WO2023180881A1 - Prédiction de qualité de service (qos) pour un réseau de communication - Google Patents

Prédiction de qualité de service (qos) pour un réseau de communication Download PDF

Info

Publication number
WO2023180881A1
WO2023180881A1 PCT/IB2023/052587 IB2023052587W WO2023180881A1 WO 2023180881 A1 WO2023180881 A1 WO 2023180881A1 IB 2023052587 W IB2023052587 W IB 2023052587W WO 2023180881 A1 WO2023180881 A1 WO 2023180881A1
Authority
WO
WIPO (PCT)
Prior art keywords
network
nnfs
location
values
metrics
Prior art date
Application number
PCT/IB2023/052587
Other languages
English (en)
Inventor
Ricardo BLASCO SERRANO
Guido CARLO FERRANTE
Cara WATERMANN
Alexandros PALAIOS
Original Assignee
Telefonaktiebolaget Lm Ericsson (Publ)
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Telefonaktiebolaget Lm Ericsson (Publ) filed Critical Telefonaktiebolaget Lm Ericsson (Publ)
Publication of WO2023180881A1 publication Critical patent/WO2023180881A1/fr

Links

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/147—Network analysis or design for predicting network behaviour
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/16—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/142—Network analysis or design using statistical or mathematical methods
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/50—Network service management, e.g. ensuring proper service fulfilment according to agreements
    • H04L41/5003—Managing SLA; Interaction between SLA and QoS
    • H04L41/5009—Determining service level performance parameters or violations of service level contracts, e.g. violations of agreed response time or mean time between failures [MTBF]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/50—Network service management, e.g. ensuring proper service fulfilment according to agreements
    • H04L41/5032—Generating service level reports
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/50—Network service management, e.g. ensuring proper service fulfilment according to agreements
    • H04L41/5061—Network service management, e.g. ensuring proper service fulfilment according to agreements characterised by the interaction between service providers and their network customers, e.g. customer relationship management
    • H04L41/5067—Customer-centric QoS measurements
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters

