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WO2025116800A1 - Nœuds de réseau radio, équipement utilisateur et procédés réalisés par ceux-ci - Google Patents

Nœuds de réseau radio, équipement utilisateur et procédés réalisés par ceux-ci Download PDF

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Publication number
WO2025116800A1
WO2025116800A1 PCT/SE2024/050981 SE2024050981W WO2025116800A1 WO 2025116800 A1 WO2025116800 A1 WO 2025116800A1 SE 2024050981 W SE2024050981 W SE 2024050981W WO 2025116800 A1 WO2025116800 A1 WO 2025116800A1
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WIPO (PCT)
Prior art keywords
network node
radio network
indication
frequency
cell
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English (en)
Inventor
Icaro Leonardo DA SILVA
Alessio Terzani
Tong Su
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Telefonaktiebolaget LM Ericsson AB
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Telefonaktiebolaget LM Ericsson AB
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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/0005—Control or signalling for completing the hand-off
    • H04W36/0083—Determination of parameters used for hand-off, e.g. generation or modification of neighbour cell lists
    • H04W36/0085—Hand-off measurements
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/0005—Control or signalling for completing the hand-off
    • H04W36/0055—Transmission or use of information for re-establishing the radio link
    • H04W36/0058—Transmission of hand-off measurement information, e.g. measurement reports
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/0005—Control or signalling for completing the hand-off
    • H04W36/0055—Transmission or use of information for re-establishing the radio link
    • H04W36/0064—Transmission or use of information for re-establishing the radio link of control information between different access points
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/0005—Control or signalling for completing the hand-off
    • H04W36/0083—Determination of parameters used for hand-off, e.g. generation or modification of neighbour cell lists
    • H04W36/0085—Hand-off measurements
    • H04W36/0094—Definition of hand-off measurement parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W8/00—Network data management
    • H04W8/22—Processing or transfer of terminal data, e.g. status or physical capabilities
    • H04W8/24—Transfer of terminal data

Definitions

  • Embodiments herein relate to a first radio network node, a second radio network node, a user equipment (UE) and methods performed therein regarding wireless communication. Furthermore, a computer program product and a computer-readable storage medium are also provided herein. In particular, embodiments herein relate to handling communication, such as handling measurements of a UE, in a wireless communications network.
  • UEs also known as wireless communication devices, mobile stations, stations (STA) and/or wireless devices, communicate via a Radio Access Network (RAN) with one or more core networks (CN).
  • the RAN covers a geographical area which is divided into service areas or cells, with each service area or cell being served by a radio network node such as an access node, e.g., a Wi-Fi access point or a Radio Base Station (RBS), which in some networks may also be called, for example, a NodeB, a gNodeB, or an eNodeB.
  • the service area or cell is a geographical area where radio coverage is provided by the radio network node.
  • the radio network node operates on radio frequencies to communicate over an air interface with the UEs within range of the radio network node.
  • the radio network node communicates over a downlink (DL) to the UE and the UE communicates over an uplink (UL) to the radio network node.
  • DL downlink
  • UL uplink
  • a Universal Mobile Telecommunications System is a third generation (3G) telecommunication network, which evolved from the second generation (2G) Global System for Mobile Communications (GSM).
  • the UMTS Terrestrial Radio Access Network (UTRAN) is essentially a RAN using Wideband Code Division Multiple Access (WCDMA) and/or High-Speed Packet Access (HSPA) for communication with user equipment.
  • WCDMA Wideband Code Division Multiple Access
  • HSPA High-Speed Packet Access
  • 3GPP Third Generation Partnership Project
  • telecommunications suppliers propose and agree upon standards for present and future generation networks and investigate, e.g., enhanced data rate and radio capacity.
  • 3GPP Third Generation Partnership Project
  • radio network nodes may be connected, e.g., by landlines or microwave, to a controller node, such as a Radio Network Controller (RNC) or a Base Station Controller (BSC), which supervises and coordinates various activities of the plural radio network nodes connected thereto.
  • RNC Radio Network Controller
  • BSC Base Station Controller
  • the RNCs are typically connected to one or more core networks.
  • the Evolved Packet System comprises the Evolved Universal Terrestrial Radio Access Network (E-UTRAN), also known as the Long-Term Evolution (LTE) radio access network, and the Evolved Packet Core (EPC), also known as System Architecture Evolution (SAE) core network.
  • E-UTRAN also known as the Long-Term Evolution (LTE) radio access network
  • EPC also known as System Architecture Evolution (SAE) core network.
  • E- UTRAN/LTE is a 3GPP radio access technology wherein the radio network nodes are directly connected to the EPC core network.
  • the RAN of an EPS has an architecture comprising radio network nodes connected directly to one or more core networks.
  • Transmit-side beamforming means that the transmitter can amplify the transmitted signals in a selected direction or directions, while suppressing the transmitted signals in other directions.
  • a receiver can amplify signals from a selected direction or directions, while suppressing unwanted signals from other directions.
  • Fig. 1 depicts the 5G reference architecture as defined by 3GPP.
  • the Network Functions (NF) shown in Fig. 1 are described below.
  • the Application Function (AF) or Application Server (AS) interacts with the 3GPP Core Network and allows external parties to use the Exposure Application Programming Interfaces (API) offered by the network operator.
  • the AF provides session related information to other nodes in the 5G core network (5GC).
  • the Network Exposure Function supports different functionalities and NEF supports different Exposure APIs.
  • NEF Network Repository Function
  • the Unified Data Repository stores data grouped into distinct collections of subscription-related information: Subscription Data; Policy Data; Structured Data for Exposure; Application Data.
  • the Session Management Function supports different functionalities, e.g. SMF receives Policy and Charging Control (PCC) rules from the Policy Control Function (PCF) and configures the User Plane Function (UPF) accordingly.
  • PCC Policy and Charging Control
  • PCF Policy Control Function
  • UPF User Plane Function
  • the UPF supports handling of user plane traffic based on the rules received from the SMF, e.g., packet inspection and different enforcement actions such as Quality of Service (QoS) handling.
  • QoS Quality of Service
  • the PCF supports a unified policy framework to govern the network behaviour. Specifically, the PCF provides PCC rules to the Policy and Charging Enforcement Function (PCEF), i.e., the SMF or UPF that enforces policy and charging decisions according to provisioned PCC rules.
  • PCEF Policy and Charging Enforcement Function
  • the Access and Mobility Management Function manages UE access, e.g., when a UE is connected through different access networks, and UE mobility aspects.
  • CAF Charging Function
  • NSSF Network Slice Selection Function
  • NSI Network Slicing Instance
  • NSSAI Network Slice Selection Assistance Information
  • the UE is required to perform serving cell measurements, e.g., reference signal received power (RSRP), reference signal received quality (RSRQ), and/or signal to interference plus noise ratio (SINR), and include these measurements in radio resource control (RRC) Measurement Reports, when the serving cell configuration includes the field servingCellMO set to the measurement object identifier of the particular serving frequency to be reported, see 3GPP TS 38.331 V17.6.0 (2023-09); 3rd Generation Partnership Project; Technical Specification Group Radio Access Network; NR; Radio Resource Control (RRC) protocol specification (Release 17).
  • RRC Radio Resource Control
  • the UE may also perform beam measurements per beam for the serving cell, see 3GPP TS 38.331 V17.6.0 (2023-09); 3rd Generation Partnership Project; Technical Specification Group Radio Access Network; NR; Radio Resource Control (RRC) protocol specification (Release 17).
  • RRC Radio Resource Control
  • the UE shall: 1 > whenever the UE has a measConfig, perform RSRP and RSRQ measurements for each serving cell for which servingCellMO is configured as follows:
  • the reportConfig associated with at least one measld included in the measIdList within VarMeasConfig contains a reportQuantityRS-lndexes and maxNrofRS- IndexesToReport and contains an rsType set to ssb:
  • the reportConfig associated with at least one measld included in the measIdList within VarMeasConfig contains a reportQuantityRS-lndexes and maxNrofRS- IndexesToReport and contains an rsType set to csi-rs:
  • the reportConfig contains rsType set to csi-rs and CSI-RS-
  • ResourceConfigMobility is configured in the servingCellMO: 3> if the reporfConf/gcontains a reportQuantityRS-lndexes and maxNrofRS- IndexesToReport.
  • the Artificial Intelligence (Al) and/or Machine Learning (ML) for physical layer (PHY) work in Release (Rel)-18 has been limited to lower layer features, such as Beam Management, which is sometimes referred to as intra-cell mobility.
  • Other features, such as Layer three (L3) handovers, RRC measurements, Layer one (L1) and/or Layer two (L2) triggered mobility (LTM), RRC measurement reporting and Conditional Handover has not been part of the Rel-18, but initial discussions seemed to indicate that higher layer features, specified by RAN2, might leverage on AI/ML functions to be possibly specified.
  • a Rel-19 Study Item to study the usage of Al/ ML for L3 Mobility and/or radio resource management (RRM) measurements is considered.
  • the potential scopes/directions of the study item include, see, e.g., RP-232622, 3GPP TSG RAN #101, Bangalore, India, September 11 - 15, 2023; Source: RAN1 Vice-chair (CMCC); Title: Moderator's summary for REL-19 RAN2 topic AI/ML for Air Interface SI (Mobility); https://www.3gpp.org/ftp/tsg_ran/TSG_RAN/TSGR_101/Docs/RP-232622.zip:
  • L3-based mobility and L1/L2-triggered mobility are both considered Handover (HO) optimization in Network side [/UE side], including o Candidate/target cell prediction in L3-based mobility, or, candidate/target beam(s) and cell(s) prediction in LTM
  • RRM measurement and event prediction including o Beam-level measurement prediction o Cell-level measurement prediction, e.g., using intra-frequency measurement results to forecast the RRM measurement of inter- frequency/inter-RAT cells o HO failure/radio link failure (RLF) prediction o Measurement events prediction
  • the UE can be configured with a maximum number of serving cells, i.e., serving frequencies, and, when the serving cell configuration includes a field ‘servingCellMO’ set to a measurement object (MO) identifier, RSRP and RSRQ needs to be measured, at least for cell measurements.
  • beam measurements per Synchronization Signal Block (SSB) and/or Channel State Information (CSI) - Reference Signal (RS), may also need to be performed, when beam measurement is configured for at least one measurement identifier.
  • SSB Synchronization Signal Block
  • CSI Channel State Information
  • RS Channel State Information
  • RRM measurement and event prediction is part of the initial scope, in particular Cell-level measurement prediction, e.g., using intra-frequency measurement results to forecast the RRM measurement of inter-frequency/inter-RAT cells.
  • Cell-level measurement prediction e.g., using intra-frequency measurement results to forecast the RRM measurement of inter-frequency/inter-RAT cells.
  • An object herein is to provide a mechanism to handle communication in an efficient manner in the wireless communications network.
  • the object is achieved, according to embodiments herein, by providing a method performed by a first radio network node for handling communication of a UE in a wireless communications network.
  • the first radio network node receives a measurement report from the UE, wherein the measurement report includes an indication of a frequency-domain prediction of one or more serving cells out of one or more configured serving cells.
  • the UE may be configured with one or more serving cells and the indication may be for a subset of the configured one or more serving cells.
  • the object is achieved, according to embodiments herein, by providing a method performed by a second radio network node for handling communication in a wireless communications network.
  • the second radio network node receives a measurement report from a first radio network node, wherein the measurement report includes an indication of a frequency-domain prediction of one or more serving cells out of one or more serving cells of the first radio network node.
  • the object is achieved, according to embodiments herein, by providing a first radio network node, a second radio network node and a UE configured to perform the methods herein, respectively.
  • the object is achieved, according to embodiments herein, by providing a UE for handling communication, e.g., measurements, in a wireless communications network.
  • the UE is configured to include in a measurement report, an indication of a frequency-domain prediction of one or more serving cells out of one or more configured serving cells; and to transmit the measurement report with the indication to a first radio network node.
  • the object is achieved, according to embodiments herein, by providing a first radio network node for handling communication of a UE in a wireless communications network.
  • the first radio network node is configured to receive a measurement report from the UE, wherein the measurement report includes an indication of a frequency-domain prediction of one or more serving cells out of one or more configured serving cells.
  • the object is achieved, according to embodiments herein, by providing a second radio network node for handling communication in a wireless communications network.
  • the second radio network node is configured to receive a measurement report from a first radio network node, wherein the measurement report includes an indication of a frequency-domain prediction of one or more serving cells out of one or more serving cells of the first radio network node.
  • a computer program product comprising instructions, which, when executed on at least one processor, cause the at least one processor to carry out the methods herein, as performed by the first radio network node, the second radio network node and the UE, respectively.
  • a computer-readable storage medium having stored thereon a computer program product comprising instructions which, when executed on at least one processor, cause the at least one processor to carry out the methods herein, as performed by the first radio network node, the second radio network node and the UE, respectively.
  • An advantage with embodiments herein is that fewer measurements need to be performed by the UE, in case the UE performs frequency domain predictions instead of measurements. That is, in case the UE is configured with N1 serving cells, among which N2 are serving cells for which measurements are to be included in the measurement reports, and the UE measures only a subset of them, since N1>N2, so the remaining ones, which are N1-N2, are serving cells for which the UE performs frequency domain predictions.
  • Another advantage is that assuming a limited number of serving cell measurements that the UE is capable of performing, the possibility to include frequency domain prediction or predictions of serving cells in a measurement report gives the possibility to report more information about more serving cells than what the UE is capable of measuring.
  • embodiments herein provide a mechanism to handle communication of UEs in an efficient manner in the wireless communications network.
