical region, latitude and longitude, antenna height, tower height, battery installation date, number of diesel generators, fuel tank size, on-air-date, number of cells and spectrum coverage, location, battery capacity, sector azimuth(s), sector spectrum, area type, radio access channel success rate over time, throughput over time, and latency over time.

The future operational condition may be any one of: power outage, sleeping cell, degradation of latency, and degradation of throughput.

According to a second aspect, it is provided an operational condition predictor for enabling prediction of a future operational condition for at least one site, each site comprising at least one radio network node of a radio access technology, RAT, of a cellular network. The operational condition predictor comprises: a processor; and a memory storing instructions that, when executed by the processor, cause the operational condition predictor to: obtain input properties of the at least one site; select a plurality of machine learning models based on the input properties; and activate the selected plurality of machine learning models in an inference engine, such that all of the selected plurality of machine learning models are collectively applicable to enable prediction of a future operational condition of the at least one site.

The operational condition predictor may further comprise instructions that, when executed by the processor, cause the operational condition predictor to: obtain a specific future operational condition to be predicted. In such a case, the instructions to select a plurality of machine learning models is also based on the specific future operational condition; and the instructions to activate the selected plurality of machine learning models enable prediction of the specific future operational condition.

In the instructions to select a plurality of machine learning models, at least one machine learning model may have been filtered to omit data according to a configuration by the source entity of each of the at least one machine learning model.

The operational condition predictor may further comprise instructions that, when executed by the processor, cause the operational condition predictor to: determine weights of each one of the selected plurality of machine learning3omodels. In such a case, the instructions to activate the selected plurality of machine learning models comprise instructions that, when executed by the processor, cause the operational condition predictor to provide the weights for the collective application of the selected plurality of machine learning models.

The operational condition predictor may further comprise instructions that, when executed by the processor, cause the operational condition predictor to: receive feedback from at least one user equipment device, UE, relating to accuracy of the collectively applied machine learning models; and adjust the weights based on the feedback.

The input properties may comprise keywords and/or key-value pairs.

The input properties may relate to at least one of: supported RATs, power source(s), geographical region, latitude and longitude, antenna height, tower height, battery installation date, number of diesel generators, fuel tank size, on-air-date, number of cells and spectrum coverage, location, battery capacity, electric power source, sector azimuth(s), sector spectrum, area type, radio access channel success rate over time, throughput over time, and latency over time.

The future operational condition may be any one of: power outage, sleeping cell, degradation of latency, and degradation of throughput.

According to a third aspect, it is provided an operational condition predictor comprising: means for obtaining input properties of at least one site, each site comprising at least one radio network node of a radio access technology, RAT, of a cellular network; means for selecting a plurality of machine learning models based on the input properties; and means for activating the selected plurality of machine learning models in an inference engine, such that all of the selected plurality of machine learning models are collectively applicable to enable prediction of a future operational condition of the at least one site.

According to a fourth aspect, it is provided a computer program for enabling prediction of a future operational condition for at least one site, each site comprising at least one radio network node of a radio access technology, RAT, of a cellular network. The computer program comprises computer program code which, when run on an operational condition predictor causes the operational condition predictor to: obtain input properties of the at least one site; select a plurality of machine learning models based on the input properties; and activate the selected plurality of machine learning models in an inference engine, such that all of the selected plurality of machine learning models are collectively applicable to enable prediction of a future operational condition of the at least one site.

According to a fifth aspect, it is provided a computer program product comprising a computer program according to the fourth aspect and a computer readable means on which the computer program is stored.

DETAILED DESCRIPTION

Embodiments presented herein enable the cross-use of machine learning models, even between different operators. The selection of what machine learning models to employ is based on the input properties of at least one site. The selected machine learning models are then collectively used to predict a future operational condition of the at least one site.

FIG. 1is a schematic diagram illustrating an environment where embodiments presented herein may be applied. A cellular network operator, hereinafter simply referred to as ‘operator’, has a number of sites5a-d, in this example four sites5a-d. In reality there are typically many more sites under control of the operator, but four sites are shown here for clarity of explanation. Hereinafter, the reference numeral5refers to any suitable site, e.g. one of the sites5a-dofFIG. 1. A site5is a location hosting equipment, in this case one or more radio network nodes. Each site5has a number of properties, e.g. based on location and technical properties of the site5and network nodes, as described in more detail below.

