WO2023211353A1

WO2023211353A1 - Reporting spatial-domain beam prediction information in beam failure recovery

Info

Publication number: WO2023211353A1
Application number: PCT/SE2023/050400
Authority: WO
Inventors: Icaro Leonardo DA SILVA; Henrik RYDÉN; Chunhui Li; Jingya Li
Original assignee: Telefonaktiebolaget LM Ericsson AB
Current assignee: Telefonaktiebolaget LM Ericsson AB
Priority date: 2022-04-29
Filing date: 2023-04-28
Publication date: 2023-11-02
Anticipated expiration: 2024-10-29
Also published as: EP4515712A1

Abstract

A method, system and apparatus for to reporting spatial domain beam prediction information in beam failure recovery (BFR) are disclosed. According to some aspects, a method in a wireless device (WD) includes performing at least one spatial domain measurement prediction for each beam of a plurality of beams in response to a beam failure detection, BFD, and transmitting to the network node an indication of at least one spatial domain measurement prediction for at least one of the plurality of beams in response to the BFD.

Description

REPORTING SPATIAL-DOMAIN BEAM PREDICTION INFORMATION IN BEAM

FAILURE RECOVERY

TECHNICAL FIELD

The present disclosure relates to wireless communications, and in particular, to reporting spatial domain beam prediction information in beam failure recovery (BFR).

BACKGROUND

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. 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. Sixth Generation (6G) wireless communication systems are also under development.

Artificial Intelligence (Al) - Machine Learning (ML) for Air Interface: 3GPP Rel-18 and 6G

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.

In 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 here depends on the use cases under consideration and could be, e.g., improved throughput, robustness, accuracy or reliability, or reduced overhead, etc. Through studying a few carefully selected use cases, assessing their performance in comparison with traditional methods and the associated potential specification impacts that enable their solutions, this study item (SI) 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. Finally, specification impact will be assessed in order to improve the overall understanding of what would be required to enable AI/ML techniques for the air-interface.

Among the initial set of use cases 3GPP RANI will be considering is beam management, e.g., beam prediction in the time, and/or spatial domain for overhead and latency reduction and beam selection accuracy improvement.

For the use cases under consideration, including beam management, 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):

■ Consider aspects related to, e.g., the specification of the Al Model lifecycle management, and dataset construction for training, validation and test for the selected use cases;

■ Use case and collaboration level specific specification impact, such as new signaling, assistance information, measurement, and feedback; o Protocol aspects including (Except use case study, RAN2 only start following general assessment after there is sufficient progress on use study in RANI):

■ Consider aspects related to, e.g., capability indication, configuration procedures (training/inference), validation and testing procedures, and management of data and AI/ML model;

■ Collaboration level specific specification impact per use case including signaling design to support the collaboration identified in RANI; and o Interoperability and testability aspects (RAN4 only start the work after there is sufficient progress on use case study in RANI and RAN2):

■ WD and network node requirements and testing frameworks to validate AI/ML based performance enhancements and ensuring that WD and network node with AI/ML meet or exceed the existing minimum requirements; and

■ Consider the need and implications for AI/ML processing capabilities definition.

ALML for 6G Air Interface

AI/ML for 6G has been considered but so far, not much has been publicly disclosed for RAN2 protocols. However, the topic is expected to be brought up in the European Union 6G project Hexa-X. According to the latest information about the project, in this area the objective is to make the most out of Al technology applied to networks - to develop methodologies, algorithms, and architectural requirements for an Al-native network, through Al-driven air interface and Al governance. 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 and Beam Failure Recoven (BFR)

In 5G NR, BFD is a WD functionality configured by a gNodeB (gNB) wherein the WD is configured with one or more beam failure detection (BFD) reference signals (RSs), which are transmitted in spatial directions (e.g., beams). Such RSs may include or be included in one or more of synchronization signal blocks (SSB) and/or channel state information reference signals (CSI-RSs). The medium access control (MAC) entity of the WD triggers a BFR when the number of beam failure instance indications received from the physical layer, also called BFIs in 3GPP Technical Standard (TS) 38.321, reaches a maximum value configured by the network, i.e., when BFI COUNTER >=beamFailure!nstanceMaxCount. In addition, a timer is also configured, and the counter is incremented while the timer is running (i.e., before a configured timer expires).

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. Each MAC layer entity at the WD (e.g., MCG MAC entity and SCG MAC entity) 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.

After BFD is declared for the SpCell (e.g., PCell), the WD performs the following actions: triggers BFR by initiating a Random Access procedure on the PCell (in case BFD is declared at the PCell); selects a suitable beam to perform BFR (if the network node has provided dedicated Random Access resources for certain beams, those will be prioritized by the WD); and includes an indication of a beam failure on PCell in a BFR MAC control element (CE) if the Random Access procedure involves contentionbased random access.

Upon completion of the Random Access procedure, beam failure recovery is considered complete.

After beam failure is detected on an SCell, the WD: triggers beam failure recovery by initiating a transmission of a BFR MAC CE for this SCell; and selects a suitable beam for this SCell (if available) and indicates it along with the information about the beam failure in the BFR MAC CE.

Upon reception of a physical downlink control channel (PDCCH) indicating an uplink grant for a new transmission for the hybrid automatic repeat request (HARQ) process used for the transmission of the BFR MAC CE, beam failure recovery for this SCell is considered complete.

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.

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). The WD transmits and the network receives the Physical Random Access Channel (PRACH) preamble in one of the configured physical random access channel (PRACH) resources corresponding to the selected SSB (or CSLRS) by the WD (e.g., PRACH-ResourceDedicatedBFR) and identifies the following: i) this random access procedure is triggered due to BFD and BFR; ii) the WD which has triggered BFR; iii) the SSB (or CSLRS) that the WD has selected. As the network identifies the selected SSB (or CSLRS), and knows its associated downlink beam/ spatial direction, the network may transmit the Random Access Response (RAR) in the downlink (DL) beam associated to that selected SSB (or CSLRS).

If the WD relies on Contention Based Random Access (CBRA) when BFR is triggered, the WD selects an SSB, also equivalent to select a beam. The network receives the PRACH preamble corresponding to the selected SSB by the WD. As the network identifies the selected SSB (or CSLRS), and knows its associated downlink beam/ spatial direction, the network may transmit the RAR in the DL beam associated to that selected SSB (or CSLRS).

However, at that point 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.

