WO2023211353A1 - Rapport d'informations de prédiction de faisceau de domaine spatial dans le cadre d'un rétablissement sur défaillance de faisceau - Google Patents

Rapport d'informations de prédiction de faisceau de domaine spatial dans le cadre d'un rétablissement sur défaillance de faisceau Download PDF

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Publication number
WO2023211353A1
WO2023211353A1 PCT/SE2023/050400 SE2023050400W WO2023211353A1 WO 2023211353 A1 WO2023211353 A1 WO 2023211353A1 SE 2023050400 W SE2023050400 W SE 2023050400W WO 2023211353 A1 WO2023211353 A1 WO 2023211353A1
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WIPO (PCT)
Prior art keywords
beams
bfd
network node
spatial domain
bfr
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PCT/SE2023/050400
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English (en)
Inventor
Icaro Leonardo DA SILVA
Henrik RYDÉN
Chunhui Li
Jingya Li
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Telefonaktiebolaget Lm Ericsson (Publ)
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Publication of WO2023211353A1 publication Critical patent/WO2023211353A1/fr

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0619—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal using feedback from receiving side
    • H04B7/0621—Feedback content
    • H04B7/063—Parameters other than those covered in groups H04B7/0623 - H04B7/0634, e.g. channel matrix rank or transmit mode selection
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0617—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal for beam forming
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G06N3/0455—Auto-encoder networks; Encoder-decoder networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/10—Interfaces, programming languages or software development kits, e.g. for simulating neural networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/373—Predicting channel quality or other radio frequency [RF] parameters

Definitions

  • the present disclosure relates to wireless communications, and in particular, to reporting spatial domain beam prediction information in beam failure recovery (BFR).
  • BFR beam failure recovery
  • the Third Generation Partnership Project (3GPP) has developed and is developing standards for Fourth Generation (4G) (also referred to as Long Term Evolution (LTE)) and Fifth Generation (5G) (also referred to as New Radio (NR)) wireless communication systems.
  • 4G Fourth Generation
  • 5G Fifth Generation
  • NR New Radio
  • Such systems provide, among other features, broadband communication between network nodes, such as base stations, and mobile wireless devices (WD), as well as communication between network nodes and between WDs.
  • 6G wireless communication systems are also under development.
  • 3GPP Rel-17 Artificial intelligence (Al) and machine learning (ML) have been studied in 3GPP in Technical Release 17 (3GPP Rel-17) and some initial functionality is planned to be standardized in 3GPP Rel-18. The outcome of the study in 3GPP Rel-17 is documented in the Technical Report (TR) 37.817 entitled “Study on enhancement for Data Collection for NR and EN-DC”.
  • 3 GPP has considered principles for radio access network (RAN) intelligence enabled by Al, the functional framework (e.g., the Al functionality and the input/output of the component for Al enabled optimization) and use cases and solutions of Al enabled RAN, based on the current architecture and interfaces of 3GPP Rel-17.
  • RAN radio access network
  • 3GPP NR standardization work there will be a new 3GPP Rel-18 Study Item on AI/ML for NR air interface starting in May 2022 (see RP-213560 SID on ALML for Air Interface), this time aiming for some impact to the air interface.
  • the goal of the study is to explore the benefits of augmenting the air-interface with features enabling improved support of AI/ML based algorithms for enhanced performance (e.g., improved throughput, robustness, accuracy or reliability) and/or reduced complexity/overhead.
  • Enhanced performance depends on the use cases under consideration and could be, e.g., improved throughput, robustness, accuracy or reliability, or reduced overhead, etc.
  • this study item will lay the foundation for future Air-Interface use cases leveraging AI/ML techniques.
  • the goal is that sufficient use cases will be considered to enable the identification of a common AI/ML framework, including functional requirements of AI/ML architecture, which could be used in subsequent projects.
  • the study should also identify areas where AI/ML could improve the performance of air interface functions.
  • the 3 GPP framework for AI/ML for air interface corresponding to each target use case is to be studied in various aspects such as performance, complexity, and potential specification impact.
  • the study seeks to identify what is required for an adequate AI/ML model characterization and description establishing pertinent notation for discussions and subsequent evaluations.
  • Various levels of collaboration between the network node (e.g., gNB) and WD are identified and considered.
  • Evaluations to exercise the attainable gains of AI/ML based techniques for the use cases under consideration will be carried out with the corresponding identification of key performance indicators (KPIs) with the goal to have a better understanding of the attainable gains and associated complexity requirements.
  • KPIs key performance indicators
  • beam management e.g., beam prediction in the time, and/or spatial domain for overhead and latency reduction and beam selection accuracy improvement.
  • 3 GPP seeks to assess potential specification impact, specifically for the considered use cases in the final representative set and for a common framework: o PHY layer aspects including (RANI):
  • AI/ML for 6G has been considered but so far, not much has been publicly disclosed for RAN2 protocols.
  • the topic is expected to be brought up in the European Union 6G project Hexa-X.
  • the 6G networks should be designed to incorporate Al operation to optimize network performance, as well as operate to optimize Al performance for other services.
  • Key targets here include embedding Al functionality into the signal processing chain and develop suitable learning methods.
  • governance and protocols for secure Al needs to be developed for the integration of Al into trustworthy network systems. Further, intelligent orchestration covering dynamic resource management, data-driven optimization, and intent-based operation will be developed to streamline operations of future networks. The potential of node programmability will be studied for improved development speed and flexibility.
  • BFD is supported for a Special Cell (SpCell), i.e., a Primary Cell (PCell) and/or a PSCell if WD is in multiple radio access technology (RAT) dual connectivity (MR-DC), and master cell group (MCG) secondary cells (SCells) and/or secondary cell group (SCG) SCells, if configured.
  • SpCell Special Cell
  • PCell Primary Cell
  • PSCell PSCell
  • MCG master cell group
  • SCells secondary cell group
  • SCG secondary cell group
  • Each MAC layer entity at the WD controls its own BFD procedures, i.e., the MAC MCG controls the BFD for the MCG, and the MAC SCG controls the BFD for the SCG.
  • a problem is that the information the WD transmits to the network during BFR about the SpCell (and/or SCell) is very limited, possibly leading to subsequent failures when the network re-configures the WD and/or activates other Open Systems Interconnection (OSI) LI (OSI LI is generally referred to herein as “LI”) configurations at the WD due to BFD.
  • OSI LI Open Systems Interconnection
  • the WD If the WD is configured with Contention-Free Random Access (CFRA), and triggers RA due to BFR, the WD selects an SSB (or CSLRS), which is equivalent to select a beam out of one of the candidate beams configured in BFR configuration (parameter candidateBeamRSList in 3GPP TS 38.331).
  • CFRA Contention-Free Random Access
  • CSLRS CSLRS
  • the network is not able to identify that this RA procedure is triggered due to BFD and BFR, nor the WD which has triggered BFR. That is why in CBRA after the WD receives the RAR, the WD transmits a BFR MAC CE (defined in 3 GPP Rel-16) indicating that this RA was triggered due to BFR.
  • the only information the WD reports to the network in BFR is a selected beam (e.g., SSB index/ identifier and/or CSI-RS resource identifier) per serving cell (e.g., for the SpCell and/or one or more SCells).
  • the network upon receiving the preamble and/or the BFR MAC CE, interprets that each indicated beam is suitable, as the WD selects if they are above the configured threshold.
  • the limited information the network node receives in BFR may lead to the misconfiguration of beam related parameters after BFD and BFR.
  • the limited information may also lead to further BFDs, Radio Link Failures, or sub-sequent reconfigurations via radio resource control (RRC) and/or activations/deactivated via MAC CEs, to re-adjust the parameters based on further CSI reports.
  • RRC radio resource control
  • More signaling from/to the WD and/or network node increases the WD power consumption, increases the risk of further failures, and degrades the data rates (as the WD is not always under the coverage of the beam providing the best radio link). See FIGS. 1 and 2, where the figures use the terminology of “user equipment” or “UE”, which are synonymous with and/or types of “wireless device” and “WD,” respectively.
  • the WD when the beams with SSBs are transmitted in different time units (e.g., time slots, subframes, OFDM symbols), the WD needs to wait a certain amount of time to measure possibly detected SSBs for a given serving cell (e.g., PCell, Scell, PScell).
  • a serving cell e.g., PCell, Scell, PScell.
  • the WD deploys beamforming at the receiver side (e.g., if WD has multiple receiver panels)
  • the WD measures for each instance of a possibly detected SSB, a number of measurements, e.g., one per Rx spatial direction (of Rx beam), as illustrated in the example FIG. 3
  • Some embodiments advantageously provide methods, systems, and apparatuses for to reporting spatial domain beam prediction information in beam failure recovery (BFR).
  • BFR beam failure recovery
  • Some embodiments include a method at a User Equipment (UE) and at a network node (e.g., a gNodeB) for reporting information based on predictions in the spatial domain of beam measurements during a Beam Failure Recovery (BFR) procedure.
  • UE User Equipment
  • BFR Beam Failure Recovery
  • the plurality of beams includes a beam selected for beam failure recovery, BFR. In some embodiments, the plurality of beams includes at least one beam not used for BFD monitoring. In some embodiments, the plurality of beams includes at least one beam configured for channel state information, CSI, reporting. In some embodiments, the radio interface is further configured to receive an indication of a configuration of beams for which to perform the spatial domain measurement predictions. In some embodiments, the spatial domain measurement predictions are predicted by a machine learning process. In some embodiments, transmitting the indication includes initiating a random access procedure on a selected beam. In some embodiments, transmitting the indication includes triggering a scheduling request. In some embodiments, transmitting the indication includes transmitting a beam failure recovery, BFR, report on a first available medium access control, MAC, control element, CE, after BFD.
  • a network node configured to communicate with a wireless device, WD.
  • the network node includes a radio interface configured to receive a beam failure recovery, BFR, report indicating beam failure detection, BFD, by the WD, the BFR report including an indication of at least one spatial domain measurement prediction for at least one of a plurality of beams.
  • the network node also includes processing circuitry in communication with the radio interface and configure to reconfigure communications with the WD in response to the indication.
  • reconfiguring communications with the WD includes reconfiguring at least one of radio link management reference signals, RLM-RSs, beam failure detection reference signals, and beamforming parameters.
  • reconfiguring communications with the WD includes at least one of changing and reconfiguring a transmission configuration indication, TCI, activation state.
  • reconfiguring communications with the WD includes reconfiguring layer 1, LI, resources for spatial domain measurements.
  • reconfiguring communications with the WD includes modifying a set of BFD reference signals via downlink signaling, the set of BFD reference signals including at least one reference signal to be monitored by the WD.
  • FIG. 3 is a diagram of beam sweeping by a network node and by a WD
  • FIG. 9 is a flowchart illustrating example methods implemented in a communication system including a host computer, a network node and a wireless device for receiving user data at a host computer according to some embodiments of the present disclosure
  • FIG. 12 is a flowchart of an example process in a wireless device for to reporting spatial domain beam prediction information in beam failure recovery (BFR);
  • FIG. 14 is a block diagram of a WD prediction model
  • FIG. 15 is a flowchart of BFR based on CBRA according to principles disclosed herein;
  • FIG. 17 is an example of spatial domain prediction for transmit beams
  • FIG. 18 is an example of spatial domain prediction of a measurement on a first beam pair based on an actual measurement on a second beam pair;
  • FIG. 19 illustrates a multi-dimensional signal quality space
  • FIG. 21 shows an example set of training samples
  • network node can be any kind of network node comprised in a radio network which may further comprise any of base station (BS), radio base station, base transceiver station (BTS), base station controller (BSC), radio network controller (RNC), g Node B (gNB), evolved Node B (eNB or eNodeB), Node B, multistandard radio (MSR) radio node such as MSR BS, multi-cell/multicast coordination entity (MCE), integrated access and backhaul (IAB) node, relay node, donor node controlling relay, radio access point (AP), transmission points, transmission nodes, Remote Radio Unit (RRU) Remote Radio Head (RRH), a core network node (e.g., mobile management entity (MME), self-organizing network (SON) node, a coordinating node, positioning node, MDT node, etc.), an external node (e.g., 3rd party node, a node external to the current network), nodes in distributed antenna system (DA).
