Patent Application
20240251376
The present disclosure relates to application of a spatial-temporal user distribution for network resource management.
Examples of embodiments relate to apparatuses, methods and computer program products relating to the application of a spatial-temporal user distribution for network resource management.
The following description of background art may include insights, discoveries, understandings or disclosures, or associations, together with disclosures not known to the relevant prior art, to at least some examples of embodiments of the present disclosure but provided by the disclosure. Some of such contributions of the disclosure may be specifically pointed out below, whereas other of such contributions of the disclosure will be apparent from the related context.
In the last years, an increasing extension of communication networks, e.g. of wire based communication networks, such as the Integrated Services Digital Network (ISDN), Digital Subscriber Line (DSL), or wireless communication networks, such as the cdma2000 (code division multiple access) system, cellular 3rd generation (3G) like the Universal Mobile Telecommunications System (UMTS), fourth generation (4G) communication networks or enhanced communication networks based e.g. on Long Term Evolution (LTE) or Long Term Evolution-Advanced (LTE-A), fifth generation (5G) communication networks, cellular 2nd generation (2G) communication networks like the Global System for Mobile communications (GSM), the General Packet Radio System (GPRS), the Enhanced Data Rates for Global Evolution (EDGE), or other wireless communication system, such as the Wireless Local Area Network (WLAN), Bluetooth or Worldwide Interoperability for Microwave Access (WiMAX), took place all over the world. Various organizations, such as the European Telecommunications Standards Institute (ETSI), the 3rd Generation Partnership Project (3GPP), Telecoms & Internet converged Services & Protocols for Advanced Networks (TISPAN), the International Telecommunication Union (ITU), 3rd Generation Partnership Project 2 (3GPP2), Internet Engineering Task Force (IETF), the IEEE (Institute of Electrical and Electronics Engineers), the WiMAX Forum and the like are working on standards or specifications for telecommunication network and access environments.
With respect thereto, 3GPP standards define a concept of Management Data Analytics (MDA) in TR 28.809 and TS 28.104 to monitor, analyze and affect recommendations, perform root cause analysis, or enact policy changes.
A Management Service (MnS) Producer generates data and performance information consumed by MDA (which may be understood to represent a (management application/element/function of a) Service Management Orchestrator (SMO)), which can potentially consume other MDA data (via other MDA Services (MDAS) Producers) and/or 5G core analytics via the Network Data Analytics Function (NWDAF) as illustrated in
The present disclose may fit within the MDA framework taking advantage of the existing 5G Performance Measurements TS 28.552 and Key Performance Indicators (KPIs) TS 28.554 related to mobility, user activity, subscriber position, throughput, and session management.
Regarding a user location, the Minimization of Drive Tests (MDT) (TS 37.320) provides a detailed location information (e.g. global navigation satellite system (GNSS) location information) to be included and reported to the management plane via the radio access network if available by a user equipment (UE) when the measurement was taken. If detailed location information is available, the reporting shall include the latitude and longitude. Depending on the availability, parameters like altitude, uncertainty, and confidence may also be included in addition.
3GPP [TS37.320] section 5.1.1.3.3 defines how UEs can report their location information, and other quality indicators (Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), Received Signal Strength Indicator (RSSI)) at regular time intervals or when needed. Also, real-time text (RTT) can be used in Observed Time Different of Arrival (OTDOA) for triangulation.
Referring now to
Alternatively, 5G Location Services (LCS) & Location Management Function (LMF) (TS 29.572) defines the functionality to determine the location of a UE. In TS 29.572 Clause 4, the LMF can obtain a location estimate from the UE itself and/or from uplink location measurements from the 5G RAN and AMF as shown in
MDA can assist the operation phase of the Slicing Lifecycle Management (TS 28.530) following the preparation and commission of slices as shown in
However, there is still the problem of managing, like e.g. configuring and/or allocating, network resources to a plurality of UEs at different locations.