Another problem is that while the WD only reports a single beam (selected beam per serving cell), most of the beam related parameters which needs to be reconfigured/ updated at the WD requires some understanding of the situation of multiple beams. These beam-related parameters may be one or more of: a) one or more Transmission Configuration Indication (TCI) states which were activated need to be deactivated, and TCI states which were deactivated need to be activated; b) BFD resources (BFR RSs) to be monitored needs to be re-configured; c) beam candidate lists for BFR needs to be re-configured; d) CSI-MeasConfig needs to be re-configured; e) SSB and/or CSI-RS resources to be LI measured and/or reported needs to be activated and/or deactivated.

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. 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.

At the same time, the WD reporting measurements on multiple beams per serving cells is also problematic, as it increases WD complexity, increases the WD’s energy consumption required to perform measurements on additional beams and may lead to longer delays to the BFR procedure. In some embodiments, in cases where both the network node and the WD have beamforming requiring Transmitter (Tx) beam sweeping and/or Receiver (Rx) beam sweeping, the delay in the procedure for performing measurements is proportional to the number of Rx and Tx beams and/or the number of time units in which the Tx beams are multiplexed. In some embodiments, 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). In addition to this, if 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

SUMMARY

Some embodiments advantageously provide methods, systems, and apparatuses for to reporting spatial domain beam prediction information in beam failure recovery (BFR).

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.

According one aspect, a method in a wireless device, WD, configured to communicate with a network node is provided. The method includes performing at least one spatial domain measurement prediction for each beam of a plurality of beams in response to a beam failure detection, BFD. The method further includes transmitting to the network node an indication of at least one spatial domain measurement prediction for at least one of the plurality of beams in response to the BFD.

According to this aspect, in some embodiments, 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. In some embodiments, the plurality of beams includes at least one transmit beam transmitted by the network node. In some embodiments, the plurality of beams includes at least one receive beam for receiving signals from the network node. In some embodiments, the plurality of beams includes at least one pair of a transmit beam and a receive beam. In some embodiments, at least one beam of the plurality of beams is configured for BFD monitoring. In some embodiments, the plurality of beams includes at least one candidate beam for beam failure recovery, BFR. In some embodiments, 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 indication includes transmitting a beam failure recovery, BFR, report on a first available medium access control, MAC, control element, CE, after BFD.

According to another aspect, a wireless device, WD, configured to communicate with a network node is provided. The WD includes processing circuitry 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 WD includes a radio interface in communication with the processing circuitry and configured to transmit to the network node an indication of at least one spatial domain measurement prediction for at least one of the plurality of beams in response to the BFD.

According to this aspect, in some embodiments, 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. In some embodiments, the plurality of beams includes at least one transmit beam transmitted by the network node. In some embodiments, the plurality of beams includes at least one receive beam for receiving signals from the network node. In some embodiments, the plurality of beams includes at least one pair of a transmit beam and a receive beam. In some embodiments, at least one beam of the plurality of beams is configured for BFD monitoring. In some embodiments, the plurality of beams includes at least one candidate beam for beam failure recovery, BFR. In some embodiments, 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.

According to yet another aspect, a method in a network node configured to communicate with a wireless device, WD, is provided. The method includes receiving 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 method also includes reconfiguring communications with the WD in response to the indication.

According to this aspect, in some embodiments, 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. In some embodiments, reconfiguring communications with the WD includes at least one of changing and reconfiguring a transmission configuration indication, TCI, activation state. In some embodiments, reconfiguring communications with the WD includes reconfiguring layer 1, LI, resources for spatial domain measurements. In some embodiments, 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.

According to another aspect, a network node configured to communicate with a wireless device, WD, is provided. 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.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present embodiments, and the attendant advantages and features thereof, will be more readily understood by reference to the following detailed description when considered in conjunction with the accompanying drawings wherein:

FIG. 1 is a flowchart of BFR based on CBRA;

FIG. 2 is a flowchart of BFR based on CFRA;

FIG. 3 is a diagram of beam sweeping by a network node and by a WD;

FIG. 4 is a schematic diagram of an example network architecture illustrating a communication system connected via an intermediate network to a host computer according to the principles in the present disclosure;

FIG. 5 is a block diagram of a host computer communicating via a network node with a wireless device over an at least partially wireless connection according to some embodiments of the present disclosure;

FIG. 6 is a flowchart illustrating example methods implemented in a communication system including a host computer, a network node and a wireless device for executing a client application at a wireless device according to some embodiments of the present disclosure;

FIG. 7 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 wireless device according to some embodiments of the present disclosure;

FIG. 8 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 from the wireless device at a host computer according to some embodiments of the present disclosure;

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. 10 is a flowchart of an example process in a network node for to reporting spatial domain beam prediction information in beam failure recovery (BFR);

FIG. 11 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. 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. 13 is a flowchart of an example process in a network node 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. 16 is a flowchart of BFR based on CFRA 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. 20 illustrates a mixed-Gaussian probability density function with two components;

FIG. 21 shows an example set of training samples; and

FIG. 22 shows an example set of noisy samples.

DETAILED DESCRIPTION

Before describing in detail example embodiments, it is noted that the embodiments reside primarily in combinations of apparatus components and processing steps related to reporting spatial domain beam prediction information in beam failure recovery (BFR). Accordingly, components have been represented where appropriate by conventional symbols in the drawings, showing only those specific details that are pertinent to understanding the embodiments so as not to obscure the disclosure with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein. Like numbers refer to like elements throughout the description.

As used herein, relational terms, such as “first” and “second,” “top” and “bottom,” and the like, may be used solely to distinguish one entity or element from another entity or element without necessarily requiring or implying any physical or logical relationship or order between such entities or elements. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the concepts described herein. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “includes,” “comprising,” “includes” and/or “including” when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

In embodiments described herein, the joining term, “in communication with” and the like, may be used to indicate electrical or data communication, which may be accomplished by physical contact, induction, electromagnetic radiation, radio signaling, infrared signaling or optical signaling, for example. One having ordinary skill in the art will appreciate that multiple components may interoperate and modifications and variations are possible of achieving the electrical and data communication.

In some embodiments described herein, the term “coupled,” “connected,” and the like, may be used herein to indicate a connection, although not necessarily directly, and may include wired and/or wireless connections.

The term “network node” used herein 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 (DAS), a spectrum access system (SAS) node, an element management system (EMS), etc. The network node may also comprise test equipment. The term “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. In some embodiments, the non-limiting terms wireless device (WD) or a user equipment (UE) are used interchangeably. 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.