  • BS base station
  • the network node may also comprise test equipment.
  • radio node used herein may be used to also denote a wireless device (WD) such as a wireless device (WD) or a radio network node.
  • WD wireless device
  • UE user equipment
  • the WD herein can be any type of wireless device capable of communicating with a network node or another WD over radio signals, such as wireless device (WD).
  • the WD may also be a radio communication device, target device, device to device (D2D) WD, machine type WD or WD capable of machine to machine communication (M2M), low-cost and/or low-complexity WD, a sensor equipped with WD, Tablet, mobile terminals, smart phone, laptop embedded equipped (LEE), laptop mounted equipment (LME), USB dongles, Customer Premises Equipment (CPE), an Internet of Things (loT) device, or a Narrowband loT (NB-IOT) device, etc.
  • D2D device to device
  • M2M machine to machine communication
  • M2M machine to machine communication
  • Tablet mobile terminals
  • smart phone laptop embedded equipped (LEE), laptop mounted equipment (LME), USB dongles
  • CPE Customer Premises Equipment
  • LME Customer Premises Equipment
  • NB-IOT Narrowband loT
  • radio network node can be any kind of a radio network node which may comprise any of base station, radio base station, base transceiver station, base station controller, network controller, RNC, evolved Node B (eNB), Node B, gNB, Multi -cell/multicast Coordination Entity (MCE), IAB node, relay node, access point, radio access point, Remote Radio Unit (RRU) Remote Radio Head (RRH).
  • RNC evolved Node B
  • MCE Multi -cell/multicast Coordination Entity
  • IAB node IAB node
  • relay node relay node
  • access point radio access point
  • RRU Remote Radio Unit
  • RRH Remote Radio Head
  • WCDMA Wide Band Code Division Multiple Access
  • WiMax Worldwide Interoperability for Microwave Access
  • UMB Ultra Mobile Broadband
  • GSM Global System for Mobile Communications
  • functions described herein as being performed by a wireless device or a network node may be distributed over a plurality of wireless devices and/or network nodes.
  • the functions of the network node and wireless device described herein are not limited to performance by a single physical device and, in fact, can be distributed among several physical devices.
  • An AI/ML model can be defined as a functionality or be part of a functionality that is deployed/implemented in a first node (e.g., a WD).
  • An AI/ML model can be defined as a feature or part of a feature that is implemented/supported in the first node.
  • An ML-model (or Model Inference function) may correspond to a function which receives one or more inputs (e.g., measurements) and provide as an outcome one or more predictions/ estimates/decisions of a certain type.
  • an ML model or Model Inference is a function that provides AI/ML model inference outputs (e.g., predictions or decisions).
  • the Model inference function is also responsible for data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) based on Inference Data delivered by a Data Collection function, if required.
  • the output may correspond to the inference output of the AI/ML model produced by a Model Inference function.
  • the predictions are spatial- domain predictions: thus, the input of the ML-model may correspond to one or more measurements at (or starting at) at a time instance tO (or a measurement period tO+T) for at least one beam (e.g., SSB identified by SSB index X).
  • an SS-RSRP of SSB index X may include one or more spatial-domain predicted measurements for that time instance tO (or that measurement period tO+T) for at least one beam (e.g., SSB identified by SSB index Y).
  • the SS-RSRP of SSB index Y (for that measurement period) may be predicted.
  • the input to the ML-model being one or more measurements should be interpreted as an example, as there may be other types of input such as positioning, Global Positioning system (GPS) positioning, etc. Further terminology may refer to an “actor”, as a function that receives the output from the Model inference function and triggers or performs corresponding actions.
  • GPS Global Positioning system
  • the Actor may trigger actions directed to other entities or to itself.
  • One actor may correspond to the BFD and/or BFR functionality at the WD 22, and/or the functionality at the WD 22 responsible for generating the data structure to transmit the one or more indications (e.g., a MAC CE) upon triggering BFR.
  • the one or more indications e.g., a MAC CE
  • an ML-model may correspond to a function receiving as input one or more measurements of at least one reference Signal (RS) at time instance tO (or a time interval starting or ending at tO, such as measurement period tO+T), associated to an RS index (possibly transmitted in a beam, spatial direction and/or with a spatial direction filter).
  • RS reference Signal
  • the RS index may be transmitted in beam-X, SSB-x, CSLRS resource index x; and provide as output a prediction of a measurements of a different RS associated to a different RS index (possibly transmitted in a different beam, a different spatial direction and/or with a different spatial direction filter), for example, transmitted in beam-Y, SSB-y, CSLRS resource index y.
  • a “beam” indicates aa beam that transmits a signal from the network node to the WD, or a beam that transmits a signal from the WD to the network node. This is mostly referred to a beam transmitted by the network which may be received by the WD. Hence, the term “beam” may be interpreted as a “Tx beam”. If the text refers to a receiver beam, it is referred as a Rx beam or receive beam.
  • a beam may be considered a particular spatial distribution of energy transmitted by an antenna. In an array antenna, the spatial distribution of energy transmitted by the array antenna is controlled by analog and/or digital beam forming.
  • Some embodiments provide to reporting spatial domain beam prediction information in beam failure recovery (BFR).
  • BFR beam failure recovery
  • Misconfiguration of beam related parameters after BFD and BFR may lead to further BFDs, Radio Link Failures, or sub-sequent re-configurations via RRC and/or activations/deactivated via MAC CEs, to re-adjust the parameters based on further CSI reports. More signaling from/to the WD and/or network increases the WD power consumption, increases the risk of further failures, and degrades the data rates (as the WD is not always under the coverage of the beam providing the best radio link).
  • better robustness in the connection is provided due to the transmission by the WD of one or more indications based on the one or more spatial domain predictions of measurements on a plurality of beams in response to a BFD.
  • the WD’s beam related parameters will not be misconfigured after BFD and BFR, so that further failures due to these possible misconfigurations may be prevented or mitigated.
  • the signaling is also reduced as the WD would be measuring and reporting according to a CSI measurement configuration (e.g., CSI-MeasConfig) associated to the beams that the WD is supposed to measure.
  • the WD’s power consumption will also be reduced, and data rates improved, e.g., as the WD will be under the coverage of the beams and beam candidates providing the best radio link.
  • the network node when the network re-configures/activates/ deactivates TCI states and LI resources to be measured/reported, the network node may reduce the number of resources to be measured and/or monitored due to the predictions reported by the WD. In that case, WD may reduce the power consumption and the latency for measuring the RSs.
  • the WD’s power or energy consumption is lessened as the WD would perform fewer measurements, since the indications reported in addition to the selected beam per serving cell is based on one or more spatial domain predictions.
  • the WD may transmit the one or more indications much faster compared to the situation where the WD would have to perform the measurements for each beam (or TX-Rx beam pair). Even in the case both the network node and the WD have beamforming requiring Transmitter (Tx) beam sweeping and/or Receiver (Rx) beam sweeping, the spatial-domain predictions may prevent the WD to wait for a whole Tx sweep (or a series of SSBs for the same serving cell in a series of subframes) and/or a whole Rx sweep (or a series of Rx directions or panels with which the WD is equipped).
  • Tx Transmitter
  • Rx Receiver
  • joint processes for beam management may be achieved.
  • the Rx beam refinement at the WD is transparent to the network node. From the network point of view, only P2 is running. But due to the spatial-domain prediction, it is possible for the WD to predict the beam pair between all TX beams and remaining Rx beams.
  • FIG. 4 a schematic diagram of a communication system 10, according to an embodiment, such as a 3 GPP -type cellular network that may support standards such as LTE and/or NR (5G), which includes an access network 12, such as a radio access network, and a core network 14.
  • the access network 12 includes a plurality of network nodes 16a, 16b, 16c (referred to collectively as network nodes 16), such as NBs, eNBs, gNBs or other types of wireless access points, each defining a corresponding coverage area 18a, 18b, 18c (referred to collectively as coverage areas 18).
  • Each network node 16a, 16b, 16c is connectable to the core network 14 over a wired or wireless connection 20.
  • a first wireless device (WD) 22a located in coverage area 18a is configured to wirelessly connect to, or be paged by, the corresponding network node 16a.
  • a second WD 22b in coverage area 18b is wirelessly connectable to the corresponding network node 16b. While a plurality of WDs 22a, 22b (collectively referred to as wireless devices 22) are illustrated in this example, the disclosed embodiments are equally applicable to a situation where a sole WD is in the coverage area or where a sole WD is connecting to the corresponding network node 16. Note that although only two WDs 22 and three network nodes 16 are shown for convenience, the communication system may include many more WDs 22 and network nodes 16.
  • a WD 22 can be in simultaneous communication and/or configured to separately communicate with more than one network node 16 and more than one type of network node 16.
  • a WD 22 can have dual connectivity with a network node 16 that supports LTE and the same or a different network node 16 that supports NR.
  • WD 22 can be in communication with an eNB for LTE/E-UTRAN and a gNB for NR/NG-RAN.
  • the communication system 10 may itself be connected to a host computer 24, which may be embodied in the hardware and/or software of a standalone server, a cloud- implemented server, a distributed server or as processing resources in a server farm.
  • the host computer 24 may be under the ownership or control of a service provider, or may be operated by the service provider or on behalf of the service provider.
  • the connections 26, 28 between the communication system 10 and the host computer 24 may extend directly from the core network 14 to the host computer 24 or may extend via an optional intermediate network 30.
  • the intermediate network 30 may be one of, or a combination of more than one of, a public, private or hosted network.
  • the intermediate network 30, if any, may be a backbone network or the Internet. In some embodiments, the intermediate network 30 may comprise two or more sub-networks (not shown).
  • the communication system of FIG. 4 as a whole enables connectivity between one of the connected WDs 22a, 22b and the host computer 24.
  • the connectivity may be described as an over-the-top (OTT) connection.
  • the host computer 24 and the connected WDs 22a, 22b are configured to communicate data and/or signaling via the OTT connection, using the access network 12, the core network 14, any intermediate network 30 and possible further infrastructure (not shown) as intermediaries.
  • the OTT connection may be transparent in the sense that at least some of the participating communication devices through which the OTT connection passes are unaware of routing of uplink and downlink communications.
  • a network node 16 may not or need not be informed about the past routing of an incoming downlink communication with data originating from a host computer 24 to be forwarded (e.g., handed over) to a connected WD 22a. Similarly, the network node 16 need not be aware of the future routing of an outgoing uplink communication originating from the WD 22a towards the host computer 24.
  • a network node 16 is configured to include a configuration unit 32 which may be configured to configure the WD with at least one parameter to be used by the WD to perform at least one spatial domain prediction of measurements on at least one beam in response to a beam failure detection, BFD, at the WD.
  • the configuration unit 32 may be configured to reconfigure communications with the WD in response to the at least one beam quality indication.
  • a wireless device 22 is configured to include a prediction unit 34 which may be configured to use at least one machine learning, ML, model to predict at least one spatial domain measurements on at least one beam in response to beam failure detection, BFD.
  • the prediction unit 34 may be configured to perform at least one spatial domain measurement prediction for each beam of a plurality of beams in response to a beam failure detection, BFD.
  • a host computer 24 includes hardware (HW) 38 including a communication interface 40 configured to set up and maintain a wired or wireless connection with an interface of a different communication device of the communication system 10.
  • the host computer 24 further includes processing circuitry 42, which may have storage and/or processing capabilities.
  • the processing circuitry 42 may include a processor 44 and memory 46.