Definitions

  • the present disclosure relates generally to the field of communication networks, and more specifically to techniques for predicting quality-of-service (QoS) that a user equipment (UE) will receive from a communication network (e.g., 5G wireless network) at various times and/or locations.
  • QoS quality-of-service
  • the fifth generation (“5G”) of cellular systems also referred to as New Radio (NR) was initially standardized 3GPP Rel-15 and continues to evolve in subsequent releases.
  • NR is developed for maximum flexibility to support a variety of different use cases including enhanced mobile broadband (eMBB), machine type communications (MTC), ultra-reliable low latency communications (URLLC), side-link device-to-device (D2D), and several other use cases.
  • eMBB enhanced mobile broadband
  • MTC machine type communications
  • URLLC ultra-reliable low latency communications
  • D2D side-link device-to-device
  • 5G/NR technology shares many similarities with fourth-generation LTE.
  • the performance of a communication network can be measured in terms of quality-of-service (QoS) delivered by the communication network or in terms of quality-of- experience (QoE) of an end user of the communication network.
  • QoS quality-of-service
  • QoE quality-of- experience
  • UL uplink
  • DL downlink
  • latency latency jitter
  • service availability packet loss
  • QoS framework a QoS framework
  • Adding equipment to improve network performance has worked well for best-effort services and for services with relatively relaxed requirements for latency, such as eMBB.
  • This approach has also worked well in controlled scenarios (e.g., factories or hot spots) for services with more strict performance requirements, such as small latency and/or latency jitter.
  • meeting relatively strict performance requirements over large areas and/or in uncontrolled environments can be very costly and/or technically challenging.
  • One use case affected by this problem is vehicular communication. Since vehicles cross areas with different topographies and different equipment deployments, it is difficult to ensure they receive QoS from the communication network at least a specified percentage of time throughout the coverage area.
  • remote (e.g., tele-operated) driving is an application of vehicular communications that typically combines UL transmission of a video stream and DL transmission driving commands.
  • Modern video codecs can adapt video quality and bit rate to available UL bandwidth.
  • the entire driving operation can be changed based on predicted QoS variations, such as decreasing the vehicle speed when the available UL bandwidth only permits a reduced video quality.
  • the route of the vehicle may be changed to avoid areas with network QoS performance is known or expected to be inadequate.
  • prediction QoS refers to the prediction of future QoS performance of the network and any adaptations resulting therefrom. Even so, predictive QoS has proved difficult for various reasons.
  • An object of embodiments of the present disclosure is to address one or more of these and related problems, issues, and/or difficulties by providing techniques and apparatus for predicting a service metric (S) that is representative of QoS provided by a communication network (e.g., 5G network).
  • S service metric
  • Some embodiments of the present disclosure include methods e.g., procedures) for predicting a service metric (S) that is representative of QoS provided by a communication network. These exemplary methods can include determining respective metrics (Li) that relate each of a plurality of values (Vs) of the service metric (S) to each of a plurality of values (VF) for one or more features (F) related to communication conditions in the communication network. These exemplary methods can also include determining one or more of the feature values (VF) that are associated with one or more location-time pairs (x, t) of interest.
  • These exemplary methods can also include, based on the determined feature values (VF) and the respective metrics (Li), determining respective metrics (L3) that relate each of the values (Vs) of the service metric (S) to each of the location-time pairs (x, t) of interest. These exemplary methods can also include predicting the service metric (S) at the one or more location-time pairs (x, t) of interest, based on the respective metrics (L3).
  • determining the respective metrics (Li) includes estimating a conditional probability function S
  • S conditional probability function
  • Vs the values (Vs) of the service metric (S) include binary indications of whether a desired service quality is met;
  • the one or more features (F) include channel quality and network load
  • VF different feature values
  • VF feature values
  • F) indicates the following metrics (Li):
  • the metrics (Li) are determined based on one or more datasets that include a plurality of entries, with each entry including one or more feature values (VF) and a corresponding value (Vs) of a service metric (S).
  • the metrics (Li) are determined during a training phase and then stored. Also, determining the respective metrics (L3) includes, during a subsequent prediction phase, retrieving stored metrics (Li) that are associated with the determined one or more feature values (VF).
  • these exemplary methods can also include determining respective metrics (L2) that relate at least one of the plurality of feature values (VF) to each of the plurality of location-time pairs (x, t).
  • determining the respective metrics (L3) is further based on the respective metrics (L2).
  • the metrics (L2) are determined based on one or more datasets having a plurality of entries, with each entry including one or more feature values (VF) associated with a particular location-time pair (x, t).
  • the metrics (L2) are determined based on the one or more datasets during a training phase and then stored.
  • determining the respective metrics (L3) includes, during a subsequent prediction phase, retrieving stored metrics (L2) that are associated with the location-time pairs (x, t) of interest.
  • determining the respective metrics (L2) includes estimating a probability function fp( ) that indicates respective likelihoods of different feature values (VF) at each of the plurality of location-time pairs (x, t). In such embodiments, determining the respective metrics (L3) is based on the estimated probability function In some of these embodiments, the following conditions apply:
  • the one or more features (F) include channel quality and network load
  • VF different feature values
  • VF feature values
  • the probability function fpf indicates the following metrics (L2):
  • these exemplary methods also includes training a neural network (NN) using one or more datasets having a plurality of entries.
  • the respective entries include one or more of the following associated with respective location-time pairs (x, t): one or more values (VF) of the features (F), and a value (Vs) of service metric (S).
  • determining the respective metrics (L3) includes applying the determined feature values (VF) at the location-time pairs (x, t) of interest as inputs to the trained NN.
  • the NN comprises a plurality of layers, including an input layer and an output layer.
  • the input layer includes a number of neurons that is less than or equal to a number of the features (F) related to the communication conditions.
  • the output layer includes a number of neurons that is less than or equal to a number of values (Vs) of the service metric (S).
  • the respective metrics (L3), for each particular location-time pair (x, t), are based on a probability function f s (s) that indicates likelihoods of all values (Vs) of the service metric (S) for the particular location-time pair (x, t).
  • determining the respective metrics (Li) is performed by a first NNFS
  • determining the one or more values (VF) is performed by a second NNFS
  • determining the respective metrics (L3) is performed by a third NNFS
  • predicting the service metric (S) is performed by a fourth NNFS.
  • at least one of the first through fourth NNFS is different than other of the first through fourth NNFS.
  • the second NNFS is a radio access network (RAN) node, such as a base station, eNB, gNB, etc.
  • RAN radio access network
  • determining the plurality of values (VF), by the second NNFS is performed based on the location-time pairs (x, t) of interest provided by the third NNFS.
  • determining the one or more values (VF) and determining the respective metrics (L3) are performed by the second and third NNFS, respectively, based on the location-time pairs (x, t) of interest provided by the fourth NNFS.
  • the one or more location-time pairs (x, t) of interest are associated with a route of a UE operating in the communication network.
  • these exemplary methods can also include adjusting one or more of the following based on the predicted service metric (S) at the one or more location-time pairs (x, t) of interest:
  • NNFS network nodes, functions, or services
  • Other embodiments include non-transitory, computer-readable media storing computerexecutable instructions that, when executed by processing circuitry, configure such NNFS to perform operations corresponding to the exemplary methods summarized above.
  • Figure 1 shows a high-level view of an exemplary 5G/NR network architecture.
  • Figure 2 shows an exemplary non-roaming 5G reference architecture with various network functions (NFs) that have service-based interfaces.
  • NFs network functions
  • Figure 3 shows an example scenario that illustrates, at a high level, various embodiments of QoS prediction techniques described herein.
  • Figure 4 shows a block diagram that illustrates, at a high level, various embodiments of QoS prediction techniques described herein.
  • FIGS 5-6 illustrate various aspects of an exemplary neural network (NN) that can be used in a QoS prediction procedure, according to various embodiments of the present disclosure.
  • FIGS 7-9 show various exemplary arrangements in which respective operations of a QoS prediction procedure are performed by different network nodes, functions, or services (NNFS), according to various embodiments of the present disclosure.
  • Figure 10 illustrates an exemplary method (e.g., procedure) for QoS prediction in a communication network, according to various embodiments of the present disclosure.
  • Figure 11 shows an exemplary communication network in which various embodiments of the present disclosure can be implemented.
  • Figures 12 shows an exemplary network node in which various embodiments of the present disclosure can be implemented
  • Figure 13 shows an exemplary host computing system in which various embodiments of the present disclosure can be implemented.
  • Figure 14 shows an exemplary virtualization environment in which various embodiments of the present disclosure can be implemented.
  • a “node” refers to any type of device or apparatus of that can operate in and/or communicate via a wired or wireless communication network, including but not limited to radio access network (RAN) nodes (e.g., base stations, eNBs, gNBs, etc.), core network nodes (e.g., MME, SGW, or a network node that hosts a network function), servers, gateways, UEs, etc. Examples of various nodes are described below with reference to various figures.
  • RAN radio access network
  • a “network node” is any node that is part of a RAN (e.g., a RAN node such as a base station name discussed above) or part of a core network e.g., a core network node discussed above) of a wireless network.
  • a network node is equipment capable, configured, arranged, and/or operable to communicate directly or indirectly with UEs and/or with other network nodes or equipment in the wireless network, to facilitate and/or provide wireless access to the UEs, to host/implement a network function, and/or to perform other functions (e.g., administration) in the wireless network.
  • the 5G System consists of an Access Network (AN) and a Core Network (CN).
  • the AN provides UEs connectivity to the CN, e.g., via base stations such as gNBs or ng-eNBs described below.
  • the CN includes a variety of Network Functions (NF) that provide a wide range of different functionalities such as session management, connection management, charging, authentication, etc.
  • NF Network Functions
  • FIG. 1 illustrates a high-level view of an exemplary 5G wireless network 100, which includes a Next Generation Radio Access Network (NG-RAN, 199) and a 5G Core (5GC, 198).
  • the NG-RAN can include one or more gNodeB’s (gNBs, e.g., 100, 152) connected to the 5GC via one or more NG interfaces (e.g., 102, 152). More specifically, gNBs can be connected to one or more access and mobility management functions (AMFs) in the 5GC via respective NG-C interfaces. Similarly, gNBs can be connected to one or more user plane functions (UPFs) in 5GC via respective NG-U interfaces.
  • Various other network functions (NFs) can be included in the 5GC, as described in more detail below.
  • each of the gNBs can be connected to each other via one or more Xn interfaces (e.g., 140 between gNBs 100 and 150).
  • the radio technology for the NG-RAN is often referred to as “New Radio” (NR).
  • NR New Radio
  • each of the gNBs can support frequency division duplexing (FDD), time division duplexing (TDD), or a combination thereof.
  • FDD frequency division duplexing
  • TDD time division duplexing
  • Each of the gNBs can serve a geographic coverage area including one or more cells and, in some cases, can also use various directional beams to provide coverage in the respective cells.
  • the NG-RAN is layered into a Radio Network Layer (RNL) and a Transport Network Layer (TNL).
  • RNL Radio Network Layer
  • TNL Transport Network Layer
  • the NG-RAN architecture i.e., the NG-RAN logical nodes and interfaces between them, is defined as part of the RNL.
  • NG, Xn, Fl the related TNL protocol and the functionality are specified.
  • the TNL provides services for user plane transport and signaling transport.
  • the NG RAN logical nodes shown in Figure 1 include a Central Unit (CU or gNB-CU) and one or more Distributed Units (DU or gNB-DU).
  • gNB 100 includes gNB-CU 110 and gNB-DUs 120 and 130.
  • CUs are logical nodes that host higher-layer protocols and perform various gNB functions such controlling the operation of DUs.
  • DUs are decentralized logical node that hosts lower layer protocols and can include, depending on the functional split option, various subsets of the gNB functions.
  • each CU or DU can include various circuitry needed to perform their respective functions, including processing circuitry, transceiver circuitry e.g., for communication), and power supply circuitry.
  • a gNB-CU connects to one or more gNB-DUs over respective Fl logical interfaces (e.g., 122, 132).
  • a gNB-DU can be connected to only a single gNB-CU.
  • the gNB-CU and connected gNB-DU(s) are only visible to other gNBs and the 5GC as a gNB. In other words, the Fl interface is not visible beyond gNB-CU.
  • 5G wireless networks e.g., in 5GC
  • SBA Service Based Architecture
  • NFs Network Functions
  • HTTP/REST Hyper Text Transfer Protocol/Representational State Transfer
  • APIs application programming interfaces
  • the services are composed of various “service operations”, which are more granular divisions of the overall service functionality.