Also, in some embodiments the generic term “radio network node” is used. It 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).

Note that although terminology from one particular wireless system, such as, for example, 3GPP LTE and/or New Radio (NR), may be used in this disclosure, this should not be seen as limiting the scope of the disclosure to only the aforementioned system. Other wireless systems, including without limitation Wide Band Code Division Multiple Access (WCDMA), Worldwide Interoperability for Microwave Access (WiMax), Ultra Mobile Broadband (UMB) and Global System for Mobile Communications (GSM), may also benefit from exploiting the ideas covered within this disclosure.

Note further, that 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. In other words, it is contemplated that 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.

As used herein, the terms “ML-model”, “Al-model”, “Model Inference”, “Model Inference function” are used interchangeable. 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. As used herein, 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). In some embodiments, an SS-RSRP of SSB index X, and the output of the ML-model, 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). In some embodiments, 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. 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.

In some embodiments, 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). In some embodiments, 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.

As used herein, 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.

Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. It will be further understood that terms used herein should be interpreted as having a meaning that is consistent with their meaning in the context of this specification and the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

Some embodiments provide to reporting spatial domain beam prediction information in beam failure recovery (BFR).

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).

Hence, in some embodiments, 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. In addition, as misconfigurations are avoided, 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.

In addition, in some embodiments, 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.

In some embodiments, 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.

In some embodiments, 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.

In some embodiments, 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).

In some embodiments, joint processes for beam management (named P2 and P3 in the literature and in 3GPP technical Reports for 5G) 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.

Returning now to the drawing figures, in which like elements are referred to by like reference numerals, there is shown in 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.

Also, it is contemplated that 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. In some embodiments, 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. As an example, 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. In some embodiments, 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.

Example implementations, in accordance with an embodiment, of the WD 22, network node 16 and host computer 24 discussed in the preceding paragraphs will now be described with reference to FIG. 5. In a communication system 10, 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. In particular, in addition to or instead of a processor, such as a central processing unit, and memory, 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. 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).

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. In some embodiments, 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. In providing the service to the remote user, 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. In some embodiments, 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.

In the embodiment shown, 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. In particular, in addition to or instead of a processor, such as a central processing unit, and memory, 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. 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).

Thus, 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. In some embodiments, 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. In some embodiments, 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. In particular, in addition to or instead of a processor, such as a central processing unit, and memory, 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).

Thus, 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. In 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. In providing the service to the user, 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. In some embodiments, 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. In some embodiments, 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.

In some embodiments, 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.

In FIG. 5, 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.

In some embodiments, 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. There may further be an optional network functionality for reconfiguring the OTT connection 52 between the host computer 24 and WD 22, in response to variations in the measurement results. 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. In embodiments, 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. In certain embodiments, measurements may involve proprietary WD signaling facilitating the host computer’s 24 measurements of throughput, propagation times, latency and the like. In some embodiments, 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.

Thus, in some embodiments, 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. In some embodiments, the cellular network also includes the network node 16 with a radio interface 62. In some embodiments, 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.

In some embodiments, 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. In some embodiments, 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.

Although 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. In a first step of the method, the host computer 24 provides user data (Block SI 00). In an optional substep of the first step, the host computer 24 provides the user data by executing a host application, such as, for example, the host application 50 (Block SI 02). In a second step, the host computer 24 initiates a transmission carrying the user data to the WD 22 (Block SI 04). In an optional third step, 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). In an optional fourth step, 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. In a first step of the method, the host computer 24 provides user data (Block SI 10). In an optional substep (not shown) the host computer 24 provides the user data by executing a host application, such as, for example, the host application 50. In a second step, 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. In an optional third step, 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. In an optional first step of the method, the WD 22 receives input data provided by the host computer 24 (Block SI 16). In an optional substep of the first step, 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). Additionally or alternatively, in an optional second step, the WD 22 provides user data (Block S120). In an optional substep of the second step, the WD provides the user data by executing a client application, such as, for example, client application 92 (Block S122). In providing the user data, the executed client application 92 may further consider user input received from the user. Regardless of the specific manner in which the user data was provided, the WD 22 may initiate, in an optional third substep, transmission of the user data to the host computer 24 (Block S124). In a fourth step of the method, 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. In an optional first step of the method, in accordance with the teachings of the embodiments described throughout this disclosure, the network node 16 receives user data from the WD 22 (Block S128). In an optional second step, the network node 16 initiates transmission of the received user data to the host computer 24 (Block SI 30). In a third step, 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).

In some embodiments, 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. In some embodiments, the method also includes transmitting to the WD, information on supported noising patterns. In some embodiments, 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)

In some embodiments, 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. In some embodiments, the indications are sent via a random access procedure. In some embodiments, 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). In some embodiments, 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. In some embodiments, the plurality of beams includes at least one transmit beam transmitted by the network node 16. In some embodiments, the plurality of beams includes at least one receive beam for receiving signals from the network node 16. In some embodiments, the plurality of beams includes at least one pair of a transmit beam and a receive beam. In some embodiments, at least one beam of the plurality of beams is configured for BFD monitoring. In some embodiments, the plurality of beams includes at least one candidate beam for beam failure recovery, BFR. In some embodiments, 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). In some embodiments, 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. In some embodiments, reconfiguring communications with the WD 22 includes at least one of changing and reconfiguring a transmission configuration indication, TCI, activation state. In some embodiments, reconfiguring communications with the WD 22 includes reconfiguring layer 1, LI, resources for spatial domain measurements. In some embodiments, 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.

Having described the general process flow of arrangements of the disclosure and having provided examples of hardware and software arrangements for implementing the processes and functions of the disclosure, the sections below provide details and examples of arrangements for to reporting spatial domain beam prediction information in beam failure recovery (BFR).

WD method

In some embodiments, a method at a WD 22 operating with at least one ML model (e.g., based on which the WD 22 performs one or more spatial-domain predictions), the method 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); one or more selected beams (selected during BFR); a plurality of beams configured for other procedures, e.g., not for BFD monitoring; and/or 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).

In some embodiments, prior to transmitting the one or more indications, the WD 22 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.

In some embodiments, 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.

In some embodiments, 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 initiating a Random Access (RA) procedure (triggered by BFR upon declaring BFD), if BFD is for a primary cell/ special cell (e.g., SpCell, PCell, PSCell as defined in 3GPP TS 38.331); the WD 22 selecting a first beam (and/or RS) and an associated RA resource (e.g., preamble, RA time and/or frequency resource) to transmit the preamble, transmitting the preamble and receiving a PDCCH or/and a RAR, so that upon receiving the PDCCH or/and the RAR, the WD 22 generates the first MAC CE and transmits it.