  • the processing circuitry 42 may comprise integrated circuitry for processing and/or control, e.g., one or more processors and/or processor cores and/or FPGAs (Field Programmable Gate Array) and/or ASICs (Application Specific Integrated Circuitry) adapted to execute instructions.
  • processors and/or processor cores and/or FPGAs Field Programmable Gate Array
  • ASICs Application Specific Integrated Circuitry
  • the processor 44 may be configured to access (e.g., write to and/or read from) memory 46, which may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).
  • memory 46 may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).
  • Processing circuitry 42 may be configured to control any of the methods and/or processes described herein and/or to cause such methods, and/or processes to be performed, e.g., by host computer 24.
  • Processor 44 corresponds to one or more processors 44 for performing host computer 24 functions described herein.
  • the host computer 24 includes memory 46 that is configured to store data, programmatic software code and/or other information described herein.
  • the software 48 and/or the host application 50 may include instructions that, when executed by the processor 44 and/or processing circuitry 42, causes the processor 44 and/or processing circuitry 42 to perform the processes described herein with respect to host computer 24.
  • the instructions may be software associated with the host computer 24.
  • the software 48 may be executable by the processing circuitry 42.
  • the software 48 includes a host application 50.
  • the host application 50 may be operable to provide a service to a remote user, such as a WD 22 connecting via an OTT connection 52 terminating at the WD 22 and the host computer 24.
  • the host application 50 may provide user data which is transmitted using the OTT connection 52.
  • the “user data” may be data and information described herein as implementing the described functionality.
  • the host computer 24 may be configured for providing control and functionality to a service provider and may be operated by the service provider or on behalf of the service provider.
  • the processing circuitry 42 of the host computer 24 may enable the host computer 24 to observe, monitor, control, transmit to and/or receive from the network node 16 and or the wireless device 22.
  • the communication system 10 further includes a network node 16 provided in a communication system 10 and including hardware 58 enabling it to communicate with the host computer 24 and with the WD 22.
  • the hardware 58 may include a communication interface 60 for setting up and maintaining a wired or wireless connection with an interface of a different communication device of the communication system 10, as well as a radio interface 62 for setting up and maintaining at least a wireless connection 64 with a WD 22 located in a coverage area 18 served by the network node 16.
  • the radio interface 62 may be formed as or may include, for example, one or more RF transmitters, one or more RF receivers, and/or one or more RF transceivers.
  • the communication interface 60 may be configured to facilitate a connection 66 to the host computer 24.
  • the connection 66 may be direct or it may pass through a core network 14 of the communication system 10 and/or through one or more intermediate networks 30 outside the communication system 10.
  • the hardware 58 of the network node 16 further includes processing circuitry 68.
  • the processing circuitry 68 may include a processor 70 and a memory 72.
  • the processing circuitry 68 may comprise integrated circuitry for processing and/or control, e.g., one or more processors and/or processor cores and/or FPGAs (Field Programmable Gate Array) and/or ASICs (Application Specific Integrated Circuitry) adapted to execute instructions.
  • FPGAs Field Programmable Gate Array
  • ASICs Application Specific Integrated Circuitry
  • the processor 70 may be configured to access (e.g., write to and/or read from) the memory 72, which may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).
  • volatile and/or nonvolatile memory e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).
  • the network node 16 further has software 74 stored internally in, for example, memory 72, or stored in external memory (e.g., database, storage array, network storage device, etc.) accessible by the network node 16 via an external connection.
  • the software 74 may be executable by the processing circuitry 68.
  • the processing circuitry 68 may be configured to control any of the methods and/or processes described herein and/or to cause such methods, and/or processes to be performed, e.g., by network node 16.
  • Processor 70 corresponds to one or more processors 70 for performing network node 16 functions described herein.
  • the memory 72 is configured to store data, programmatic software code and/or other information described herein.
  • the software 74 may include instructions that, when executed by the processor 70 and/or processing circuitry 68, causes the processor 70 and/or processing circuitry 68 to perform the processes described herein with respect to network node 16.
  • processing circuitry 68 of the network node 16 may include a configuration unit 32 which may be configured to configure the WD with at least one parameter to be used by the WD to perform at least one spatial domain prediction of measurements on at least one beam in response to a beam failure detection, BFD, at the WD.
  • the configuration unit 32 may be configured to reconfigure communications with the WD in response to the at least one beam quality indication.
  • the communication system 10 further includes the WD 22 already referred to.
  • the WD 22 may have hardware 80 that may include a radio interface 82 configured to set up and maintain a wireless connection 64 with a network node 16 serving a coverage area 18 in which the WD 22 is currently located.
  • the radio interface 82 may be formed as or may include, for example, one or more RF transmitters, one or more RF receivers, and/or one or more RF transceivers.
  • the hardware 80 of the WD 22 further includes processing circuitry 84.
  • the processing circuitry 84 may include a processor 86 and memory 88.
  • the processing circuitry 84 may comprise integrated circuitry for processing and/or control, e.g., one or more processors and/or processor cores and/or FPGAs (Field Programmable Gate Array) and/or ASICs (Application Specific Integrated Circuitry) adapted to execute instructions.
  • the processor 86 may be configured to access (e.g., write to and/or read from) memory 88, which may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).
  • memory 88 may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).
  • the WD 22 may further comprise software 90, which is stored in, for example, memory 88 at the WD 22, or stored in external memory (e.g., database, storage array, network storage device, etc.) accessible by the WD 22.
  • the software 90 may be executable by the processing circuitry 84.
  • the software 90 may include a client application 92.
  • the client application 92 may be operable to provide a service to a human or non-human user via the WD 22, with the support of the host computer 24.
  • an executing host application 50 may communicate with the executing client application 92 via the OTT connection 52 terminating at the WD 22 and the host computer 24.
  • the client application 92 may receive request data from the host application 50 and provide user data in response to the request data.
  • the OTT connection 52 may transfer both the request data and the user data.
  • the client application 92 may interact with the user to generate the user data that it provides.
  • the processing circuitry 84 may be configured to control any of the methods and/or processes described herein and/or to cause such methods, and/or processes to be performed, e.g., by WD 22.
  • the processor 86 corresponds to one or more processors 86 for performing WD 22 functions described herein.
  • the WD 22 includes memory 88 that is configured to store data, programmatic software code and/or other information described herein.
  • the software 90 and/or the client application 92 may include instructions that, when executed by the processor 86 and/or processing circuitry 84, causes the processor 86 and/or processing circuitry 84 to perform the processes described herein with respect to WD 22.
  • the processing circuitry 84 of the wireless device 22 may include a prediction unit 34 which may be configured to use at least one machine learning, ML, model to predict at least one spatial domain measurements on at least one beam in response to beam failure detection, BFD.
  • the prediction unit 34 may be configured to perform at least one spatial domain measurement prediction for each beam of a plurality of beams in response to a beam failure detection, BFD.
  • the inner workings of the network node 16, WD 22, and host computer 24 may be as shown in FIG. 5 and independently, the surrounding network topology may be that of FIG. 4.
  • the OTT connection 52 has been drawn abstractly to illustrate the communication between the host computer 24 and the wireless device 22 via the network node 16, without explicit reference to any intermediary devices and the precise routing of messages via these devices.
  • Network infrastructure may determine the routing, which it may be configured to hide from the WD 22 or from the service provider operating the host computer 24, or both. While the OTT connection 52 is active, the network infrastructure may further take decisions by which it dynamically changes the routing (e.g., on the basis of load balancing consideration or reconfiguration of the network).
  • the wireless connection 64 between the WD 22 and the network node 16 is in accordance with the teachings of the embodiments described throughout this disclosure.
  • One or more of the various embodiments improve the performance of OTT services provided to the WD 22 using the OTT connection 52, in which the wireless connection 64 may form the last segment. More precisely, the teachings of some of these embodiments may improve the data rate, latency, and/or power consumption and thereby provide benefits such as reduced user waiting time, relaxed restriction on file size, better responsiveness, extended battery lifetime, etc.
  • 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 52 may be implemented in the software 48 of the host computer 24 or in the software 90 of the WD 22, or both.
  • sensors (not shown) may be deployed in or in association with communication devices through which the OTT connection 52 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 48, 90 may compute or estimate the monitored quantities.
  • the reconfiguring of the OTT connection 52 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not affect the network node 16, and it may be unknown or imperceptible to the network node 16. Some such procedures and functionalities may be known and practiced in the art.
  • measurements may involve proprietary WD signaling facilitating the host computer’s 24 measurements of throughput, propagation times, latency and the like.
  • the measurements may be implemented in that the software 48, 90 causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection 52 while it monitors propagation times, errors, etc.
  • the host computer 24 includes processing circuitry 42 configured to provide user data and a communication interface 40 that is configured to forward the user data to a cellular network for transmission to the WD 22.
  • the cellular network also includes the network node 16 with a radio interface 62.
  • the network node 16 is configured to, and/or the network node’s 16 processing circuitry 68 is configured to perform the functions and/or methods described herein for preparing/initiating/maintaining/ supporting/ending a transmission to the WD 22, and/or preparing/terminating/ maintaining/ supporting/ ending in receipt of a transmission from the WD 22.
  • the host computer 24 includes processing circuitry 42 and a communication interface 40 that is configured to a communication interface 40 configured to receive user data originating from a transmission from a WD 22 to a network node 16.
  • the WD 22 is configured to, and/or includes a radio interface 82 and/or processing circuitry 84 configured to perform the functions and/or methods described herein for preparing/initiating/maintaining/ supporting/ending a transmission to the network node 16, and/or preparing/ terminating/maintaining/supporting/ending in receipt of a transmission from the network node 16.
  • FIGS. 4 and 5 show various “units” such as configuration 32, and prediction unit 34 as being within a respective processor, it is contemplated that these units may be implemented such that a portion of the unit is stored in a corresponding memory within the processing circuitry. In other words, the units may be implemented in hardware or in a combination of hardware and software within the processing circuitry.
  • FIG. 6 is a flowchart illustrating an example method implemented in a communication system, such as, for example, the communication system of FIGS. 4 and 5, in accordance with some embodiments.
  • the communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIG. 5.
  • the host computer 24 provides user data (Block SI 00).
  • the host computer 24 provides the user data by executing a host application, such as, for example, the host application 50 (Block SI 02).
  • the host computer 24 initiates a transmission carrying the user data to the WD 22 (Block SI 04).
  • the network node 16 transmits to the WD 22 the user data which was carried in the transmission that the host computer 24 initiated, in accordance with the teachings of the embodiments described throughout this disclosure (Block SI 06).
  • the WD 22 executes a client application, such as, for example, the client application 92, associated with the host application 50 executed by the host computer 24 (Block SI 08).
  • FIG. 7 is a flowchart illustrating an example method implemented in a communication system, such as, for example, the communication system of FIG. 4, in accordance with some embodiments.
  • the communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIGS. 4 and 5.
  • the host computer 24 provides user data (Block SI 10).
  • the host computer 24 provides the user data by executing a host application, such as, for example, the host application 50.
  • the host computer 24 initiates a transmission carrying the user data to the WD 22 (Block SI 12).
  • the transmission may pass via the network node 16, in accordance with the teachings of the embodiments described throughout this disclosure.
  • the WD 22 receives the user data carried in the transmission (Block SI 14).
  • FIG. 8 is a flowchart illustrating an example method implemented in a communication system, such as, for example, the communication system of FIG. 4, in accordance with some embodiments.
  • the communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIGS. 4 and 5.
  • the WD 22 receives input data provided by the host computer 24 (Block SI 16).
  • the WD 22 executes the client application 92, which provides the user data in reaction to the received input data provided by the host computer 24 (Block SI 18).
  • the WD 22 provides user data (Block S120).
  • the WD provides the user data by executing a client application, such as, for example, client application 92 (Block S122).
  • client application 92 may further consider user input received from the user.