Each site5a-dis used to provide cellular network coverage using one or more radio access technologies (RAT). The operator can support one or more different types of cellular networks. Each type of cellular network utilises a RAT. For instance, one or more RATs can be selected from the list of 5G NR (New Radio), LTE (Long Term Evolution), LTE-Advanced, W-CDMA (Wideband Code Division Multiplex), EDGE (Enhanced Data Rates for GSM (Global System for Mobile communication) Evolution), GPRS (General Packet Radio Service), CDMA2000 (Code Division Multiple Access 2000), GSM, or any other current or future wireless network, as long as the principles described hereinafter are applicable. The site5is responsible for providing a suitable environment, e.g. in the form of a building, for the radio network nodes to be able to provide coverage. For power, each site5a-dis usually connected to an electric grid as a primary power source. Additionally, the site5can also provide one or more secondary power sources, such as solar power, wind generator, batteries, and diesel generator.

Since many operators provide coverage using several RATs, each site5a-dcan host several radio network nodes, where each radio network node can support a different RAT. In the example ofFIG. 1, a first site5ahosts a first radio network node1aand a second radio network node ib. A second site5bhosts a third radio network node1c, a fourth radio network node id and a fifth radio network node1e. A third site5chosts a sixth radio network node if, a seventh radio network node1gand an eighth radio network node1g. A fourth site5dhosts a ninth radio network node ii and a tenth radio network node1j.

The radio network nodes1a-jare in the form of radio base stations being any one of evolved Node Bs, also known as eNode Bs or eNBs, g Node Bs, Node Bs, BTSs (Base Transceiver Stations) and/or BSSs (Base Station Subsystems), etc.

The radio network nodes1a-jprovide radio connectivity over a wireless interface to a plurality of instances of user equipment (UE)2. The term UE is also known as mobile communication terminal, mobile terminal, user terminal, user agent, subscriber terminal, subscriber device, wireless device, wireless terminal, machine-to-machine device etc., and can e.g. be in the form of what today are commonly known as a mobile phone, smart phone or a tablet/laptop with wireless connectivity.

Over the wireless interface, downlink (DL) communication occurs from the radio network nodes1a-jto the UE2and uplink (UL) communication occurs from the UE2to the radio network nodes1a-j. The quality of the wireless radio interface to each UE2can vary over time and depending on the position of the UE2, due to effects such as fading, multipath propagation, interference, etc.

For each RAT, a number of network nodes are connected to a core network (CN)3for connectivity to central functions and a wide area network7, such as the Internet. A Network Operations Centre (NOC)4is connected to the core network3to monitor and control the cellular networks of the operator. A single NOC4can be employed for several different cellular networks of the operator or different NOCs can be used for different cellular networks.

According to embodiments presented herein, it is provided an operational condition predictor to predict when problems in sites5of the cellular network are likely to occur.

FIGS. 2A-Care schematic diagrams illustrating embodiments of where the operational condition predictor11can be implemented.

InFIG. 2A, the operational condition predictor11is shown implemented in a radio network node1, which e.g. can be any one of the radio network nodes ofFIG. 1. The radio network node1is thus the host device for the operational condition predictor11in this implementation. This embodiment corresponds to an edge network implementation.

InFIG. 2B, the operational condition predictor11is shown implemented in the NOC4. The NOC4is thus the host device for the operational condition predictor11in this implementation.

InFIG. 2C, the operational condition predictor11is shown implemented as a stand-alone device. The operational condition predictor11thus does not have a host device in this implementation. The operational condition predictor11can thus be implemented anywhere suitable, e.g. in the cloud.

FIGS. 3A-Bare flow charts illustrating embodiments of methods for enabling prediction of a future operational condition one or more sites5. The method is performed for a set of the one or more sites5, which can be all, or a subset of all, sites5of the operator. The future operational condition can e.g. be any one (or a combination) of: power outage, or sleeping cell, degradation of latency, and degradation of throughput. As described above, each site5comprises at least one radio network node1of an RAT of a cellular network. The methods are performed in the operational condition predictor.

In an obtain input properties step40, the operational condition predictor obtains input properties of the at least one site5. The input can be received from an operator terminal (e.g. of the NOC4) or from a server instructing the operational condition predictor to perform this method, e.g. on a scheduled basis or based on a certain condition. The input properties can comprise keywords. Each keywords is a property which either exists or not for the site5. Alternatively or additionally, the input properties comprise key-value pairs. Each key-value pair is made up of a key and a value, where the key is a label indicating the use of the key-value pair and the value is a measurement for that particular key. The input properties relate to at least one of:

supported RATs, power source(s), geographical region, latitude and longitude, antenna height, tower height, battery installation date, number of diesel generators, fuel tank size (for the generator(s)), on-air-date, number of cells and spectrum coverage, location, battery capacity, sector azimuth(s), sector spectrum, area type (e.g. urban, rural, suburban), radio access channel success rate over time, throughput over time, and latency over time.