In some embodiments, 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.

In some embodiments, 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.

In some embodiments, 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.).

In some embodiments, 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.

In some embodiments, 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.

Note: 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 transmission configuration indicator (TCI) state of activation or deactivation.

Network method

In some embodiments, a method at a network node 16 (e.g., gNodeB, RAN node in a 6G radio access network) 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.

In some embodiments, a method at a network node 16 includes configuring the WD 22 with one or more parameter for the WD 22 to transmit 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.

In some embodiments, a method in a network node 16 includes configuring the WD 22 with one or more parameters for the WD 22 to perform one or more spatial domain predictions of measurements on a plurality of beams, for deriving one or more indications and report in response to a BFD at the WD 22. In some embodiments, the one or more parameters assist the WD 22 to perform the one or more spatial-domain predictions.

In some embodiments, the network node 16 performs one or more actions in response to the reception of the one or more indications from a WD 22 based on one or spatial domain predictions of measurements on a plurality of beams in response to a BFD at the WD 22, including: reconfigure RLM-RSs; reconfigure BFD RSs; reconfigure one or more antenna parameters related to the

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.

In the example of FIG. 15, the WD 22 is configured to transmit one or more indications of candidate beams based on the one or more spatial domain predictions and/or estimation of measurements on a plurality of beams in response to a BFD. The candidate beams are used for beam recovery (also known as link recovery or beam repair).

In the example of FIG. 15, upon receiving BFD reference signals, 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. For example, 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). The network node 16 may transmit the RAR as well as one or more indications based on one or more spatial domain predictions of measurements on a plurality of beams. The WD 22 may also identify the WD 22 and BFR (Block SI 60). The network node 16 may transmit a MAC CE for TCI state and/or a MAC CE for LI measConfig update based on the indications based on one or more spatial domain predictions of measurements on a plurality of beams. The WD 22 may monitor BFD (Block S162). The WD 22 may also receive BFD reference signals based on the selected synchronization signal block (SSB-x) and other reported beams. The WD 22 may then, not declare BFD for a period of time (Block SI 64).

In the example flow diagram of FIG. 16, the network, e.g., via the network node 16, configures the WD 22 for the transmissions, and, the network (optionally) performs further actions in response to the one or more indications based on the one or spatial domain predictions of measurements on a plurality of beams in response to a BFD.

In the example of FIG. 16, upon receiving BFD reference signals, the WD 22 may declare BFD (Block S166). The WD 22 may receive an indication of candidate beams from the NN 16. For example, 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 68). The WD 22 may set the random access preamble index to ra-Preamblelndex associated with the selected SSB (Block SI 70) The WD 22 may then initiate a CFRA 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 determines the WD 22 (because it is CFRA) (i.e., the network node 16 knows the WD’s C-RNTI (Block S172). The network node 16 may transmit the RAR as well as one or more indications based on one or more spatial domain predictions of measurements on a plurality of beams, network node 16 may transmit a MAC CE for TCI state and/or a MAC CE for LI measConfig update based on the indications based on one or more spatial domain predictions of measurements on a plurality of beams. The WD 22 may monitor BFD (Block S174). The WD 22 may also receive BFD reference signals based on the selected synchronization signal block (SSB-x) and other reported beams. The WD 22 may then, not declare BFD for a period of time (Block SI 76).

Note that the WD 22 receiving the configuration and the reporting may be seen as one procedure from the 3 GPP/ specifications perspective. Also, the WD 22 receiving a reconfiguration from the network in response may be viewed as another procedure, which is an optional step triggered by the network.

Some embodiments include a method at a WD, e.g., User Equipment (UE), operating with at least one ML model (e.g., based on which the WD 22 performs one or more spatial-domain predictions) for transmitting predicted information during a Beam Failure Recovery (BFR) procedure. The method may include 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.

Some embodiments include spatial domain predictions of measurements.

Some embodiments include indications based on spatial domain predictions of measurements.

Some embodiments include BFD, BFR and reporting of one or more indications based on spatial domain predictions of measurements.

Some embodiments include configurations from the network for the reporting and the predictions.

Some embodiments include capability signaling.

Some embodiments include re-configurations and updates the WD 22 may receive in response to the transmitted indications.

Some embodiments include aspects related to the ML-model/ Model inference function for spatial-domain prediction of beam measurements.

Some embodiments include how the WD 22 may obtain (or may be equipped or deployed with or implemented with) the ML-models. Spatial domain predictions of measurements

Prior to transmitting the one or more indications, the WD 22 may perform one or more spatial domain predictions of measurements on a plurality of beams, such as spatial-domain predictions/ estimates of SS-RSRP, CSI-RSRP, SS-RSRQ, CSI-RSRQ, SS-SINR, CSI-SINR, as defined in 3GPP TS 38.215. In some embodiments, the WD 22 performs measurements on at least one beam (e.g., an RS associated to an RS index, RS identifier or beam identifier, like an SSB index X), such as an SS-RSRP, and spatial- domain predictions of measurements on at least one beam, such as the prediction of an SS-RSRP for that SSB index (e.g., SSB-index Y). The prediction of a measurement in spatial domain may either be related to the Rx beam, the Tx beam or both (i.e., for a beam pair). In other words, based on the SS-RSRP for beam X (e.g., SSB index X), the WD 22 may predict the SS-RSRP, e.g., for beam Y (or SSB index Y). In some embodiments, the spatial-domain prediction is performed for a given measurement period, so if the actual measurement for SSB index X is performed during measurement period tO+T, the spatial-domain prediction for the SS-RSRP of SSB index Y is associated with the same measurement period.