  • the interactions between service consumers and producers can be of the type “request/response” or “subscribe/notify”.
  • network repository functions (NRF) allow every network function to discover the services offered by other network functions
  • DFS Data Storage Functions
  • This 5G SBA model is based on principles including modularity, reusability and self-containment of NFs, which can enable network deployments to take advantage of the latest virtualization and software technologies.
  • FIG. 2 shows an exemplary non-roaming architecture for a 5G wireless network (200) with service-based interfaces.
  • This architecture includes the following NFs:
  • Application Function interacts with the 5GC to provision information to the network operator and to subscribe to certain events happening in operator's network.
  • An AF offers applications for which service is delivered in a different layer (i.e., transport layer) than the one in which the service has been requested (i.e., signaling layer), the control of flow resources according to what has been negotiated with the network.
  • An AF communicates dynamic session information to PCF (via N5 interface), including description of media to be delivered by transport layer.
  • PCF Policy Control Function
  • Npcf interface supports unified policy framework to govern the network behavior, via providing PCC rules (e.g., on the treatment of each service data flow that is under PCC control) to the SMF via the N7 reference point.
  • PCF provides policy control decisions and flow based charging control, including service data flow detection, gating, QoS, and flow-based charging (except credit management) towards the SMF.
  • the PCF receives session and media related information from the AF and informs the AF of traffic (or user) plane events.
  • User Plane Function supports handling of user plane traffic based on the rules received from SMF, including packet inspection and different enforcement actions (e.g., event detection/reporting, QoS handling, etc.).
  • UPFs communicate with the RAN (e.g., NG-RAN/gNBs) via the N3 reference point, with SMFs (discussed below) via the N4 reference point, and with an external packet data network (PDN) via the N6 reference point.
  • the N9 reference point is for communication between two UPFs.
  • Session Management Function interacts with the decoupled traffic (or user) plane, including creating, updating, and removing Protocol Data Unit (PDU) sessions and managing session context with the User Plane Function (UPF), e.g., for event reporting.
  • SMF performs data flow detection (based on filter definitions included in PCC rules), online and offline charging interactions, and policy enforcement.
  • Charging Function (CHF, with Nchf interface) is responsible for converged online charging and offline charging functionalities. It provides quota management (for online charging), re-authorization triggers, rating conditions, etc. and is notified about usage reports from the SMF. Quota management involves granting a specific number of units (e.g., bytes, seconds) for a service. CHF also interacts with billing systems.
  • Access and Mobility Management Function terminates the RAN CP interface and handles all mobility and connection management of UEs (similar to MME in EPC).
  • AMFs communicate with UEs via the N1 reference point and with the RAN (e.g., NG-RAN) via the N2 reference point.
  • NEF Network Exposure Function
  • Nnef interface - acts as the entry point into operator's network, by securely exposing to AFs the network capabilities and events provided by 3GPP NFs and by providing ways for the AF to securely provide information to 3GPP network.
  • NEF provides a service that allows an AF to provision specific subscription data (e.g., expected UE behavior) for various UEs.
  • NRF Network Repository Function
  • Network Slice Selection Function with Nnssf interface - a “network slice” is a logical partition of a 5G network that provides specific network capabilities and characteristics, e.g., in support of a particular service.
  • a network slice instance is a set of NF instances and the required network resources (e.g., compute, storage, communication) that provide the capabilities and characteristics of the network slice.
  • the NSSF enables other NFs (e.g., AMF) to identify a network slice instance that is appropriate for a UE’s desired service.
  • AUSF Authentication Server Function
  • HPLMN home network
  • Network Data Analytics Function (NWDAF, 210) with Nnwdaf interface - interacts with other NFs to collect relevant data and provides network analytics information (e.g., statistical information of past events and/or predictive information) to other NFs.
  • NWAF Network Data Analytics Function
  • Location Management Function with Nlmf interface - supports various functions related to determination of UE locations, including location determination for a UE and obtaining any of the following: DL location measurements or a location estimate from the UE; UL location measurements from the NG RAN; and non-UE associated assistance data from the NG RAN.
  • the Unified Data Management (UDM) function supports generation of 3GPP authentication credentials, user identification handling, access authorization based on subscription data, and other subscriber-related functions. To provide this functionality, the UDM uses subscription data (including authentication data) stored in the 5GC unified data repository (UDR). In addition to the UDM, the UDR supports storage and retrieval of policy data by the PCF, as well as storage and retrieval of application data by NEF.
  • the NRF allows every NF to discover the services offered by other NFs, and Data Storage Functions (DSF) allow every NF to store its context.
  • DSF Data Storage Functions
  • the NEF provides exposure of capabilities and events of the 5GC to AFs within and outside of the 5GC. For example, NEF provides a service that allows an AF to provision specific subscription data (e.g., expected UE behavior) for various UEs.
  • Densification can be used to increase, improve, and/or maintain more consistent QoS for a 5G network deployment.
  • additional gNBs can be added to increase capacity in certain coverage areas and/or frequency bands.
  • more NF instances e.g., UPFs
  • UPFs can be added to increase data throughput.
  • various services and/or use cases can adapt to 5G network QoS variations that can be predicted in advanced.
  • remote (e.g., tele-operated) driving which is a vehicular communications application that typically combines UL transmission of a video stream and DL transmission of driving commands.
  • Modern video codecs can adapt video quality and bit rate to UL bandwidth available according to actual or predicted QoS.
  • the entire driving operation can be adapted based on predicted QoS variations, such as by decreasing vehicle speed when the available UL bandwidth only permits a reduced video quality.
  • the route of the vehicle may be changed (e.g., with new driving commands) to avoid areas where network QoS performance is known or predicted to be inadequate.
  • existing QoS prediction techniques are inadequate for these and other relevant use cases.
  • existing QoS predictions typically have poor resolution, e.g., whether a desired QoS level will be met, one of a small number of QoS “grades”, etc.
  • a prediction that a desired QoS level will not be met may be insufficient information for demanding use cases that require a prediction of QoS level that will be provided.
  • QoS typically requires performing extensive, system-wide, service-specific measurement campaigns to generate data from which predictions can be made. These campaigns are costly, time consuming, and may not be feasible for large networks that have already been deployed. Many of the conducted QoS studies assume access to data without considering the costs of acquiring the data.
  • embodiments of the present disclosure overcome these problems, issues, and/or difficulties by providing techniques for predicting QoS to be experienced by a UE in a particular location at a particular future time.
  • these techniques determine information related to a communication condition (e.g., whether or not a channel quality is adequate, whether a network is loaded) at the particular position and time.
  • the communication condition can also be referred to as a “feature,” as explained in more detail below.
  • the feature-related information is a likelihood that the communication condition will occur at the particular position and time.
  • the feature-related information is data (e.g., raw, collected, etc.) associated with and/or related to the communication condition.
  • the data can have a probability density function (PDF) from which the likelihood of occurrence can be derived, as in the first variant.
  • PDF probability density function
  • the techniques also determine likelihoods for different values of a service -related metric S (e.g., a QoS KPI) that correspond to different values of the communication condition or feature.
  • a service -related metric S e.g., a QoS KPI
  • the metric S has an X% likelihood of having a value Y (e.g., acceptable, non- acceptable) when the feature has a value of Z.
  • the techniques determine respective likelihoods for values of metric S at various locations and (future) times based combining the feature-related information and the likelihoods previously determined. The resulting likelihoods can then be used for making QoS predictions.
  • these techniques can provide various benefits, advantages, and/or solutions to problems described herein. For example, these techniques split a complex problem of predicting service quality into three steps, simplifying the implementation. The use of three steps reduces the need for system-wide, extensive, service-specific measurement campaigns. Rather, it merely requires: 1) a service-specific measurement campaign performed on a small but representative part of the system (e.g., RAN, CN, UE, application, etc.); and 2) a system- wide understanding of operating conditions in different location at different times. This information may be gathered opportunistically by the network as a by-product or typical operation, by crowdsourcing, etc.
  • a service-specific measurement campaign performed on a small but representative part of the system (e.g., RAN, CN, UE, application, etc.); and 2) a system- wide understanding of operating conditions in different location at different times. This information may be gathered opportunistically by the network as a by-product or typical operation, by crowdsourcing, etc.
  • FIG. 3 shows an example scenario that illustrates various embodiments at a high level.
  • QoS is predicted for two different locations and times:
  • each of the possible values of a feature is assigned a likelihood for a particular position and time.
  • the feature is typically a measurable quantity representing a communication condition that influences the QoS of interest, when occurring at the particular position and time.
  • Example features include channel quality, signal strength, interference, network load or congestion, etc. In the context of the example scenario shown in Figure 3, it is determined that:
  • a probability function ⁇ (F) can describe the likelihood of the values of one or more features (F) for a location/time pair (x, t).
  • the probability function / can also be written as f ; ( x ,t)( ) to emphasize the dependency on (x, t).
  • the probability function/ can be a probability density function (PDF), a probability mass function, etc.
  • PDF probability density function
  • metric L2 that measures the likelihood of VF occurring at (x, t).
  • the metric L2 can be determined from the probability function. More generally, the metric L2 relates feature value VF to the particular location-time pairs (x, t).
  • the feature-related information is data (e.g., raw, collected, etc.) associated with the feature.
  • the data can be measurements representing a communication condition that influences the QoS of interest, such as measurements of channel quality, signal strength, interference, network load or congestion, etc.
  • the relevant data is the data (e.g., measurements) associated with the feature at location-time pair (x, t).
  • the data can be associated with probability function / from which a likelihood of occurrence can be derived, as in the above-described embodiments, but it is not necessary to determine the probability function in these embodiments. In other words, the probability function/of the data can be considered unknown in these embodiments.
  • the techniques also determine likelihoods for different values of a service-related metric S (e.g., a QoS KPI) that correspond to different values of the one or more features (F).
  • a service-related metric S e.g., a QoS KPI
  • F) can describe likelihood for values of service metric S conditioned on different values of the feature(s) F.
  • the conditional probability function can be a conditional probability density function, a conditional probability mass function, etc.
  • the metric Li may be associated with a single (x, t) pair, a plurality of (x, t) pairs, or not associated with any particular (x, t) pair. More generally, the metric Li relates the particular value Vs of service metric S to the particular value VF of feature F.
  • the techniques also determine likelihoods for different values of the service-related metric S (e.g., a QoS KPI) at the various location-time pairs (x, t). In general, this is done by combining the previously determined feature -related information and the conditional probability function /S
  • s (s) can be a PDF, a probability mass function, etc. according to its constituent probability functions.
  • these operations determine a metric (called L3) that relates a value Vs of service metric S to a particular location-time pair (x, t), e.g., how likely it is that a service requirement is met at (x, t).
  • L3 s(Ps) for various location-time pairs (x, t).
  • S service metric
  • an example prediction for the service metric is S > 10 Mb/s with 90% probability.
  • metric L3 can include likelihoods for every value of S, i.e., f(S). In such case, predicting whether the service metric S > 10 Mb/s can be based on computing (or estimating) the integral of f(S) over the range 10 ⁇ S ⁇ infinity.
  • Figure 4 shows a block diagram that illustrates various embodiments of the techniques at a high level. Operation 1 involves estimation and/or determination of metric Li based on the conditional probability function s
  • Operations 1-2 can be performed in either order (or even concurrently) and can be performed using the same data, different data, overlapping data, etc.
  • Operation 3 involves estimation and/or determination of metric L3 based on the probability function f s (s) , using inputs from operations 1-2. This prediction output can be provided to a UE, a network node, a NF, an AF, etc.
  • the feature-related information can include the probability function fp( ") determined in operation 1.
  • F) and fp( ") in operation 3 can collect different combinations of values for these functions.
  • the feature -related information can be data (e.g., raw, collected, etc.) associated with the feature and the probability function.