  • the WD 22 may initiate, in an optional third substep, transmission of the user data to the host computer 24 (Block S124).
  • the host computer 24 receives the user data transmitted from the WD 22, in accordance with the teachings of the embodiments described throughout this disclosure (Block S126).
  • FIG. 9 is a flowchart illustrating an example method implemented in a communication system, such as, for example, the communication system of FIG. 4, in accordance with some embodiments.
  • the communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIGS. 4 and 5.
  • the network node 16 receives user data from the WD 22 (Block S128).
  • the network node 16 initiates transmission of the received user data to the host computer 24 (Block SI 30).
  • the host computer 24 receives the user data carried in the transmission initiated by the network node 16 (Block SI 32).
  • FIG. 10 is a flowchart of an example process in a network node 16 for to reporting spatial domain beam prediction information in beam failure recovery (BFR).
  • One or more blocks described herein may be performed by one or more elements of network node 16 such as by one or more of processing circuitry 68 (including the configuration unit 32), processor 70, radio interface 62 and/or communication interface 60.
  • Network node 16 such as via processing circuitry 68 and/or processor 70 and/or radio interface 62 and/or communication interface 60 is configured to configure the WD with at least one parameter to be used by the WD to perform at least one spatial domain prediction of measurements on at least one beam in response to a beam failure detection, BFD, at the WD (Block SI 34).
  • the process also includes receiving the at least one spatial domain prediction (Block S136).
  • the process further includes performing at least one action based at least in part on the at least one spatial domain prediction (Block S138).
  • the at least one action includes at least one of reconfiguring radio link monitoring, RLM, reference signals, reconfiguring BFD reference signals, reconfiguring at least one antenna parameter related to one of RLM and BFD, one of activating and deactivating transmission configuration indicator, TCI, states, reconfiguring layer 1 resources, and modify at least one BFD reference signal to monitored.
  • the method also includes transmitting to the WD, information on supported noising patterns.
  • the information includes an indication of which beams for which measurements are taken.
  • FIG. 11 is a flowchart of an example process in a wireless device 22 according to some embodiments of the present.
  • One or more blocks described herein may be performed by one or more elements of wireless device 22 such as by one or more of processing circuitry 84 (including the prediction unit 34), processor 86, radio interface 82 and/or communication interface 60.
  • Wireless device 22 such as via processing circuitry 84 and/or processor 86 and/or radio interface 82 is configured to use at least one machine learning, ML, model to predict at least one spatial domain measurements on at least one beam in response to beam failure detection, BFD (Block S140).
  • the process also includes transmitting indications of the at least one spatial domain measurement prediction to the network node (Block SI 42)
  • the at least one beam corresponds to at least one transmit beam, at least one receive beam, a pair of beams, beams configured for BFD monitoring, at least one candidate beam to be selected during beam failure recover, BFR, beams configured for procedures other than BFD monitoring and at least one beam for channel state information, CSI, reporting.
  • the indications are sent via a random access procedure.
  • the random access procedure is one of contention free random access and contention based random access.
  • FIG. 12 is a flowchart of an example process in a wireless device 22 according to some embodiments of the present.
  • One or more blocks described herein may be performed by one or more elements of wireless device 22 such as by one or more of processing circuitry 84 (including the prediction unit 34), processor 86, radio interface 82 and/or communication interface 60.
  • Wireless device 22 such as via processing circuitry 84 and/or processor 86 and/or radio interface 82 is configured to perform at least one spatial domain measurement prediction for each beam of a plurality of beams in response to a beam failure detection, BFD (Block S144).
  • the method further includes transmitting to the network node 16 an indication of at least one spatial domain measurement prediction for at least one of the plurality of beams in response to the BFD(Block S146).
  • the spatial domain measurement predictions are predictions of at least one of a reference signal received power, RSRP, a reference signal received quality and a signal to interference plus noise ratio, SINR, for each of the plurality of beams.
  • the plurality of beams includes at least one transmit beam transmitted by the network node 16.
  • the plurality of beams includes at least one receive beam for receiving signals from the network node 16.
  • the plurality of beams includes at least one pair of a transmit beam and a receive beam.
  • at least one beam of the plurality of beams is configured for BFD monitoring.
  • the plurality of beams includes at least one candidate beam for beam failure recovery, BFR.
  • the plurality of beams includes a beam selected for beam failure recovery, BFR. In some embodiments, the plurality of beams includes at least one beam not used for BFD monitoring. In some embodiments, the plurality of beams includes at least one beam configured for channel state information, CSI, reporting. In some embodiments, the method includes receiving an indication of a configuration of beams for which to perform the spatial domain measurement predictions. In some embodiments, the spatial domain measurement predictions are predicted by a machine learning process. In some embodiments, transmitting the indication includes initiating a random access procedure on a selected beam. In some embodiments, transmitting the indication includes triggering a scheduling request. In some embodiments, transmitting the at least one beam quality indication includes transmitting a beam failure recovery, BFR, report on a first available medium access control, MAC, control element, CE, after BFD.
  • FIG. 13 is a flowchart of an example process in a network node 16 for to reporting spatial domain beam prediction information in beam failure recovery (BFR).
  • One or more blocks described herein may be performed by one or more elements of network node 16 such as by one or more of processing circuitry 68 (including the configuration unit 32), processor 70, radio interface 62 and/or communication interface 60.
  • Network node 16 such as via processing circuitry 68 and/or processor 70 and/or radio interface 62 and/or communication interface 60 is configured to receive a beam failure recovery, BFR, report indicating beam failure detection, BFD, by the WD 22, the BFR report including an indication of at least one spatial domain measurement prediction for at least one of a plurality of beams (Block S148).
  • the method also includes reconfiguring communications with the WD 22 in response to the indication (Block S150).
  • reconfiguring communications with the WD 22 includes reconfiguring at least one of radio link management reference signals, RLM-RSs, beam failure detection reference signals, and beamforming parameters.
  • reconfiguring communications with the WD 22 includes at least one of changing and reconfiguring a transmission configuration indication, TCI, activation state.
  • reconfiguring communications with the WD 22 includes reconfiguring layer 1, LI, resources for spatial domain measurements.
  • reconfiguring communications with the WD 22 includes modifying a set of BFD reference signals via downlink signaling, the set of BFD reference signals including at least one reference signal to be monitored by the WD 22.
  • a method at a WD 22 operating with at least one ML model comprising: transmitting one or more indications based on one or more spatial-domain predictions of measurements on a plurality of beams in response to a BFD;
  • the a plurality of beams may correspond to at least the following: one or more Tx beams transmitted by the network; one or more Rx beams which may be used for detecting at least one signal transmitted by the network; one or more beam pairs i.e. a Tx Beam (transmitted by the network), associated to an Rx beam (at the WD 22, used for receiving signals from the network); a plurality of beams configured for BFD monitoring; one or more candidate beams (candidates to be selected during
  • BFR BFR
  • selected beams selected during BFR
  • a plurality of beams configured for other procedures, e.g., not for BFD monitoring
  • a plurality of beams configured for CSI report.
  • the signal transmitted on a plurality of beams can be CSI-RS (including a tracking reference signal (TRS) (CSI-RS for tracking)), SSB, cell-specific reference signal (CRS), demodulation reference signal (DMRS), PTRS (phase-tracking RS) and/or discovery reference signal (DRS).
  • TRS tracking reference signal
  • CRS cell-specific reference signal
  • DMRS demodulation reference signal
  • PTRS phase-tracking RS
  • DRS discovery reference signal
  • the WD 22 prior to transmitting the one or more indications, performs one or more spatial domain predictions of measurements on a plurality of beams, such as spatial-domain predictions/ estimates of SS-reference signal received power (RSRP), CSI-RSRP, SS-reference signal received quality (RSRQ), CSI-RSRQ, SS-signal to interference plus noise ratio (SINR), CSI-SINR, as defined in 3GPP Technical Standard (TS) 38.215. Then, in some embodiments, the WD 22 generates the one or more indications based on one or spatial domain predictions of measurements on a plurality of beams, to be transmitted to the network in response to the BFD. Referring to FIG. 14, the one or more spatial-domain predict!
  • ons/estimates on a plurality of beams may be the output of an ML-model 94 (or Al-model, or Model Inference function) determined by the prediction unit 34 at the WD 22.
  • the outputs are received by the function 96 which generates the one or more indications, which may correspond to the “actor” in this process.
  • transmitting the one or spatial domain predictions of measurements on a plurality of beams in response to a BFD include the WD 22 including the one or more indications based on spatial domain predictions of measurements on the plurality of beams in a first Medium Access Control (MAC) Control Element (in Block 98), and the WD 22 transmitting the first MAC CE to the network node 16.
  • MAC Medium Access Control
  • transmitting one or more indications based on the one or spatial domain predictions of measurements on a plurality of beams in response to a BFD includes the WD 22 triggering at least one scheduling request (SR) (e.g., over physical uplink control channel (PUCCH), physical uplink shared channel (PUSCH), or PRACH) if BFD is for an SCell and no uplink scheduling (UL)-SCH resource is available for a new transmission or if the WD 22 initiates a CFRA procedure for BFR; the WD 22 receives an uplink scheduling grant from the network and transmits its first MAC CE on the scheduled PUSCH.
  • SR scheduling request
  • transmitting one or more indications based on the one or spatial domain predictions of measurements on a plurality of beams in response to a BFD includes the WD 22 transmitting the first MAC CE on the first available PUSCH for a new transmission, if BFD is for an SCell and UL-SCH resources are available for a new transmission.
  • the WD 22 is configured by a network node 16 with one or more parameters and/or configurations associated to the transmitting step. These may be parameters indicating what to transmit and/or how to transmit (e.g., in which message, in which format of a given message, in which protocol layer, etc.).
  • the WD 22 is configured by a network node 16 with one or more parameters and/or configurations for the WD 22 to perform the one or spatial domain predictions of measurements on a plurality of beams.
  • the WD 22 receives a reconfiguration (and/or an update command) from the network, in response to transmitting the one or more indications based on the one or spatial domain predictions of measurements (e.g., in the first MAC CE).
  • the response may indicate one or more of reconfigure RLM-RSs; reconfigure BFD RSs; reconfigure one or more antenna parameters related to the
  • RLM/BFD RSs for selecting antenna parameters that forms more wider/ narrow beams; activate/ deactivate TCI states; re-configure TCI states; re-configure LI resources to be measured/ reports; and/or modify at least one of the BFD-RSs to be monitored, e.g., via transmissions of a downlink control information (DCI) message or MAC CE.
  • DCI downlink control information
  • a method at a network node 16 includes one or more indications from a WD 22 based on one or more spatial domain predictions of measurements on a plurality of beams in response to a BFD at the WD 22.
  • RLM/BFD RSs for selecting antenna parameters that forms a more wider/narrow beam; activate/ deactivate TCI states; re-configure TCI states; re-configure LI resources to be measured/ reports; and/or modify at least one of the BFD-RSs to be monitored.
  • An example of how the WD 22 and network methods could be combined for BFR based on CBRA and CFRA is shown in the flow diagram of FIG. 15.
  • the WD 22 may declare BFD and/or trigger BFR (Block SI 54).
  • the WD 22 may receive an indication of candidate beams from the network node (NN) 16.
  • a candidate beam may be an SSB-x or SSB-y of a serving cell.
  • the WD 22 may select a candidate beam at a time to (Block SI 56).
  • the WD 22 may then initiate a CBRA with a preamble/PRACH mapped to SSB-x, for example.
  • the network node 16 may determine a beam for SSB-x to transmit RAR, and cannot determine the WD 22 (CBRA) (Block SI 58).
  • Some embodiments include configurations from the network for the reporting and the predictions.
  • Some embodiments include capability signaling.