  • Fig. 1 shows an architecture according to prior art
  • Fig. 2 shows a wireless communications network according to embodiments herein;
  • Fig. 3 shows a combined signalling scheme and flow chart according to embodiments herein;
  • Fig. 4 is depicting a method performed by a UE according to embodiments herein
  • Fig. 5 is depicting a method performed by a first radio network node according to embodiments herein;
  • Fig. 6 is depicting a method performed by a second radio network node according to embodiments herein;
  • Fig. 7 shows an example of a measurement report according to embodiments herein;
  • Fig. 8a shows an overview depicting some embodiments herein
  • Fig. 8b shows an overview depicting some embodiments herein;
  • Fig. 9a shows an overview depicting some embodiments herein;
  • Fig. 9b shows an overview depicting some embodiments herein;
  • Figs. 10a-10b show examples of Neural Networks used for designing AI/ML models for FD beam prediction
  • Figs. 10c-10d show examples of transformer based Neural Networks used for designing AI/ML models for FD beam prediction
  • Figs. 11a-11 b show multi hear self-attention and scaled dot-product attention in a transformer encoder based neural network
  • Fig. 12 shows a block diagram depicting the UE according to embodiments herein;
  • Fig. 13 shows a block diagram depicting the first radio network node according to embodiments herein;
  • Fig. 14 shows a block diagram depicting the second radio network node according to embodiments herein;
  • Fig. 16 shows a UE QQ200 in accordance with some embodiments
  • Fig. 17 shows a network node QQ300 in accordance with some embodiments
  • Fig. 18 is a block diagram of a host QQ400, which may be an embodiment of the host QQ116 of Fig. 15, in accordance with various aspects described herein;
  • Fig. 19 is a block diagram illustrating a virtualization environment QQ500 in which functions implemented by some embodiments may be virtualized.
  • Fig. 20 shows a communication diagram of a host QQ602 communicating via a network node QQ604 with a UE QQ606 over a partially wireless connection in accordance with some embodiments.
  • Embodiments herein relate to wireless communications networks in general.
  • Fig. 2 is a schematic overview depicting a wireless communications network 1.
  • the wireless communications network 1 comprises one or more RANs and one or more CNs.
  • the wireless communications network 1 may use one or a number of different technologies.
  • Embodiments herein relate to recent technology trends that are of particular interest in an NR context. However, embodiments are also applicable in existing wireless communications systems such as e.g. LTE or WCDMA, and developments thereof.
  • a UE 10 exemplified herein as a wireless device such as a mobile station, a non-access point (non-AP) station (STA), a STA and/or a wireless terminal, is comprised communicating via, e.g., one or more Access Networks (AN), e.g. RAN, to one or more CNs.
  • AN Access Networks
  • UE is a non-limiting term which means any terminal, wireless communications terminal, user equipment, NarrowBand Internet of Things (NB-loT) device, Machine Type Communication (MTC) device, Device to Device (D2D) terminal, or node e.g. smart phone, laptop, mobile phone, sensor, relay, mobile tablets or even a small base station capable of communicating using radio communication with a radio network node within an area served by the radio network node.
  • NB-loT NarrowBand Internet of Things
  • MTC Machine Type Communication
  • D2D Device to Device
  • the wireless communications network 1 comprises a first radio network node 12 providing radio coverage over a geographical area, a first service area 11 or first cell, of a first Radio Access Technology (RAT), such as 6G, NR, LTE, or similar.
  • the radio network node 12 may be a transmission and reception point such as an access node, an access controller, a base station, e.g.
  • a radio base station such as a gNodeB (gNB), an evolved Node B (eNB, eNode B), a NodeB, a base transceiver station, a radio remote unit, an Access Point Base Station, a base station router, a Wireless Local Area Network (WLAN) access point or an Access Point Station (AP STA), a transmission arrangement of a radio base station, a stand-alone access point or any other network unit or node capable of communicating with a wireless device within the area served by the radio network node depending e.g. on the first radio access technology and terminology used.
  • gNB gNodeB
  • eNB evolved Node B
  • eNode B evolved Node B
  • NodeB a NodeB
  • a base transceiver station such as a radio remote unit, an Access Point Base Station, a base station router, a Wireless Local Area Network (WLAN) access point or an Access Point Station (AP STA), a transmission arrangement of a radio base station, a
  • the radio network node may be referred to as a serving radio network node wherein the service area may be referred to as a serving cell such as a primary cell (PCell) and/or a primary secondary cell (PSCell), and the serving network node communicates with the UE in form of DL transmissions to the UE and UL transmissions from the UE.
  • a serving cell such as a primary cell (PCell) and/or a primary secondary cell (PSCell)
  • PCell primary cell
  • PSCell primary secondary cell
  • the wireless communications network 1 comprises a second radio network node 13 providing radio coverage over a geographical area, a second service area 14 or second cell, of a second RAT, such as 6G, NR, LTE, or similar.
  • the second radio network node 12 may be a transmission and reception point such as an access node, an access controller, a base station, e.g.
  • a radio base station such as a gNodeB (gNB), an evolved Node B (eNB, eNode B), a NodeB, a base transceiver station, a radio remote unit, an Access Point Base Station, a base station router, a Wireless Local Area Network (WLAN) access point or an Access Point Station (AP STA), a transmission arrangement of a radio base station, a stand-alone access point or any other network unit or node capable of communicating with a wireless device within the area served by the radio network node depending e.g. on the first radio access technology and terminology used.
  • gNB gNodeB
  • eNB evolved Node B
  • eNode B evolved Node B
  • NodeB a NodeB
  • a base transceiver station such as a radio remote unit, an Access Point Base Station, a base station router, a Wireless Local Area Network (WLAN) access point or an Access Point Station (AP STA), a transmission arrangement of a radio base station, a
  • the radio network node may be referred to as a target radio network node wherein the service area may be referred to as a target cell such as a secondary cell (SCell), and the target radio network node communicates with the UE in form of DL transmissions to the UE and UL transmissions from the UE.
  • SCell secondary cell
  • a service area may be denoted as cell, beam, beam group or similar to define an area of radio coverage.
  • the UE 10 includes an indication in a measurement report to the first radio network node 12.
  • the indication indicates a frequency domain (FD) prediction of one or more serving cells out of one or more configured serving cells for the UE 10.
  • An FD prediction may, be a prediction of, or comprise, a parameter related to a performance of the one or more serving cells, and the indication may comprise channel estimation, a throughput indication, a beam ID, a cell ID and/or a reference signal strength/quality estimation of the one or more serving cells.
  • An FD prediction may be a forecast or calculation of a measurement on one or more first serving frequencies based on measurements on one or more second serving frequencies.
  • a serving cell corresponds to a cell, or any other network entity the UE 10 is considered to be connected with and/or being served by.
  • a connected state e.g., RRC_CONNECTED, not configured with Carrier Aggregation (CA) or Multi-Radio Dual Connectivity (MR-DC)
  • CA Carrier Aggregation
  • MR-DC Multi-Radio Dual Connectivity
  • serving cells is used to denote the set of cells comprising of one or more Special Cells and all secondary cells.
  • a Special Cell refers to the PCell of the Master Cell Group (MCG) or the PSCell of the Secondary Cell Group (SCG), otherwise the term Special Cell refers to the PCell.
  • MCG Master Cell Group
  • SCG Secondary Cell Group
  • the UE 10 is configured with an MCG
  • the UE is configured with a PCell, which is the MCG SpCell, and one or more SCells, called MCG SCells
  • MCG SCells SCells
  • the UE 10 is configured with a PSCell, which is the SCG SpCell, and one or more SCells, called SCG SCells.
  • an SCell may correspond to a cell providing additional radio resources on top of the Special Cell.
  • the subset of serving cells comprising of the PSCell and zero or more secondary cells are referred to as SCG, so the SCells may be referred to as the SCells of the SCG.
  • that may be characterized as a representative frequency, e.g., an Absolute frequency, possibly represented by an absolute radio-frequency channel number (ARFCN), in which a Reference Signal (RS) which is being measured is to be detected/received by the UE 10.
  • the RS received by the UE 10, which is transmitted in, or with, that frequency may encode one or more identifiers e.g. a beam identifier, a beam group identifier, a cell identifier, etc.
  • RS may also refer to a synchronization signal or synchronization signal block, with multiple signals, such as an SSB.
  • the frequency may be an SSB frequency, as defined above, and the identifiers may be an SSB index and a Physical Cell Identity (PCI).
  • PCI Physical Cell Identity
  • the CSI-RS frequency may also be characterized as the bandwidth of the CSI-RS and its subcarrier spacing.
  • an AI/ML model can be defined, in the context of embodiments herein, as a functionality or be part of a functionality that is deployed/implemented in the UE 10.
  • An AI/ML model can be defined as a feature or part of a feature that is implemented/supported in the UE 10, in which it may be called a UE-sided AI/ML model or simply UE-side model.
  • an ML model or Model Inference is a function that provides AI/ML model inference output, e.g., predictions or decisions.
  • the Model inference function may also be responsible for data preparation, e.g., data pre-processing and cleaning, formatting, and transformation, based on Inference Data delivered by a Data Collection function.
  • the output may correspond to the inference output of the AI/ML model produced by a Model Inference function.
  • An AI/ML-model, or a Model Inference function, for one or more FD predictions may correspond to a function which receives one or more inputs related to a first frequency in which a reference signal is being transmitted, e.g., SSB frequency, and provides as outcome or output, one or more predictions/estimates/decisions of a certain type related to a second frequency.
  • a first frequency in which a reference signal is being transmitted e.g., SSB frequency
  • an actor may correspond to measurement reporting of one or more FD predictions of one or more serving cells functionality at the UE 10, and/or the functionality at the UE 10 responsible for generating the data structure to transmit one or more information derived based on one or more FD predictions of one or more serving cells.
  • an ML-model may correspond to a function receiving as input, one or more measurements of at least one DL RS in a serving frequency F_0 and provide as output the prediction of another RS measurement frequency F_1.
  • the first radio network node 12, and/or the second radio network node 13 may correspond to one or more of:
  • a Centralized Unit (CU) gNodeB e.g. a source gNB-CU in case of inter-CU, or simply CU in case of intra-CU.
  • Fig. 3 shows a combined flowchart and signalling scheme according to embodiments herein.
  • the first radio network node 12 may configure the UE 10 with a measurement configuration comprising a configuration indication based on which the UE 10 triggers the measurement report.
  • the UE 10 may determine to perform the frequency-domain prediction based on one or more criteria.
  • the UE may thus perform measurement on some of the serving cells and may perform FD prediction on others (taking the measurement into account).
  • the FD prediction may be a forecast or calculation of a measurement on one or more first serving frequencies based on measurements on one or more second serving frequencies.
  • the UE 10 includes or adds in the measurement report, the indication of the frequency-domain prediction of the one or more serving cells out of the one or more configured serving cells.
  • the UE 10 further transmits the measurement report with the indication to the first radio network node 12.
  • the first radio network node 12 may forward the indication to the second radio network node 13.
  • UE 10 in the wireless communications network 1 will now be described with reference to a flowchart depicted in Fig. 4.
  • the actions do not have to be taken in the order stated below but may be taken in any suitable order.
  • Dashed boxes indicate optional features.
  • the UE 10 may obtain the measurement configuration comprising a configuration indication based on which the UE 10 triggers the measurement report.
  • the measurement configuration may comprise a value or index indicating the configuration indication and/or prediction configuration.
  • the UE 10 may receive from the first radio network node 12 the measurement configuration, including the configuration indication based on which the UE 10 triggers the measurement report.
  • the UE 10 may perform the frequency-domain prediction based on one or more criteria. For example, the UE 10 may determine to perform one or more FD predictions of the subset of the configured one or more serving cells based on one or more criteria, wherein the performed one or more FD predictions are to be included in a measurement report. The UE 10 may use an AI/ML model for FD prediction.
  • the UE 10 may determine to include the indication of the frequencydomain prediction in the measurement report based on the one or more criteria. For example, the UE 10 may determine to include the one or more FD predictions of the subset of the configured one or more serving cells in the measurement report based on the one or more criteria.
  • the UE 10 includes or adds in the measurement report, the indication of the frequency-domain prediction of the one or more serving cells out of the one or more configured serving cells.
  • the one or more serving cells may be fewer than the one or more configured serving cells.
  • the UE 10 may include one or more FD predictions of a subset of the configured one or more serving cells.
  • the UE 10 may further include in the measurement report one or more measurements of another subset, such as the remaining ones, of the configured one or more serving cells. Hence, the another subset are fewer than the configured one or more serving cells.
  • the UE 10 may include an associated information indicating that this indication is for a frequency-domain prediction (and not an actual measurement).
  • the UE 10 may be configured with one or more serving cells, in which the UE 10 includes in a measurement report, e.g., an RRC MeasurementReport, one or more FD predictions of a subset of the configured one or more serving cells.
  • the serving cell may be associated with a serving frequency and/or a subcarrier spacing.
  • the UE 10 transmits the measurement report with the indication to the first radio network node 12.
  • the one or more criteria may be based on one or more of the following (or combinations of the following):
  • the one or more criteria based on which the UE 10 determines to include the one or more FD predictions of the subset of the configured one or more serving cells in a measurement report and/or to perform one or more FD predictions of the subset of the configured one or more serving cells may be based on one or more of the above.
  • the one or more criteria based on which the UE 10 determines to include the one or more FD predictions of the subset of the configured one or more serving cells in a measurement report and/or to perform one or more FD predictions of the subset of the configured one or more serving cells may be associated to one or more parameters in the measurement and/or prediction configuration received by the UE 10.
  • the method actions performed by the first radio network node 12 for handling communication of the UE 10 in the wireless communications network 1 will now be described with reference to a flowchart depicted in Fig. 5.
  • the actions do not have to be taken in the order stated below but may be taken in any suitable order.
  • Dashed boxes indicate optional features.
  • the first radio network node 12 may configure the UE 10 with the measurement configuration comprising the configuration indication based on which the UE 10 triggers the measurement report.
  • the measurement configuration may comprise a value or index indicating the configuration indication and/or prediction configuration.
  • the first radio network node 12 may transmit to the UE 10 the measurement configuration, including the configuration indication based on which the UE 10 triggers the measurement report.
  • the first radio network node 12 may configure the UE 10 with one or more parameters associated to the one or more criteria for the UE 10 to determine to include the one or more FD predictions of the subset of the configured one or more serving cells in a measurement report; and/or to determine to perform the one or more FD predictions of the subset of the configured one or more serving cells in a measurement report.
