Beam management enhancements in model-based channel tracking

Apparatus, methods, and computer-readable media for facilitating beam management enhancements in model-based channel tracking are disclosed herein. An example method for wireless communication at a first network entity includes receiving from a second network entity, a model configuration indicative of a model condition of a channel between the first network entity and the second network entity for multiple beam pairs. The example method also includes tracking a variation in a channel condition relative to the model condition of the channel based on the model configuration for each of multiple beam pairs separately in multiple tracking sessions that overlap in time. Each beam pair may include a transmission beam and a reception beam.

TECHNICAL FIELD

The present disclosure relates generally to communication systems, and more particularly, to wireless communications utilizing channel variation tracking.

INTRODUCTION

BRIEF SUMMARY

In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided for wireless communication. An apparatus may include a first network entity. The example apparatus may receive, from a second network entity, a model configuration indicative of a model condition of a channel between the first network entity and the second network entity for multiple beam pairs. The apparatus may also track a variation in a channel condition relative to the model condition of the channel based on the model configuration for each of the multiple beam pairs separately in multiple tracking sessions that overlap in time. Each beam pair may include a transmission beam and a reception beam.

In another aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided for wireless communication. An apparatus may include a second network entity. The example apparatus may output for transmission, one or more model configurations indicative of a model condition of a channel between a first network entity and the second network entity for multiple beam pairs separately in multiple tracking sessions. Each beam pair of the multiple beam pairs may have a separate tracking session of the multiple tracking sessions. The multiple tracking sessions for the multiple beam pairs may overlap in time. The apparatus may also obtain one or more updated parameters for the one or more model configurations for one of the multiple beam pairs based on a variation, observed at the first network entity, of a channel condition in a corresponding tracking session of the multiple tracking sessions.

DETAILED DESCRIPTION

The measurement and reporting of channel state information (CSI) may be used to adjust and improve communication, such as communication between a UE and network. In some aspects, such as high mobility situations, performance loss may occur based on channel variations that may occur more frequently than CSI updates. Although the CSI reporting rate can be increased, the increased uplink and downlink CSI overhead may reduce system throughput. Additionally, more frequent measurements, transmissions (e.g., of reference signals), and/or reporting uses additional battery power at a UE. As an example, a UE may transmit SRS to enable uplink based measurements. Aspects presented herein provide for improved CSI feedback and tracking efficiency with less overhead. The aspects presented herein provide a framework for multiple beam tracking and beam management improvements using model based reporting. As an example, a first network entity may receive, from a second network entity, a model configuration indicative of a model condition of a channel between the first network entity and the second network entity for multiple beam pairs, e.g., receiving one or more model configurations for each of the multiple beam pairs. The first network entity may track a variation in a channel condition relative to the model condition of the channel based on the model configuration for each of multiple beam pairs separately in multiple tracking sessions that overlap in time. Each beam pair may include a transmission beam and a reception beam.

FIG.1is a diagram100illustrating an example of a wireless communications system and an access network. The illustrated wireless communications system includes a disaggregated base station architecture. The disaggregated base station architecture may include one or more CUs (e.g., a CU110) that can communicate directly with a core network120via a backhaul link, or indirectly with the core network120through one or more disaggregated base station units (such as a Near-Real Time (Near-RT) RAN Intelligent Controller (RIC) (e.g., a Near-RT RIC125) via an E2 link, or a Non-Real Time (Non-RT) RIC115associated with a Service Management and Orchestration (SMO) Framework (e.g., an SMO Framework105), or both). A CU110may communicate with one or more DUs (e.g., a DU130) via respective midhaul links, such as an F1 interface. The DU130may communicate with one or more RUs (e.g., an RU140) via respective fronthaul links. The RU140may communicate with respective UEs (e.g., a UE104) via one or more radio frequency (RF) access links. In some implementations, the UE104may be simultaneously served by multiple RUs.