  • the spatial-domain prediction of a measurement on a beam pair may correspond to the spatial-domain prediction of a measurement on a first Tx beam and a first Rx beam; a plurality of beams configured for BFD monitoring: o
  • these are the beams (indicated by one or more RS indices) the WD 22 has been monitoring, as indicated by the parameter failureDetectionResourcesToAddModList, or beams associated to RSs configured as QCL source for active TCI states before the detection of beam failure; o
  • these are indicated to the WD 22 by a set of periodic CSI-RS resource configuration indexes and/or a set of periodic CSI-RS resource configuration indexes and/or SS/physical broadcast channel (PBCH) block indexes; o
  • at least one of these beams is configured as a RS for Radio Link Monitoring (RLM-RSs), which may be monitored for RLM; o
  • at least one of these beams is
  • the WD 22 selects a beam whose RSRP is above a configurable threshold; o
  • the selected beam corresponds to the SSB which is the WD 22 selects with SS-RSRP above rsrp- ThresholdSSB amongst the SSBs in candidateBeamRSList or a CSLRS with CSI-RSRP above rsrp-ThresholdCSI-RS amongst the CSLRSs in candidateBeamRSList; o
  • the select beam corresponds to the SSB with SS-RSRP above rsrp-ThresholdSSB which is available; a plurality of beams configured for other procedures e.g., not for BFD monitoring; a plurality of beams configured for CSI report: o
  • the a plurality of beams configured for CSI report comprise at least one beam whose RS is configured as part of the CSI resource configuration (e.g., within the IE CSL MeasConfig.
  • Each beam may be indicated by an RS ID (e.g., an SSB-Index, a CSI resource identifier, a NZP-CSLRS-Resourceld).
  • the RS e.g., an SSB with SSB-index X
  • the RS may be transmitted in a spatial direction (also called a beam) by the network (and received by the WD 22).
  • an RS identifier may correspond to a beam identifier and vice versa, as a beam is used to transmitting a given RS with an RS index.
  • Measurements on a plurality of beams corresponds to measurement of one or more measurement quantities, e.g., RSRP and/or RSRQ, and/or received signal strength indicator (RSSI), and/or SINR, measured on one or more RSs, e.g., SSB, CSI-RS, Cellspecific Reference Signal (CRS), Discovery Reference Signal (DRS), Demodulation Reference Signal (DMRS), wherein the one or more measured RSs may be transmitted in different spatial directions, which may be referred as different beams.
  • RSRP and/or RSRQ and/or received signal strength indicator (RSSI), and/or SINR
  • RSs e.g., SSB, CSI-RS, Cellspecific Reference Signal (CRS), Discovery Reference Signal (DRS), Demodulation Reference Signal (DMRS)
  • a measurement on a beam may correspond to a SS-RSRP (Synchronization Signal Reference Signal Received Power) on an SSB index X of a cell Z, wherein the SSB of SSB index X is transmitted in a beam/ spatial direction.
  • More examples of measurements in the context of the invention may be the ones in 3GPP TS 38.215, such a SS-RSRQ, SS-SINR, CSI-RSRP, CSI-RSRQ, CSLSINR.
  • Measurements and spatial-domain prediction of measurements on a plurality of beams may be obtained during a measurement period, as defined in TS 38.133.
  • the invention refers to a spatial-domain measurement prediction at time tO, it may refer to a measurement period which has ended at time tO e.g., the end of a time window, moving average of measurement samples, etc.
  • the WD 22 predicts one or more measurements on a first beam (or signal, reference signal, synchronization signal, synchronization sequences), wherein the first beam has at least a first transmission property (e.g., a wide beam, a beam transmitting one or more SSBs, periodicity Tl):
  • a first transmission property e.g., a wide beam, a beam transmitting one or more SSBs, periodicity Tl
  • the WD 22 may predict one or more measurements of wide beams (e.g., transmitting SSBs) based on the measurements of narrow beams (e.g., transmitting CSI-RS). To do the prediction the WD 22 may be aware that there is a correlation and/or overlapping coverage in the wide and narrow beams;
  • the first and/or the second transmission property is indicated to the WD 22, e.g., in a dedicated or broadcasted RRC message received by the WD 22 and transmitted by the network. That may be received in a system information message or within an RRCReconfiguration message (e.g., in a serving cell configuration for SSBs of that serving cell, like the PCell, PScells or MCG SCells, or SCG SCells);
  • the WD 22 may predict one or more measurements of wide beams (e.g., transmitting SSBs) based on the measurements of other wide beams;
  • the WD 22 may predict one or more measurements of narrow beams (e.g., transmitting SSBs) based on the measurements of other narrow beams;
  • the WD 22 measures at least one CSI-RS then WD 22 performs the prediction of the SSB (this narrow beam is within this predicted SSB) or the nearby SSBs (this narrow beam is not within the predicted SSBs);
  • WD 22 performs the prediction of the CSI-RS (this narrow beam is within this measured SSB) or the nearby CSI-RSs (predicted narrow beams is (are) not within this measured SSB);
  • the first and/or second beams are beams which the network uses for transmitting one or more of: reference signals, synchronization signals, control channels, data channels, etc.; and/or
  • the first and/or second beams are beams which the WD 22 uses for receiving one or more of: reference signals, synchronization signals, control channels, data channels, etc.
  • the spatial-domain prediction of a measurements on a first beam which is the output of the ML-model, is produced in a measurement period without the WD 22 having measured the beam which is being transmitted by the network (e.g., beam transmitting SSB index Y), which may include one of more of (i.e., are not mutually exclusive):
  • the WD 22 does not detect the first beam
  • the WD 22 detects at least one different beam (second beam), wherein the detection of the second beam may be used as input to produce the ML-model spatial-domain prediction of the first beam; In some embodiments, the WD 22 does not monitor or receive in the direction of the first beam;
  • the WD 22 monitors or receives in the direction of a second beam, which may be used as input to the ML-model which produced the spatial-domain prediction of a measurement for the first beam.
  • a second beam which may be used as input to the ML-model which produced the spatial-domain prediction of a measurement for the first beam.
  • An example is shown below where the WD 22 receives beam x, and produces spatial-domain predictions for beams Y and Z;
  • the WD 22 does not monitor or receive in the time resource in which the first beam is supposed to be transmitted. In some embodiments, the WD 22 is aware that a given SSB is transmitted in a given time frame and/or time slot and/or subframe and/or OFDM symbol and does not need to receive the SSB in any of these time units to predict the SSB measurement;
  • the WD 22 monitors or receives in the time resource in which a second beam is being transmitted, wherein at least one property of the second beam may be used as input to produce the ML-model spatial-domain prediction of the first beam;
  • the WD 22 detects the first beam, but does not perform the measurements on the first beam.
  • the detection may be used as input to the ML-model, to indicate that it is possible to produce a spatial-domain prediction of a measurement of the first beam;
  • the WD 22 detects the first beam, performs at least one measurement on the first beam, but does not perform LI filtering on the measurements.
  • a measurement may be at least one sample, while a LI filtering could be interpreted as multiple samples, in time and/or frequency domain.
  • a RX beam may correspond to a receiver direction at the WD 22 (Rx direction A), or a Rx spatial filter/ direction at the WD 22.
  • the spatial-domain prediction of measurements on a first Tx beam transmitted by the network (Tx Beam X), which is the output of the ML-model, is associated to a first Rx beam (Rx beam Z) at the WD 22, and is produced in a measurement period without the WD 22 having measured the first Tx beam on that first Rx beam.
  • That process may include one of more of:
  • the WD 22 does not detect the first Tx beam (Tx Beam X) in the first Rx Beam (Rx beam Z). Note: That does not preclude the WD 22 detecting the first Tx beam in another Rx beam;
  • the WD 22 detects the first Tx beam (Tx Beam X) in at least one different Rx beam (second Rx beam), wherein the detection in the second Rx beam may be used as input to produce the ML-model spatial-domain prediction of the measurement of the first Tx beam associated to the first Rx beam.
  • the WD 22 may have multiple RX beams, and in that sense, the second Rx beam may be any of the beams, except the first Rx beam.
  • the WD 22 detects the Tx Beam X in the Rx Beam W, and produces the spatial-domain prediction/ estimate of measurements on Tx Beam X in the Rx Beam Z;
  • the WD 22 does not monitor or receive the first Tx beam in the direction of the first Rx beam;
  • the WD 22 monitors or receives the first Tx Beam (Tx Beam X) in the direction of a second Rx beam, which may be used as input to the ML-model which produced the spatial-domain prediction of a measurement for the first Tx Beam associated to the first Rx beam.
  • the output may correspond to the estimate of a first beam pair (e.g., SS-RSRP estimate of beam pair first Tx Beam and First Rx Beam) based on the actual measurements of a second beam pair (e.g., SS-RSRP of beam pair first Tx Beam and Second Rx Beam).
  • the WD 22 receives the Tx beam X in Rx Beam W (second Rx beam), and produces spatial-domain predictions for the measurement on Tx beam X on Rx Beam Z.
  • the beam pair where the measurement is performed is associated to the same Tx Beam X, but solutions where a different Tx Beam is used are not precluded;
  • the beam pair where the measurement is performed is associated to the same Tx Beam X, but solutions where a different Tx Beam is used are not precluded.
  • the WD 22 does not monitor or receive in the time resource in which the first Tx beam is supposed to be transmitted.
  • the WD 22 is aware that a given SSB is transmitted in a given time frame and/or time slot and/or subframe and/or OFDM symbol and does not need to receive the SSB in any of these time units (in any of the Rx beams) to predict the SSB measurement;
  • the WD 22 detects the Tx Beam in multiple Rx beams (K Rx beams), where K ⁇ N, wherein N is the number of Rx beams the WD 22 is equipped with. Then, based on the measurements on the Tx Beam on the K Rx beams (e.g., K values of SS-RSRP for the Tx Beam) the WD 22 produces as output of the ML-model the N-K outputs which are the N-K spatial- domain predictions of measurements for the other K RX beams. This in essence is the ability to predict a set of radio signal qualities, based on a subset of radio signal measurements. As shown in the example of FIG. 19, each dimension can represent a TX-RX pair.
  • the WD 22 may generate the one or more indications based on one or more spatial domain predictions of measurements on a plurality of beams, to be transmitted to the network in response to the BFD.
  • the one or more spatial-domain predictions/ estimates on a plurality of beams may be the output of an ML-model (or Al-model) the WD 22 is deployed with.
  • the configuration is indicated per cell e.g., serving cell (PCell, PSCell, SCells) or cell group (Master Cell Group, Secondary Cell Group);
  • serving cell PCell, PSCell, SCells
  • cell group Master Cell Group, Secondary Cell Group
  • the configuration indicates one or more Tx beams for which the measurement needs to be performed
  • the configuration indicates one or more Rx beams for which the measurement needs to be performed
  • the configuration indicates one or more beam pairs (Tx beam, Rx beam) for which the measurement needs to be performed;
  • the configuration indicates a minimum number of beams (Tx beams) for which the measurement needs to be performed.
  • Tx beams a minimum number of beams
  • the configuration may indicate that the WD 22 may measure N1 beams, so the measurements for the remaining N-Nl may be the output of the ML-model, i.e., the spatial-domain predictions: o
  • this minimum number may be associated to a WD 22 capability (which is reported to the network by the WD 22); o
  • this minimum number may be defined per one or more of: a subcarrier spacing, a carrier frequency, a frequency range (FR1, FR2), STMC periodicity, DRX configurations, maximum number of SSBs in a cell, etc.; o
  • WD 22 could only measure part of N1 beams (i.e., N2, where N2 ⁇ Nl) even N1 beams are configured to be measured, so the measurements
  • the configuration indicates a minimum number of beams (Rx beams) for which the measurement needs to be performed.
  • the configuration may indicate that the WD 22 may measure a Tx beam using at least KI Rx beams, so the measurements for the remaining K-Kl for a given Tx beam may be the output of the ML-model i.e. the spatial-domain predictions.