  • the first radio network node 12 may configure the UE 10 to transmit the measurement report, e.g. an RRC MeasurementReport, which includes one or more FD prediction(s) of a subset of the configured one or more serving cells, wherein a serving cell is associated with a serving frequency and/or a subcarrier spacing.
  • the measurement report e.g. an RRC MeasurementReport, which includes one or more FD prediction(s) of a subset of the configured one or more serving cells, wherein a serving cell is associated with a serving frequency and/or a subcarrier spacing.
  • the first radio network node 12 receives the measurement report from the UE 10, wherein the measurement report includes the indication of the frequencydomain prediction of the one or more serving cells out of the one or more configured serving cells.
  • the measurement report may include one or more FD predictions of a subset of the configured one or more serving cells.
  • the measurement report may include one or more measurements of another subset of the configured one or more serving cells.
  • the measurement report may further include an associated information indicating that this indication is for a frequency-domain prediction, and not an actual measurement.
  • the first radio network node 12 may further transmit to the second radio network node 13, the measurement report including the indication of a frequencydomain prediction of one or more serving cells out of one or more serving cells of the first radio network node.
  • the first radio network node 12 may transmit the indication, such as one or more FD predictions, of the subset of the configured one or more serving cells to the second radio network node 13 operating as a target node: in a handover preparation; in a secondary node (SN) addition, or a secondary node change.
  • SN secondary node
  • the first radio network node 12 may perform an action related to communication served by the first radio network node 12 based on the received indication.
  • the first network node 12 may use the indication when performing load balancing, early data forwarding, traffic steering, mobility, updates on the SCell state, decisions on SCells to be Conditional Handover candidates, LTM candidates, and/or similar.
  • the method actions performed by the second radio network node 13 for handling communication of the UE 10 in the wireless communications network 1 will now be described with reference to a flowchart depicted in Fig. 6.
  • the actions do not have to be taken in the order stated below but may be taken in any suitable order. Dashed boxes indicate optional features.
  • the second radio network node 13 receives the measurement report from the first radio network node 12, wherein the measurement report includes the indication of the frequency-domain prediction of the one or more serving cells out of one or more serving cells of the first radio network node.
  • the measurement report may include one or more FD predictions of a subset of the configured one or more serving cells.
  • the measurement report may further include the associated information indicating that this a FD prediction, and not an actual measurement.
  • the second radio network node 13 may be a target network node in a handover, in an SN addition or an SN change.
  • the second radio network node 13 may perform an action related to communication served by the second radio network node 13 based on the received indication.
  • the second radio network node 13 may use the indication to decide whether to accept a handover request from the first radio network node 12; and/or when performing: load balancing, early data forwarding, traffic steering, mobility, updates on the SCell state, decisions on SCells to be Conditional Handover candidates, LTM candidates and/or similar.
  • Embodiments herein disclose a method in the UE 10, wherein the UE 10 is configured with one or more serving cells, e.g., PCell, SCells of the MCG, SCells of the SCG, PSCell, etc..
  • the UE 10 includes in the measurement report, e.g., an RRC MeasurementReport, one or more FD predictions of a subset of the configured one or more serving cells.
  • the serving cell may be associated to a serving frequency, e.g., an SSB frequency, and/or a subcarrier spacing.
  • the UE 10 may further include in the measurement report one or more measurements of another subset of the configured one or more serving cells.
  • the UE 10 may include measurements of some of the serving cells and FD predictions of other serving cells.
  • the UE 10 when the UE 10 is configured with one or more serving cells in serving frequencies F_0, F_1 , .., F_k1, F_k2, F_(k2+1), ... , F_N the UE 10 may include in the measurement report, FD predictions for the serving cells in the serving frequencies F_k2, F_(k2+1), ..., F_N, and, in addition, one or more measurements of serving cells in serving frequencies F_0, F_1 , .., F_k1, as shown in Fig. 7.
  • Embodiments herein may use one or more criteria based on which the UE 10 determines to include the one or more FD predictions of the subset of the configured one or more serving cells in a measurement report.
  • the one or more criteria may be used by the UE 10 to determine to perform one or more FD predictions of the subset of the configured one or more serving cells, wherein the performed one or more FD predictions are to be included in a measurement report.
  • the one or more criteria may be based on one or more of the following, or combinations of the following:
  • a type of serving cell may be: a Secondary Cell, a special cell (SpCell), a serving cell is a PCell, or a PSCell; o
  • the UE 10 may include (or is allowed to include) FD prediction for a serving cell when the serving cell is an SCell. Thanks to that, the UE 10 may not need to measure all SCells which the UE 10 would have to measure e.g. SCells whose a field ‘servingCellMO’ is included in the SCell configuration and points to a measurement object identifier (measObjectld)
  • a state of a serving cell may be: activated, deactivated, dormant.
  • the UE 10 may include, or is allowed to include, FD prediction for a SCell, which is deactivated. Thanks to that, the UE 10 could still benefit from the power savings, because the UE 10 would not have to measure deactivated SCells, but the UE 10 would report FD predictions for them, enriching the information to the network and making it possible to have better decisions in terms of load balancing, traffic steering, mobility, updates on the SCell state, decisions on SCells to be Conditional Handover candidates, LTM candidates, etc..
  • the UE 10 would not be required to perform measurements on SCells which are deactivated and consequently, would not report measurement information about these SCells.
  • the UE 10 may include, or is allowed to include, FD prediction for an SCell which is dormant. Thanks to that, the UE 10 could achieve a power saving gains similar to deactivated state, because when the SCell is dormant the UE 10 may skip some actions with the SCell, e.g. no need to monitor downlink control channels, like Physical Downlink Control Channel (PDCCH), but it would need to perform measurements.
  • the UE 10 may include, or is allowed to include, FD prediction for a SCell which is activated.
  • the UE 10 may achieve a power saving gain similar to dormant state, at least in what concerns the performance of measurements, because when the SCell is activated the UE 10 would need to perform measurements. This may also be used in combination with one or more other criteria, such as the SCell being in an activated state, but not configured to be reported, e.g., SCell configuration does not include a servingCellMO set to a measurement object identifier.
  • the Cell Group of a serving cell may be an MCG and/or SCG.
  • the UE 10 may include FD predictions when a serving cell is part of the MCG.
  • the UE 10 may include FD predictions when a serving cell is part of the SCG.
  • the UE 10 may include FD predictions when a serving cell is an SCell of the MCG.
  • the UE 10 may include FD predictions when a serving cell is an SCell of the SCG.
  • the UE 10 may include FD predictions of a serving cell of a first cell group when the Measurement Report is triggered by a measurement configuration of the other cell group.
  • the UE 10 may benefit from power savings, as the UE 10 would not need to perform measurements of serving cells of the SCG when the report is triggered by the MCG measurement configuration, assuming that the Master Node, the first radio network node 12 receiving the report, would not need SCG measurements with as high a level of accuracy as the MCG measurements which it has configured the UE to measure and report.
  • the state of Cell Group may be: activated, deactivated.
  • the UE 10 may include FD predictions of a serving cell of an SCG which is deactivated when the Measurement Report is triggered. Thanks to that, the UE 10 may benefit even more from power savings gains when the SCG is deactivated, since it would perform fewer measurements of the SCG serving cells and rely on the FD predictions to be reported.
  • the UE 10 may include FD predictions of a serving cell of an MCG which is deactivated when the Measurement Report is triggered.
  • the UE 10 may include FD predictions of an SCell of an SCG which is deactivated, such as deactivated SCG, when the Measurement Report is triggered. o In one option, the UE 10 may include FD predictions of a PSCell for which the SCG which is deactivated, such as deactivated SCG, when the Measurement Report is triggered.
  • the indication may be the presence of a field or parameter indicating that the serving cell is to be measured and/or included in a measurement report by the UE 10.
  • the presence of the field ‘servingCellMO’ in a serving cell configuration such as the field ‘servingCellMO’ being set to a measurement object identifier.
  • the UE 10 may include FD predictions of a serving cell for which its configuration includes the presence of the field ‘servingCellMO’, such as set to a measurement object identifier.
  • an indication may be the absence of a field or parameter indicating that the serving cell is to be measured and/or included in a measurement report by the UE 10. For example, the absence of the field ‘servingCellMO’ in a serving cell configuration, for example, the field ‘servingCellMO’ being set to a measurement object identifier.
  • the UE 10 may include FD predictions of a serving cell for which its configuration does not include the presence of the field ‘servingCellMO’, for example, the field ‘servingCellMO’ being set to a measurement object identifier.
  • the UE 10 may perform one or more FD predictions, which enriches the information included in the measurement reports about serving cells without sacrificing additional UE power savings, as no additional serving cell measurements are needed for these cells.
  • the indication may be the presence of a field or parameter indicating that the UE 10 is allowed to include in a FD prediction, instead of one or more measurements.
  • the UE 10 may include FD predictions of a serving cell for which its configuration includes the indication. According to embodiments herein, the UE 10 may not be required to perform one or more serving cell measurements for that particular serving cell, which leads to gains in terms of UE power savings.
  • the UE 10 may include FD predictions of a serving cell for which its configuration includes the indication, instead of a serving cell measurement, the UE 10 may include in the measurement report an indication that this is an FD prediction and/or estimate instead of a measurement for that serving cell. Thanks to this, the UE 10 can make the network aware that this is an FD prediction and/or estimate.
  • the indication may be a measurement object identifier, wherein its presence in a serving cell configuration indicates that the serving cell is a cell which is to be measured.
  • An indication may be the presence of a field or parameter indicating that a measurement object with a given frequency, e.g., SSB frequency, the serving cell is to be measured and/or included in a measurement report by the UE 10. For example, the presence of a measurement object identifier set to the field ‘servingCellMO’ in a serving cell configuration.
  • the UE 10 may include FD predictions of a serving cell for which its configuration includes a measurement object identifier e.g. set to the field ‘servingCellMO’. o In one option, the UE 10 may include FD predictions of a serving cell for which its configuration includes a measurement object identifier e.g. set to the field ‘servingCellMO’ AND the measurement object configuration, e.g., instance of the Information element MeasObjectNR, associated to that measurement object identifier, also includes an indication that the serving cell associated to that measurement object is to be measured and/or that the UE 10 is allowed to perform one or more FD predictions for that frequency.
  • the measurement object configuration e.g., instance of the Information element MeasObjectNR, associated to that measurement object identifier
  • the indication may be a field and/or parameter, e.g., ‘allowlnterruptions’, included in a serving cell configuration which indicates that the UE 10 is allowed to cause interruptions to serving cells when performing measurements e.g. of deactivated SCell.
  • the indication may be a field and/or parameter, e.g. ‘allowlnterruptions’, included in a measurement object configuration associated to an SCell which indicates that the UE 10 is allowed to cause interruptions to serving cells when performing measurements e.g. of deactivated SCell.
  • An indication of a number of serving cells for which the UE 10 is allowed to transmit one or more FD predictions may be a field and/or parameter included in a reporting configuration and/or in a measurement object associated to a serving frequency.
  • the parameter and/or field indicates a maximum number of serving cells for which the UE 10 may include FD predictions, e.g. instead of measurements, the UE 10 may select one or more serving cells up to that maximum number for performing one or more FD predictions instead of measurements.
  • a frequency band associated to one or more serving cells may be a frequency band indicated in the IE FreqBandlndicatorNR, e.g., an NR frequency band number as defined in TS 38.101-1 and TS 38.101-2, such as an integer from 1 to 1024 associated to a frequency band.
  • the UE 10 may perform one or more FD predictions of a first set of one or more serving cells in serving frequencies, e.g., SSB frequencies, in a first frequency band, when the UE 10 is configured with at least one serving cell in a serving frequency in the first frequency band in which the UE 10 needs to perform measurements.
  • the UE 10 may perform one or more predictions on the second serving cell in the same frequency band.
  • the UE 10 may determine to either perform one or more FD predictions or measurements on a serving cell, depending on whether there are multiple serving cells with serving frequencies in associated to the same frequency band. For example, when multiple serving cells have their respective SSB frequencies in the same frequency band, the UE 10 may perform measurements on one of the serving cells in that frequency band, and perform one or more FD predictions on one or more other serving cells in that frequency band.
  • a frequency range associated to one or more serving cells may be a frequency range 1 (FR1) or a frequency range 2 (FR2).
  • the UE 10 may perform one or more FD predictions of a first set of one or more serving cells in serving frequencies, e.g., SSB frequencies, in a first frequency range, e.g., FR1 , when the UE 10 is configured with at least one serving cell in a serving frequency in the first frequency range which the UE 10 needs to perform measurements.
  • the UE 10 may perform one or more FD predictions on the second serving cell in the same frequency range.
  • the UE 10 may receive an RRC message, e.g. RRC Reconfiguration message, like an RRCReconfiguration, including a measurement configuration, e.g., IE MeasConfig, which includes one or more parameters for the one or more criteria, wherein the one or more criteria are based on which the UE 10 determines to perform one or more FD predictions of the subset of the configured one or more serving cells.
  • RRC message e.g. RRC Reconfiguration message, like an RRCReconfiguration, including a measurement configuration, e.g., IE MeasConfig, which includes one or more parameters for the one or more criteria, wherein the one or more criteria are based on which the UE 10 determines to perform one or more FD predictions of the subset of the configured one or more serving cells.
  • the one or more parameters may be included in a reporting configuration, e.g., IE ReportConfigNR.
  • IE ReportConfigNR e.g., IE ReportConfigNR.
  • the UE 10 may perform one or more FD predictions for the subset of configured one or more serving cells.
  • the one or more parameters may be included in a measurement object configuration, e.g., IE MeasObjectNR.
  • the measurement object configuration includes an SSB frequency associated to the SSB frequency of the serving cell for which the one or more parameters are related to.
  • the UE 10 may receive an RRC message, e.g., RRC Reconfiguration message, like an RRCReconfiguration, including a measurement configuration, e.g., IE MeasConfig, which includes one or more parameters for the one or more criteria, wherein the criteria are based on which the UE 10 determines to include one or more FD predictions of the subset of the configured one or more serving cells.