The input properties can contain static or configurable information obtained from a database. Alternatively or additionally, the input properties can contain dynamic information, e.g. obtained by querying the site5and/or radio network nodes1of the site5.

In a select ML models step42, the operational condition predictor selects a plurality of machine learning (ML) models based on the input properties. The ML models can be ML models from different operators. The different ML models can be stored centrally or in different locations, e.g. at each respective operator being a source for the ML model. This allows each operator to not only use its own ML models, but also to use the ML models of other operators to improve the prediction of operating conditions of sites5. Since the selection of ML models is performed based on the input properties, ML models matching the one or more sites5are preferred. For instance, if the one or more sites5are in a rural location with a single diesel generator as backup power at a latitude of 35 degrees, ML models with similar characteristics are preferred.

For instance, a look up function can be used to compare the input parameters of the ML models available. Using a similarity technique, the top-k models are selected that match the one or more sites5depending on the future operational condition to be predicted.

In one embodiment, at least one ML model has been filtered to omit data according to a configuration by the source entity of each of the at least one ML model. In other words, each operator can then configure what data should form part of the ML model to be shared. This configuration can be based on business decisions and/or on regulations on what data that can be shared. The sharing of data across operators can be sensitive, which is mitigated in this way.

The ML models are already in a state to be used, i.e. have been appropriately set up and trained in any suitable way. For instance, the models might have been trained using counters such as RachSuccRate, UlSchedulerActivityRate_EWMALastiweek. RachSuccRate denotes a percentage of successful radio access establishments using random access. UlSchedulerActivityRate_EWMALastiweek denotes an aggregate counter measuring the Uplink Scheduler Activity Rate for the past week. A time window is used which aggregates data over a period. This counter measures how many times different uplink tasks have been scheduled. The counter used to train the ML model can be one of the input parameters of step40above.

Examples of potentially sensitive data include mobile subscriber location, type of traffic generated by subscribes, call data records, etc. It is to be noted that the filtering of data can imply removing data, or anonymising data (e.g. by means of k-anonymization such as suppression and generalization).

In an activate ML models collectively step44, the operational condition predictor activates the selected plurality of ML models in an inference engine, such that all of the selected plurality of ML models are collectively applicable to enable prediction of a future operational condition of the at least one site5. The combining of the ML models can e.g. be performed using boosting or bagging, as known in the art per se.

In boosting some points from a dataset are selected at random, resulting in learning and building a model, and then testing the model against the selected points. For any incorrect predictions, the boosting procedure will pay more attention. The process is repeated until all predictions are correct, or the rate of correct predictions is greater than a threshold. Subsequently, a consensus model is built. In case of classification problems (e.g. trying to identify root cause of an issue), a voting process can be used, wherein each individual model identifies the root cause and the root cause with the most votes wins. In case of regression problems (e.g. estimating churn propensity scores for mobile subscribers) a consensus model can be built either by simple averaging (e.g. mean computation) or weighed averaging of the produced models. The consensus model is more accurate than the individual models, as it eliminates bias of individual models, thus improving predictions at-large.

The inference engine is the entity which performs the actual prediction based on the ML models. The inference engine can form part of the NOC4or can be implemented in a separate device located elsewhere. Optionally, the inference engine can be implemented in the same physical device as the operational conditional predictor.

Given the predictive nature of ML models, these can be triggered ahead of time based on the validity of the prediction. For instance, if a prediction is meant to be valid (to a certain degree of certainty) for X hours, inference can be triggered X hours ahead of time minus the time it takes for the actual computation for the prediction to be generated. Aside from the temporal dimension, additional criteria can be considered for triggering this process. In one embodiment, specific alarms popping up on a NOC4are considered. In one embodiment, specific sites5that have been addressed as a consequence of an ML prediction are considered. This can be used to verify the quality of the prediction as well as the resolution that has been applied.

Looking now toFIG. 3B, only new or modified steps compared to the steps ofFIG. 3Awill be described.

In an optional obtain specific future operational condition step41, the operational condition predictor obtains a specific future operational condition to be predicted. In such a case, the select ML models step42is also based on the specific future operational condition. Furthermore, the activate ML models collectively step44, enables prediction of the specific future operational condition.