  • This minimum number of Rx beams to be used may also be associated to a WD 22 capability (which is reported to the network by the WD 22): o In some embodiments, this minimum number may be associated to a WD 22 capability (which is reported to the network by the WD 22); o In some embodiments, this minimum number may be defined per one or more of: a subcarrier spacing, a carrier frequency, a frequency range (FR1, FR2), STMC periodicity, DRX configurations, maximum number of SSBs in a cell, etc.; o In some embodiments, WD 22 may only measure a TX beam using part of KI Rx beams (i.e., K2, where K2 ⁇ KI) even KI Rx beams are configured to be used to perform the measurement, so the measurements for the remaining K-K2 for a given Tx beam may be the output of the ML-model i.e. the spatial-domain predictions;
  • the configuration indicates one or more Tx beam for which the spatial-domain prediction may be performed
  • the configuration indicates one or more Tx beam with additional beam-related info (e.g., the correlation between Tx beams) for which the spatial-domain prediction may be performed;
  • additional beam-related info e.g., the correlation between Tx beams
  • the configuration indicates one or more Rx beam for which the spatial-domain prediction may be performed
  • the configuration indicates one or more beam pairs (Tx beam, Rx beam) for which the spatial-domain prediction may be performed;
  • the configuration indicates a maximum number of beams (Tx beams) for which the measurement may be performed;
  • the configuration indicates a maximum number of beams (Tx beams) with additional beam-related info (e.g., the correlation between Tx beams) for which the measurement may be performed;
  • the configuration indicates a maximum number of Rx beams for which the measurement may be performed
  • the configuration indicates the accuracy needed for when the spatial-domain prediction may be performed. In some embodiments, the configuration indicates that a spatial-domain prediction could be performed if such predictions mean-squared error is within a certain threshold value;
  • the configuration indicates a minimum number of beam pairs (Rx beam, Tx Beam) for which the measurement may be performed;
  • the configuration indicates a threshold associated to a measurement quantity e.g., RSRP threshold, wherein if the measurement of a Tx Beam (e.g., SSB index X) is above the threshold on Rx Beam Z, the WD 22 is allowed to produce a spatial-domain prediction of the Tx Beam for another Rx Beam e.g., Rx Beam Z. Else, if the measurement of a Tx Beam (e.g., SSB index X) is worse than the threshold on Rx Beam Z, the WD 22 is not allowed to produce a spatial-domain prediction of the Tx Beam for another Rx Beam e.g., Rx Beam Z i.e. the WD 22 measures the Tx Beam in Rx Beam Z.
  • RSRP threshold e.g., RSRP threshold
  • a plurality of beams may be indicated by one or more of: a list of SSB indexes, CSI-RS resource identifier, RS Indexes, beam indexes, bit string in which the positions set to a value indicate that the beam is indicated, etc.
  • the spatial-domain prediction of a beam corresponds to the prediction or estimate of an SS-RSRP for that SSB, which is an estimate of the linear average over the power contributions (in Watts) of the resource elements that carry secondary synchronization signals (SSSs) of the SSB.
  • the prediction/ estimate may be performed for SS-RSRQ, SS-SINR, CSI- RSRP, CSI-RSRQ, CSI-SINR of an SSB.
  • the WD 22 may start to perform the spatial-domain predictions when it is configured e.g., for performing BFD.
  • the WD 22 has the spatial-domain predictions ready and available to be included in the message to be transmitted (e.g., the first MAC CE) when BFD is declared, i.e., there is no need to wait extra time for performing the predictions before indicating to the network.
  • the WD 22 performs the one or more spatial-domain predictions (or estimate) of a measurement before BFD occurs for the beams the WD 22 is monitoring for BFD. This may be the case if the input measurements to the ML-model that generates the predictions are being generated for BFD, so there would be no need for extra measurement related efforts to generate the outputs of the ML-model.
  • the WD 22 starts performing one or more spatial domain predictions (or estimate) of a measurement at tO, wherein tO is after the WD 22 declares BFD.
  • tO is after the WD 22 declares BFD.
  • the WD 22 performs the one or more spatial-domain predictions (or estimate) of a measurement after BFD is declared for the candidate beams or selected beams. This may be the case if the input measurements to the ML-model that generates the predictions are being generated after BFD is declared, so there would be no need for extra measurement related efforts to generate the outputs of the ML-model.
  • the WD 22 starts performing one or more spatial domain predictions (or estimate) of a measurement at tO, wherein tO is upon the detection of a first beam failure instance (BFI) indication.
  • BFI beam failure instance
  • the WD 22 has the spatial-domain predictions ready to be included in the first MAC CE when BFD is declared, but at the same time, only starts performing the predictions when there is some evidence that BFD may be declared. This may be seen as a case wherein the WD 22 starts performing the one or more spatial domain predictions (or estimate) before BFR is triggered (or BFD is declared), except when the max number of beam failure instances (e.g., beamFailurelnstanceMaxCount) is set to 1.
  • the max number of beam failure instances e.g., beamFailurelnstanceMaxCount
  • RSRP e.g., SS-RSRP, CSLRSRP
  • SSB is usually use as an example of RS which is beamformed, but other RSs may also be equally considered such as CSL RS, DRMS, CRS, DRS, etc.
  • the one or more indications includes at least one of the spatial domain predictions of measurements.
  • the WD 22 transmits the predicted RSRP for SSB-X e.g., predicted SS-RSRP.
  • the one or more indications are for beams (RSs) that may be associated to one or more of:
  • a first cell group with an SpCell (Special Cell, as defined in 3GPP TS 38.331, or any other cell with equivalent properties) and one or more Secondary Cells (SCells, as defined in 3GPP TS 38.331, or any other cell with equivalent properties).
  • the first cell group is a Master Cell Group (MCG)
  • the SpCell is a Primary Cell (PCell).
  • the first cell group is a Secondary Cell Group (SCG)
  • SCG Secondary Cell Group
  • the SpCell is a SpCell of the SCG (PSCell); and/or
  • a serving cell e.g., SpCell of MCG, SpCel of the SCG, SCell of the MCG; SCell of the SCG.
  • the one or more indications includes an average (e.g., moving average, filtered averaged, weighted average) based on at least one spatial domain predictions of measurements.
  • the WD 22 transmits an average of the RSRP for SSB-X (SS-RSRP) which includes at least one predicted value, possibly based on the predicted value associated to an Rx beam in which the Tx beam has not been received / measured (possibly including measurements for that Tx Beam in other Rx beams in which the signal has been received /measured). That may also include an indication of the RS index/ identifier.
  • the one or more indications includes a statistical metric derived based on the distribution of the multiple spatial domain predictions of measurements.
  • the WD 22 transmits a statistical metric of the predicted RSRPs for SSB-X for different Rx beams e.g., predicted SS-RSRP for Rx Beam X in Rx beam Yl, predicted SS-RSRP for Rx Beam X in Rx beam Y2, . . ., etc. That may also include an indication of the RS index/ identifier.
  • the statistical metric could comprise, for each time instance, the average value and standard deviation of such value.
  • the confidence interval of the expected value e.g., 90% probability that the value is within a certain range.
  • the statistics of a predicted value can be reported as the below probability density function, using e.g., Gaussian mixtures for one or more Rx beams, as shown below. The prediction is then reported using the parameters describing the mixed gaussian components. Its mean, variation and component weight for each of the components.
  • FIG. 20 is a graph showing an example of a mixed gaussian with two components.
  • the one or more indications include at least one time instance (or indications of a time instance) during which the WD 22 has performed spatial domain predictions of measurements.
  • the one or more indications includes at least one metric (value, parameter, indication) which is derived (generated) by the WD 22 based on one or more spatial domain predictions of measurements.
  • the one or more indications includes a beam identifier, derived (generated) by the WD 22 based on one or more spatial domain predictions of measurements.
  • a beam identifier may correspond to a RS ID, e.g., an SSB index, CSI- RS resource identifier.
  • the one or more indications based on spatial domain predictions of measurements includes an indication of the RS index/ identifier (e.g., SSB identifier).
  • LIST [SSB index-5, SSB index-12, SSB index-60]
  • the WD 22 derives the one or more indications based on at least a threshold associated to a measurement quantity (e.g., RSRP, RSRQ, SINR, RSSI) which is to be predicted or estimated.
  • a measurement quantity e.g., RSRP, RSRQ, SINR, RSSI
  • the WD 22 predicts the RSRP of at least one SSB in the spatial- domain (SS-RSRP)
  • the WD 22 derives the one or more indications by comparing the predictions/ estimates with an RSRP threshold:
  • the one or more indications indicates the one or more RSRP predictions/ estimations above the threshold (e.g., good predictions). If the WD 22 generates [SS-RSRP for Tx beam XI, SS-RSRP for Tx beam X2, . . ., SS-RSRP for Tx beam Xk], and/or [SS-RSRP for Rx beam XI, SS-RSRP for Rx beam Y2, ..., SS-RSRP for Rx beam Ym], and/or [SS- RSRP for beam pair 1, SS-RSRP for beam pair 2, . . .
  • the WD 22 may transmit only the subset (or the subset is a candidate from which the WD 22 further selects the predictions to be reported, based on one or more additional rules).
  • the threshold can also have an associated uncertainty in the prediction.
  • the probability that a prediction is above a threshold with a certain probability The RSRP prediction could be highly uncertain and potentially below the indicated threshold, with a certain probability.
  • the network knows which beams are above and/or below the threshold and prepare for counter-actions such as beam switching and/or TCI state activation / deactivation; or, it is able to configure multiple beams for the WD 22 to measure, monitor and report, for the different procedures such as BFD, RLM, TCI state monitoring, RRM measurements (based on Measurement Configuration, IE MeasConfig, etc.).
  • the one or more indications indicates of the number of beams (Rx, Tx) and/or beam pairs in which the predictions are above the threshold (e.g., above a suitability threshold means that a number of beams or beam pairs are suitable).
  • a suitability threshold means that a number of beams or beam pairs are suitable.
  • a low number of indications would indicate to the network that for a given Tx beam, the WD 22 the Tx beam would not be suitable in many other Rx beams, which makes the link (or the Tx beam) less robust.
  • a high number of indications would indicate to the network that the situation is stable, as in addition to the selected beam (known to the network via BFR MAC CE and/or random access preamble reception), a high number of Tx beams are also suitable.
  • a low number of indications would indicate to the network that the situation is not very stable, as only the selected beam (known to the network via BFR MAC CE and/or random access preamble reception), or perhaps a few more are suitable.
  • “High” and “low” as used in this context can be indications with respect to one another, or greater/lower than a predetermined value based on design criteria/objectives. o In cases where the network considers the link as not very robust some counter-actions could be taken, such as the configuration of multiple TRPs, SCells in serving frequencies, dual connectivity, etc.
  • the one or more indications includes an indication of ratio of predictions above the threshold divided by the total number of predictions (K).
  • the indication of ratio is the actual ratio.
  • the indication of the ratio is derived from the actual ratio compared to a ratio threshold. In some embodiments, the indication of the ratio is set to 1 if the ratio is higher than the threshold, or 0 otherwise.
  • the ratio threshold can be configurable which depends on the required level of stability. If the level of accuracy of ML model is high, e.g., it requires 7 out of 8 predictions to be higher than their corresponding thresholds, then the network can know the ML model is very good if the indication bit reported by the WD 22 is set to 1.
  • the indication of the ratio is the number of predictions that is within a certain range of the actual value, where the range is defined by the threshold. For example the prediction should be within a certain threshold to the actual value, this could also be seen as a confidence interval. The ratio can be seen as the ratio of the predictions that are within a confidence interval defined by the threshold.
  • the one or more indications the WD 22 transmits includes an indication of at least one candidate beam (e.g., SSB-X), i.e., the RS IDs or an associated identifier known by the WD 22 to be associated to the RS IDs, wherein the WD 22 includes the RS ID based on spatial-domain RSRP predictions/ estimates.