  • RRC message e.g., RRC Reconfiguration message, like an RRCReconfiguration, including a measurement configuration, e.g., IE MeasConfig, which includes one or more parameters for the one or more criteria, wherein the criteria are based on which the UE 10 determines to include one or more FD predictions of the subset of the configured one or more serving cells.
  • the one or more parameters may be included in a reporting configuration, e.g., IE ReportConfigNR.
  • a reporting configuration e.g., IE ReportConfigNR.
  • the UE 10 may rely on the associated reporting configuration to determine to include the one or more FD predictions.
  • the one or more parameters may be included in a measurement object configuration, e.g., IE MeasObjectNR.
  • the measurement object configuration includes an SSB frequency associated to the SSB frequency of the serving cell for which the one or more parameters are related to.
  • the UE 10 may receive an RRC message including a measurement configuration which includes one or more parameters based on which the UE 10 determines which serving cells are cells for which the UE 10 is to perform one or more FD predictions and/or serving cell measurements.
  • the measurement report may correspond to an RRC MeasurementReport message, as defined in TS 38.331.
  • the measurement report corresponds to a different, e.g., new, RRC message for including one or more FD predictions of one or more serving cells.
  • the UE 10 may include in the measurement report, a serving frequency identifier, e.g., serving cell identifier, or a serving frequency identifier, servCellld, physical cell identifier, and/or global cell identifier, associated to the one or more FD predictions which is being included in the measurement report.
  • a serving frequency identifier e.g., serving cell identifier, or a serving frequency identifier, servCellld, physical cell identifier, and/or global cell identifier, associated to the one or more FD predictions which is being included in the measurement report.
  • the UE 10 may include in the measurement report, a serving cell identifier, e.g., serving cell identifier, or a serving frequency identifier, servCellld, physical cell identifier, and/or global cell identifier, associated to the one or more FD predictions which is being included in the measurement report.
  • a serving cell identifier e.g., serving cell identifier, or a serving frequency identifier, servCellld, physical cell identifier, and/or global cell identifier, associated to the one or more FD predictions which is being included in the measurement report.
  • the UE 10 may include in the measurement report, a beam identifier for a serving cell, e.g. SSB index, CSI-RS resource identifier, and/or a beam identifier, associated to the one or more FD predictions which is being included in the measurement report.
  • a beam identifier for a serving cell e.g. SSB index, CSI-RS resource identifier, and/or a beam identifier, associated to the one or more FD predictions which is being included in the measurement report.
  • the UE 10 may include in the measurement report, one or more FD predictions for a serving cell, wherein the FD prediction comprises FD predictions of one or more beams of the serving cell.
  • the UE 10 may include in the measurement report, one or more FD predictions for a serving cell, wherein the FD prediction comprises FD predictions of one or more beams of the serving cell, in addition to one or more FD predictions of that serving cell.
  • the UE 10 may include in the measurement report, one or more FD predictions for a serving cell, wherein the FD prediction comprises FD predictions of one or more beams of the serving cell, in addition to measurements of that serving cell.
  • the UE 10 may include in the measurement report, one or more FD predictions for a serving cell, wherein the FD prediction comprises FD predictions of cell measurements of the serving cell, in addition to one or more FD predictions of beams of that serving cell.
  • the first radio network node 12 receives the measurement report from the UE 10, which may include one or more FD predictions of a subset of the configured one or more serving cells.
  • the first radio network node 12 may use the one or more FD predictions of one or more beams and/or one or more FD predictions of cell measurement, to select the target node in a handover preparation, in SN addition or SN change.
  • the first radio network node 12 may use the one or more FD predictions to trigger the Early Data Forwarding towards the target node, such as the second radio network node 13, so that data is available when the handover, SN addition or SN change is executed.
  • the one or more measurements, used by the UE 10 as input; or related to the FD predictions or estimates, produced as the output of the AI/ML model for one or more FD predictions e.g. FD prediction of a measurement, may comprise one or more of the following:
  • a radio measurement i.e. a measurement performed on one or more reference signals received by the UE over the radio interface
  • a reference signal type e.g. SSB, CSI-RS, or Mobility Reference Signal (MRS).
  • MRS Mobility Reference Signal
  • SS-RSRP Synchronization Signal reference signal received power
  • this may be defined as the linear average over the power contributions (in [W]) of the resource elements that carry secondary synchronization signals.
  • SS-RSRQ SS reference signal received quality
  • SS-RSRQ SS reference signal received quality
  • this may be defined as the ratio of NxSS-RSRP / NR carrier Received Signal Strength Indicator (RSSI), where N is the number of resource blocks in the NR carrier RSSI measurement bandwidth.
  • RSSI Received Signal Strength Indicator
  • SS-SINR SS signal-to- interference plus noise ratio
  • a cell-based measurement quantity e.g. cell based RSRP, cell based RSRQ, cell based SINR,
  • a cell-based measurement quantity derived as the highest beam measurement quantity value e.g. highest SS-RSRP of the cell, highest SS-RSRQ of the cell, highest SS-SINR of the cell
  • a cell-based measurement quantity derived as the linear power scale average of the highest beam measurement quantity values above a threshold e.g., absThreshSS- BlocksConsolidation, where the total number of averaged beams shall not exceed an integer threshold, e.g., nrofSS- BlocksToAverage, e.g., average of SS-RSRP values of the cell.
  • CSI reference signal received power o
  • this may be defined as the linear average over the power contributions (in [W]) of the resource elements of one or more antenna ports that carry CSI reference signals configured for RSRP measurements within the considered measurement frequency bandwidth in the configured CSI-RS occasions.
  • CSI-RSRQ CSI reference signal received quality
  • this may be defined as CSI-RSRQ, which is defined as the ratio of NxCSI-RSRP to CSI- RSSI, where N is the number of resource blocks in the CSI-RSSI measurement bandwidth.
  • the measurements in the numerator and denominator shall be made over the same set of resource blocks.
  • CSI-SINR CSI signal-to- interference plus noise ratio
  • a cell-based measurement quantity e.g., cell based RSRP, cell based RSRQ, cell based SINR,
  • a cell-based measurement quantity derived as the highest beam measurement quantity value e.g., highest CSI-RSRP of the cell, highest CSI-RSRQ of the cell, highest CSI-SINR of the cell,
  • a cell-based measurement quantity derived as the linear power scale average of the highest beam measurement quantity values above a threshold e.g., absThreshSS- BlocksConsolidation, where the total number of averaged beams shall not exceed an integer threshold, e.g., nrofSS- BlocksToAverage, e.g., average of CSI-RSRP values of the cell.
  • a measurement quantity such as RSRP, RSRQ or SINR.
  • a measurement corresponds to an RSRP value, so that a measurement of a neighbor cell corresponds to an RSRP value of the neighbor cell.
  • a measurement quantity such as an RSRP and/or RSRQ and/or SINR and/or RSSI value in dB and/or dBm.
  • a measurement of a cell which may also be called cell quality or cell measurement result, wherein the measurement of a cell may be performed based on one or more beam measurements.
  • a measurement which is filtered according to one or more filter parameters configured by the network e.g., a L3 filtered measurement, with a time-domain filtered.
  • a cell-based measurement result or cell measurement wherein a measurement value represents a cell quality e.g. RSRP of a cell, RSRQ of a cell
  • a beam-based measurement result or beam measurement wherein a measurement value represents a beam quality e.g. RSRP of a beam, RSRQ of a beam, SINR of a beam.
  • a beam-based measurement may also be an RS based measurement when the RS is transmitted in a spatial direction or beam e.g. SSB measurement may correspond to a measurement associated to an SSB index, like an SS-RSRP value; CSI-RS measurement may correspond to a measurement associated to an CSI-RS resource index/ identifier, like a CSI-RSRP value
  • a L1-RSRP comprises an RSRP measurement on an SSB of a cell, wherein such as an SS-RSRP wherein required are defined specifically for L1-RSRP, in contrast to SS-RSRP values used for deriving cell measurement results and/or L3 filtered SS-RSRP measurements.
  • a beam may be characterized as a spatial direction in which a network entity, e.g., a UE and/or a base station, is transmitting a reference signal and/or a direction in which a network entity is receiving a reference signal, so that the RS transmitted in a beam may include an RS identifier which represents the beam, so that it may be considered as a beam identifier.
  • a network entity e.g., a UE and/or a base station
  • the one or more inputs, of the AI/ML model for FD prediction e.g. of inter-frequency RRM measurements may correspond to one or more measurements, e.g. RSRP, RSRQ, SINR, RSSI, and/or similar, performed on one or more reference signals of a first serving cell a UE is configured with, such as SSBs and/or CSI-RSs measurements of an SpCell or an SCell, e.g., of a MCG or a SCG, wherein the one or more RSs are in a serving frequency F_0, e.g., SSB frequency, initial frequency in a frequency grid, point A frequency, etc., and/or a subcarrier spacing (SCS) SCS_0.
  • F_0 serving frequency
  • F_0 e.g., SSB frequency, initial frequency in a frequency grid, point A frequency, etc.
  • SCS subcarrier spacing
  • the one or more predictions/ estimates/decisions of a certain type related to a second frequency may correspond to one or more predictions or estimates, so called FD predictions or estimates, of one or more measurements, e.g. RSRP, RSRQ, SINR, and/or RSSI, etc., of a second serving cell the UE is configured with, wherein the second serving cell is in a second serving frequency F_1 , e.g. SSB frequency, initial frequency in a frequency grid, and/or a point A frequency, etc., and/or has a subcarrier spacing SCS_1.
  • F_1 e.g. SSB frequency, initial frequency in a frequency grid, and/or a point A frequency, etc.
  • both the SSB frequency and the SCS are different for the first serving cell and the second serving cell.
  • the first and the second serving cells have the same SSB frequency, but different SCSs.
  • the first and the second serving cells have the same SCSs, but different SSB frequencies.
  • FIG. 8a shows input and output of an AI/ML model for FD prediction.
  • the one or more inputs may correspond to one or more measurements, e.g. RSRP, RSRQ, SINR, RSSI, etc., performed on one or more reference signals of a first set of serving cells the UE 10 is configured with, such as SSBs and/or CSI-RSs measurements of an SpCell or an SCell, e.g. of a MCG or a SCG, wherein the one or more RSs are in a serving frequency F_0, F_1, F_k1 , e.g., SSB frequency, initial frequency in a frequency grid, point A frequency, etc., and/or a subcarrier spacing SCS_0, SCS_1, SCS_k1.
  • the UE 10 could measure multiple serving cells and these measurements are used as input to the AI/ML model, so that the model produces FD predictions or estimates of measurements in for another serving cell, e.g., in another frequency, which is not being measured.
  • the one or more predictions/ estimates/decisions of a certain type related to a second frequency may correspond to one or more FD predictions or estimates of one or more measurements a second serving cell the UE 10 is configured with and which is not in the first set of serving cells, wherein the second serving cell is in a second serving frequency F_k2, e.g., SSB frequency, initial frequency in a frequency grid, point A frequency, etc., and/or has a subcarrier spacing SCS_k2.
  • F_k2 e.g., SSB frequency, initial frequency in a frequency grid, point A frequency, etc.
  • the SSB frequency of the second serving cell is different from the SSB frequencies of the cells in the first set of serving cells, but the SCS_k2 may either be different from all, or be the same as one or more of the serving cells in the first set of serving cells.
  • the SCS_k2 of the second serving cell is different from the SCS of the cells in the first set of serving cells, but the SSB frequency F_k2 may either be different from all the serving cells or be the same as one or more of the serving cells in the first set of serving cells.
  • FIG. 8b shows input and output of an AI/ML model for FD prediction.
  • the one or more FD predictions of a serving cell may comprise one or more of the following:
  • the FD DL beam prediction for a Set A e.g., in a first RS frequency
  • the FD DL beam prediction for a Set A e.g., in a first RS frequency
  • the FD DL beam prediction for a Set A e.g., in a first RS frequency
  • the FD DL beam prediction for a Set A e.g., in a first RS frequency
  • the FD DL beam prediction for a Set A e.g., in a first RS frequency
  • Set B of beams e.g., in a second RS frequency. See Fig. 9a.
  • Fig. 9a shows an example of the AI/ML model using the RSRP measurements, e.g., L1-RSRP values, from beams in Set B in a second frequency, as input predicts the best, e.g., strongest RSRP, beam in set A, in a second RS frequency.
  • the output of the AI/ML model could be predicted beam IDs with/without predicted RSRP values, e.g., with/without predicted L1-RSRP values, for the beams in the other frequency.
  • Fig. 9a shows an example of the AI/ML model using the RSRP measurements, e.g., L1-RSRP values, from beams in Set B in a second frequency, as input predicts the best, e.g., strongest RSRP, beam in set A, in a second RS frequency.
  • the output of the AI/ML model could be predicted beam IDs with/without predicted RSRP values, e.g., with/without predicted L1-RSRP values, for the beams in
  • 9b shows an example of the AI/ML model using the RSRP measurements, e.g., L1-RSRP values, from beams in Set B, in a second RS frequency, e.g., SSB frequency and/or SCS, as input predicts the Top-K beams, e.g. in terms of RSRP, in set A, in a second RS frequency, e.g., SSB frequency and/or SCS.
  • the output of the AI/ML model could be predicted beam IDs with/without predicted RSRP values.
  • the Set A of beams and the Set B of beams may be of different serving cells, each in different RS frequencies and/or different SCSs.
  • the Set A of beams are DL beams transmitting SSBs of a first serving cell, i.e., each SSB of the Set A comprises a PCI of the first serving cell, e.g., PCell, and is transmitted in a first SSB frequency
  • each SSB of the Set B comprises a PCI of the second serving cell, e.g. PCell, and is transmitted in a second SSB frequency.
  • the different SSBs are transmitted in different beams, meaning they are transmitted in different spatial directions.
  • the FD beam prediction may comprise a prediction of a measurement quantity of a reference signal, such as an SSB or CSI-RS, e.g., predicted L1 -RSRP values/L1 -based SS-RSRP.
  • a reference signal such as an SSB or CSI-RS, e.g., predicted L1 -RSRP values/L1 -based SS-RSRP.