  • the integration operation above is merely a theoretical representation and f s (s) numerically approximated by repeatedly using a neural network (NN) over the data associated with the feature for a particular location-time pair (x, t) and averaging the results.
  • NN neural network
  • Figures 5-6 illustrate various aspects of an exemplary NN that can be used in these embodiments of the present disclosure.
  • Figure 5 shows the three primary operations performed by the NN, namely data analysis/feature extraction, training, and prediction.
  • feature extraction is performed on raw data to extract parts that are relevant to the features of interest at location-time pairs of interest.
  • the data includes features (e.g., measurements, KPIs, etc.) and corresponding labels (e.g., measured QoS associated with the measurements, KPIs) associated with different location-time pairs.
  • the NN is used on data that includes only features.
  • the NN classifies the feature to a label using probabilistic (soft) values, e.g., the output is the probability (or likelihood) that each input feature corresponds to a particular label.
  • the NN is run on each piece of data separately and all likelihoods are stored.
  • the predicted likelihoods are averaged.
  • a running average of the likelihoods can be computed at the previous step as new data is processed.
  • Figure 6 shows an exemplary architecture for a NN that can be used in these embodiments of the present disclosure.
  • This architecture has layers 1-N, with features being input to layer 1 and the prediction(s) being output from layer N.
  • the number of neurons in layer 1 is equal to the dimensionality of the state space, i.e., the number of features of interest.
  • the number of neurons in layer N is less than or equal to the dimensionality of the prediction space. For example, if the prediction is binary (e.g., service satisfactory or not), then the number of neurons in the last layer is (at most) two.
  • the number of layers (N), the number of neurons per layer, and non-linearities with the layers are tuneable parameters.
  • one or more metrics L3 may be used to predict a service metric (S, e.g., QoS KPI), such as described above.
  • S service metric
  • operation 3 described above may be executed before the service metric prediction or as part of the service metric prediction.
  • the prediction may be requested by a UE, an application or user of the UE, a network node, a function of a network node (e.g., a gNB scheduler), a NF (e.g., UPF), an AF, an exposure function (e.g., NEF, SCEF), an application or AF (e.g., running on a cloud server), etc.
  • a service metric e.g., QoS KPI
  • operation 3 described above may be executed before the service metric prediction or as part of the service metric prediction.
  • the prediction may be requested by a UE, an application or user of the UE, a network node, a function of a network node (e.g
  • the request can include information relevant for the prediction.
  • the request can include one or more location-time pairs (x, t) of interest (e.g., a route or part thereof).
  • the prediction can be performed for each of the requested location-time pairs.
  • the prediction may be associated with a subscription model.
  • the requester may subscribe to a network service that provides predictions in response to some condition (e.g., timer expiration, predefined and/or periodic scheduled, predefined changed in conditions, etc.) being fulfilled.
  • some condition e.g., timer expiration, predefined and/or periodic scheduled, predefined changed in conditions, etc.
  • the corresponding location and time (x, t) may be provided periodically, based on conditions, on request, etc.
  • operations 1-2 described above may be performed independent from operation 3.
  • one or more of operations 1-2 can be performed as part of a training or initialization procedure or phase, in advance of any prediction requests.
  • operations 1-2 can be performed as part of an initialization/training procedure requiring dedicated measurements.
  • operations 1-2 can be performed as part of a training procedure using measurements obtained opportunistically from UEs in the network (i.e., not associated with a dedicated measurement campaign) and/or using data from a measurement campaign.
  • one or more of operations 1-2 can be performed during regular operations of the network, without a corresponding request for prediction, which may come at an undetermined time in the future).
  • observed values for metric S together with corresponding observed values VF for a certain feature may be collected during normal network operations (e.g., Si observed together with Vi, S2 observed together with V2, etc.). All these measurements may be used to determine likelihood value Li.
  • observed values VF for a certain feature may be collected at different location-time pairs (x, t). For instance, a first user measures value V a at (x, t), a second user measures value Vb at (x, t), etc. All these measurements may be used to determine likelihood value L2.
  • the values V s of metric S may be limited to a small and/or discrete set (e.g., whether or not a data rate is below a threshold; whether or not a latency is below a threshold; whether or not the service meets a requirement, etc.). In other embodiments, the values V s of metric S may be continuous or at least include a relatively large number of values (e.g., with high resolution). In some embodiments, a multi-dimensional set of features can be used for the prediction; as discussed above, the dimensionality of the feature set can affect the complexity of probability function estimation in certain embodiments.
  • operations 1-3 can be performed by the same network node, function, or service (NNFS) or one or more of operations 1-3 may be performed by different NNFS than others of operations 1-3.
  • the predictor (operation 3) can be performed by a RAN node (e.g., base station, gNB, etc.), a NF in a core network (e.g., UPF in 5GC), a server or AF in a cloud environment, etc.
  • Figures 7-9 show various exemplary arrangements in which the respective operations are performed by different NNFS.
  • a first NNFS (710) performs operation 1 that produces metric Li
  • a second NNFS (720) performs operation 2 that produces the feature- related information
  • a third NNFS (730) performs operation 3 that produces metric L3 based on the outputs of operations 1-2.
  • the third NNFS may receive the outputs of operations 1-2 directly from the first and second NNFS or through an exposure function (e.g., NEF, via subscription).
  • the third NNFS may also use the location-time pairs of interest (x, t) to select relevant data from a training dataset.
  • the second NNFS (e.g., RAN node) can determine the feature- related information (e.g., data or metric L2) based on its own measurements and/or measurements reported by UEs.
  • the first NNFS can determine metric Li based on data collected from other network nodes, NFs, services, etc. including but not limited to the second and third NNFS shown in the figures.
  • a fourth NNFS (740) may generate the service metric prediction (S) based on metric L3 output by the third NNFS.
  • the prediction (after step 3) may be performed by a different entity (i.e., a fourth entity) that gathers the third likelihood metric from the third entity, directly or indirectly (e.g., through an exposure function).
  • the predictor may be a different entity than the one executing operation 3. This entity receives the corresponding information (e.g., metric L3) and makes use of it to make a prediction.
  • the third NNFS provides the location-time pairs of interest (x, t) to the second NNFS to facilitate the second NNFS selection and/or determination of feature -related information relevant to the prediction.
  • the second NNFS may receive this information from the same source as the third NNFS.
  • the fourth NNFS provides the location-time pairs of interest (x, t) to the second and third NNFS to facilitate the second NNFS selection and/or determination of feature-related information relevant to the prediction, as well as the third NNFS determination of metric L3).
  • the second and third NNFS may receive this information from the same source as the fourth NNFS.
  • Figure 10 depicts an exemplary method e.g., procedures) for predicting a service metric (S) that is representative of quality of service (QoS) provided by a communication network, according to various embodiments of the present disclosure.
  • S service metric
  • QoS quality of service
  • various features of the operations described below correspond to various embodiments described above.
  • the exemplary method is illustrated in Figure 10 by specific blocks in a particular order, the operations corresponding to the blocks can be performed in a different order than shown and can be combined and/or divided into blocks having different functionality than shown. Optional blocks and/or operations are indicated by dashed lines.
  • the exemplary method illustrated by Figure 10 can be performed by one or more NNFS of the communication network (e.g., 5G wireless network), such as described in the examples above.
  • NNFS the communication network
  • the singular term “NNFS” refers to one or more NNFS unless otherwise noted.
  • the exemplary method can include the operations of block 1010, where the NNFS can determine respective metrics (Li) that relate each of a plurality of values (Vs) of the service metric (S) to each of a plurality of values (VF) for one or more features (F) related to communication conditions in the communication network.
  • the exemplary method can also include the operations of block 1040, where the NNFS can determine one or more of the feature values (VF) that are associated with one or more location-time pairs (x, t) of interest.
  • the exemplary method can also include the operations of block 1050, where the NNFS can, based on the determined feature values (VF) and the respective metrics (Li), determine respective metrics (L3) that relate each of the values (Vs) of the service metric (S) to each of the locationtime pairs (x, t) of interest.
  • the exemplary method can also include the operations of block 1060, where the NNFS can predict the service metric (S) at the one or more location-time pairs (x, t) of interest, based on the respective metrics (L3).
  • determining the respective metrics (Li) in block 1010 includes the operations of sub-block 1011, where the NNFS can estimate a conditional probability function S
  • S conditional probability function
  • Vs the values (Vs) of the service metric (S) include binary indications of whether a desired service quality is met;
  • the one or more features (F) include channel quality and network load
  • VF different feature values
  • VF different feature values
  • F) indicates the following metrics (Li):
  • the metrics (Li) are determined based on one or more datasets that include a plurality of entries, with each entry including one or more feature values (VF) and a corresponding value (Vs) of a service metric (S).
  • the metrics (Li) are determined during a training phase and then stored.
  • determining the respective metrics (L3) in block 1050 includes the operations of sub-block 1051, where during a subsequent prediction phase, the NNFS can retrieve stored metrics (Li) that are associated with the determined one or more feature values (VF).
  • the exemplary method can also include the operations of block 1020, where the NNFS can determine respective metrics (L2) that relate at least one of the plurality of feature values (VF) to each of the plurality of location-time pairs (x, t). In such embodiments, determining the respective metrics (L3) in block 1050 is further based on the respective metrics (L2).
  • the metrics (L2) are determined based on one or more datasets having a plurality of entries, with each entry including one or more feature values (VF) associated with a particular location-time pair (x, t). In some variants, the metrics (L2) are determined based on the one or more datasets during a training phase and then stored. Also, determining the respective metrics (L3) in block 1050 includes the operations of sub-block 1052, where during a subsequent prediction phase, the NNFS can retrieve stored metrics (L2) that are associated with the location-time pairs (x, t) of interest.
  • determining the respective metrics (L2) in block 1020 includes the operations of sub-block 1021, where the NNFS can estimate a probability function f ( ") that indicates respective likelihoods of different feature values (VF) at each of the plurality of location-time pairs (x, t).
  • determining the respective metrics (L3) in block 1050 is based on the estimated probability function fp( ). In some of these embodiments, the following conditions apply:
  • the one or more features (F) include channel quality and network load
  • VF different feature values
  • VF feature values
  • the probability function f F (F) indicates the following metrics (L2):
  • the exemplary method also includes the operations of block 1030, where the NNFS can train a neural network (NN) using one or more datasets having a plurality of entries.
  • the respective entries include one or more of the following associated with respective location-time pairs (x, t): one or more values (VF) of the features (F), and a value (Vs) of service metric (S).
  • determining the respective metrics (L3) in block 1050 includes the operations of sub-block 1051, where the NNFS can apply the determined feature values (VF) at the location-time pairs (x, t) of interest as inputs to the trained NN.
  • the NN comprises a plurality of layers, including an input layer and an output layer.
  • the input layer includes a number of neurons that is less than or equal to a number of the features (F) related to the communication conditions.
  • the output layer includes a number of neurons that is less than or equal to a number of values (Vs) of the service metric (S).
  • the respective metrics (L3), for each particular location-time pair (x, t), are based on a probability function f s (s) that indicates likelihoods of all values (Vs) of the service metric (S) for the particular location-time pair (x, t).
  • each of the metrics (Li) is associated with one of the following: a single location-time pair, a plurality of location-time pairs, or no location-time pair.
  • the one or more features (F) include at least two of the following: channel quality, signal strength, interference, and network load.
  • determining the respective metrics (Li) in block 1010 is performed by a first NNFS of the communication network
  • determining the one or more values (VF) in block 1040 is performed by a second NNFS of the communication network
  • determining the respective metrics (L3) in block 1050 is performed by a third NNFS of the communication network
  • predicting the service metric (S) in block 1060 is performed by a fourth NNFS of the communication network.
  • at least one of the first through fourth NNFS is different than other of the first through fourth NNFS.
  • Figures 7-9 show examples of these embodiments.
  • the second NNFS (i.e., that performs operations of block 1040) is a RAN node, such as a base station, eNB, gNB, etc.
  • determining the plurality of values (VF), by the second NNFS is performed based on the location-time pairs (x, t) of interest provided by the third NNFS.
  • Figure 8 shows an example of these embodiments.