The a plurality of beams referred above may correspond to: one or more Tx beams (simply referred as “beams” in the following examples) which may be transmitted by the network and possibly configured to be monitored by the WD 22 for one or more processes (e.g., BFD, radio link monitoring (RLM), CSI measurements and reporting, as shown in the example diagram of FIG. 17; 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 node 16 and a Rx beam received by the WD 22. In some embodiments, 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 In some embodiments, 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 In some embodiments, 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 In some embodiments, at least one of these beams is configured as a RS for Radio Link Monitoring (RLM-RSs), which may be monitored for RLM; o In some embodiments, at least one of these beams is configured as part of the RLM-RSs configurations; o In some embodiments, these are indicated per Bandwidth Part (BWP) and/or per serving cell (e.g., PCell, PScell, SCell); o In some embodiments, these are the beams associated to the RSs of one or more active beams in which the WD 22 is monitoring PDCCH (or any other control channel in which the WD 22 may receive scheduling information); o In some embodiments, these are the beams associated to the RSs of one or more active/ activated Transmission Configuration Indication (TCI) states, e.g., wherein the WD 22 is monitoring PDCCH associated to the activated TCI states; o In some embodiments, these are the beams associated to the RSs configured as Quasi-Co-Location source of one or more active/ activated Transmission Configuration Indication (TCI) states; o In some embodiments, the WD 22 determines the set to include periodic CSI-RS resource configuration indexes with same values as the RS indexes in the RS sets indicated by TCLState for respective CORESETs that the WD 22 uses for monitoring PDCCH and, if there are two RS indexes in a TCI state, the set includes RS indexes with QCL-TypeD configuration for the corresponding TCI states. The WD 22 expects the set to include up to two RS indexes; one or more candidate beams (candidates to be selected during

BFR); o In some embodiments, the one or more candidate beams to be selected during BFR are indicated by the parameter candidateBeamRSList and/or for each beam the information element (IE), PRACH-ResourceDedicatedBFR, at least for CFRA for BFR; o In some embodiments, for CBRA for BFR any SSB which is transmitted and/or to be measured may be considered a candidate beam; o In some embodiments, any SSB of a serving cell can correspond to a candidate beam; one or more selected beams (selected during BFR): o In some embodiments, a beam is selected by the WD 22 out of the candidate beams, during RA (more specifically during RA resource selection, e.g., as defined in 3GPP TS 38.321, § 5.2.1. The WD 22 selects a beam whose RSRP is above a configurable threshold; o In some embodiments, 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 In some embodiments, 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 In some embodiments, 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 RS may be configured by an RS index or identifier, or identity, or a representation of the identity/ index or identifier e.g., SSB-Index in case of SSB, or CSLRS resource index, for the case of CSLRS. One option is to configure at least one resource set (or a plurality of sets), wherein each set includes multiple SSBs and/or multiple CSLRS or any other RSs.

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) may be transmitted in a spatial direction (also called a beam) by the network (and received by the WD 22). Hence, 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. In some embodiments, 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. Thus, when 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.

In some embodiments, 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):

In some embodiments, the one or more predictions are based on one or more measurements on a second beam, wherein the second beam has at least a second transmission property (e.g., a wide beam, a beam transmitting one or more SSBs, periodicity Tl), which may differ or not from the first transmission property;

In some embodiments, 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;

In some embodiments, 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);

In some embodiments, the WD 22 may predict one or more measurements of wide beams (e.g., transmitting SSBs) based on the measurements of other wide beams;

In some embodiments, the WD 22 may predict one or more measurements of narrow beams (e.g., transmitting SSBs) based on the measurements of other narrow beams;

In some embodiments, 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);

UE measures at least one SSB then 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);

(Tx beams) In on embodiment, 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

(Rx beams) In on embodiment, 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.

Tx spatial predictions

In some embodiments, 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):

In some embodiments, the WD 22 does not detect the first beam;

In some embodiments, 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;

In some embodiments, 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. An example is shown below where the WD 22 receives beam x, and produces spatial-domain predictions for beams Y and Z;

In some embodiments, 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;

In some embodiments, 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;

In some embodiments, 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;

In some embodiments, 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. In this case, a measurement may be at least one sample, while a LI filtering could be interpreted as multiple samples, in time and/or frequency domain.

Note that 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.

Rx spatial predictions

In some embodiments, 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:

In some embodiments, 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;

In some embodiments, 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. In some embodiments, 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;

In some embodiments, the WD 22 does not monitor or receive the first Tx beam in the direction of the first Rx beam;

In some embodiments, 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). FIG. 18 shows one example where 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. In this example, 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;

In some embodiments, 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. In other words, the WD 22 does not monitor or receive in the time resource in which the first Tx 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 (in any of the Rx beams) to predict the SSB measurement;

In some embodiments, 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.

Then, 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.

In some embodiments, 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 configuration may comprise on or more parameters, fields or information elements, received in a higher layer message (e.g., RRC message). The configuration may be activated and/or deactivated and/or replaced and/or switched by the reception of a lower layer signaling, such as a MAC Control Element (MAC CE) or Downlink Control Information (DCI):

In some embodiments, the configuration is indicated per cell e.g., serving cell (PCell, PSCell, SCells) or cell group (Master Cell Group, Secondary Cell Group);

In some embodiments, the configuration indicates one or more Tx beams for which the measurement needs to be performed;

In some embodiments, the configuration indicates one or more Rx beams for which the measurement needs to be performed;

In some embodiments, the configuration indicates one or more beam pairs (Tx beam, Rx beam) for which the measurement needs to be performed;

In some embodiments, the configuration indicates a minimum number of beams (Tx beams) for which the measurement needs to be performed. In some embodiments, for a serving cell with N SSBs (e.g., N=64) 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 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 could only measure part of N1 beams (i.e., N2, where N2 < Nl) even N1 beams are configured to be measured, so the measurements for the remaining N-N2 may be the output of the ML-model i.e. the spatial-domain predictions;

In some embodiments, the configuration indicates a minimum number of beams (Rx beams) for which the measurement needs to be performed. In some embodiments, for a WD 22 with K Rx beams 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;

In some embodiments, the configuration indicates a minimum number of beam pairs (Rx beam, Tx Beam) 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.;

In some embodiments, the configuration indicates one or more Tx beam for which the spatial-domain prediction may be performed;

In some embodiments, 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;

In some embodiments, the configuration indicates one or more Rx beam for which the spatial-domain prediction may be performed;

In some embodiments, the configuration indicates one or more beam pairs (Tx beam, Rx beam) for which the spatial-domain prediction may be performed;

In some embodiments, the configuration indicates a maximum number of beams (Tx beams) for which the measurement may be performed;

In some embodiments, 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;

In some embodiments, the configuration indicates a maximum number of Rx beams for which the measurement may be performed;

In some embodiments, 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;

In some embodiments, the configuration indicates a minimum number of beam pairs (Rx beam, Tx Beam) for which the measurement may be performed;

In some embodiments, 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.

Note that 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.

In some embodiments, the spatial-domain prediction of a beam (associated to an SSB) 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. In another example, the prediction/ estimate may be performed for SS-RSRQ, SS-SINR, CSI- RSRP, CSI-RSRQ, CSI-SINR of an SSB.