  • the FD prediction information the UE 10 derives may correspond to L1-RSRP for SSB (1), to L1-RSRP for SSB (2), to L1-RSRP for SSB (3), to L1-RSRP for SSB (4), based on the L1- RSRP for SSB (5), to L1-RSRP for SSB (6), to L1-RSRP for SSB (7), to L1-RSRP for SSB (8).
  • the FD DL beam prediction may comprise a beam identifier, e.g., SSB index, CSI-RS resource identity, beam ID, of a first SSB frequency, derived based on a prediction of a measurement quantity of an RS in which the beam is transmitted.
  • Set A and Set B may be different beams and/or have different beam identifiers i.e.
  • Set B is not a subset of Set A.
  • Set B consists of SSB beams
  • Set A consists of CSI-RS beams, wherein SSB beams are transmitted in a first frequency and CSI-RS beams are transmitted in a second frequency. Notice that the fact that the sets A and B are transmitted in different frequencies, does not necessarily imply that they are not correlated.
  • Set B may be a subset of Set A, in terms of beam indexes.
  • Set A and Set B may be transmitted in different frequencies, but the beam indexes may be the same, which could imply some correlation, for example: Set A corresponds to [SSB(1), SSB(2), SSB (3), SSB (4)] in the first RS frequency, e.g., SSB frequency, and that Set B corresponds to [SSB(1), SSB(2), SSB (3), SSB (4)] in the second RS frequency, e.g., SSB frequency.
  • the AI/ML model may receive as input one or more of the following:
  • the one or more FD DL beam predictions may be one or more outputs of an AI/ML model
  • a FD DL beam prediction may correspond to one or more of the following:
  • the predicted L1-RSRP of the N predicted one or more DL Tx and/or Rx beams e.g., top N predicted beams, strongest N predicted beams; •
  • N predicted RSRP values, Layer 1 RSRP, or other measurement quantities per beam and/or per RS transmitted on a spatial direction or beam such as SS-RSRP, SS-RSRQ, SS-SINR, CSI-RSRP, CSI-RSRQ, CSI-SINR.
  • an FD prediction for a cell X also denoted as FD cell prediction, may:
  • the FD prediction for a serving cell X may correspond to the value of a measurement quantity, e.g., an RSRP value, an RSRQ value, an SINR value, representing the measurement quantity of the serving cell X, or a cell quality or cell measurement result, wherein the FD prediction for cell X may be calculated based on one or more FD beam predictions of a Set A of beams of cell X, e.g., predicted RSRP values for each beam in the Set A of beams.
  • a measurement quantity e.g., an RSRP value, an RSRQ value, an SINR value
  • the FD prediction for cell X may be calculated based on one or more FD beam predictions of a Set A of beams of cell X, e.g., predicted RSRP values for each beam in the Set A of beams.
  • the one or more FD beam predictions for a Set A of beams of cell X is calculated based on measurements of a Set B of beams of cell X, wherein Sets A and B are in different frequencies and/ or SCSs.
  • the UE 10 derives as prediction information the values of SS-RSRP for SSB (1), SS-RSRP for SSB (2), SS- RSRP for SSB (3), SS-RSRP for SSB (4), based on the SS-RSRP for SSB (5), SS-RSRP for SSB (6), SS-RSRP for SSB (7), SS- RSRP for SSB (8).
  • the UE 10 uses the obtained predictions of SS-RSRP for SSB (1), SS-RSRP for SSB (2), to SS-RSRP for SSB (3), to SS-RSRP for SSB (4) to calculate the FD prediction of cell X e.g. highest SS-RSRP value out of SS-RSRP for SSB (1), SS-RSRP for SSB (2), SS-RSRP for SSB (3), to SS-RSRP for SSB (4).
  • SS-RSRP for example, may correspond to the L1-RSRP of an SSB, which is the quantity used for LTM execution decisions.
  • the one or more FD beam predictions for a Set A of beams of cell X in a first RS frequency is calculated based on measurements of a Set B of beams of cell Y in a second RS frequency.
  • the UE 10 derives as prediction information the values of SS-RSRP for SSB (1), SS-RSRP for SSB (2), SS- RSRP for SSB (3), SS-RSRP for SSB (4) of Cell X, based on the SS-RSRP for SSB (5), SS-RSRP for SSB (6), SS-RSRP for SSB (7), SS-RSRP for SSB (8)
  • the UE 10 uses the obtained predictions of SS-RSRP for SSB (1), SS-RSRP for SSB (2), SS- RSRP for SSB (3), SS-RSRP for SSB (4) to calculate the prediction of cell X e.g. highest SS-RSRP value out of SS-RSRP for SSB (1), SS-RSRP for SSB (2), SS-RSRP for SSB (3), to SS-RSRP for SSB (4).
  • SS-RSRP for example, may correspond to the L1-RSRP of an SSB, which is the quantity used for LTM execution decisions.
  • the FD prediction for a Set A of cells may be based on measurement results, e.g., cell quality, of Set B of cells.
  • the FD prediction information the UE 10 derives may correspond to cell quality for Cell (1), to cell quality for Cell(2), to cell quality for Cell (3), to cell quality for Cell (4), based on the cell quality for Cell (5), cell quality for Cell (6), cell quality for Cell (7), cell quality for Cell (8).
  • the prediction information can be best cell ID/Top-K cell IDs with/without predicted cell quality.
  • Set A of Cells and Set B of Cells are different (Set B is NOT a subset of Set A).
  • Set B is a subset of Set A
  • Set A and Set B are the same
  • the input to the AI/ML model may be measurements on one/more Set B of beams and cell quality of cells in set B of cells.
  • the beams in the one/more set B are from cells in set B of cells. For example, assuming set B of cells corresponding to [Cell(1 ),Cell(2)].
  • the AI/ML model input are: cell quality of Cell(1) and Cell(2), and measurements of SSB(1), SSB(2), SSB(3), SSB(4), SSB(5), SSB(6).
  • An AI/ML model may be designed to realize the beam-level measurement prediction in the FD. Utilizing the one or more predicted beam-level measurement qualities or/and beam IDs generated from the AI/ML model output, a predicted cell-level measurement quality for a cell X can be derived using the approaches described above.
  • An AI/ML model can also be designed to directly output the predicted cell-level measurement by taking L3 measurements of a set of beams as model input. Besides predicted beam-level and/or cell-level measurement quantities and beam/cell IDs, the model may also provide additional information like confidence level of the model output, the validation time of the predicted measurements, etc.
  • the designed AI/ML model can be deployed at the UE 10 and associated to an FD beam prediction feature or an RRM prediction feature.
  • the UE 10 may report its support of the AI/ML model for the FD beam prediction feature or RRM prediction feature to the network node via UE capability reporting, e.g., in a UE Capability Information message.
  • the first radio network node 12 may make decisions on whether to configure/activate the AI/ML model at the UE 10 or not.
  • there may be different capabilities e.g. indication of a maximum number of FD predictions for one or more serving cells.
  • the UE 10 is capable of performing and/or reporting indication of the UE 10 being capable of performing one or more FD predictions of a serving cell when the UE 10 is configured with at least another serving cell in the same frequency band for which the UE 10 perform measurements, etc.
  • the AI/ML model used for FD prediction, or FD cell prediction in an example, it is composed of multiple connected neurons. Optionally it contains one or a few of input layer, one or a few of hidden layer, and one output layer.
  • the input layer it takes UE measurements results as the model input, where the measurement results are obtained based on measuring one or more RSs, e.g., SSBs and/or CSI-RSs in a first RS frequency, for performing FD predictions in another RS frequency.
  • the measurement results would be normalized before input to the hidden layers. The normalization can change the value of the numeric variable in the dataset to a typical scale which improve model training.
  • the function applies weights to the inputs and directs them through an activation function to the output layer.
  • activation function can be one of Softmax function, Sigmoid function, ReLU function, Leaky ReLLI, tanh function and Maxout.
  • the output can be predicted RSRP values for each beam.
  • the output can be the probability values where each value means the probability of the beam to be the best beam.
  • the AI/ML model used for FD prediction is based on convolutional neural networks, optionally it contains one or a few of input layers, one of a few of convolution layer, one or a few of pooling layer and output layer.
  • the input layer it takes UE measurements results performed in a first RS frequency, e.g., first SSB frequency, as the model input, where the measurement results are obtained based on measuring some reference signals, e.g., SSBs and/or CSI-RSs, for obtaining output for a second RS frequency.
  • the measurement results would be normalized before input to the convention layers.
  • the normalization can change the value of the numeric variable in the dataset to a typical scale which improve model training.
  • the one or more convention layers it is used to extract the feature from the input. It applies a set of learnable filters, known as the kernels, to the input with smaller size than the whole input. These filters and kernels slide over the input data and compute the dot product between kernel weight and the corresponding input.
  • the output of convention layer is referred as feature maps coming from the input measured RSRP values.
  • For pooling layers it involves sliding a two-dimensional filter over each channel of feature map and summarizing the features lying within the region covered by the filter. Before the output layer, there can be a fully connected layer to interpret/summarize the features obtained and directs them through activation function to the output layer.
  • the output can be predicted RSRP values for each beam.
  • the output can be the probability values where each value means the probability of the beam to be the best beam.
  • an AI/ML model is designed to realize the beam-level measurement prediction in the FD.
  • a predicted cell-level measurement quality for a cell X, in a first SSB frequency can be derived based on the one or more predicted beam-level measurement qualities or/and beam IDs generated from the AI/ML model output in that first SSB frequency.
  • Different design options for AI/ML based spatial domain beam prediction can be considered. Three example design options are mentioned below.
  • the AI/ML model predicts one or more top-1/K beam IDs, where the model takes the, postprocessed, RSRP measurements of the beams in set B, in a second RS frequency and/or SCS, as model input and directly outputs the one or more top-1/K beam IDs of the beams in Set A, in a first RS frequency and/or SCS.
  • the AI/ML model predicts one or more top-1/K beam IDs and the associated predicted RSRP values, where the model takes the postprocessed RSRP measurements of the beams in set B, in a second RS frequency and/or SCS, as model input and directly outputs the one or more top-1/K beam IDs and the predicted RSRP values of these beams in set A, in a first RS frequency and/or SCS.
  • the AI/ML model predicts one or more top-1/K beam IDs with/without the associated predicted RSRP values, where the model takes the, e.g., postprocessed, RSRP measurements of the beams in set B, e.g., in a second RS frequency and/or SCS, or/and the assistance information like UE position as model input.
  • the AI/ML model mentioned in the above three design options can be based on neural network architectures, e.g., convolutional neural network (CNN), fully connected neural network (NN), Residual Networks (ResNet).
  • CNN convolutional neural network
  • NN fully connected neural network
  • ResNet Residual Networks
  • the Figs. 10a and 10b show two examples of model architectures, where Neural network B (NN B) is a model with higher complexity in comparison to Neural network A (NN A).
  • the number of nodes in the dense layers equals the number of beams in Set A, N SetA .
  • the model input takes RSRP of SSB and/or CSI RS of set B beams, one real value per measured beam, normalized based on min and max values per sample, wherein these are in a second RS frequency.
  • Normalization is based on scaling the beam RSRP values in dB per sample to yield the range 0.0 to 1.0 for RSRP values for each sample.
  • assistance information such as UE location information
  • that information is concatenated to the RSRP values after being separately scaled by a fixed scaling factor designed to yield values with maximum magnitudes in the order of 1.
  • a softmax cross-entropy function is used to generate the probability of a beam being the strongest beam, used to derive top-1/K beams.
  • Figs. 10a-10b show examples of Neural Networks used for designing AI/ML models for FD beam prediction.
  • the number of nodes in the dense layers equals the number of beams N SetA in Set A.
  • the AI/ML model mentioned in the above design options can be based on transformer encoder architecture.
  • the encoder may be composed of a stack of identical layers where each layer has some sub-layers.
  • Fig. 10c-10d show examples of transformer encoder based NN and the encoder architecture.
  • Fig. 10c-10d show examples of transformer based Neural Networks used for designing AI/ML models for FD beam prediction.
  • the number of nodes in the dense layers equals the number of beams N SetA in Set A.
  • Figs. 11a and 11b show an example of multi hear self-attention and scaled dot-product attention.
  • Figs. 11a-11b show multi hear self-attention and scaled dot-product attention in a transformer encoder based neural network.
  • Fig. 12 is a block diagram depicting embodiments of the UE 10 for handling communication of the UE 10 in the wireless communications network 1 according to embodiments herein.
  • the UE 10 may comprise processing circuitry 901 , e.g., one or more processors, configured to perform the methods herein.
  • processing circuitry 901 e.g., one or more processors, configured to perform the methods herein.
  • the UE 10 and/or the processing circuitry 901 may be configured to obtain the measurement configuration comprising the configuration indication based on which the UE 10 triggers the measurement report.
  • the measurement configuration may comprise the value or index indicating the configuration indication and/or the prediction configuration.
  • the UE 10 and/or the processing circuitry 901 may be configured to receive from the first radio network node 12 the measurement configuration, including the configuration indication based on which the UE 10 triggers the measurement report.
  • the UE 10 and/or the processing circuitry 901 may be configured to perform the FD prediction based on one or more criteria. For example, the UE 10 and/or the processing circuitry 901 may be configured to determine to perform one or more FD predictions of the subset of the configured one or more serving cells based on one or more criteria, wherein the performed one or more FD predictions are to be included in a measurement report.
  • the UE 10 and/or the processing circuitry 901 may be configured to determine to include the indication of the FD prediction in the measurement report based on the one or more criteria.
  • the UE 10 and/or the processing circuitry 901 may be configured to determine to include the (one or more) FD predictions of the subset of the configured one or more serving cells in the measurement report based on the one or more criteria.
  • the UE 10 and/or the processing circuitry 901 is configured to include or add in the measurement report, the indication of the FD prediction of the one or more serving cells out of the one or more configured serving cells.
  • the one or more serving cells may be fewer than the one or more configured serving cells.
  • the UE 10 and/or the processing circuitry 901 may be configured to include one or more FD predictions of a subset of the configured one or more serving cells.