  • the SSB index is configured in a list
  • the WD 22 may indicate a position in that list, so the network knows that the report is associated to that SSB index.
  • transmitting the one or more indications based on the one or more spatial domain predictions of measurements on a plurality of beams in response to a BFD wherein the beam failure is detected at least by: the WD 22 counting beam failure instance (BFI) indications e.g., from the lower layers to the MAC entity.
  • BFI beam failure instance
  • a BFI indication is received (e.g., at the MAC entity of the WD 22) from lower layers (e.g., Layer 1) at the WD 22, the WD 22 i) starts or restart a beam failure timer (e.g., beamFailureDetectionTimer); ii) increment the counter for BFI by 1; and iii) if the counter for BFI is greater (or equal) than a configurable count value the WD 22 initiates beam failure recovery (BFR).
  • a beam failure timer e.g., beamFailureDetectionTimer
  • BFR is for a primary cell (e.g., SpCell, PCell, PSCell): o The WD 22 initiates a random access procedure; If BFR is for a secondary cell (e.g., SCell of MCG, SCell of SCG): o The WD 22 initiates BFR for SCell;
  • a primary cell e.g., SpCell, PCell, PSCell
  • a secondary cell e.g., SCell of MCG, SCell of SCG
  • transmitting the one or more indications based on the one or more spatial domain predictions of measurements on a plurality of beams in response to a BFD wherein in response the BFD the WD 22 transmits a BFR MAC CE, as defined in TS 38.321.
  • transmitting the one or more indications based on the one or more spatial domain predictions of measurements on a plurality of beams in response to a BFD includes the WD 22 including the one or spatial domain predictions of measurements on the plurality of beams in a first Medium Access Control (MAC) Control Element (MAC CE), and the WD 22 (e.g., the WD’s MAC entity) transmitting the first MAC CE to the network.
  • the first MAC CE is a BFR MAC CE, e.g., associated to a logical channel identify or identifier.
  • This BFR MAC CE includes the one or more indications, and further info, e.g., the occurrence of BFR for at least one SCell, the occurrence of BFR for a special cell.
  • the first MAC CE is multiplexed with a BFR MAC CE (as defined in 3GPP TS 38.321) e.g., in the same MAC PDU.
  • the MAC PDU is transmitted when the WD 22 declares a BFD and triggers BFR. If BFR is for a primary cell (e.g., SpCell, PCell, PSCell) the WD 22 initiates a Random Access procedure and the MAC PDU is part of Msg3 (transmitted by the WD 22 in response to the reception of the RAR) for a CBRA triggered by BFR.
  • the first MAC CE is transmitted when the WD 22 declares a BFD and/or triggers BFR based on CFRA. If BFR is for a primary cell (e.g., SpCell, PCell, PSCell) the WD 22 initiates a Random Access procedure and the first MAC CE is transmitted by the WD 22 in response to the reception detecting of the PDCCH addressed to the cell radio network temporary identifier (C-RNTI) on the search space for beam failure recovery.
  • BFR is for a primary cell (e.g., SpCell, PCell, PSCell)
  • C-RNTI cell radio network temporary identifier
  • the first MAC CE is multiplexed with a BFR MAC CE (for example as defined in 3GPP TS 38.321), e.g., in the same MAC PDU.
  • the MAC PDU is transmitted when the WD 22 declares a BFD and triggers BFR. If BFR is for a secondary cell (e.g., MCG SCell, SCG SCell) the WD 22 does not initiate a Random Access procedure and only transmits the MAC PDU.
  • the MAC entity at the WD 22 which transmits the first MAC CE is associated to a first cell group, with an SpCell (Special Cell, as defined in 3GPP TS 38.331, or any other cell with equivalent properties) and one or more Secondary Cells (SCells, for example as defined in 3GPP TS 38.331, or any other cell with equivalent properties).
  • the first cell group is a Master Cell Group (MCG)
  • the SpCell is a Primary Cell (PCell).
  • the first cell group is a Secondary Cell Group (SCG)
  • the SpCell is a Primary Cell (PCell).
  • the WD 22 is configured to report these by both MAC entities when the WD 22 is configured with multi-radio Dual Connectivity (MR-DC) e.g., with an MCG and an SCG, which implies an MCG MAC entity, and an SCG MAC entity.
  • MR-DC multi-radio Dual Connectivity
  • transmitting the one or more indications based on one or more spatial domain predictions of measurements on a plurality of beams in response to a BFD includes the WD 22 including the one or more indications of the one or more spatial domain predictions of measurements on the plurality of beams in an RRC message.
  • the RRC message is an RRC SCG Failure message which is transmitted by the WD 22 when the SCG fails, such as when a Radio Link Failure (RLF) is declared for the SCG, i.e. PSCell.
  • RLF Radio Link Failure
  • the action may be performed when BFD is declared for an SCG which is deactivated (UE configured with MR-DC).
  • UE configured with MR-DC
  • the WD 22 transmits the RRC SCG Failure message to the network node 16 operating as the Master Node (MN) including the one or more indications.
  • MN Master Node
  • the deactivated SCG For the deactivated SCG, this may be important as the WD 22 is not reporting typical CSI measurements for the SCG (as that is deactivated).
  • the one or more indications would be quite relevant for the network (e.g., the network node 16 operating as the Secondary Node, SN) to reconfigure and/or update the beam related parameters at the WD 22, which would have been typically done with the assistance of previously received CSI reports (not available within a reasonable time window because the WD 22 was in SCG deactivated state);
  • the RRC message is an RRC MCG Failure message which is transmitted by the WD 22 when the MCG fails, such as when a Radio Link Failure (RLF) is declared for the MCG, i.e. PCell.
  • RLF Radio Link Failure
  • the action may possibly be performed when BFD is declared for an MCG which is deactivated (UE configured with MR-DC).
  • UE configured with MR-DC
  • the WD 22 transmits the RRC MCG Failure message to the network node 16 operating as the SN including the one or more indications.
  • the deactivated MCG For the deactivated MCG, this may be important as the WD 22 is not reporting typical CSI measurements for the MCG (as that is deactivated).
  • the one or more indications would be quite relevant for the network (e.g., the network node 16 operating as the MN) to re-configure and/or update the beam related parameters for multiple beams at the WD 22, which would have been typically done with the assistance of previously received CSI reports (not available within a reasonable time window because the WD 22 was in MCG deactivated state);
  • the RRC message is a Measurement
  • the RRC message is a WD 22 Assistance Information.
  • transmitting the one or more indications based on one or more spatial domain predictions of measurements on a plurality of beams in response to a BFD includes the WD 22 including the one or more indications of the one or more spatial domain predictions of measurements on the plurality of beams in a message to the transmitted Over the Top, to a server e.g., transparent to the mobile network. Configurations from the network
  • the network node 16 described herein may correspond to a gNodeB, an eNodeB, a Radio Access Node for 6G radio, a Radio Access Node connected to a 6G Core Network, a Distributed Unit (e.g., wherein a radio access node has a Central Unit associated and that Distributed Unit), a Baseband Unit, a Radio unit.
  • the WD 22 is configured by a network node 16 with one or more parameters and/or configurations associated to the transmitting step. These may be parameters indicating what to transmit and/or how to transmit (e.g., in which message, in which format of a given message, in which protocol layer, etc.).
  • the WD 22 is configured by a network node 16 with one or more parameters and/or configurations for the WD 22 to perform the one or spatial domain predictions of measurements on a plurality of beams.
  • performing one or more spatial domain predictions of measurements on a plurality of beams is based on one or more configurations received from a network node 16 to which the WD 22 is connected.
  • the configuration is received in an RRC message (e.g., RRC Reconfiguration, RRC Resume, RRC Setup). This may be received during a transition to RRC CONNECTED (from RRC IDLE or RRC INACTIVE), while the WD 22 is in RRC CONNECTED, or during a mobility procedure (e.g., reconfiguration with sync, PCell change, handover);
  • RRC message e.g., RRC Reconfiguration, RRC Resume, RRC Setup.
  • RRC CONNECTED from RRC IDLE or RRC INACTIVE
  • a mobility procedure e.g., reconfiguration with sync, PCell change, handover
  • the configuration is received as part of the BFR configuration
  • the configuration is received as part of the BFD configuration; In some embodiments, the configuration is received as part of the Radio Link Monitoring (RLM) configuration; and/or
  • RLM Radio Link Monitoring
  • the configuration is received as part of a Prediction configuration.
  • the WD 22 is configured by a network node 16 with one or more parameters and/or configurations via RRC, and at least one of the one or more parameters and/or configurations may be activated by the reception of a MAC CE and/or a DCI, defined for that purpose.
  • the WD 22 may report at least one capability indication, indicating one or more of the following: the WD 22 is capable of performing one or more spatial-domain predictions of measurements on a plurality of beams for BFD/ BFR; the WD 22 is capable of generating one or more indications based on one or more spatial-domain predictions of measurements on a plurality of beams for BFD/ BFR; and/o the WD 22 is capable of reporting one or more indications based on one or more spatial-domain predictions of measurements on a plurality of beams for BFD/ BFR.
  • a WD 22 may implement multiple methods and report multiple capabilities. Re-configurations / updates in response to the one or more indications
  • a reconfiguration may correspond to an RRC message (e.g., RRCReconfiguration, RRCConnectionReconfiguration).
  • An update command may correspond to a MAC Control Element (MAC CE) indicating the activation and/or deactivation and/or switching of one or more configurations, such as a TCI state activation/ deactivation.
  • MAC CE MAC Control Element
  • the reception of the reconfiguration from the network may be an optional step after the WD 22 transmits the first MAC CE, and depends on the network, e.g., the network may decide to transmit the reconfiguration to the WD 22 or not.
  • the second MAC CE corresponds to a SP CSI-RS/CSI-IM Resource Set Activation/Deactivation MAC CE; o In some embodiments, the second MAC CE corresponds to a SP ZP CSLRS Resource Set Activation/Deactivation MAC CE; o Due to the fact that the network knows more beams via the report, the network can confidently activate multiple CSI reports; activate/ deactivate one or more reporting configurations for CSI reporting: o In some embodiments, the second MAC CE corresponds to a SP CSI reporting on PUCCH Activation/Deactivation MAC CE; modify at least one of the RLM-RSs to be monitored: o In some embodiments, the second MAC CE corresponds to a TCI State Indication for WD 22-specific PDCCH MAC CE, wherein the WD 22 performs RLM based on one or more RSs configured in the QCL configuration of the TCI states being activated;
  • the reconfiguration from the network is an RRC message (e.g., RRCReconfiguration) the WD 22 receives, wherein the RRC message indicates: reconfigure RLM-RSs: o
  • the WD 22 receives an RRC message including the IE RadioLinkMonitoringConfig (used to configure radio link monitoring for detection of beam- and/or cell radio link failure); reconfigure BFD RSs: o
  • the WD 22 receives an RRC message including the IE RadioLinkMonitoringConfig (used to configure radio link monitoring for detection of beam- and/or cell radio link failure).
  • the WD 22 receives an RRC message including at least one IE TCLState, wherein at least one parameter/ field/ configuration within is included (which indicates that it is being modified, added or removed); o In some embodiments the WD 22 receives an RRC message indicating that a previously configured TCI state is being modified; o In some embodiments the WD 22 receives an RRC message indicating that a new TCI state is being added and/or that previously configured TCI state is being associated to a Downlink control channel (PDCCH) or data channel (PDSCH); re-configure LI resources to be measured/ reports; o In some embodiments the WD 22 receives an RRC message including the IE CSI-MeasConfig:
  • ⁇ that includes configuration of resources to be measured, such as one or more SSBs and/or one or more CSLRS resources e.g., in the csi- ResourceConfigToAddModList, of IE SEQUENCE (SIZE (L.maxNrofCSI-ResourceConfigurations)) OF CSL ResourceConfig;
  • ⁇ that includes configuration of CSI reports, such as one or more periodic, aperiodic and/or semi-persistent, event-triggered reporting over PUCCH and/or PUSCH e.g., csi-ReportConfigToAddModList SEQUENCE (SIZE (L.maxNrofCSI-ReportConfigurations)) OF IE CSI-ReportConfig; re-configure the measurement configuration (MeasConfig) for RRC measurement reporting, over OSI L3 : o
  • that includes the number of beams to be combined (e.g., averaged) for performing cell quality derivation for example as defined in 3GPP TS 38.331, 6.3.2, e.g., nrofSS- BlocksTo Av erage,
  • Some embodiments include a method at a network node 16, the method comprising the network receiving one or more indications from a WD 22 based on one or more spatial domain predictions of measurements on a plurality of beams in response to a BFD at the WD 22.