Each of the units, i.e., the CUs (e.g., a CU110), the DUs (e.g., a DU130), the RUs (e.g., an RU140), as well as the Near-RT RICs (e.g., the Near-RT RIC125), the Non-RT RICs (e.g., the Non-RT RIC115), and the SMO Framework105, may include one or more interfaces or be coupled to one or more interfaces configured to receive or to transmit signals, data, or information (collectively, signals) via a wired or wireless transmission medium. Each of the units, or an associated processor or controller providing instructions to the communication interfaces of the units, can be configured to communicate with one or more of the other units via the transmission medium. For example, the units can include a wired interface configured to receive or to transmit signals over a wired transmission medium to one or more of the other units. Additionally, the units can include a wireless interface, which may include a receiver, a transmitter, or a transceiver (such as an RF transceiver), configured to receive or to transmit signals, or both, over a wireless transmission medium to one or more of the other units.

Lower-layer functionality can be implemented by one or more RUs. In some deployments, an RU140, controlled by a DU130, may correspond to a logical node that hosts RF processing functions, or low-PHY layer functions (such as performing fast Fourier transform (FFT), inverse FFT (iFFT), digital beamforming, physical random access channel (PRACH) extraction and filtering, or the like), or both, based at least in part on the functional split, such as a lower layer functional split. In such an architecture, the RU140can be implemented to handle over the air (OTA) communication with one or more UEs (e.g., the UE104). In some implementations, real-time and non-real-time aspects of control and user plane communication with the RU140can be controlled by a corresponding DU. In some scenarios, this configuration can enable the DU(s) and the CU110to be implemented in a cloud-based RAN architecture, such as a vRAN architecture.

The SMO Framework105may be configured to support RAN deployment and provisioning of non-virtualized and virtualized network elements. For non-virtualized network elements, the SMO Framework105may be configured to support the deployment of dedicated physical resources for RAN coverage requirements that may be managed via an operations and maintenance interface (such as an O1 interface). For virtualized network elements, the SMO Framework105may be configured to interact with a cloud computing platform (such as an open cloud (O-Cloud)190) to perform network element life cycle management (such as to instantiate virtualized network elements) via a cloud computing platform interface (such as an O2 interface). Such virtualized network elements can include, but are not limited to, CUs, DUs, RUs and Near-RT RICs. In some implementations, the SMO Framework105can communicate with a hardware aspect of a 4G RAN, such as an open eNB (O-eNB)111, via an O1 interface. Additionally, in some implementations, the SMO Framework105can communicate directly with one or more RUs via an O1 interface. The SMO Framework105also may include a Non-RT RIC115configured to support functionality of the SMO Framework105.

The wireless communications system may further include a Wi-Fi AP150in communication with a UE104(also referred to as Wi-Fi stations (STAs)) via communication link154, e.g., in a 5 GHz unlicensed frequency spectrum or the like. When communicating in an unlicensed frequency spectrum, the UE104/Wi-Fi AP150may perform a clear channel assessment (CCA) prior to communicating in order to determine whether the channel is available.

Referring again toFIG.1, in certain aspects, a device in communication with a base station, such as a UE104in communication with a base station102or a component of a base station (e.g., a CU110, a DU130, and/or an RU140), may be configured to manage one or more aspects of wireless communication. For example, the UE104may include a UE channel tracking component198configured to facilitate beam management of multiple beam pairs associated with model-based channel tracking while also conserving wireless resources and reducing signaling overhead.

In certain aspects, the UE channel tracking component198may be configured to receive, from a second network entity, a model configuration indicative of a model condition of a channel between the first network entity and the second network entity. The example UE channel tracking component198may also be configured to track a variation in a channel condition relative to the model condition of the channel based on the model configuration for each of multiple beam pairs separately in multiple tracking sessions that overlap in time. Each beam pair may include a transmission beam and a reception beam.

In another configuration, a base station, such as the base station102or a component of a base station (e.g., a CU110, a DU130, and/or an RU140), may be configured to manage or more aspects of wireless communication. For example, the base station102may include a BS channel tracking component199configured to facilitate beam management of multiple beam pairs associated with model-based channel tracking while also conserving wireless resources and reducing signaling overhead.

In certain aspects, the BS channel tracking component199may be configured to output for transmission, a model configuration indicative of a model condition of a channel between a first network entity and the second network entity for multiple beam pairs separately in multiple tracking sessions. Each beam pair of the multiple beam pairs may have a separate tracking session of the multiple tracking sessions. The multiple tracking sessions for the multiple beam pairs may overlap in time. The example BS channel tracking component199may also be configured to obtain one or more updated parameters for the model configuration for one of the multiple beam pairs based on a variation, observed at the first network entity, of a channel condition in a corresponding tracking session of the multiple tracking sessions.