  • the UE 10 and/or the processing circuitry 901 may be configured to include in the measurement report one or more measurements of another subset of the configured one or more serving cells. Hence, the another subset are fewer than the configured one or more serving cells.
  • the UE 10 and/or the processing circuitry 901 may be configured to include the associated information indicating that this indication is for the FD prediction, and not an actual measurement.
  • the UE may be configured with one or more serving cells, in which the UE includes in the measurement report, e.g., an RRC MeasurementReport, one or more FD predictions of a subset of the configured one or more serving cells.
  • the serving cell may be associated with a serving frequency and/or a subcarrier spacing.
  • the UE 10 and/or the processing circuitry 901 is configured to transmit the measurement report with the indication to the first radio network node 12.
  • the one or more criteria may be based on one or more of the following (or combinations of the following):
  • the UE 10 may comprise a memory 906.
  • the memory 906 comprises one or more units to be used to store data on, such as FD predictions, measurements, measurement reports, beam information, cell information, indications, time indications, HO information, mobility events, measurements, events and applications to perform the methods disclosed herein when being executed, and similar.
  • the UE 10 may comprise a communication interface 907 comprising such as a transmitter, a receiver, a transceiver and/or one or more antennas.
  • the methods according to the embodiments described herein for the UE 10 are respectively implemented by means of e.g., a computer program product 908 or a computer program, comprising instructions, i.e. , software code portions, which, when executed on at least one processor, cause the at least one processor to carry out the actions described herein, as performed by the UE 10.
  • the computer program product 908 may be stored on a computer-readable storage medium 909, e g., a disc, a universal serial bus (USB) stick or similar.
  • the computer-readable storage medium 909, having stored thereon the computer program product may comprise the instructions which, when executed on at least one processor, cause the at least one processor to carry out the actions described herein, as performed by the UE 10.
  • the computer-readable storage medium may be a transitory or a non-transitory computer- readable storage medium.
  • embodiments herein may disclose a UE for handling communication in a wireless communications network, wherein the UE comprises processing circuitry and a memory, said memory comprising instructions executable by said processing circuitry whereby said UE is operative to perform any of the methods herein.
  • Fig. 13 is a block diagram depicting embodiments of the first radio network node 12, such as an eNB or a gNB, for handling communication of the UE 10 in the wireless communications network 1 according to embodiments herein.
  • the first radio network node 12 such as an eNB or a gNB
  • the first radio network node 12 may comprise processing circuitry 911 , e.g., one or more processors, configured to perform the methods herein.
  • processing circuitry 911 e.g., one or more processors, configured to perform the methods herein.
  • the first radio network node 12 and/or the processing circuitry 911 may be configured to configure the UE 10 with the measurement configuration comprising the configuration indication based on which the UE 10 triggers the measurement report.
  • the measurement configuration may comprise the value or index indicating the configuration indication and/or the prediction configuration.
  • the first radio network node 12 and/or the processing circuitry 911 may be configured to transmit to the UE 10 the measurement configuration, including the configuration indication based on which the UE 10 triggers the measurement report.
  • the first radio network node 12 and/or the processing circuitry 911 may be configured to configure the UE 10 with one or more parameters associated to the one or more criteria for the UE 10 to determine to include the one or more FD predictions of the subset of the configured one or more serving cells in a measurement report; and/or to determine to perform the one or more FD predictions of the subset of the configured one or more serving cells in a measurement report.
  • the first radio network node 12 and/or the processing circuitry 911 may be configured to configure the UE 10 to transmit the measurement report, e.g. an RRC MeasurementReport, which includes one or more FD predictions of a subset of the configured one or more serving cells, wherein a serving cell is associated with a serving frequency and/or a subcarrier spacing.
  • the first radio network node 12 and/or the processing circuitry 911 is configured to receive the measurement report from the UE 10, wherein the measurement report includes the indication of the FD prediction of the one or more serving cells out of the one or more configured serving cells.
  • the measurement report may include one or more FD predictions of a subset of the configured one or more serving cells.
  • the measurement report may include one or more measurements of another subset of the configured one or more serving cells.
  • the measurement report may further include the associated information indicating that this indication is for a FD prediction, and not an actual measurement.
  • the first radio network node 12 and/or the processing circuitry 911 may be configured to transmit the measurement report including the indication of the FD prediction of one or more serving cells out of one or more serving cells of the first radio network node.
  • the first radio network node 12 and/or the processing circuitry 911 may be configured to transmit the indication, such as one or more FD predictions, of the subset of the configured one or more serving cells to the second radio network node 13 operating as a target node: in a handover preparation; in a secondary node (SN) addition or a secondary node change.
  • SN secondary node
  • the first radio network node 12 and/or the processing circuitry 911 may be configured to perform the action related to communication served by the first radio network node 12 based on the received indication.
  • the first radio network node 12 and/or the processing circuitry 911 may be configured to use the indication when performing load balancing, traffic steering, mobility, updates on the SCell state, decisions on SCells to be Conditional Handover candidates, LTM candidates and /or similar.
  • the first radio network node 12 may comprise a memory 916.
  • the memory 916 comprises one or more units to be used to store data on, such as measurements, measurement reports, beam information, FD predictions, actions, cell information, indications, time indications, HO information, mobility events, events and applications to perform the methods disclosed herein when being executed, and similar.
  • the first radio network node 12 may comprise a communication interface 917 comprising such as a transmitter, a receiver, a transceiver and/or one or more antennas.
  • the methods according to the embodiments described herein for the first radio network node 12 are respectively implemented by means of e.g., a computer program product 918 or a computer program, comprising instructions, i.e., software code portions, which, when executed on at least one processor, cause the at least one processor to carry out the actions described herein, as performed by the first radio network node 12.
  • the computer program product 918 may be stored on a computer-readable storage medium 919, e.g., a disc, a universal serial bus (USB) stick or similar.
  • the computer- readable storage medium 919 may comprise the instructions which, when executed on at least one processor, cause the at least one processor to carry out the actions described herein, as performed by the first radio network node 12.
  • the computer-readable storage medium may be a transitory or a non-transitory computer-readable storage medium.
  • embodiments herein may disclose a first radio network node 12 for handling communication in a wireless communications network, wherein the first radio network node 12 comprises processing circuitry and a memory, said memory comprising instructions executable by said processing circuitry whereby said first radio network node is operative to perform any of the methods herein.
  • Fig. 14 is a block diagram depicting embodiments of the second radio network node 13, such as an eNB or a gNB, for handling communication of the UE 10 in the wireless communications network 1 according to embodiments herein.
  • the second radio network node 13 such as an eNB or a gNB
  • the second radio network node 13 may comprise processing circuitry 921 , e.g., one or more processors, configured to perform the methods herein.
  • the second radio network node 13 and/or the processing circuitry 921 is configured to receive the measurement report from the first radio network node 12, wherein the measurement report includes the indication of the FD prediction of one or more serving cells out of one or more serving cells of the first radio network node 12.
  • the measurement report may include one or more FD predictions of a subset of the configured one or more serving cells.
  • the measurement report may further include the associated information indicating that this is a FD prediction, and not an actual measurement.
  • the second radio network node 13 may be a target network node in a handover, in SN addition, or SN change.
  • the second radio network node 13 and/or the processing circuitry 921 may be configured to perform the action related to communication served by the second radio network node 13 based on the received indication.
  • the second radio network node 13 and/or the processing circuitry 921 may be configured to use the indication to decide handover, and/or when performing: load balancing, traffic steering, mobility, updates on the SCell state, decisions on SCells to be Conditional Handover candidates, LTM candidates and /or similar.
  • the second radio network node 13 may comprise a memory 926.
  • the memory 926 comprises one or more units to be used to store data on, such as measurements, measurement reports, beam information, FD predictions, actions, cell information, indications, time indications, HO information, mobility events, events and applications to perform the methods disclosed herein when being executed, and similar.
  • the second radio network node 13 may comprise a communication interface 927 comprising such as a transmitter, a receiver, a transceiver and/or one or more antennas.
  • the methods according to the embodiments described herein for the second radio network node 13 are respectively implemented by means of e.g., a computer program product 928 or a computer program, comprising instructions, i.e., software code portions, which, when executed on at least one processor, cause the at least one processor to carry out the actions described herein, as performed by the second radio network node 13.
  • the computer program product 928 may be stored on a computer-readable storage medium 929, e.g., a disc, a universal serial bus (USB) stick or similar.
  • the computer- readable storage medium 929 may comprise the instructions which, when executed on at least one processor, cause the at least one processor to carry out the actions described herein, as performed by the second radio network node 13.
  • the computer-readable storage medium may be a transitory or a non-transitory computer-readable storage medium.
  • embodiments herein may disclose a second radio network node 13 for handling communication in a wireless communications network, wherein the second radio network node 13 comprises processing circuitry and a memory, said memory comprising instructions executable by said processing circuitry whereby said second radio network node 13 is operative to perform any of the methods herein.
  • a more general term “network node” is used and it can correspond to any type of radio-network node or any network node, which communicates with a wireless device and/or with another network node.
  • network nodes are NodeB, MeNB, SeNB, a network node belonging to Master Cell Group (MCG) or Secondary Cell Group (SCG), Base Station (BS), Multi-Standard Radio (MSR) radio node such as MSR BS, eNodeB, gNodeB, network controller, RNC, BSC, relay, donor node controlling relay, Base Transceiver Station (BTS), Access Point (AP), transmission points, transmission nodes, Remote Radio Unit (RRU), Remote Radio Head (RRH), nodes in Distributed Antenna System (DAS), etc.
  • MCG Master Cell Group
  • SCG Secondary Cell Group
  • BS Base Station
  • MSR Multi-Standard Radio
  • the non-limiting term wireless device or UE refers to any type of wireless device communicating with a network node and/or with another wireless device in a cellular or mobile communication system.
  • Examples of UE are target device, D2D UE, proximity capable UE (aka ProSe UE), machine type UE or UE capable of Machine to Machine (M2M) communication, Tablet, mobile terminals, smart phone, Laptop Embedded Equipped (LEE), Laptop Mounted Equipment (LME), USB dongles etc.
  • Embodiments are applicable to any RAT or multi-RAT systems, where the wireless device receives and/or transmit signals (e.g. data) e.g. NR, Wi-Fi, LTE, LTE-Advanced, WCDMA, GSM/Enhanced Data rate for GSM Evolution (GSM/EDGE), Worldwide Interoperability for Microwave Access (WiMax), or Ultra Mobile Broadband (UMB), just to mention a few possible implementations.
  • signals e.g. NR, Wi-Fi, LTE, LTE-Advanced, WCDMA, GSM/Enhanced Data rate for GSM Evolution (GSM/EDGE), Worldwide Interoperability for Microwave Access (WiMax), or Ultra Mobile Broadband (UMB), just to mention a few possible implementations.
  • signals e.g. NR, Wi-Fi, LTE, LTE-Advanced, WCDMA, GSM/Enhanced Data rate for GSM Evolution (GSM/EDGE), Worldwide Interoperability for Microwave Access (WiMax), or Ultra Mobile Broadband
  • the functions means or circuits may be implemented using digital logic and/or one or more microcontrollers, microprocessors, or other digital hardware. In some embodiments, several or all of the various functions may be implemented together, such as in a single Application-Specific Integrated Circuit (ASIC), or in two or more separate devices with appropriate hardware and/or software interfaces between them. Several of the functions may be implemented on a processor shared with other functional components of a wireless device or network node, for example.
  • ASIC Application-Specific Integrated Circuit
  • processors or “controller” as used herein does not exclusively refer to hardware capable of executing software and may implicitly include, without limitation, Digital Signal Processor (DSP) hardware and/or program or application data. Other hardware, conventional and/or custom, may also be included. Designers of communications devices will appreciate the cost, performance, and maintenance trade-offs inherent in these design choices.
  • DSP Digital Signal Processor
  • 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 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 to one or more embodiments of the present disclosure.
  • FIG. 15 shows an example of a communication system QQ100 in accordance with some embodiments.
  • the communication system QQ100 includes a telecommunication network QQ102 that includes an access network QQ104, such as a radio access network (RAN), and a core network QQ106, which includes one or more core network nodes QQ108.
  • the access network QQ104 includes one or more access network nodes, such as network nodes QQ110a and QQ110b (one or more of which may be generally referred to as network nodes QQ110) being examples of the first radio network node 12 and second radio network node 13, or any other similar 3 rd Generation Partnership Project (3GPP) access nodes or non-3GPP access points.
  • 3GPP 3 rd Generation Partnership Project
  • a network node being examples of the entities herein, is not necessarily limited to an implementation in which a radio portion and a baseband portion are supplied and integrated by a single vendor.
  • network nodes include disaggregated implementations or portions thereof.
  • the telecommunication network QQ102 includes one or more Open-RAN (ORAN) network nodes.
  • ORAN Open-RAN
  • An ORAN network node is a node in the telecommunication network QQ102 that supports an ORAN specification (e.g., a specification published by the O-RAN Alliance, or any similar organization) and may operate alone or together with other nodes to implement one or more functionalities of any node in the telecommunication network QQ102, including one or more network nodes QQ110 and/or core network nodes QQ108.
  • ORAN specification e.g., a specification published by the O-RAN Alliance, or any similar organization
  • Examples of an ORAN network node include an open radio unit (0-Rll), an open distributed unit (0-Dll), an open central unit (O-CU), including an O-CU control plane (O-CU-CP) or an O-CU user plane (O-CU-UP), a RAN intelligent controller (near- real time or non-real time) hosting software or software plug-ins, such as a near-real time control application (e.g., xApp) or a non-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification).
  • a near-real time control application e.g., xApp
  • rApp non-real time control application
  • the network node may support a specification by, for example, supporting an interface defined by the ORAN specification, such as an A1 , F1 , W1 , E1, E2, X2, Xn interface, an open fronthaul user plane interface, or an open fronthaul management plane interface.
  • an ORAN access node may be a logical node in a physical node.
  • an ORAN network node may be implemented in a virtualization environment (described further below) in which one or more network functions are virtualized.