The following meanings for the abbreviations used in this specification apply:
Various examples of embodiments of the present disclosure aim at addressing at least part of the above issues and/or problems and drawbacks.
Various aspects of examples of embodiments of the present disclosure are set out in the appended claims.
According to examples of embodiments, there is provided, for example, a method according to claim 1 and a method according to claim 10. Further advantageous developments with respect to the methods are defined in the respective dependent claims 2 to 9 and 11 to 16.
In addition, according to examples of embodiments, there is provided, for example, an apparatus according to claim 17 and an apparatus according to claim 26. Further advantageous developments with respect to the apparatuses are defined in the respective dependent claims 18 to 25 and 27 to 32.
Furthermore, according to examples of embodiments, there is provided, for example, a computer program product according to claims 33 and 34.
Any one of the above mentioned aspects enables a time-related management of network resources, like allocation of network resources and/or a time-related configuration of network slicing to a time-related distribution of a plurality of mobile devices, like mobile terminal endpoint devices or user equipment, thereby allowing to solve at least part of the problems and drawbacks as identified/derivable from above.
Thus, improvement is achieved by apparatuses, methods, and computer program products enabling a time-related allocation of network resources.
Some embodiments of the present disclosure are described below, by way of example only, with reference to the accompanying drawings, in which:
Basically, for properly establishing and handling a communication between two or more end points (e.g. communication stations or elements or functions, such as terminal devices, user equipments (UEs), or other communication network elements, a database, a server, host etc.), one or more network elements or functions (e.g. virtualized network functions), such as communication network control elements or functions, for example access network elements like access points (APs), radio base stations (BSs), relay stations, eNBs, gNBs etc., and core network elements or functions, for example control nodes, support nodes, service nodes, gateways, user plane functions, access and mobility functions etc., may be involved, which may belong to one communication network system or different communication network systems.
In this context, as already outlined above with reference to
In this regard, in more detail, the present disclosure deals with the problem of user distribution in geographical space and time considering the user activity, and potentially the aggregated throughput, session, and application characteristics. It introduces an aggregated or group view of a three-dimensional user distribution with the target to assist the network resource allocation MnS, unlike current solutions for example mobility analytics that focus on a single user for enhancing or assuring the user experience. In other words, the proposed weighted STUD applies per aggregated group of UEs creating a map of user gravity in a 3D geographical space and time.
The meta data that needs to accompany the proposed STUD would require enhancements on the respective interfaces either in the (i) tenant interfaces that interacts with the management system and/or (ii) MDA producer and MDA consumer interfaces. The meta data used depends on the usage of the STUD, which may be used to address several problems, which fall into the following three basic categories:
1. Slice request template: Currently, slices are specified and requested using the GSMA NG.116 Template, which defines performance parameters that apply across the entire slice area. Furthermore, it considers a maximum uniform UE distribution based on a fixed upper bound limit. These problems can be addressed by the idea as disclosed in the present specification. Defining parameters which apply to an entire area may prove to be problematic if the area contains smaller geographical coverage points or areas that influence significantly different the user behavior at distinct points in time, as shown in
2. Resource optimization: A common 5G challenge is resource management, e.g., to deploy and host network slices, while achieving optimal network and service performance. Slicing optimization is a “big” topic in 5G networks considering how to setup a network to provide optimal functionality, what sorts of slices should be setup and how to best set them up to achieve product differentiation. Service providers compete for how to optimally setup to slices to provide the optimal throughput and UE experience, while conserving the maximum amount of resources.
3. MDA: MDA today does not utilize time series or changing distributions of an aggregate UE location. Analysis is only based on single UEs in cells. MDA are performed without spatial-temporal information.
The present disclosure thus provides, according to various examples of embodiments as outlined below in detail, a flexible and/or efficient solution to manage, i.e. allocate time-related network resources and/or time-related network slices to a plurality of UEs based on a time-related spatial distribution of the plurality of UEs.