  • determining the one or more values (VF) and determining the respective metrics (L3) are performed by the second and third NNFS, respectively, based on the location-time pairs (x, t) of interest provided by the fourth NNFS.
  • Figure 9 shows an example of these embodiments.
  • the one or more location-time pairs (x, t) of interest are associated with a route of a UE operating in the communication network.
  • the exemplary method can also include the operations of block 1070, where the NNFS can adjust one or more of the following based on the predicted service metric (S) at the one or more location-time pairs (x, t) of interest:
  • FIG. 11 shows an example of a communication system 1100 in accordance with some embodiments.
  • communication system 1100 includes a telecommunication network 1102 that includes an access network 1104 (e.g., RAN) and a core network 1106, which includes one or more core network nodes 1108.
  • Access network 1104 includes one or more access network nodes, such as network nodes l l lOa-b (one or more of which may be generally referred to as network nodes 1110), or any other similar 3GPP access node or non-3GPP access point.
  • Network nodes 1110 facilitate direct or indirect connection of UEs, such as by connecting UEs 1112a-d (one or more of which may be generally referred to as UEs 1112) to core network 1106 over one or more wireless connections.
  • 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.
  • communication system 1100 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.
  • Communication system 1100 may include and/or interface with any type of communication, telecommunication, data, cellular, radio network, and/or other similar type of system.
  • UEs 1112 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and/or operable to communicate wirelessly with network nodes 1110 and other communication devices.
  • network nodes 1110 are arranged, capable, configured, and/or operable to communicate directly or indirectly with UEs 1112 and/or with other network nodes or equipment in telecommunication network 1102 to enable and/or provide network access, such as wireless network access, and/or to perform other functions, such as administration in telecommunication network 1102.
  • core network 1106 connects network nodes 1110 to one or more hosts, such as host 1116. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts.
  • Core network 1106 includes one more core network nodes (e.g., core network node 1108) 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 core network node 1108.
  • Example core network nodes include functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier De-concealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and/or a User Plane Function (UPF).
  • MSC Mobile Switching Center
  • MME Mobility Management Entity
  • HSS Home Subscriber Server
  • AMF Access and Mobility Management Function
  • SMF Session Management Function
  • AUSF Authentication Server Function
  • SIDF Subscription Identifier De-concealing function
  • UDM Unified Data Management
  • SEPP Security Edge Protection Proxy
  • NEF Network Exposure Function
  • UPF User Plane Function
  • Host 1116 may be under the ownership or control of a service provider other than an operator or provider of access network 1104 and/or telecommunication network 1102, and may be operated by the service provider or on behalf of the service provider.
  • Host 1116 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.
  • one or more of network nodes l l lOa-b, core network node 1108, and host 1116 can perform operations corresponding to any of the exemplary methods or procedures described above in relation to Figures 4-10.
  • each network node 1110, core network node 1108, or host 1116 can perform any or all of the operations associated with the first through fourth network nodes, functions, or service (NNFS) described above.
  • host 1116 may be part of a cloud computing apparatus, system, or environment.
  • communication system 1100 of Figure 11 enables connectivity between the UEs, network nodes, and hosts.
  • 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); Fong 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.
  • GSM Global System for Mobile Communications
  • UMTS Universal Mobile Telecommunications System
  • LTE Long Term Evolution
  • telecommunication network 1102 is a cellular network that implements 3GPP standardized features. Accordingly, telecommunication network 1102 may support network slicing to provide different logical networks to different devices that are connected to telecommunication network 1102. For example, telecommunication network 1102 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.
  • URLLC Ultra Reliable Low Latency Communication
  • eMBB Enhanced Mobile Broadband
  • mMTC Massive Machine Type Communication
  • UEs 1112 are configured to transmit and/or receive information without direct human interaction.
  • a UE may be designed to transmit information to access network 1104 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from access network 1104.
  • a UE may be configured for operating in single- or multi-RAT or multi-standard mode.
  • 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).
  • MR-DC multi-radio dual connectivity
  • hub 1114 communicates with access network 1104 to facilitate indirect communication between one or more UEs (e.g., UE 1112c and/or 1112d) and network nodes (e.g., network node 1110b).
  • hub 1114 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs.
  • hub 1114 may be a broadband router enabling access to core network 1106 for the UEs.
  • hub 1114 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 1110, or by executable code, script, process, or other instructions in hub 1114.
  • hub 1114 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.
  • hub 1114 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, hub 1114 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which hub 1114 then provides to the UE either directly, after performing local processing, and/or after adding additional local content.
  • hub 1114 acts as a proxy server or orchestrator for the UEs, in particular in if one or more of the UEs are low energy loT devices.
  • Hub 1114 may have a constant/persistent or intermittent connection to network node 1110b. Hub 1114 may also allow for a different communication scheme and/or schedule between hub 1114 and UEs (e.g., UE 1112c and/or 1112d), and between hub 1114 and core network 1106. In other examples, hub 1114 is connected to core network 1106 and/or one or more UEs via a wired connection. Moreover, hub 1114 may be configured to connect to an M2M service provider over access network 1104 and/or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with network nodes 1110 while still connected via hub 1114 via a wired or wireless connection.
  • UEs may establish a wireless connection with network nodes 1110 while still connected via hub 1114 via a wired or wireless connection.
  • hub 1114 may be a dedicated hub - that is, a hub whose primary function is to route communications to/from the UEs from/to network node 1110b.
  • hub 1114 may be a nondedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 1110b, but which is additionally capable of operating as a communication start and/or end point for certain data channels.
  • FIG 12 shows a network node 1200 in accordance with some embodiments.
  • network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (e.g., radio base stations, Node Bs, eNBs, and gNBs).
  • APs access points
  • base stations e.g., radio base stations, Node Bs, eNBs, and gNBs.
  • 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 Test (MDTs).
  • network nodes include any network node that hosts a network function (NF), such as any of the NFs that were described above.
  • NF network function
  • Network node 1200 includes a processing circuitry 1202, a memory 1204, a communication interface 1206, and a power source 1208.
  • Network node 1200 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.
  • network node 1200 comprises multiple separate components (e.g., BTS and BSC components)
  • one or more of the separate components may be shared among several network nodes.
  • a single RNC may control multiple NodeBs.
  • each unique NodeB and RNC pair may in some instances be considered a single separate network node.
  • network node 1200 may be configured to support multiple radio access technologies (RATs).
  • RATs radio access technologies
  • some components may be duplicated (e.g., separate memory 1204 for different RATs) and some components may be reused (e.g., a same antenna 1210 may be shared by different RATs).
  • Network node 1200 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 1200, 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 1200.
  • RFID Radio Frequency Identification
  • Processing circuitry 1202 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 1200 components, such as memory 1204, to provide network node 1200 functionality.
  • processing circuitry 1202 includes a system on a chip (SOC). In some embodiments, processing circuitry 1202 includes one or more of radio frequency (RF) transceiver circuitry 1212 and baseband processing circuitry 1214. In some embodiments, the radio frequency (RF) transceiver circuitry 1212 and baseband processing circuitry 1214 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 1212 and baseband processing circuitry 1214 may be on the same chip or set of chips, boards, or units.
  • SOC system on a chip
  • processing circuitry 1202 includes one or more of radio frequency (RF) transceiver circuitry 1212 and baseband processing circuitry 1214.
  • the radio frequency (RF) transceiver circuitry 1212 and baseband processing circuitry 1214 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
  • Memory 1204 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 (collectively denoted computer program 1204a, which may be in the form of a computer program product) that may be used by processing circuitry 1202.
  • RAM random access memory
  • ROM read-only memory
  • 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)
  • computer program 1204a which may be in the form of a computer program product
  • Memory 1204 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 processing circuitry 1202 and utilized by network node 1200. Memory 1204 may be used to store any calculations made by processing circuitry 1202 and/or any data received via communication interface 1206. In some embodiments, processing circuitry 1202 and memory 1204 is integrated.
  • Communication interface 1206 is used in wired or wireless communication of signaling and/or data between a network node, access network, and/or UE. As illustrated, communication interface 1206 comprises port(s)/terminal(s) 1216 to send and receive data, for example to and from a network over a wired connection. Communication interface 1206 also includes radio front-end circuitry 1218 that may be coupled to, or in certain embodiments a part of, antenna 1210. Radio front-end circuitry 1218 comprises filters 1220 and amplifiers 1222. Radio frontend circuitry 1218 may be connected to an antenna 1210 and processing circuitry 1202. The radio front-end circuitry may be configured to condition signals communicated between antenna 1210 and processing circuitry 1202.
  • Radio front-end circuitry 1218 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. Radio front-end circuitry 1218 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 1220 and/or amplifiers 1222. The radio signal may then be transmitted via antenna 1210. Similarly, when receiving data, antenna 1210 may collect radio signals which are then converted into digital data by radio front-end circuitry 1218. The digital data may be passed to processing circuitry 1202. In other embodiments, the communication interface may comprise different components and/or different combinations of components.
  • network node 1200 does not include separate radio front-end circuitry 1218, instead, processing circuitry 1202 includes radio front-end circuitry and is connected to antenna 1210. Similarly, in some embodiments, all or some of RF transceiver circuitry 1212 is part of communication interface 1206. In still other embodiments, communication interface 1206 includes one or more ports or terminals 1216, radio front-end circuitry 1218, and RF transceiver circuitry 1212, as part of a radio unit (not shown), and communication interface 1206 communicates with baseband processing circuitry 1214, which is part of a digital unit (not shown).
  • Antenna 1210 may include one or more antennas, or antenna arrays, configured to send and/or receive wireless signals. Antenna 1210 may be coupled to radio front-end circuitry 1218 and may be any type of antenna capable of transmitting and receiving data and/or signals wirelessly. In certain embodiments, antenna 1210 is separate from network node 1200 and connectable to network node 1200 through an interface or port.
  • Antenna 1210, communication interface 1206, and/or processing circuitry 1202 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, antenna 1210, communication interface 1206, and/or processing circuitry 1202 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.
  • Power source 1208 provides power to the various components of network node 1200 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). Power source 1208 may further comprise, or be coupled to, power management circuitry to supply the components of network node 1200 with power for performing the functionality described herein.
  • network node 1200 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 power source 1208.
  • power source 1208 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.
  • Embodiments of network node 1200 may include additional components beyond those shown in Figure 12 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.
  • network node 1200 may include user interface equipment to allow input of information into network node 1200 and to allow output of information from network node 1200. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for network node 1200.
  • network node 1200 can be configured to perform operations corresponding to any of the exemplary methods or procedures described above in relation to Figures 4-10. As a more specific example, network node 1200 can perform any or all of the operations associated with the first through fourth NNFS described above.
  • FIG. 13 is a block diagram of a host 1300, in accordance with various aspects described herein.
  • host 1300 may be or comprise various combinations hardware and/or software, including a standalone server, a blade server, a cloud-implemented server, a distributed server, a virtual machine, container, or processing resources in a server farm.
  • Host 1300 may provide one or more services to one or more UEs, to nodes or NFs of a communication network, and/or to a service provider.
  • the host may be part of a cloud computing apparatus, system, or environment.
  • Host 1300 includes processing circuitry 1302 that is operatively coupled via a bus 1304 to an input/output interface 1306, a network interface 1308, a power source 1310, and a memory 1312.
  • Other components may be included in other embodiments. Features of these components may be substantially similar to those described with respect to the devices of previous figures, such as Figures 11-12, such that the descriptions thereof are generally applicable to the corresponding components of host 1300.