  • a method includes using different ML-model or prediction models, based on different set of parameters known at the WD 22.
  • the method includes the usage of “real/current measurements” as input parameters for the mobility prediction model (e.g., RSRP, RSRQ, SINR at a certain Tx and/or Rx beam for other Tx and/or Rx beams for which the WD 22 perform predictions, based on an RS type like SSB and/or CSLRS and/or DRMS), either instantaneous values or filtered values (e.g., with LI filter parameters).
  • the mobility prediction model e.g., RSRP, RSRQ, SINR at a certain Tx and/or Rx beam for other Tx and/or Rx beams for which the WD 22 perform predictions, based on an RS type like SSB and/or CSLRS and/or DRMS
  • instantaneous values e.g., with LI filter parameters
  • Some embodiments include the usage of parameters from sensors, such as WD 22 positioning information (e.g., GPS coordinates, barometric sensor information or other indicators of height), rotation sensors, proximity sensors, and mobility such as, location information, previous connected BSs history, speed and mobility direction, information from mapping/guiding applications (e.g., Google maps, Apple maps).
  • sensors such as WD 22 positioning information (e.g., GPS coordinates, barometric sensor information or other indicators of height), rotation sensors, proximity sensors, and mobility such as, location information, previous connected BSs history, speed and mobility direction, information from mapping/guiding applications (e.g., Google maps, Apple maps).
  • mapping/guiding applications e.g., Google maps, Apple maps.
  • Some embodiments include the usage of WD 22 mobility history information such as last visited beams, LI measurements, CSI measurements, etc. Some embodiments include the usage of time information such as the current time (e.g., 10: 15 am) and associated time zone (e.g., 10: 15 GMT). That may be relevant if the WD 22 has a predictable trajectory and it is typical that at a certain time the WD 22 is in a certain location.
  • time information such as the current time (e.g., 10: 15 am) and associated time zone (e.g., 10: 15 GMT). That may be relevant if the WD 22 has a predictable trajectory and it is typical that at a certain time the WD 22 is in a certain location.
  • the WD 22 may be configured (e.g., by the network, via an RRC message) to utilize at least one of the above parameters as input to the ML-model for beam management (in this particular case, for BFD).
  • the availability of these parameters may depend on a capability information indicated to the network. If network is aware that the WD 22 is capable of performing certain measurements (like based on sensors) and, if the network is aware that a WD 22 benefits in using a parameter in an ML-model, WD 22 may be configured to use at least one of these input parameters in the ML-model for which the network is configuring the WD 22 to report.
  • the WD 22 indicates to the network a capability related information i.e. WD 22 indicates to the network that it can download / receive a prediction model from the network (for example, for mobility prediction information) according to the method.
  • This capability may be related to the software and hardware aspects at the WD 22, availability of sensors, etc.
  • the WD 22 may be further configured by the network to use it e.g., in a measurement configuration like reporting configuration, measurement object configuration, etc.
  • An autoencoder is a type of machine learning algorithm that may be used to learn efficient data representations, that is to concentrate data. Autoencoders are trained to take a set of input features and reduce the dimensionality of the input features, with minimal information loss.
  • An autoencoder is divided into two parts, an encoding part or encoder and a decoding part or decoder.
  • the encoder and decoder may comprise, for example, deep neural networks comprising layers of neurons.
  • An encoder successfully encodes or compresses the data if the decoder is able to restore the original data stream with a tolerable loss of data.
  • the AE typically learns identity function, which implies that the output equals the input.
  • a Denoising Autoencoder includes corrupting the input data on purpose by randomly turning some of the input values to zero. This can enable the neural network to reconstruct the zero-valued input features (perform denoising).
  • a denoising autoencoder (DAE) has been shown to improve image quality of low-resolution pictures.
  • the WD 22 may train a DAE to perform spatial predictions on its SSB-beams. For example 4 SSB-beams as shown in the example dataset in FIG. 21.
  • the WD 22 first collects a set of measurements comprising RSRP data for all 4 beams.
  • the WD 22 performs noising of the measurements, more specifically, it creates a pattern where one of the 4 beams can be omitted/predicted.
  • One example dataset and the noised samples, after applying the 4 noising patterns [0,1, 1,1], [1,0, 1,1], [1, 1,0,1] , [1,1, 1,0] are shown in the example of FIG. 22.
  • the WD 22 may build a denoising autoencoder model F able to predict the actual values from the noised samples, F(xnoise)-> x.
  • F(xnoise)-> x By using the DAE, it enables the WD 22 to use one model, to be able to predict any combination of measured vs predicted beam information.
  • the network node 16 in case the model is downloaded to the WD 22 from network node 16, the network node 16 also includes information on the supported noising patterns. For example what beams that can be omitted and instead be predicted.
  • the denoising autoencoder might only be trained to support a certain combination of beams to be predicted /measured.
  • the WD 22 may need to measure on at least one beam, hence not all patterns are valid.
  • Each pattern can also be signalled along with its prediction performance.
  • [1,1, 1,0] has a mean prediction accuracy on the 4 th beam of x dBm, and variance of y dBM.
  • the network may only include patterns that fulfil a certain accuracy level.
  • the WD 22 may obtain from a network node 16 (e.g., the RAN node, gNodeB, core network (CN) node, OTT server) the ML-model (Inference Model) to be used for performing the one or spatial domain predictions of measurements on a plurality of beams in response to a BFD.
  • the WD 22 could download the ML-model from the network node 16 (e.g., in the RAN or in the CN), or an OTT server.
  • Alternative 1 - WD 22 receives one or more ML-model parameters/ configurations:
  • An ML-model could be signaled using existing model formats such as Open Neural Network Exchange (ONNX), or formats used in commonly used toolboxes such as Keras or Pytorch (See https://onnx.ai for further details).
  • the ML-model is signaled using a high-level model description, plus a detailed information regarding the weights of each layer if the model includes a neural network.
  • the high-level model description (model parameter vector) may for example comprise parameters defining the structure and characteristics of the model, such as for example number of layers, activation function of respective layer, nature of connections between nodes of respective layer, weights, loss function, just to mention a few, of a neural network.
  • the detailed information can comprise the values for each parameter in the ML-model.
  • the network node 16 can in some embodiments, create a containerized image with the ML-model.
  • the network node 16 can for example use Docker containers to create, and signal to the WD 22 an image capable of executing the trained ML-model.
  • the WD 22 may ensure that it has the correct libraries, runtimes, and other technical dependencies are installed in order to execute the ML-model.
  • Another approach is to use a so-called container. Docker is one such example, where the Docker containers contain all components which may be needed for the ML model, including code, libraries, runtimes, and system tools. Containers can therefore be used to ensure that the WD 22 don’t risk of missing or having incompatible libraries leading to errors. However, since the containers may support more than only the model parameters, the over-the-air signaling size may be larger in comparison to alternative 1.
  • the WD 22 is equipped a set of ML-models (e.g., from factory, obtained from the USIM card) each capable of predicting a time series of RSRP measurements, where the model parameters could be specified in existing standards.
  • the WD 22 can thus be equipped with a set of ML-models with a general configuration, e.g., trained on an aggregated dataset from multiple deployment scenarios (real data or simulations).
  • the network does not need to transmit the model parameters to the WD 22 but could instead transmit an index of which ML-models, in the set of ML-models that it should use.
  • Some embodiments may include one or more of the following:
  • a network node configured to communicate with a wireless device (WD), the network node configured to, and/or comprising a radio interface and/or comprising processing circuitry configured to: configure the WD with at least one parameter to be used by the WD to perform at least one spatial domain prediction of measurements on at least one beam in response to a beam failure detection, BFD, at the WD; receive the at least one spatial domain prediction; and perform at least one action based at least in part on the at least one spatial domain prediction.
  • WD wireless device
  • BFD beam failure detection
  • Embodiment A2 The network node of Embodiment Al, wherein the at least one action includes at least one of reconfiguring radio link monitoring, RLM, reference signals, reconfiguring BFD reference signals, reconfiguring at least one antenna parameter related to one of RLM and BFD, one of activating and deactivating transmission configuration indicator, TCI, states, reconfiguring layer 1 resources, and modify at least one BFD reference signal to monitored.
  • Embodiment Bl A method implemented in a network node configured to communicate with a wireless device, WD, the method comprising: configuring the WD with at least one parameter to be used by the WD to perform at least one spatial domain prediction of measurements on at least one beam in response to a beam failure detection, BFD, at the WD; receiving the at least one spatial domain prediction; and performing at least one action based at least in part on the at least one spatial domain prediction.
  • Embodiment B4 The method of Embodiment B3, wherein the information includes an indication of which beams for which measurements are taken.
  • a wireless device configured to communicate with a network node, the WD configured to, and/or comprising a radio interface and/or processing circuitry configured to: use at least one machine learning, ML, model to predict at least one spatial domain measurement on at least one beam in response to beam failure detection, BFD; and transmit indications of the at least one spatial domain measurement prediction to the network node.
  • a wireless device configured to communicate with a network node, the WD configured to, and/or comprising a radio interface and/or processing circuitry configured to: use at least one machine learning, ML, model to predict at least one spatial domain measurement on at least one beam in response to beam failure detection, BFD; and transmit indications of the at least one spatial domain measurement prediction to the network node.
  • Embodiment C4 The WD of Embodiment C3, wherein the random access procedure is one of contention free random access and contention based random access.
  • Embodiment D3 The method of any of Embodiments DI and D2, wherein the indications are sent via a random access procedure.
  • These computer program instructions may also be stored in a computer readable memory or storage medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks.
  • Computer program code for carrying out operations of the concepts described herein may be written in an object oriented programming language such as Python, Java® or C++.
  • the computer program code for carrying out operations of the disclosure may also be written in conventional procedural programming languages, such as the "C" programming language.
  • the program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer.
  • the remote computer may be connected to the user's computer through a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).
  • LAN local area network
  • WAN wide area network
  • Internet Service Provider for example, AT&T, MCI, Sprint, EarthLink, MSN, GTE, etc.

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

L'invention concerne un procédé, un système et un appareil servant à rapporter des informations de prédiction de faisceau de domaine spatial dans le cadre d'un rétablissement sur défaillance de faisceau (BFR). Selon certains aspects, un procédé dans un dispositif sans fil (WD) comprend la réalisation d'au moins une prédiction de mesure de domaine spatial pour chaque faisceau d'une pluralité de faisceaux en réponse à une détection de défaillance de faisceau, BFD, et la transmission, au nœud de réseau, d'une indication d'au moins une prédiction de mesure de domaine spatial pour au moins un faisceau de la pluralité de faisceaux en réponse à la BFD.
PCT/SE2023/050400 2022-04-29 2023-04-28 Rapport d'informations de prédiction de faisceau de domaine spatial dans le cadre d'un rétablissement sur défaillance de faisceau WO2023211353A1 (fr)

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WO2021118418A1 (fr) * 2019-12-10 2021-06-17 Telefonaktiebolaget Lm Ericsson (Publ) Procédés, ue et premier nœud de réseau pour gérer des informations de mobilité dans un réseau de communication
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