In the following, different exemplifying embodiments will be described using, as an example of a communication network to which examples of embodiments may be applied, a communication network architecture based on 3GPP standards for a communication network, such as a 5G/NR, without restricting the embodiments to such an architecture, however. It is obvious for a person skilled in the art that the embodiments may also be applied to other kinds of communication networks like 4G and/or LTE where mobile communication principles are integrated, e.g. Wi-Fi, worldwide interoperability for microwave access (WiMAX), Bluetooth®, personal communications services (PCS), ZigBee®, wideband code division multiple access (WCDMA), systems using ultra-wideband (UWB) technology, mobile ad-hoc networks (MANETs), wired access, etc. Furthermore, without loss of generality, the description of some examples of embodiments is related to a mobile communication network, but principles of the disclosure can be extended and applied to any other type of communication network, such as a wired communication network or datacenter networking.
The following examples and embodiments are to be understood only as illustrative examples. Although the specification may refer to “an”, “one”, or “some” example(s) or embodiment(s) in several locations, this does not necessarily mean that each such reference is related to the same example(s) or embodiment(s), or that the feature only applies to a single example or embodiment. Single features of different embodiments may also be combined to provide other embodiments. Furthermore, terms like “comprising” and “including” should be understood as not limiting the described embodiments to consist of only those features that have been mentioned; such examples and embodiments may also contain features, structures, units, modules etc. that have not been specifically mentioned.
A basic system architecture of a (tele)communication network including a mobile communication system where some examples of embodiments are applicable may include an architecture of one or more communication networks including wireless access network subsystem(s) and core network(s). Such an architecture may include one or more communication network control elements or functions, access network elements, radio access network elements, access service network gateways or base transceiver stations, such as a base station (BS), an access point (AP), a NodeB (NB), an eNB or a gNB, a distributed or a centralized unit, which controls a respective coverage area or cell(s) and with which one or more communication stations such as communication elements or functions, like user devices, mobile devices, or terminal devices, like a UE, or another device having a similar function, such as a modem chipset, a chip, a module etc., which can also be part of a station, an element, a function or an application capable of conducting a communication, such as a UE, an element or function usable in a machine-to-machine communication architecture, or attached as a separate element to such an element, function or application capable of conducting a communication, or the like, are capable to communicate via one or more channels via one or more communication beams for transmitting several types of data in a plurality of access domains. Furthermore, (core) network elements or network functions ((core) network control elements or network functions, (core) network management elements or network functions), such as gateway network elements/functions, mobility management entities, a mobile switching center, servers, databases and the like may be included.
The general functions and interconnections of the described elements and functions, which also depend on the actual network type, are known to those skilled in the art and described in corresponding specifications, so that a detailed description thereof is omitted herein. However, it is to be noted that several additional network elements and signaling links may be employed for a communication to or from an element, function or application, like a communication endpoint, a communication network control element, such as a server, a gateway, a radio network controller, and other elements of the same or other communication networks besides those described in detail herein below.
A communication network architecture as being considered in examples of embodiments may also be able to communicate with other networks, such as a public switched telephone network or the Internet. The communication network may also be able to support the usage of cloud services for virtual network elements or functions thereof, wherein it is to be noted that the virtual network part of the telecommunication network can also be provided by non-cloud resources, e.g. an internal network or the like. It should be appreciated that network elements of an access system, of a core network etc., and/or respective functionalities may be implemented by using any node, host, server, access node or entity etc. being suitable for such a usage. Generally, a network function can be implemented either as a network element on a dedicated hardware, as a software instance running on a dedicated hardware, or as a virtualized function instantiated on an appropriate platform, e.g., a cloud infrastructure.