  • Memory 1312 may include one or more computer programs including one or more host application programs 1314 and data 1316, which may include user data, e.g., data generated by a UE for host 1300 or data generated by host 1300 for a UE. Embodiments of host 1300 may utilize only a subset or all of the components shown. Host application programs 1314 may be implemented in a container-based architecture.
  • the containerized host application programs may provide support for video codecs (e.g., Versatile Video Coding (VVC), High Efficiency Video Coding (HEVC), Advanced Video Coding (AVC), MPEG, VP9) and audio codecs (e.g., FLAC, Advanced Audio Coding (AAC), MPEG, G.711), including transcoding for multiple different classes, types, or implementations of UEs (e.g., handsets, desktop computers, wearable display systems, heads-up display systems).
  • Host application programs 1314 may also provide for user authentication and licensing checks and may periodically report health, routes, and content availability to a central node, such as a device in or on the edge of a core network.
  • host 1300 may select and/or indicate a different host for over-the-top services for a UE.
  • Host application programs 1314 may support various protocols, such as the HTTP Live Streaming (HLS) protocol, Real- Time Messaging Protocol (RTMP), Real-Time Streaming Protocol (RTSP), Dynamic Adaptive Streaming over HTTP (MPEG-DASH), etc.
  • HTTP Live Streaming HLS
  • RTMP Real- Time Messaging Protocol
  • RTSP Real-Time Streaming Protocol
  • MPEG-DASH Dynamic Adaptive Streaming over HTTP
  • the containerized applications running in host 1300 can include one or more applications that include operations corresponding to any of the exemplary methods or procedures described above in relation to Figures 4-10.
  • the containerized applications running in host 1300 can perform any or all of the operations associated with the first through fourth NNFS described above.
  • FIG 14 is a block diagram illustrating a virtualization environment 1400 in which functions implemented by some embodiments may be virtualized.
  • virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources.
  • 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 1400 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.
  • VMs virtual machines
  • the virtual node does not require radio connectivity (e.g., a core network node or host)
  • the node may be entirely virtualized.
  • Applications 1402 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment 1400 to implement some of the features, functions, and/or benefits of some of the embodiments disclosed herein.
  • any of the exemplary methods or procedures described above in relation to Figures 4-10 can be instantiated as application(s) 1402 running in virtualization environment 1400, such as in the form of an application function (AF) or a virtual network function (NF).
  • AF application function
  • NF virtual network function
  • virtualization environment 1400 may be (or be part of) a cloud computing system or environment that hosts various applications, including but not limited to instantiations of the exemplary methods or procedures described herein.
  • any or all of the operations associated with the first through fourth NNFS described above can be instantiated as application(s) 1402 running in virtualization environment 1400.
  • Hardware 1404 includes processing circuitry, memory that stores software and/or instructions (collectively denoted computer program 1404a, which may be in the form of a computer program product) 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 1406 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs 1408a-b (one or more of which may be generally referred to as VMs 1408), and/or perform any of the functions, features and/or benefits described in relation with some embodiments described herein.
  • the virtualization layer 1406 may present a virtual operating platform that appears like networking hardware to the VMs 1408.
  • VMs 1408 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 1406.
  • VMs 1402 may be implemented on one or more of VMs 1408, 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.
  • NFV network function virtualization
  • VM 1408 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine.
  • Each VM 1408, and that part of hardware 1404 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.
  • a virtual network function is responsible for handling specific network functions that run in one or more VMs 1408 on top of the hardware 1404 and corresponds to the application 1402.
  • Hardware 1404 may be implemented in a standalone network node with generic or specific components. Hardware 1404 may implement some functions via virtualization. Alternatively, hardware 1404 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 1410, which, among others, oversees lifecycle management of applications 1402.
  • hardware 1404 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.
  • some signaling can be provided with the use of a control system 1412 which may alternatively be used for communication between hardware nodes and radio units.
  • the term unit can have conventional meaning in the field of electronics, electrical devices and/or electronic devices and can include, for example, electrical and/or electronic circuitry, devices, modules, processors, memories, logic solid state and/or discrete devices, computer programs or instructions for carrying out respective tasks, procedures, computations, outputs, and/or displaying functions, and so on, as such as those that are described herein.
  • any appropriate steps, methods, features, functions, or benefits disclosed herein may be performed through one or more functional units or modules of one or more virtual apparatuses.
  • Each virtual apparatus may comprise a number of these functional units.
  • These functional units may be implemented via processing circuitry, which may include one or more microprocessor or microcontrollers, as well as other digital hardware, which may include Digital Signal Processor (DSPs), special-purpose digital logic, and the like.
  • the processing circuitry may be configured to execute program code stored in memory, which may include one or several types of memory such as Read Only Memory (ROM), Random Access Memory (RAM), cache memory, flash memory devices, optical storage devices, etc.
  • Program code stored in memory includes program instructions for executing one or more telecommunications and/or data communications protocols as well as instructions for carrying out one or more of the techniques described herein.
  • the processing circuitry may be used to cause the respective functional unit to perform corresponding functions according one or more embodiments of the present disclosure.
  • device and/or apparatus can be represented by a semiconductor chip, a chipset, or a (hardware) module comprising such chip or chipset; this, however, does not exclude the possibility that a functionality of a device or apparatus, instead of being hardware implemented, be implemented as a software module such as a computer program or a computer program product comprising executable software code portions for execution or being run on a processor.
  • functionality of a device or apparatus can be implemented by any combination of hardware and software.
  • a device or apparatus can also be regarded as an assembly of multiple devices and/or apparatuses, whether functionally in cooperation with or independently of each other.
  • devices and apparatuses can be implemented in a distributed fashion throughout a system, so long as the functionality of the device or apparatus is preserved. Such and similar principles are considered as known to a skilled person.
  • a method for predicting a service metric (S) that is representative of quality of service (QoS) provided by a wireless network the method being performed by one or more network nodes, functions, or services (NNFS) of the wireless network and comprising: determining respective metrics (Li) that relate each of a plurality of values (Vs) of the service metric (S) to each of a plurality of values (VF) for one or more features (F) related to communication conditions in the wireless network; and determining one or more of the feature values (VF) that are associated with one or more location-time pairs (x, t) of interest; based on the determined feature values (VF) and the respective metrics (Li), determining respective metrics (L3) that relate each of the values (Vs) of the service metric (S) to each of the location-time pairs (x, t) of interest; and predicting the service metric (S) at the one or more location-time pairs (x, t) of interest, based on the respective metrics (L3).
  • determining the respective metrics (Li) comprises estimating a conditional probability function S
  • the values (Vs) of the service metric (S) include binary indications of whether a desired service quality is met; the one or more features (F) include channel quality and network load; the different feature values (VF) for channel quality include high channel quality and low channel quality; the different feature values (VF) for network load include high network load and low network load; and the conditional probability function /S
  • A5. The method of embodiment A4 wherein: the metrics (Li) are determined during a training phase and then stored; and determining the respective metrics (L3) comprises, during a subsequent prediction phase, retrieving stored metrics (Li) that are associated with the determined one or more feature values (VF).
  • A6. The method of any of embodiments A1-A5, wherein: the method further comprises determining respective metrics (L2) that relate at least one of the plurality of feature values (VF) to each of the plurality of location-time pairs (x, t); and determining the respective metrics (L3) is further based on the respective metrics (L2).
  • the metrics (L2) are determined based on the one or more datasets during a training phase and then stored; and determining the respective metrics (L3) comprises, during a subsequent prediction phase, retrieving stored metrics (L2) that are associated with the location-time pairs (x, t) of interest.
  • determining the respective metrics (L2) comprises estimating a probability function fp ⁇ F) that indicates respective likelihoods (L2) of different feature values (VF) at each of the plurality of location-time pairs (x, t); and determining the respective metrics (L3) is based on the estimated probability function
  • the one or more features (F) include channel quality and network load
  • the different feature values (VF) for channel quality include high channel quality and low channel quality
  • the different feature values (VF) for network load include high network load and low network load
  • the metrics (L2) include: a first likelihood (L2) for low channel quality and high network load at a first location-time pair (x, t); and a second likelihood (L2) for high channel quality and low network load at a second location-time pair (x, t).
  • the method further comprises training a neural network (NN) using one or more datasets having a plurality of entries, with the respective entries including one or more of the following associated with respective location-time pairs (x, t): one or more values (VF) of the features (F), and a value (Vs) of service metric (S); and determining the respective metrics (L3) comprises applying the determined feature values (VF) at the location-time pairs (x, t) of interest as inputs to the trained NN.
  • NN neural network
  • the respective entries including one or more of the following associated with respective location-time pairs (x, t): one or more values (VF) of the features (F), and a value (Vs) of service metric (S)
  • determining the respective metrics (L3) comprises applying the determined feature values (VF) at the location-time pairs (x, t) of interest as inputs to the trained NN.
  • the NN comprises a plurality of layers, including an input layer and an output layer; the input layer includes a number of neurons that is less than or equal to a number of the features (F) related to the communication conditions; and the output layer includes a number of neurons that is less than or equal to a number of values (Vs) of the service metric (S).
  • each of the metrics (Li) is associated with one of the following: a single location-time pair, a plurality of location-time pairs, or no location-time pair.
  • a 16 The method of any of embodiments Al -Al 5, wherein: determining the respective metrics (Li) is performed by a first NNFS of the wireless network; determining the one or more values (VF) is performed by a second NNFS of the wireless network; determining the respective metrics (L3) is performed by a third NNFS of the wireless network; and predicting the service metric (S) is performed by a fourth NNFS of the wireless network.
  • a 17 The method of embodiment A 16, wherein at least one of the first through fourth NNFS is different than other of the first through fourth NNFS.
  • A19 The method of any of embodiments A16-A18, wherein determining the plurality of values (VF), by the second NNFS, is performed based on the location-time pairs (x, t) of interest provided by the third NNFS.
  • A20 The method of any of embodiments Al 6-Al 8, wherein determining the one or more values (VF) and determining the respective metrics (L3), by the second and third NNFS respectively, are based on the location-time pairs (x, t) of interest provided by the fourth NNFS.
  • NNFS network nodes, functions, or services
  • S service metric
  • QoS quality of service
  • each NNFS is implemented by communication interface circuitry and processing circuitry that are operably coupled and arranged to communicate with at least one other of the NNFS; and the processing circuitry and the communication interface circuitry, for the respective NNFS, are configured to perform operations corresponding to the method of any of the embodiments A1-A20.
  • One or more network nodes, functions, or services (NNFS) of a wireless network that are configured to predict a service metric (S) representative of quality of service (QoS) provided by the wireless network, the one or more NNFS being further configured to perform operations corresponding to the method of any of the embodiments A1-A20.
  • B3. A non-transitory, computer-readable medium storing computer-executable instructions that, when executed by processing circuitry associated with one or more network nodes, functions, or services (NNFS) of a wireless network that are configured to predict a service metric (S) representative of quality of service (QoS) provided by the wireless network, configure the one or more NNFS to perform operations corresponding to any of the methods of embodiments A1-A20.
  • a computer program product comprising computer-executable instructions that, when executed by processing circuitry associated with one or more network nodes, functions, or services (NNFS) of a wireless network that are configured to predict a service metric (S) representative of quality of service (QoS) provided by the wireless network, configure the one or more NNFS to perform operations corresponding to any of the methods of embodiments Al- A20.
  • NNFS network nodes, functions, or services