In some embodiments, the WD 22 starts performing one or more spatial domain predictions (or estimate) of a measurement at tO, wherein tO is before BFD occurs or BFR is triggered (i.e. before BFI COUNTER >= the max value configured by the network, or when a number of BFIs are received at the MAC entity of the WD 22). Thus, the WD 22 may start to perform the spatial-domain predictions when it is configured e.g., for performing BFD. One advantage is that 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. In some embodiments, 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.

In some embodiments, 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. One advantage is that efforts at the WD 22 before BFD is declared may not be needed, which is beneficial as BFD should be a rare event. In some embodiments, 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.

In some embodiments, 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. One advantage is that 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.

One or more indications based on spatial domain predictions of measurements

RSRP (e.g., SS-RSRP, CSLRSRP) is usually used as an example of a measurement quantity, but other measurement quantities may also be equally considered such as RSRQ, SINR, RSSI. Similarly, 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.

In some embodiments, the one or more indications includes at least one of the spatial domain predictions of measurements. In some embodiments, for a given beam (SSB-X, whose SSB index = X) the WD 22 transmits the predicted RSRP for SSB-X e.g., predicted SS-RSRP.

In some embodiments, 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). In one option, the first cell group is a Master Cell Group (MCG), and the SpCell is a Primary Cell (PCell). In another option, the first cell group is a Secondary Cell Group (SCG), and 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.

In some embodiments, 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. In some embodiments, for a given beam (SSB-X, whose SSB index = X) transmitted by the network, 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.

In some embodiments, the one or more indications includes a statistical metric derived based on the distribution of the multiple spatial domain predictions of measurements. In some embodiments, for a given beam (SSB-X, whose SSB index = X) 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. Or for example the confidence interval of the expected value, e.g., 90% probability that the value is within a certain range. In another related embodiment, 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. Component 1 : mean =-100, sigma = 1, component weight = 1/3. Component 2: mean =-90, sigma = 1, component weight = 2/3. In some embodiments, 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.

In some embodiments, 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.

In some embodiments, 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.

In some embodiments, the one or more indications based on spatial domain predictions of measurements includes an indication of the RS index/ identifier (e.g., SSB identifier). In some embodiments, the indication of the RS index/ identifier corresponds to the actual the RS index/ identifier e.g., an SSB is indicated by its explicit SSB identifier (X, for SSB index=X). In some embodiments, the indication of the RS index/ identifier corresponds to a configuration identifier, based on a mapping provided in the RRC configuration: for example, the WD 22 is configured with a list of SSB indexes, e.g., LIST = [SSB index-5, SSB index-12, SSB index-60], so that what is reported is the position the list e.g., if the WD 22 sends the prediction for SSB index- 12 it indicates the prediction is for the SSB in position 1, if the WD 22 sends the prediction for SSB index-5 it indicates the prediction is for the SSB in position 0, if the WD 22 sends the prediction for SSB index-60 it indicates the prediction is for the SSB in position 2. This allows fewer bits to be included in the first MAC CE.

In some embodiments, 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. In some embodiments, if RSRP is the measurement quantity, the WD 22 predicts the RSRP of at least one SSB in the spatial- domain (SS-RSRP), then the WD 22 derives the one or more indications by comparing the predictions/ estimates with an RSRP threshold:

In some embodiments, 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, . . . , SS-RSRP for beam pair Z], and only a subset of the predictions are above the threshold, 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). o The threshold can also have an associated uncertainty in the prediction. In some embodiments, 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.

By receiving these, 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.).

In some embodiments, 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). o For cases of Rx beams, for example, a high number of indications would indicate to the network that for a given Tx beam, the WD 22 detects the Tx beam in its Rx beams with suitable values, as most predictions are above the threshold. In the case of Rx beams, there may be multiple values per Tx beam. In that case, the network knows the link is robust. o For the case of Rx beams, for example, 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. o For the case of Tx beams, for example, 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. o For the case of Tx beams, for example, 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.

In some embodiments, there may be different threshold for different predictions quantities, such as RSRP, RSRQ, SINR, RS SI, etc.

In some embodiments, the one or more indications includes an indication of ratio of predictions above the threshold divided by the total number of predictions (K).

In some embodiments, the indication of ratio is the actual ratio. One advantage, compared to some of the previous embodiments, is that fewer bits could be used to encode the indication when this is transmitted by the WD 22 to the network. For example: 3 bits are needed if K=8 [1001 1101], the index of measured RSs [1, 5, 6] with “0” represent that the predicted RSRP is lower than its expected threshold. Given the indication, the network can exactly know the index of measured RSs that is larger or lower than the threshold.

In some embodiments, 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. By receiving that the network knows whether most predictions are good or not. 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.

In some embodiments, 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. In some embodiments, 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. In some embodiments, if 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.

BFD, BFR and reporting of one or more indications

In some embodiments, 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.

In some embodiments, if 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). There may be different set of actions from that point:

If 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;

In some embodiments, 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.

In some embodiments, 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. In some embodiments, 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.

In some embodiments, 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.

In some embodiments, 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.

In some embodiments, 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.

In some embodiments, 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). In one option, the first cell group is a Master Cell Group (MCG), and the SpCell is a Primary Cell (PCell). In one option, the first cell group is a Secondary Cell Group (SCG), and the SpCell is a Primary Cell (PCell). In one option, 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. This may be useful as the reliability of an SCG may be lower than the reliability of the MCG e.g., when the SCG is deployed in Frequency Range 2 (FR2).

In some embodiments, 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.

In some embodiments, 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. The action may be performed when BFD is declared for an SCG which is deactivated (UE configured with MR-DC). In other words, if the WD 22 is configured with a deactivated SCG and performing the monitoring of BFD for the SCG, and BFD is detected, 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. 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). Hence, 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);

In some embodiments, 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. The action may possibly be performed when BFD is declared for an MCG which is deactivated (UE configured with MR-DC). In other words, if the WD 22 is configured with a deactivated MCG and performing the monitoring of BFD for the SCG, and BFD is detected, the WD 22 transmits the RRC MCG Failure message to the network node 16 operating as the SN including the one or more indications. 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). Hence, 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);

In some embodiments, the RRC message is a Measurement

Report; and/or

In some embodiments, the RRC message is a WD 22 Assistance Information.

In some embodiments, 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.

In some embodiments, 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.