  • the virtualization environment may include an O-Cloud computing platform orchestrated by a Service Management and Orchestration Framework via an 0-2 interface defined by the O-RAN Alliance or comparable technologies.
  • the network nodes QQ110 facilitate direct or indirect connection of the user equipment (UE) 10, such as by connecting UEs QQ112a, QQ112b, QQ112c, and QQ112d (one or more of which may be generally referred to as UEs QQ112) to the core network QQ106 over one or more wireless connections.
  • UE user equipment
  • 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.
  • the communication system QQ100 may include any number of wired or wireless networks, network nodes, UEs, and/or any other components or systems that may facilitate or participate in the communication of data and/or signals whether via wired or wireless connections.
  • the communication system QQ100 may include and/or interface with any type of communication, telecommunication, data, cellular, radio network, and/or other similar type of system.
  • the UEs QQ112 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and/or operable to communicate wirelessly with the network nodes QQ110 and other communication devices.
  • the network nodes QQ110 are arranged, capable, configured, and/or operable to communicate directly or indirectly with the UEs QQ112 and/or with other network nodes or equipment in the telecommunication network QQ102 to enable and/or provide network access, such as wireless network access, and/or to perform other functions, such as administration in the telecommunication network QQ102.
  • the core network QQ106 connects the network nodes QQ110 to one or more hosts, such as host QQ116. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts.
  • the core network QQ106 includes one more core network nodes (e.g., core network node QQ108) such as network node 15 that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and/or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node QQ108.
  • Example core network nodes include functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (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
  • the host QQ116 may be under the ownership or control of a service provider other than an operator or provider of the access network QQ104 and/or the telecommunication network QQ102, and may be operated by the service provider or on behalf of the service provider.
  • the host QQ116 may host a variety of applications to provide one or more service. Examples of such applications include live and prerecorded 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.
  • the communication system QQ100 of FIG. 15 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); Long Term Evolution (LTE), and/or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standard (e.g., 6G); wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (WiFi); and/or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, LiFi, and/or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox.
  • GSM Global System for Mobile Communications
  • UMTS Universal Mobile Telecommunications System
  • LTE Long Term
  • the telecommunication network QQ102 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network QQ102 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network QQ102. For example, the telecommunications network QQ102 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and/or Massive Machine Type Communication (mMTC)/Massive loT services to yet further UEs.
  • URLLC Ultra Reliable Low Latency Communication
  • eMBB Enhanced Mobile Broadband
  • mMTC Massive Machine Type Communication
  • the UEs QQ112 are configured to transmit and/or receive information without direct human interaction.
  • a UE may be designed to transmit information to the access network QQ104 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network QQ104.
  • 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
  • the hub QQ114 communicates with the access network QQ104 to facilitate indirect communication between one or more UEs (e.g., UE QQ112c and/or QQ112d) and network nodes (e.g., network node QQ110b).
  • the hub QQ114 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs.
  • the hub QQ114 may be a broadband router enabling access to the core network QQ106 for the UEs.
  • the hub QQ114 may be a controller that sends commands or instructions to one or more actuators in the UEs.
  • the hub QQ114 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data.
  • the hub QQ114 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub QQ114 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub QQ114 then provides to the UE either directly, after performing local processing, and/or after adding additional local content.
  • the hub QQ114 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy loT devices.
  • the hub QQ114 may have a constant/persistent or intermittent connection to the network node QQ110b.
  • the hub QQ114 may also allow for a different communication scheme and/or schedule between the hub QQ114 and UEs (e.g., UE QQ112c and/or QQ112d) , and between the hub QQ114 and the core network QQ106.
  • the hub QQ114 is connected to the core network QQ106 and/or one or more UEs via a wired connection.
  • the hub QQ114 may be configured to connect to an M2M service provider over the access network QQ104 and/or to another UE over a direct connection.
  • UEs may establish a wireless connection with the network nodes QQ110 while still connected via the hub QQ114 via a wired or wireless connection.
  • the hub QQ114 may be a dedicated hub - that is, a hub whose primary function is to route communications to/from the UEs from/to the network node QQ110b.
  • the hub QQ114 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node QQ110b, but which is additionally capable of operating as a communication start and/or end point for certain data channels.
  • FIG. 16 shows a UE QQ200 in accordance with some embodiments.
  • a UE refers to a device capable, configured, arranged and/or operable to communicate wirelessly with network nodes and/or other UEs.
  • Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), smart device, wireless customer-premise equipment (CPE), vehicle, vehicle-mounted or vehicle embedded/integrated wireless device, etc.
  • VoIP voice over IP
  • LME laptop-embedded equipment
  • LME laptop-mounted equipment
  • CPE wireless customer-premise equipment
  • UEs identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-loT) UE, a machine type communication (MTC) UE, and/or an enhanced MTC (eMTC) UE.
  • 3GPP 3rd Generation Partnership Project
  • NB-loT narrow band internet of things
  • MTC machine type communication
  • eMTC enhanced MTC
  • a UE may support device-to-device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle-to-everything (V2X).
  • D2D device-to-device
  • DSRC Dedicated Short-Range Communication
  • V2V vehicle-to-vehicle
  • V2I vehicle-to-infrastructure
  • V2X vehicle-to-everything
  • a UE may not necessarily have a user in the sense of a human user who owns and/or operates the relevant device.
  • a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller).
  • a UE may represent a device that is not intended for sale
  • the UE QQ200 includes processing circuitry QQ202 that is operatively coupled via a bus QQ204 to an input/output interface QQ206, a power source QQ208, a memory QQ210, a communication interface QQ212, and/or any other component, or any combination thereof.
  • Certain UEs may utilize all or a subset of the components shown in FIG 16. The level of integration between the components may vary from one UE to another UE. Further, certain UEs may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.
  • the processing circuitry QQ202 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs in the memory QQ210.
  • the processing circuitry QQ202 may be implemented as one or more hardware-implemented state machines (e.g., in discrete logic, field-programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs, general-purpose processors, such as a microprocessor or digital signal processor (DSP), together with appropriate software; or any combination of the above.
  • the processing circuitry QQ202 may include multiple central processing units (CPUs).
  • the input/output interface QQ206 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and/or output devices.
  • Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof.
  • An input device may allow a user to capture information into the UE QQ200.
  • Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like.
  • the presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user.
  • a sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof.
  • An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device.
  • USB Universal Serial Bus
  • the power source QQ208 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used.
  • the power source QQ208 may further include power circuitry for delivering power from the power source QQ208 itself, and/or an external power source, to the various parts of the UE QQ200 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source QQ208.
  • Power circuitry may perform any formatting, converting, or other modification to the power from the power source QQ208 to make the power suitable for the respective components of the UE QQ200 to which power is supplied.
  • the memory QQ210 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth.
  • the memory QQ210 includes one or more application programs QQ214, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data QQ216.
  • the memory QQ210 may store, for use by the UE QQ200, any of a variety of various operating systems or combinations of operating systems.
  • the memory QQ210 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual inline memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and/or ISIM, other memory, or any combination thereof.
  • RAID redundant array of independent disks
  • HD-DVD high-density digital versatile disc
  • HDDS holographic digital data storage
  • DIMM external mini-dual inline memory module
  • SDRAM synchronous dynamic random access memory
  • SDRAM synchronous dynamic random access memory
  • the UICC may for example be an embedded UICC (eUlCC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’
  • the memory QQ210 may allow the UE QQ200 to access instructions, application programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data.
  • An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory QQ210, which may be or comprise a device-readable storage medium.
  • the processing circuitry QQ202 may be configured to communicate with an access network or other network using the communication interface QQ212.
  • the communication interface QQ212 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna QQ222.
  • the communication interface QQ212 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another UE or a network node in an access network).
  • Each transceiver may include a transmitter QQ218 and/or a receiver QQ220 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth).
  • the transmitter QQ218 and receiver QQ220 may be coupled to one or more antennas (e.g., antenna QQ222) and may share circuit components, software or firmware, or alternatively be implemented separately.
  • communication functions of the communication interface QQ212 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof.
  • GPS global positioning system
  • Communications may be implemented in according to one or more communication protocols and/or standards, such as IEEE 802.11 , Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol/internet protocol (TCP/IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.
  • CDMA Code Division Multiplexing Access
  • WCDMA Wideband Code Division Multiple Access
  • GSM Global System for Mobile communications
  • LTE Long Term Evolution
  • NR New Radio
  • UMTS Worldwide Interoperability for Microwave Access
  • WiMax Ethernet
  • TCP/IP transmission control protocol/internet protocol
  • SONET synchronous optical networking
  • ATM Asynchronous Transfer Mode
  • QUIC Hypertext Transfer Protocol
  • HTTP Hypertext Transfer Protocol
  • a UE may provide an output of data captured by its sensors, through its communication interface QQ212, via a wireless connection to a network node.
  • Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE.
  • the output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature), random (e.g., to even out the load from reporting from several sensors), in response to a triggering event (e.g., when moisture is detected an alert is sent), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient).
  • a UE comprises an actuator, a motor, or a switch, related to a communication interface configured to receive wireless input from a network node via a wireless connection.
  • the states of the actuator, the motor, or the switch may change.
  • the UE may comprise a motor that adjusts the control surfaces or rotors of a drone in flight according to the received input or to a robotic arm performing a medical procedure according to the received input.
  • a UE when in the form of an Internet of Things (loT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application and healthcare.
  • loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door/window sensor, a flood/moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smartwatch, a fitness tracker, a head-mounted display for Augmented Reality (AR) or Virtual Reality (VR), a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item-tracking
  • AR Aug
  • a UE may represent a machine or other device that performs monitoring and/or measurements, and transmits the results of such monitoring and/or measurements to another UE and/or a network node.
  • the UE may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device.
  • the UE may implement the 3GPP NB-loT standard.
  • a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and/or reporting on its operational status or other functions associated with its operation.
  • any number of UEs may be used together with respect to a single use case.
  • a first UE might be or be integrated in a drone and provide the drone’s speed information (obtained through a speed sensor) to a second UE that is a remote controller operating the drone.
  • the first UE may adjust the throttle on the drone (e.g. by controlling an actuator) to increase or decrease the drone’s speed.
  • the first and/or the second UE can also include more than one of the functionalities described above.
  • a UE might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators.
  • FIG. 17 shows a network node QQ300 in accordance with some embodiments.
  • network node refers to equipment capable, configured, arranged and/or operable to communicate directly or indirectly with a UE and/or with other network nodes or equipment, in a telecommunication network.
  • network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NR NodeBs (gNBs)), O-RAN nodes or components of an O-RAN node (e.g., O-RU, O- DU, O-CU).
  • APs access points
  • BSs base stations
  • eNBs evolved Node Bs
  • gNBs NR NodeBs
  • O-RAN nodes or components of an O-RAN node e.g., O-RU, O- DU, O-CU.
  • Base stations may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations.
  • a base station may be a relay node or a relay donor node controlling a relay.
  • a network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units, distributed units (e.g., in an O-RAN access node) and/or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio.
  • Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).
  • DAS distributed antenna system
  • network nodes include multiple transmission point (multi- TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi-cell/multicast coordination entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and/or Minimization of Drive Tests (MDTs).
  • MSR multi-standard radio
  • RNCs radio network controllers
  • BSCs base station controllers
  • BTSs base transceiver stations
  • OFDM Operation and Maintenance
  • OSS Operations Support System
  • SON Self-Organizing Network
  • positioning nodes e.g., Evolved Serving Mobile Location Centers (E-SMLCs)
  • the network node QQ300 includes a processing circuitry QQ302, a memory QQ304, a communication interface QQ306, and a power source QQ308.
  • the network node QQ300 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components.
  • the network node QQ300 comprises multiple separate components (e.g., BTS and BSC components)
  • one or more of the separate components may be shared among several network nodes.
  • a single RNC may control multiple NodeBs.
  • each unique NodeB and RNC pair may in some instances be considered a single separate network node.
  • the network node QQ300 may be configured to support multiple radio access technologies (RATs).
  • RATs radio access technologies
  • some components may be duplicated (e.g., separate memory QQ304 for different RATs) and some components may be reused (e.g., a same antenna QQ310 may be shared by different RATs).
  • the network node QQ300 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node QQ300, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node QQ300.
  • RFID Radio Frequency Identification
  • the processing circuitry QQ302 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and/or encoded logic operable to provide, either alone or in conjunction with other network node QQ300 components, such as the memory QQ304, to provide network node QQ300 functionality.
  • the processing circuitry QQ302 includes a system on a chip (SOC). In some embodiments, the processing circuitry QQ302 includes one or more of radio frequency (RF) transceiver circuitry QQ312 and baseband processing circuitry QQ314. In some embodiments, the radio frequency (RF) transceiver circuitry QQ312 and the baseband processing circuitry QQ314 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry QQ312 and baseband processing circuitry QQ314 may be on the same chip or set of chips, boards, or units.
  • SOC system on a chip
  • the processing circuitry QQ302 includes one or more of radio frequency (RF) transceiver circuitry QQ312 and baseband processing circuitry QQ314.
  • the radio frequency (RF) transceiver circuitry QQ312 and the baseband processing circuitry QQ314 may be on separate chips (or sets of chips
  • the memory QQ304 may comprise any form of volatile or non-volatile computer-readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and/or any other volatile or non-volatile, non-transitory device-readable and/or computer-executable memory devices that store information, data, and/or instructions that may be used by the processing circuitry QQ302.
  • 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
  • the memory QQ304 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and/or other instructions capable of being executed by the processing circuitry QQ302 and utilized by the network node QQ300.
  • the memory QQ304 may be used to store any calculations made by the processing circuitry QQ302 and/or any data received via the communication interface QQ306.
  • the processing circuitry QQ302 and memory QQ304 is integrated.
  • the communication interface QQ306 is used in wired or wireless communication of signalling and/or data between a network node, access network, and/or UE.
  • the communication interface QQ306 comprises port(s)/terminal(s) QQ316 to send and receive data, for example to and from a network over a wired connection.
  • the communication interface QQ306 also includes radio frontend circuitry QQ318 that may be coupled to, or in certain embodiments a part of, the antenna QQ310.