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Databases & Information Systems (AREA)
  • Evolutionary Computation (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

Des modes de réalisation comprennent des procédés de prédiction d'une métrique de service (S) qui est représentative de la qualité de service (QoS) fournie par un réseau de communication. De tels procédés comprennent la détermination de métriques respectives (L1) qui associent chacune d'une pluralité de valeurs (VS) de S à chacune d'une pluralité de valeurs (VF) pour une ou plusieurs caractéristiques (F) associées à des conditions de communication dans le réseau de communication, et la détermination d'une ou plusieurs des valeurs de caractéristique (VF) qui sont associées à une ou plusieurs paires emplacement-temps (x, t) d'intérêt. De tels procédés comprennent, sur la base des valeurs de caractéristiques déterminées et des métriques respectives (L1), la détermination de métriques respectives (L3) qui associent chacun des VS à chacune des paires de temps de localisation (x, t) d'intérêt, et la prédiction de S au niveau de la ou des paires emplacement-temps (x, t) d'intérêt sur la base des métriques respectives (L3). D'autres modes de réalisation comprennent des nœuds de réseau, des fonctions ou des services configurés pour mettre en œuvre de tels procédés.
PCT/IB2023/052587 2022-03-24 2023-03-16 Prédiction de qualité de service (qos) pour un réseau de communication WO2023180881A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US202263323102P 2022-03-24 2022-03-24
US63/323,102 2022-03-24