In some embodiments, 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);

In some embodiments, the configuration is received as part of the BFR configuration;

In some embodiments, 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

In some embodiments, the configuration is received as part of a Prediction configuration.

In some embodiments, 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.

Capability signaling

In some embodiments, 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.

For each of the embodiments or set of embodiments, associated to the different manners to perform the spatial-domain predictions these may be different capabilities. Thus, a WD 22 may implement multiple methods and report multiple capabilities. Re-configurations / updates in response to the one or more indications

In some embodiments, the WD 22 receives from the network node 16 (i.e. the network node 16 transmits to the WD 22) a reconfiguration (and/or an update command), in response to transmitting the one or more indications based on the one or more spatial domain predictions of measurements (e.g., in the first MAC CE), in which 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 DCI or MAC CE.

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.

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.

In some embodiments, the reconfiguration from the network is a second MAC CE or in a Downlink Control Information (DCI) the WD 22 receives, wherein the MAC CE or the DCI indicates: activate/ deactivate TCI states; o In some embodiments, the second MAC CE corresponds to a TCI States Activation/Deactivation for WD 22-specific PDSCH MAC CE; o In some embodiments, the second MAC CE corresponds to a TCI State Indication for WD 22-specific PDCCH MAC CE; o Due to the fact that the network knows more beams via the report, the network can confidently activate multiple TCI states. activate/ deactivate one or more resource configurations for CSI reporting; o In some embodiments, 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; o Due to the fact that the network knows more beams via the report, the network can confidently add more RLM-RS resources (and not rely only on a resource associated to the selected beam); modify at least one of the BFD-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 BFD based on one or more RSs configured in the QCL configuration of the TCI states being activated; o Due to the fact that the network knows more beams via the report, the network can confidently add more BFD RS resources (and not rely only on a resource associated to the selected beam).

In some embodiments, 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 In some embodiments 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 In some embodiments 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). In addition, that may include the indication of at least one RadioLinkMonitoringRS with purpose set to BeamFailure; re-configure TCI states: o In some embodiments 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:

■ In some embodiments, 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;

■ In some embodiments, 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 In some embodiments, that includes at least an IE ReportConfig (or ReportConfigNR); o In some embodiments, that includes an indication for the WD 22 to include beam measurement information in RRC measurement reports; o In some embodiments, 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, nrofCSI-RS-ResourcesToAverage); o In some embodiments, that includes the consolidation threshold for selecting beams for performing cell quality derivation (for example as defined in 3GPP TS 38.331, 6.3.2, e.g., absThreshSS- BlocksConsolidation, absThreshCSI-RS-Consolidation); o In some embodiments, that includes the consolidation threshold for selecting beams to be included in measurement reports per cell (for example as defined in 3GPP TS 38.331, 6.3.2, e.g., absThreshSS-BlocksConsolidation, absThreshCSI-RS-Consolidation); o In some embodiments, that includes further parameters for beam reporting, as in the IE ReportConfigNR (for example as defined in 3GPP TS 38.331, 6.3.2, e.g., reportQuantityRS-Indexes and/or maxNrofRS-IndexesToReport and/or includeBeamMeasurements, or equivalent fields with similar functionality).

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.

ML-model for spatial-domain prediction of beam measurements

In some embodiments, 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).

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).

Some embodiments include the usage of metrics related to WD 22 connection, such as the input from sensors such as rotation potential blockers such as human body for a hand-held device, movement, etc. WD 22 uses some route information (e.g., current location, final destination and route) as input.

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.

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 (e.g., in case of sensors, the availability at the WD 22 of a sensor, like barometric sensor) 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.

In that case, there could be a procedure where 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. Once the WD 22 has the function available, it may be further configured by the network to use it e.g., in a measurement configuration like reporting configuration, measurement object configuration, etc.

Example ML-model implementation using denoising autoencoder

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. In AE where there are more nodes in the hidden layer than there are inputs, 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.

In some embodiments, 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. Next, 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.

Next, the WD 22 may build a denoising autoencoder model F able to predict the actual values from the noised samples, 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.

In some embodiments, 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 noising pattern transmitted to the WD 22 can comprise a bitwise vector comprising which radio signal qualities that should be measured, optionally also an associated performance for each pattern. For example in case the WD 22 is configured to measure one SSB 1-4, there can be 2^A4 patterns [bi, b2, bs ,b4], where b=l(measure) /0 (predict). Naturally, 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. For example, [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.

WD 22 obtaining the ML-model

In some embodiments, 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.

The following alternatives may be defined. 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). In general, 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.

Alternative 2 - container-based signaling:

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.

When specifying the model parameters as described in alternative 1, 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.

Alternative 3 - ML-model at the WD 22:

In some embodiments, 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, in this embodiment, 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:

Embodiment Al . 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.

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 A3. The network node of any of Embodiments Al and A2, wherein the network node, radio interface and/or processing circuitry are further configured to transmit to the WD, information on supported noising patterns.

Embodiment A4. The network node of Embodiment A3, wherein the information includes an indication of which beams for which measurements are taken.

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 B2. The method of Embodiment Bl, 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 B3. The method of any of Embodiments Bl and B2, further comprising to transmit to the WD, information on supported noising patterns.

Embodiment B4. The method of Embodiment B3, wherein the information includes an indication of which beams for which measurements are taken.

Embodiment Cl . A wireless device (WD) 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 C2. The WD of Embodiment Cl, wherein 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.

Embodiment C3. The WD of any of Embodiments Cl and C2, wherein the indications are sent via a random access procedure.

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 DI . A method implemented in a wireless device (WD), the method comprising: using 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 transmitting indications of the at least one spatial domain measurement prediction to the network node.

Embodiment D2. The method of Embodiment DI, wherein 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.

Embodiment D3. The method of any of Embodiments DI and D2, wherein the indications are sent via a random access procedure.

Embodiment D4. The method of Embodiment D3, wherein the random access procedure is one of contention free random access and contention based random access.

As will be appreciated by one of skill in the art, the concepts described herein may be embodied as a method, data processing system, computer program product and/or computer storage media storing an executable computer program. Accordingly, the concepts described herein may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects all generally referred to herein as a “circuit” or “module.” Any process, step, action and/or functionality described herein may be performed by, and/or associated to, a corresponding module, which may be implemented in software and/or firmware and/or hardware. Furthermore, the disclosure may take the form of a computer program product on a tangible computer usable storage medium having computer program code embodied in the medium that can be executed by a computer. Any suitable tangible computer readable medium may be utilized including hard disks, CD-ROMs, electronic storage devices, optical storage devices, or magnetic storage devices.