Furthermore, a network element, such as communication elements, like a UE, a mobile device, a terminal device, control elements or functions, such as access network elements, like a base station (BS), an eNB/gNB, a radio network controller, a core network control element or function, such as a gateway element, or other network elements or functions, as described herein, (core) network management element or function, such as a session management function or a SMO, and any other elements, functions or applications may be implemented by software, e.g. by a computer program product for a computer, and/or by hardware. For executing their respective processing, correspondingly used devices, nodes, functions or network elements may include several means, modules, units, components, etc. (not shown) which are required for control, processing and/or communication/signaling functionality. Such means, modules, units and components may include, for example, one or more processors or processor units including one or more processing portions for executing instructions and/or programs and/or for processing data, storage or memory units or means for storing instructions, programs and/or data, for serving as a work area of the processor or processing portion and the like (e.g. ROM, RAM, EEPROM, and the like), input or interface means for inputting data and instructions by software (e.g. floppy disc, CD-ROM, EEPROM, and the like), a user interface for providing monitor and manipulation possibilities to a user (e.g. a screen, a keyboard and the like), other interface or means for establishing links and/or connections under the control of the processor unit or portion (e.g. wired and wireless interface means, radio interface means including e.g. an antenna unit or the like, means for forming a radio communication part etc.) and the like, wherein respective means forming an interface, such as a radio communication part, can be also located on a remote site (e.g. a radio head or a radio station etc.). It is to be noted that in the present specification processing portions should not be only considered to represent physical portions of one or more processors, but may also be considered as a logical division of the referred processing tasks performed by one or more processors.
It should be appreciated that according to some examples, a so-called “liquid” or flexible network concept may be employed where the operations and functionalities of a network element, a network function, or of another entity of the network, may be performed in different entities or functions, such as in a node, host or server, in a flexible manner. In other words, a “division of labor” between involved network elements, functions or entities may vary case by case.
According to at least some examples of embodiments, the idea underlying the present disclosure is to utilize 5G geolocation methods of the UE, such as MDT or OTDOA, which can be used to pinpoint where mobile devices, like UEs are in a cell and/or geographical area that is served by plurality of different cells. The UE spatial/geospatial location data is gathered over time, which creates a spatial-temporal data series of UE locations. It shall be noted that the spatial-temporal data series may also be statistics or predictions. This data could include drones/planes (i.e. drones/planes may be considered as UEs). This results in a 4-dimensional distribution function and geospatial “UE Gravity Map” which can then be used as input to other MnS including MDA functions to optimize network slices and other use cases.
The present disclosure introduces the notion of weighted STUD in the 3GPP management plane, which can be communicated between an MnS producer and an MnS consumer (which may be an external tenant), e.g. MDAS producer and MDAS consumer. Such STUD shall include meta data, which can assist in using it, e.g., in relation with network slicing and resource allocation. The proposed STUD applies per aggregated group of UEs creating a gravity map (“UE Gravity Map”) in geographical space (including the altitude dimension for drones) and time. The perception of weight can represent a UE group aggregated (i) throughput and/or (ii) Quality of Service (QoS) flow or sessions types or (iii) application types, or any combination thereof.
According to various examples of embodiments, in brief, the solution disclosed herein may in general (but not exclusively) follow the steps as outlined below. The solution addresses the problems as outlined above.
Location Information (first step): the Radio Access Network (RAN) sends UE location measurements to a management layer (Service Management Orchestrator, SMO).
Monitoring (second step): the SMO monitors and collects this (sent) data over time.
Analyzing (third step): the analytics layers may build a weighted spatial-temporal distribution function (weighted time series cumulative distribution function) or a prediction of a weighted spatial-temporal distribution function. The distribution function D(x,y,z), or cumulative distribution function (CDF) is the probability that the UE position given in variables X,Y,Z takes on a value less than or equal to a certain position:
It may be noted, however, that the use of a cartesian coordinate system is not obligatory, but a different coordinate system, like e.g. a spherical coordinate system, may in general be used as well.
Optimizing (fourth step): the Artificial Intelligence/Machine Learning (AI/ML) analytics can then determine and send optimal slice configurations.
Configuring (fifth step): then, slice configurations is sent (TS 28.541). Thus, UE slice assignments can be adjusted. Further, policies, root causes and analytic options can be provided to MDA consumers based on the distribution function. Also, concepts of Queuing theory are applicable in this context.