Publications (1)

Publication Number Publication Date
WO2023180881A1 true WO2023180881A1 (fr) 2023-09-28

Family

ID=85873885

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/IB2023/052587 WO2023180881A1 (fr) 2022-03-24 2023-03-16 Prédiction de qualité de service (qos) pour un réseau de communication

Country Status (1)

Country Link
WO (1) WO2023180881A1 (fr)

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2006015427A1 (fr) * 2004-08-11 2006-02-16 National Ict Australia Limited Detecteur de qualite de service
EP3742767A1 (fr) * 2019-05-21 2020-11-25 Volkswagen AG Procédé de prédiction de la qualité de service pour une communication entre au moins deux partners de communication, l'un d'entre eux pouvant être un véhicule en mouvement; dispositif pour mettre en oeuvre les étapes dudit procédé et programme informatique

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2006015427A1 (fr) * 2004-08-11 2006-02-16 National Ict Australia Limited Detecteur de qualite de service
EP3742767A1 (fr) * 2019-05-21 2020-11-25 Volkswagen AG Procédé de prédiction de la qualité de service pour une communication entre au moins deux partners de communication, l'un d'entre eux pouvant être un véhicule en mouvement; dispositif pour mettre en oeuvre les étapes dudit procédé et programme informatique

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
5G AUTOMOTIVE ASSOCIATION: "5GS Enhancements for providing predictive QoS in C-V2X", WORKING GROUP SYSTEM ARCHITECTURE AND SOLUTION DEVELOPMENT, 30 May 2020 (2020-05-30), XP055839752, Retrieved from the Internet <URL:https://5gaa.org/wp-content/uploads/2020/05/5GAA_A-200055_eNESQO_TR_final.pdf> [retrieved on 20230606] *
5GAA: "Making 5G Proactive and Predictive for the Automotive Industry", 9 December 2019 (2019-12-09), XP055790418, Retrieved from the Internet <URL:https://5gaa.org/wp-content/uploads/2020/01/5GAA_White-Paper_Proactive-and-Predictive_v04_8-Jan.-2020-003.pdf> [retrieved on 20210326] *
BLASCO RICARDO ET AL: "Predictive Quality of Service in Cellular Networks: Challenges, Framework, and Application in Vehicular Communications", IEEE COMMUNICATIONS MAGAZINE., vol. 61, no. 3, 1 March 2023 (2023-03-01), US, pages 44 - 49, XP093042415, ISSN: 0163-6804, Retrieved from the Internet <URL:https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=10032072&ref=aHR0cHM6Ly9pZWVleHBsb3JlLmllZWUub3JnL3hwbC90b2NyZXN1bHQuanNwP2lzbnVtYmVyPTEwMDgwODcyJnB1bnVtYmVyPTM1> [retrieved on 20230606], DOI: 10.1109/MCOM.004.2200180 *
KOUSARIDAS APOSTOLOS ET AL: "QoS Prediction for 5G Connected and Automated Driving", IEEE COMMUNICATIONS MAGAZINE, IEEE SERVICE CENTER, PISCATAWAY, US, vol. 59, no. 9, 11 October 2021 (2021-10-11), pages 58 - 64, XP011882807, ISSN: 0163-6804, [retrieved on 20211009], DOI: 10.1109/MCOM.110.2100042 *

Similar Documents

Publication Publication Date Title
US20220103653A1 (en) Service Delivery with Joint Network and Cloud Resource Management
US11792612B2 (en) Method of updating a background data transfer policy negotiated between an application function and a core network, a policy control function, and an application function
US20220345865A1 (en) Provisioning and Exposing User Equipment (UE) Communication Pattern Associated with an Application to Request Traffic of the Application to be Analyzed in the Core Network (CN)
EP3935886B1 (fr) Transfert de données rentable, prédictif et mis en mémoire cache
US11929938B2 (en) Evaluating overall network resource congestion before scaling a network slice
WO2022248118A1 (fr) Autorisation de fonctions de réseau de consommateur
US20230099649A1 (en) Application Awareness of Credit Conditions in Communication Network
WO2023143806A1 (fr) Mise à jour d&#39;indicateur de routage par l&#39;intermédiaire d&#39;une procédure de mise à jour de paramètres d&#39;ue (upu)
WO2023113674A1 (fr) Fonctionnement d&#39;équipement utilisateur (ue) avec configuration d&#39;économie d&#39;énergie de station de base
WO2023180881A1 (fr) Prédiction de qualité de service (qos) pour un réseau de communication
WO2024089563A1 (fr) Gestion de l&#39;efficacité énergétique au niveau d&#39;un service dans un réseau de communication
WO2023206238A1 (fr) Procédé et appareil de configuration dynamique de tranche dans un réseau de communication
WO2023161733A1 (fr) Optimisation de trafic sensible à l&#39;encombrement dans des réseaux de communication
WO2023142676A1 (fr) Élimination d&#39;autorisation spécifique à un service dans un cœur de réseau 5g (5gc)
WO2024023555A1 (fr) Gestion de ressources de réseau de communication par session d&#39;utilisateur sur la base d&#39;une qualité d&#39;expérience d&#39;utilisateur (qoe)
WO2023198733A1 (fr) Détermination efficace d&#39;informations d&#39;abonnement d&#39;utilisateur dans un réseau multi-domaine
WO2023223081A1 (fr) Établissement amélioré de rapports de mesures de qualité d&#39;expérience (qoe)
WO2023180115A1 (fr) Procédés d&#39;exposition de données/analyse d&#39;un réseau de communication dans des scénarios d&#39;itinérance
WO2023187548A1 (fr) Enregistrement de surveillance de dérive de modèle d&#39;apprentissage automatique (ml)
WO2023144035A1 (fr) Automatisation de groupe de réseau virtuel (vn) pour des données partagées dynamiques dans un réseau central 5g (5gc)
WO2024075130A1 (fr) Optimisation de violations d&#39;accord de niveau de service d&#39;équipement utilisateur pour une attribution de tranche de réseau
WO2022238161A1 (fr) Autorisation d&#39;accès aux données d&#39;une fonction de coordination de collecte de données (dccf) sans structure de messagerie
WO2023217557A1 (fr) Traducteur d&#39;intelligence artificielle/apprentissage automatique (ia/aa) pour réseau central 5g (5gc)
WO2023099970A1 (fr) Gestion de modèle d&#39;apprentissage automatique (ml) dans un réseau central 5g
WO2024047392A1 (fr) Détection d&#39;application assistée par nwdaf basée sur un service de nom de domaine (dns)

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 23715235

Country of ref document: EP

Kind code of ref document: A1