  • Radio front-end circuitry QQ318 comprises filters QQ320 and amplifiers QQ322.
  • the radio front-end circuitry QQ318 may be connected to an antenna QQ310 and processing circuitry QQ302.
  • the radio front-end circuitry may be configured to condition signals communicated between antenna QQ310 and processing circuitry QQ302.
  • the radio front-end circuitry QQ318 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection.
  • the radio front-end circuitry QQ318 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters QQ320 and/or amplifiers QQ322.
  • the radio signal may then be transmitted via the antenna QQ310.
  • the antenna QQ310 may collect radio signals which are then converted into digital data by the radio front-end circuitry QQ318.
  • the digital data may be passed to the processing circuitry QQ302.
  • the communication interface may comprise different components and/or different combinations of components.
  • the network node QQ300 does not include separate radio front-end circuitry QQ318, instead, the processing circuitry QQ302 includes radio front-end circuitry and is connected to the antenna QQ310. Similarly, in some embodiments, all or some of the RF transceiver circuitry QQ312 is part of the communication interface QQ306. In still other embodiments, the communication interface QQ306 includes one or more ports or terminals QQ316, the radio front-end circuitry QQ318, and the RF transceiver circuitry QQ312, as part of a radio unit (not shown), and the communication interface QQ306 communicates with the baseband processing circuitry QQ314, which is part of a digital unit (not shown).
  • the antenna QQ310 may include one or more antennas, or antenna arrays, configured to send and/or receive wireless signals.
  • the antenna QQ310 may be coupled to the radio front-end circuitry QQ318 and may be any type of antenna capable of transmitting and receiving data and/or signals wirelessly.
  • the antenna QQ310 is separate from the network node QQ300 and connectable to the network node QQ300 through an interface or port.
  • the antenna QQ310, communication interface QQ306, and/or the processing circuitry QQ302 may be configured to perform any receiving operations and/or certain obtaining operations described herein as being performed by the network node. Any information, data and/or signals may be received from a UE, another network node and/or any other network equipment.
  • the antenna QQ310, the communication interface QQ306, and/or the processing circuitry QQ302 may be configured to perform any transmitting operations described herein as being performed by the network node. Any information, data and/or signals may be transmitted to a UE, another network node and/or any other network equipment.
  • the power source QQ308 provides power to the various components of network node QQ300 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component).
  • the power source QQ308 may further comprise, or be coupled to, power management circuitry to supply the components of the network node QQ300 with power for performing the functionality described herein.
  • the network node QQ300 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source QQ308.
  • the power source QQ308 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail.
  • Embodiments of the network node QQ300 may include additional components beyond those shown in FIG. 17 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.
  • the network node QQ300 may include user interface equipment to allow input of information into the network node QQ300 and to allow output of information from the network node QQ300. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node QQ300.
  • FIG. 18 is a block diagram of a host QQ400, which may be an embodiment of the host QQ116 of FIG. 15, in accordance with various aspects described herein.
  • the host QQ400 may be or comprise various combinations hardware and/or software, including a standalone server, a blade server, a cloud-implemented server, a distributed server, a virtual machine, container, or processing resources in a server farm.
  • the host QQ400 may provide one or more services to one or more UEs.
  • the host QQ400 includes processing circuitry QQ402 that is operatively coupled via a bus QQ404 to an input/output interface QQ406, a network interface QQ408, a power source QQ410, and a memory QQ412.
  • 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 FIG. 16 and 17, such that the descriptions thereof are generally applicable to the corresponding components of host QQ400.
  • the memory QQ412 may include one or more computer programs including one or more host application programs QQ414 and data QQ416, which may include user data, e.g., data generated by a UE for the host QQ400 or data generated by the host QQ400 for a UE.
  • Embodiments of the host QQ400 may utilize only a subset or all of the components shown.
  • the host application programs QQ414 may be implemented in a container-based architecture and may provide support for video codecs (e.g., Versatile Video Coding (VVC), High Efficiency Video Coding (HEVC), Advanced Video Coding (AVC), MPEG, VP9) and audio codecs (e.g., FLAG, Advanced Audio Coding (AAC), MPEG, G.711), including transcoding for multiple different classes, types, or implementations of UEs (e.g., handsets, desktop computers, wearable display systems, heads-up display systems).
  • the host application programs QQ414 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.
  • the host QQ400 may select and/or indicate a different host for over-the-top services for a UE.
  • the host application programs QQ414 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.
  • HLS HTTP Live Streaming
  • RTMP Real-Time Messaging Protocol
  • RTSP Real-Time Streaming Protocol
  • MPEG-DASH Dynamic Adaptive Streaming over HTTP
  • FIG. 19 is a block diagram illustrating a virtualization environment QQ500 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 QQ500 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 virtualization environment QQ500 includes components defined by the O-RAN Alliance, such as an O-Cloud environment orchestrated by a Service Management and Orchestration Framework via an 0-2 interface.
  • Applications QQ502 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment Q400 to implement some of the features, functions, and/or benefits of some of the embodiments disclosed herein.
  • Hardware QQ504 includes processing circuitry, memory that stores software and/or instructions executable by hardware processing circuitry, and/or other hardware devices as described herein, such as a network interface, input/output interface, and so forth.
  • Software may be executed by the processing circuitry to instantiate one or more virtualization layers QQ506 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs QQ508a and QQ508b (one or more of which may be generally referred to as VMs QQ508), and/or perform any of the functions, features and/or benefits described in relation with some embodiments described herein.
  • the virtualization layer QQ506 may present a virtual operating platform that appears like networking hardware to the VMs QQ508.
  • the VMs QQ508 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer QQ506.
  • Different embodiments of the instance of a virtual appliance QQ502 may be implemented on one or more of VMs QQ508, 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
  • a VM QQ508 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, nonvirtualized machine.
  • Each of the VMs QQ508, and that part of hardware QQ504 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 QQ508 on top of the hardware QQ504 and corresponds to the application QQ502.
  • Hardware QQ504 may be implemented in a standalone network node with generic or specific components. Hardware QQ504 may implement some functions via virtualization. Alternatively, hardware QQ504 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 QQ510, which, among others, oversees lifecycle management of applications QQ502. In some embodiments, hardware QQ504 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 signalling can be provided with the use of a control system QQ512 which may alternatively be used for communication between hardware nodes and radio units.
  • FIG. 20 shows a communication diagram of a host QQ602 communicating via a network node QQ604 with a UE QQ606 over a partially wireless connection in accordance with some embodiments.
  • UE such as a UE QQ112a of FIG. 15 and/or UE QQ200 of FIG. 16
  • network node such as network node QQ110a of FIG. 15 and/or network node QQ300 of FIG. 17
  • host such as host QQ116 of FIG. 15 and/or host QQ400 of FIG
  • host QQ602 Like host QQ400, embodiments of host QQ602 include hardware, such as a communication interface, processing circuitry, and memory.
  • the host QQ602 also includes software, which is stored in or accessible by the host QQ602 and executable by the processing circuitry.
  • the software includes a host application that may be operable to provide a service to a remote user, such as the UE QQ606 connecting via an over-the-top (OTT) connection QQ650 extending between the UE QQ606 and host QQ602.
  • OTT over-the-top
  • a host application may provide user data which is transmitted using the OTT connection QQ650.
  • the network node QQ604 includes hardware enabling it to communicate with the host QQ602 and UE QQ606.
  • the connection QQ660 may be direct or pass through a core network (like core network QQ106 of FIG. 15) and/or one or more other intermediate networks, such as one or more public, private, or hosted networks.
  • an intermediate network may be a backbone network or the Internet.
  • the UE QQ606 includes hardware and software, which is stored in or accessible by UE QQ606 and executable by the UE’s processing circuitry.
  • the software includes a client application, such as a web browser or operator-specific “app” that may be operable to provide a service to a human or non-human user via UE QQ606 with the support of the host QQ602.
  • a client application such as a web browser or operator-specific “app” that may be operable to provide a service to a human or non-human user via UE QQ606 with the support of the host QQ602.
  • an executing host application may communicate with the executing client application via the OTT connection QQ650 terminating at the UE QQ606 and host QQ602.
  • the UE's client application may receive request data from the host's host application and provide user data in response to the request data.
  • the OTT connection QQ650 may transfer both the request data and the user data.
  • the UE's client application may interact with
  • the OTT connection QQ650 may extend via a connection QQ660 between the host QQ602 and the network node QQ604 and via a wireless connection QQ670 between the network node QQ604 and the UE QQ606 to provide the connection between the host QQ602 and the UE QQ606.
  • the connection QQ660 and wireless connection QQ670, over which the OTT connection QQ650 may be provided, have been drawn abstractly to illustrate the communication between the host QQ602 and the UE QQ606 via the network node QQ604, without explicit reference to any intermediary devices and the precise routing of messages via these devices.
  • the host QQ602 provides user data, which may be performed by executing a host application.
  • the user data is associated with a particular human user interacting with the UE QQ606.
  • the user data is associated with a UE QQ606 that shares data with the host QQ602 without explicit human interaction.
  • the host QQ602 initiates a transmission carrying the user data towards the UE QQ606.
  • the host QQ602 may initiate the transmission responsive to a request transmitted by the UE QQ606.
  • the request may be caused by human interaction with the UE QQ606 or by operation of the client application executing on the UE QQ606.
  • the transmission may pass via the network node QQ604, in accordance with the teachings of the embodiments described throughout this disclosure. Accordingly, in step QQ612, the network node QQ604 transmits to the UE QQ606 the user data that was carried in the transmission that the host QQ602 initiated, in accordance with the teachings of the embodiments described throughout this disclosure. In step QQ614, the UE QQ606 receives the user data carried in the transmission, which may be performed by a client application executed on the UE QQ606 associated with the host application executed by the host QQ602.
  • the UE QQ606 executes a client application which provides user data to the host QQ602.
  • the user data may be provided in reaction or response to the data received from the host QQ602.
  • the UE QQ606 may provide user data, which may be performed by executing the client application.
  • the client application may further consider user input received from the user via an input/output interface of the UE QQ606. Regardless of the specific manner in which the user data was provided, the UE QQ606 initiates, in step QQ618, transmission of the user data towards the host QQ602 via the network node QQ604.
  • step QQ620 in accordance with the teachings of the embodiments described throughout this disclosure, the network node QQ604 receives user data from the UE QQ606 and initiates transmission of the received user data towards the host QQ602. In step QQ622, the host QQ602 receives the user data carried in the transmission initiated by the UE QQ606.
  • One or more of the various embodiments improve the performance of OTT services provided to the UE QQ606 using the OTT connection QQ650, in which the wireless connection QQ670 forms the last segment. More precisely, the teachings of these embodiments may improve operations by the UE and/or the network node not requiring so many measurements and thereby provide benefits such as reduced user waiting time, better responsiveness, and/or extended battery lifetime.
  • factory status information may be collected and analyzed by the host QQ602.
  • the host QQ602 may process audio and video data which may have been retrieved from a UE for use in creating maps.
  • the host QQ602 may collect and analyze real-time data to assist in controlling vehicle congestion (e.g., controlling traffic lights).
  • the host QQ602 may store surveillance video uploaded by a UE.
  • the host QQ602 may store or control access to media content such as video, audio, VR or AR which it can broadcast, multicast or unicast to UEs.
  • the host QQ602 may be used for energy pricing, remote control of non-time critical electrical load to balance power generation needs, location services, presentation services (such as compiling diagrams etc. from data collected from remote devices), or any other function of collecting, retrieving, storing, analyzing and/or transmitting data.
  • a measurement procedure may be provided for the purpose of monitoring data rate, latency and other factors on which the one or more embodiments improve.
  • the measurement procedure and/or the network functionality for reconfiguring the OTT connection may be implemented in software and hardware of the host QQ602 and/or UE QQ606.
  • sensors may be deployed in or in association with other devices through which the OTT connection QQ650 passes; the sensors may participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or supplying values of other physical quantities from which software may compute or estimate the monitored quantities.
  • the reconfiguring of the OTT connection QQ650 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not directly alter the operation of the network node QQ604. Such procedures and functionalities may be known and practiced in the art.
  • measurements may involve proprietary UE signalling that facilitates measurements of throughput, propagation times, latency and the like, by the host QQ602.
  • the measurements may be implemented in that software causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection QQ650 while monitoring propagation times, errors, etc.
  • computing devices described herein may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and/or software needed to perform the tasks, features, functions and methods disclosed herein. Determining, calculating, obtaining or similar operations described herein may be performed by processing circuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and/or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination.
  • processing circuitry may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and/or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination.
  • computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitioned between separate components.
  • a communication interface may be configured to include any of the components described herein, and/or the functionality of the components may be partitioned between the processing circuitry and the communication interface.
  • non-computationally intensive functions of any of such components may be implemented in software or firmware and computationally intensive functions may be implemented in hardware.
  • processing circuitry executing instructions stored on in memory, which in certain embodiments may be a computer program product in the form of a non- transitory computer-readable storage medium.
  • some or all of the functionality may be provided by the processing circuitry without executing instructions stored on a separate or discrete device-readable storage medium, such as in a hard-wired manner.
  • the processing circuitry can be configured to perform the described functionality. The benefits provided by such functionality are not limited to the processing circuitry alone or to other components of the computing device, but are enjoyed by the computing device as a whole, and/or by end users and a wireless network generally.

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
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Abstract

Selon des modes de réalisation, un procédé mis en œuvre par un UE (10) peut être fourni pour gérer une communication de l'UE (10) dans un réseau de communication sans fil (1). L'UE (10) comprend, dans un rapport de mesure, une indication d'une prédiction de domaine fréquentiel d'une ou de plusieurs cellules de desserte parmi une ou plusieurs cellules de desserte configurées ; et transmet le rapport de mesure avec l'indication à un premier nœud de réseau radio (12).
PCT/SE2024/050981 2023-11-30 2024-11-19 Nœuds de réseau radio, équipement utilisateur et procédés réalisés par ceux-ci Pending WO2025116800A1 (fr)

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