According to at least some examples of embodiments, the following points represent analytic requests and analytic reports relevant for the present specification.
The following is with regard to a slice request. Accordingly, the tenant may specify a network slice in a request adopting the expected STUD (that can be pre-calculated) per geographical area that can be represented by at least one distribution function including the following meta data:
The following is with regard to STUD prediction (representing an analytics request according to the present disclosure). Accordingly, an MDA type focusing on the creation of the STUD considering the following MDA request as input is provided herewith:
The following is with regard to resource optimization (representing an MDA report as disclosed herewith). Accordingly, a STUD aims to achieve resource optimization, e.g. for network slicing by adjusting configuration parameters (for e.g. network resource allocations). This represents an MDA report as disclosed herewith. Such STUD MDA report towards a MDA consumer should include the following attributes and meta data:
With reference to
With reference to
With reference to
With reference to
Hence, a spatial-temporal UE distribution is built up through the collection of location and weight data over time. A corresponding simplified example (for reasons of understandability only) is provided in
With reference to
In this regard, it is herewith referred to
Moreover, according to various examples of embodiments, there are disclosed several MDA Output and Measurements.
In this context, it shall be noted that the notion of the expression “similar” as applied directly above can be defined with a degree of similarity, e.g., based on (but not exclusively) at least one of a threshold, a geographical distance, a speed variation, and a deviation value.
According to various examples of embodiments, the below-outlined table summarizes the desired input for allowing MDA to compute the weighted STUD. Accordingly, two herewith disclosed Performance Measurements are new and novel, which are the handover-based UE flocking (handover-based mobile device flocking measurements) and weight-based UE flocking (weighted-based mobile device flocking measurements). The other measurements are used according to at least some examples of embodiments.
In the following, according to at least some examples of embodiments, there is described a data type report as disclosed herewith that is created in relation to the present disclosure, which that will be contributed to 3GPP TS28.104.
According to various examples of embodiments, the MDAS producer provides the following report with the analytics results to the MnS consumer:
Referring now to
Referring now to
There are a variety of use cases covered by TS 28.809, that STDU can assist as summarized below.
The above outlined solution for time-related allocation of network resources to a plurality of time-dependent distributed UEs as an example according to at least some examples of embodiments provides the following advantageous.
Namely, network slicing is touted to be a key technology differentiator for 5G. Allowing for more custom slices that are targeted or more appropriate for where UEs are located and how they behave will provide better service utilizing system resources optimally and help to optimize network resources, utilization and achieve SLA agreements. Resource optimality also helps operators to conserve resources, which can be used to serve more slices enhancing the profitability. Numerous potential use cases apply. The distribution function suggests that slices can be adjusted in a diurnal manner catering to the movement of UEs (that also cover Drones) and regular geospatial-temporal patterns identified. The geospatial aspect can also include aerial position.
Solutions available prior to the solution as disclosed herein would have had to rely on uniform assumptions of UE distribution, allocating the maximum amount of resources. So even the MDA analysis for resource allocation would be flawed. This specification introduces the way to represent shifting UE clustering and so more accurately represents a UE distribution allowing a more accurate way to allocate resources, which serves both resource conservation and service assurance.
In the following, further exemplary embodiments are described in relation to the above described methods and/or apparatuses.
Referring now to
In particular, according to
In S1120, there is developed, based on the plurality of time-related geospatial location information, a time series cumulative distribution function. It shall be noted that the time series cumulative distribution function indicates a probability that a geospatial location defined by its coordinate values of a respective one out of the plurality of mobile devices has coordinate values less than or equal to a selected geospatial location's coordinate values.
In S1130, configuration information for the access network element are generated. The generated configuration information is indicative of a time-related allocation of network resources to the plurality of mobile devices based on the time series cumulative distribution function.
In S1140, the configuration information is provided to the access network element.