In an optional determine weights step43, the operational condition predictor determines weights of each one of the selected plurality of ML models. When weights are determined, the activate ML models collectively step44comprises providing the weights for the collective application of the selected3oplurality of ML models. For instance, an ML model which best matches the one or more sites5can be weighted higher than an ML model which does not match as well.

In an optional receive feedback step46, the operational condition predictor receives feedback from at least one UE2. The feedback relates to accuracy of the collectively applied ML models. For instance, information relating to the predicted operational condition (e.g. sleeping cell, reduced throughput, etc.) can form part of the feedback, to allow evaluation of the ML models.

In an optional adjust weights step48, the operational condition predictor adjusts the weights based on the feedback. In this way, the ML models are to rewarded or penalised according to its accuracy (which is checked with the feedback). After the weights are adjusted, the method returns to the activate ML models collectively step44, to thereby apply the adjusted weights.

By using the feedback to adjust the weights, the operator can get feedback as to how robust a particular ML model is. This may be particularly useful when the model is deployed in a new setting, even with data that is has never seen. A general risk of ML models is that they can be over fitted or develop biases to the input training dataset, which is mitigated using this feedback loop. Using the weight adjustment, the performance of a combination of ML models2is improved, compensating for any inefficacies identified in step46.

According to embodiments presented herein, it is made possible to re-use ML models developed to predict problems for different operators. Moreover, this enables further improvement on these models by combining them and by evaluating their efficiency. The embodiments thus enable the transfer of learning between operators and/or for different deployments within the domain of an operator. In other words, models are used to solve a different problem without exposing the data used for the initial training of the ML model. Optionally, an ML model can be further trained after deployment. Consequently, the embodiments presented herein are beneficial both for new operators deploying a network and for existing operators expanding their networks or for continuous performance improvements.

FIG. 4is a schematic diagram illustrating components of the operational condition predictor ofFIGS. 2A-Caccording to one embodiment. It is to be noted that one or more of the mentioned components can be shared with the host device, such as for the embodiments illustrated inFIGS. 2A-Band described above. A processor60is provided using any combination of one or more of a suitable central processing unit (CPU), multiprocessor, microcontroller, digital signal processor (DSP), etc., capable of executing software instructions67stored in a memory64, which can thus be a computer program product. The processor60could alternatively be implemented using an application specific integrated circuit (ASIC), field programmable gate array (FPGA), etc. The processor60can be configured to execute the method described with reference toFIGS. 3A-Babove.

The memory64can be any combination of random access memory (RAM) and/or read only memory (ROM). The memory64also comprises persistent storage, which, for example, can be any single one or combination of magnetic memory, optical memory, solid-state memory or even remotely mounted memory.

A data memory66is also provided for reading and/or storing data during execution of software instructions in the processor60. The data memory66can be any combination of RAM and/or ROM.

The operational condition predictor11further comprises an I/O interface62for communicating with external and/or internal entities. Optionally, the I/O interface62also includes a user interface.

Other components of the operational condition predictor11are omitted in order not to obscure the concepts presented herein.

FIG. 5is a schematic diagram showing functional modules of the operational condition predictor ofFIGS. 2A-Caccording to one embodiment. The modules are implemented using software instructions such as a computer program executing in the operational condition predictor11. Alternatively or additionally, the modules are implemented using hardware, such as any one or more of an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or discrete logical circuits. The modules correspond to the steps in the methods illustrated inFIGS. 3A and 3B.

An input properties obtainer70corresponds to step40. A specific future operational condition obtainer71corresponds to step41. An ML model selector72corresponds to step42. A weights determiner73corresponds to step43. An ML model activator74corresponds to step44. A feedback receiver76corresponds to step46. A weights adjuster78corresponds to step48.

FIG. 6shows one example of a computer program product comprising computer readable means. On this computer readable means, a computer program91can be stored, which computer program can cause a processor to execute a method according to embodiments described herein. In this example, the computer program product is an optical disc, such as a CD (compact disc) or a DVD (digital versatile disc) or a Blu-Ray disc. As explained above, the computer program product could also be embodied in a memory of a device, such as the computer program product ### of Fig ###. While the computer program91is here schematically shown as a track on the depicted optical disk, the computer program can be stored in any way which is suitable for the computer program product, such as a removable solid state memory, e.g. a Universal Serial Bus (USB) drive.