Some embodiments are described herein with reference to flowchart illustrations and/or block diagrams of methods, systems and computer program products. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer (to thereby create a special purpose computer), special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

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.

The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

It is to be understood that the functions/acts noted in the blocks may occur out of the order noted in the operational illustrations. In some embodiments, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Although some of the diagrams include arrows on communication paths to show a primary direction of communication, it is to be understood that communication may occur in the opposite direction to the depicted arrows.

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++. However, 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. In the latter scenario, 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).

Many different embodiments have been disclosed herein, in connection with the above description and the drawings. It will be understood that it would be unduly repetitious and obfuscating to literally describe and illustrate every combination and subcombination of these embodiments. Accordingly, all embodiments can be combined in any way and/or combination, and the present specification, including the drawings, shall be construed to constitute a complete written description of all combinations and subcombinations of the embodiments described herein, and of the manner and process of making and using them, and shall support claims to any such combination or subcombination.

It will be appreciated by persons skilled in the art that the embodiments described herein are not limited to what has been particularly shown and described herein above. In addition, unless mention was made above to the contrary, it should be noted that all of the accompanying drawings are not to scale. A variety of modifications and variations are possible in light of the above teachings without departing from the scope of the following claims.

Claims

What is claimed is:

1. A method in a wireless device, WD (22), configured to communicate with a network node (16), the method comprising: performing (S144) at least one spatial domain measurement prediction for each beam of a plurality of beams in response to a beam failure detection, BFD; and transmitting (S146) 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.

2. The method of Claim 1, wherein 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.

3. The method of any of Claims 1 and 2, wherein the plurality of beams includes at least one transmit beam transmitted by the network node (16).

4. The method of any of Claims 1-3, wherein the plurality of beams includes at least one receive beam for receiving signals from the network node (16).

5. The method of any of Claims 1-4, wherein the plurality of beams includes at least one pair of a transmit beam and a receive beam.

6. The method of any of Claims 1-5, wherein at least one beam of the plurality of beams is configured for BFD monitoring.

7. The method of any of Claims 1-6, wherein the plurality of beams includes at least one candidate beam for beam failure recovery, BFR.

8. The method of any of Claims 1-7, wherein the plurality of beams includes a beam selected for beam failure recovery, BFR.

9. The method of any of Claims 1-8, wherein the plurality of beams includes at least one beam not used for BFD monitoring.

10. The method of any of Claims 1-9, wherein the plurality of beams includes at least one beam configured for channel state information, CSI, reporting.

11. The method of any of Claims 1-10, further comprising receiving an indication of a configuration of beams for which to perform the spatial domain measurement predictions.

12. The method of any of Claims 1-11, wherein the spatial domain measurement predictions are predicted by a machine learning process.

13. The method of any of Claims 1-12, wherein transmitting the indication includes initiating a random access procedure on a selected beam.

14. The method of any of Claims 1-13, wherein transmitting the indication includes triggering a scheduling request.

15. The method of any of Claims 1-14, wherein transmitting the indication includes transmitting a beam failure recovery, BFR, report on a first available medium access control, MAC, control element, CE, after BFD.

16. A wireless device, WD (22), configured to communicate with a network node (16), the WD (22) comprising: processing circuitry (84) 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; and a radio interface (82) in communication with the processing circuitry (84) and configured to transmit 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.

17. The WD (22) of Claim 16, wherein 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.

18. The WD (22) of any of Claims 16 and 17, wherein the plurality of beams includes at least one transmit beam transmitted by the network node (16).

19. The WD (22) of any of Claims 16-18, wherein the plurality of beams includes at least one receive beam for receiving signals from the network node (16).

20. The WD (22) of any of Claims 16-19, wherein the plurality of beams includes at least one pair of a transmit beam and a receive beam.

21. The WD (22) of any of Claims 16-20, wherein at least one beam of the plurality of beams is configured for BFD monitoring.

22. The WD (22) of any of Claims 16-21, wherein the plurality of beams includes at least one candidate beam for beam failure recovery, BFR.

23. The WD (22) of any of Claims 16-22, wherein the plurality of beams includes a beam selected for beam failure recovery, BFR.

24. The WD (22) of any of Claims 16-23, wherein the plurality of beams includes at least one beam not used for BFD monitoring.

25. The WD (22) of any of Claims 16-24, wherein the plurality of beams includes at least one beam configured for channel state information, CSI, reporting.

26. The WD (22) of any of Claims 16-25, wherein the radio interface (82) is further configured to receive an indication of a configuration of beams for which to perform the spatial domain measurement predictions.

27. The WD (22) of any of Claims 16-26, wherein the spatial domain measurement predictions are predicted by a machine learning process.

28. The WD (22) of any of Claims 16-27, wherein transmitting the indication includes initiating a random access procedure on a selected beam.

29. The WD (22) of any of Claims 16-28, wherein transmitting the indication includes triggering a scheduling request.

30. The WD (22) of any of Claims 16-29, wherein transmitting the indication includes transmitting a beam failure recovery, BFR, report on a first available medium access control, MAC, control element, CE, after BFD.

31. A method in a network node (16) configured to communicate with a wireless device, WD (22), the method comprising: receiving (S148) 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 ; and reconfiguring (SI 50) communications with the WD (22) in response to the indication.

32. The method of Claim 31, wherein 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.

33. The method of any of Claims 31 and 32, wherein reconfiguring communications with the WD (22) includes at least one of changing and reconfiguring a transmission configuration indication, TCI, activation state.

34. The method of any of Claims 31-33, wherein reconfiguring communications with the WD (22) includes reconfiguring layer 1, LI, resources for spatial domain measurements.

35. The method of any of Claims 31-34, wherein 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).

36. A network node (16) configured to communicate with a wireless device, WD (22), the network node (16) comprising: a radio interface (62) 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 ; and processing circuitry (68) in communication with the radio interface (62) and configure to reconfigure communications with the WD (22) in response to the indication.

37. The network node (16) of Claim 36, wherein 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.

38. The network node (16) of any of Claims 36 and 37, wherein reconfiguring communications with the WD (22) includes at least one of changing and reconfiguring a transmission configuration indication, TCI, activation state.

39. The network node (16) of any of Claims 36-38, wherein reconfiguring communications with the WD (22) includes reconfiguring layer 1, LI, resources for spatial domain measurements.

40. The network node (16) of any of Claims 36-39, wherein 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.