Moreover, according to at least some examples of embodiments, the method may further comprise the steps of further collecting, from the access network element and/or a network management element, a plurality of time-related network resource requirement information associated to a respective one out of the plurality of mobile devices. It shall be noted that the plurality of time-related network resource requirement information indicate the respective mobile devices' network resource requirements at certain times. Additionally, the method further comprises developing, from the time series cumulative distribution function, a weighted time series cumulative distribution function, wherein a weighting of the time series cumulative distribution function is based on the plurality of time-related network resource requirement information. Moreover, there is comprised the method step of generating the configuration information to be further indicative of a weighted time-related allocation of network resources to the plurality of mobile devices based on the weighted time series cumulative distribution function.
Furthermore, according to various examples of embodiments, the method may further comprise the steps of predicting geospatial locations for the plurality of mobile devices based on one of predicted locations of a respective one out of the plurality of mobile devices or predicted locations of a respective group of at least some mobile devices out of the plurality of mobile devices, as well as generating the configuration information to be further indicative of the plurality of mobile device's predicted geospatial locations. It shall be noted that the prediction is based on applying predictive analysis on the time series cumulative distribution function or on the weighted time series cumulative distribution function. Alternatively, the prediction is based on developing an associated vectorised acceleration profile indicative of the respective mobile devices' accelerations in a geospatial direction based on applying predictive analysis on the time series cumulative distribution function or on the weighted time series cumulative distribution function.
Additionally, according to various examples of embodiments, the method may further comprise the steps of grouping, by using the time series cumulative distribution function or the weighted time series cumulative distribution function, at least some out of the plurality of mobile devices into groups of mobile devices based on at least one of:
Optionally, according to at least some examples of embodiments, the method may further comprise the steps of associating the time series cumulative distribution function or the weighted time series cumulative distribution function with at least one of the following types of meta data:
Moreover, according to various examples of embodiments, the method may further comprise the steps of requesting, from a core network management element, input data to be associated with the developed time series cumulative distribution function or the developed weighted time series cumulative distribution function, wherein the input data comprise at least one of information for
Moreover, according to at least some examples of embodiments, the method may further comprise the steps of providing, to a core network management element and/or a network consumer element, output data to be associated with the developed time series cumulative distribution function or the developed weighted time series cumulative distribution function, wherein the output data comprise at least one of information for
Moreover, according to at least some examples of embodiments, the method may further comprise the steps of developing a plurality of different time series cumulative distribution functions and/or a plurality of different weighted time series cumulative distribution functions. The method further comprises generating a plurality of different configuration information based on the plurality of different distribution functions, as well as providing the plurality of different configuration information to the access network element.
Furthermore, according to various examples of embodiments, wherein the configuration information may represent network slicing configuration information for time-related or weighted time-related adjustment of network slice characteristics, operation and behavior.
The above outlined solution for time-related and/or weighted time-related allocation of network resources and or network slices to a plurality of mobile devices based on a time-dependent distribution of the mobile devices as an example according to at least some examples of embodiments provides the following advantages (in addition to the advantages already outlined above).
Namely, an efficient and dynamic allocation of network resources and/or network slices is achieved, which allows to efficiently (resource optimal) achieve, for predetermined geospatial locations, desired service levels and/or desired data throughput. Moreover, most reliable network related communications based on available/allocated network resources (network resource capacities) may be achieved. In brief, the usage of available network resources may be optimized in relation to meeting time-related demands at geospatial locations by reducing/minimizing the risk of provided network resources remaining unused.
Referring now to
In particular, according to
Further, in S1220, the plurality of time-related geospatial location information is provided to a network management element.
Additionally, in S1230, there is obtained, from the network management element, configuration information indicative of a time-related allocation of network resources to the plurality of mobile devices.
Furthermore, in S1240, the network resources are configured according to the configuration information.
Moreover, according to at least some examples of embodiments, the method may further comprise the steps of obtaining the time-related geospatial location information for a respective one out of the plurality of mobile devices based on triggering a geolocation determination process at the respective mobile device and based on associating a timestamp to the geospatial location information obtained through the triggering.
Furthermore, according to various examples of embodiments, the method may further comprise the steps of further obtaining a plurality of time-related network resource requirement information associated to a plurality of mobile devices. It shall be noted that the plurality of time-related network resource requirement information indicate the respective mobile devices' network resource requirements at certain times. In addition, the method comprises further providing the plurality of time-related network resource requirement information to the network management element.
Additionally, according to various examples of embodiments, the method may further comprise the steps of obtaining the time-related network resource requirement information for a respective one out of the plurality of mobile devices based on triggering network resource requirement measurements at the respective mobile device and based on associating a timestamp to the network resource requirement information obtained through the triggering.
Further, according to various examples of embodiments, the method may further comprise the steps of further obtaining confidence degree information based on handover-based mobile device flocking measurements, indicating a number of handover processes related to the plurality of mobile devices within a preselected time interval towards an area served by the access network element with network resources; and/or weighted-based mobile device flocking measurements, indicating a number of modifications of network resource requirements related to the plurality of mobile devices within a preselected time interval in an area served by the access network element with network resources. The method further comprises providing the confidence degree information to the network management element.
Optionally, according to at least some examples of embodiments, the method may further comprise the steps of further configuring the network resources according to the configuration information, wherein the configuration information is further indicative of at least one of the following:
Moreover, according to at least some examples of embodiments, the time-related geospatial location information may indicate for a respective one out of the plurality of mobile devices geospatial location for a certain time in (x, y, z)-coordinates; and/or the time-related geospatial location information for a respective one out of the plurality of mobile devices may be obtained based on utilizing, as a geolocation determination method, Minimization of Drive Tests and/or Observed Time Different of Arrival and/or 5G functionality of Location Services and Location Management Function; and/or the time-related network resource requirement information for a respective one out of the plurality of mobile devices may be related to the respective mobile device's data throughput and/or quality of service flows; and/or the configuration of the network resources according to the configuration information may represent a time-related configuration for a dynamic allocation of network resources.
The above outlined solution for time-related allocation of network resources to a time-dependent distribution of mobile devices as an example according to at least some examples of embodiments provides the following advantages (in addition to the advantages already outlined above).
Namely, an efficient and dynamic allocation of network resources and/or network slices is achieved, which allows to efficiently (resource optimal) achieve, for predetermined geospatial locations, desired service levels and/or desired data throughput. Moreover, most reliable network related communications based on available/allocated network resources (network resource capacities) may be achieved. In brief, the usage of available network resources may be optimized in relation to meeting time-related demands at geospatial locations by reducing/minimizing the risk of provided network resources remaining unused.
Specifically,
The above described method, which may be implemented at a network management element like a SMO, thus provides, according to various examples of embodiments, a time-related allocation of network resources to time-dependent distributed mobile devices.
The apparatus 1300 shown in
The processor or processing function 1310 is configured to execute processing related to the above described processing. In particular, the processor or processing circuitry or function 1310 includes one or more of the following sub-portions. Sub-portion 1311 is a processing portion which is usable as a portion for collecting time-related geospatial location information. The portion 1311 may be configured to perform processing according to S1110 of
Referring now to
Specifically,
The above described method, which may be implemented at an access network element like a gNB and/or may be applied to a RAN, thus provides, according to various examples of embodiments, a time-related allocation of network resources to time-dependent distributed mobile devices.
The apparatus 1400 shown in
The processor or processing function 1410 is configured to execute processing related to the above described processing. In particular, the processor or processing circuitry or function 1410 includes one or more of the following sub-portions. Sub-portion 1411 is a processing portion which is usable as a portion for obtaining time-related geospatial location information. The portion 1411 may be configured to perform processing according to S1210 of
It should be appreciated that
Although the present disclosure has been described herein before with reference to particular embodiments thereof, the present disclosure is not limited thereto and various modifications can be made thereto.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2021/068501 | 7/5/2021 | WO |