The aspects presented herein may enable beam management of multiple beams pairs and tracking channel variations, which may facilitate adjusting and improving communication between a UE and network, for example, by conserving wireless resources and reducing signaling overhead.

Although the following description provides examples directed to 5G NR (and, in particular, to channel tracking), the concepts described herein may be applicable to other similar areas, such as LTE, LTE-A, CDMA, GSM, and/or other wireless technologies, in which a UE and network may adjust wireless communication based on channel variations.

FIG.3is a block diagram that illustrates an example of a first wireless device that is configured to exchange wireless communication with a second wireless device. In the illustrated example ofFIG.3, the first wireless device may include a base station310, the second wireless device may include a UE350, and the base station310may be in communication with the UE350in an access network. As shown inFIG.3, the base station310includes a transmit processor (TX processor316), a transmitter318Tx, a receiver318Rx, antennas320, a receive processor (RX processor370), a channel estimator374, a controller/processor375, and memory376. The example UE350includes antennas352, a transmitter354Tx, a receiver354Rx, an RX processor356, a channel estimator358, a controller/processor359, memory360, and a TX processor368. In other examples, the base station310and/or the UE350may include additional or alternative components.

Channel estimates derived by the channel estimator358from a reference signal or feedback transmitted by the base station310may be used by the TX processor368to select the appropriate coding and modulation schemes, and to facilitate spatial processing. The spatial streams generated by the TX processor368may be provided to different antenna of the antennas352via separate transmitters (e.g., the transmitter354Tx). Each transmitter354Tx may modulate an RF carrier with a respective spatial stream for transmission.

The measurement and reporting of CSI may be used to adjust and improve communication, such as communication between a UE and network. In some aspects, such as high mobility situations, performance loss may occur based on channel variations that may occur more frequently than CSI updates. Although the CSI reporting rate can be increased, the increased uplink and downlink CSI overhead may reduce system throughput. Additionally, more frequent measurements, transmissions (e.g., of reference signals), and/or reporting uses additional battery power at a UE. As an example, a UE may transmit SRS to enable uplink based measurements. Aspects presented herein provide for model based CSI tracking that may address channel variations while also conserving wireless resources and avoiding additional CSI overhead.

Reducing an overhead associated with channel state information (CSI) measurement and CSI reporting may increase a performance of a first network entity, such as a UE, and/or a second network entity, such as a base station or a component of a base station. For example, reducing a number of CSI measurements may increase a system throughput between the first network entity and the second network entity. However, reducing the number of CSI measurements may also reduce a quality of the CSI, as more CSI measurements may provide increased measurement accuracy, but may also increase the overhead. A reduction of the overhead may be advantageous for high mobility use cases, such as for UEs moving at speeds of 30-500 kilometers per hour (kmph), applications associated with Industrial IoT (IIoT) procedures, automotive applications, highway applications, high-speed train applications, etc.

Some Type II CSI feedback procedures may experience a performance loss even at moderate speeds of the UE, such as 10-30 kmph. Performance loss may occur based on a channel variation being too fast for a CSI measurement/update rate (i.e., a frequency at which the CSI measurement and CSI reporting is performed by the UE). By a time that the UE performs the CSI measurement and reports the CSI measurement to the scheduling entity (e.g., second network entity), the CSI report may become outdated. Thus, subsequent transmissions or pre-coding procedures that are based on the CSI measurement and the CSI report may not be accurate. For example, a mismatch may occur between the indicated CSI measurement and the actual channel conditions through which a signal may be propagated. Type II/enhanced TypeII (eType II) procedures for tracking the CSI feedback may also include increased CSI processing times in comparison to Type-I single panel (SP) CSI feedback procedures.

The CSI measurement/update rate may be increased based on a channel variation rate. As an example, a UE may be requested to report the CSI feedback to the network more frequently for tracking an increased variability of the channel. A request for tracking the increased variability of the channel may also be transmitted in the reverse direction. Such requests may generate an increased downlink/uplink resource overhead affecting system throughput (e.g., may decrease system throughput). Additionally, more frequent reporting occasions by the UE may also increase UE battery consumption. Aspects presented herein help to improve CSI feedback and tracking procedures with less associated overhead.

Although the above description describes an example in which the network node requests the UE to report CSI feedback, in other examples, the request for more frequent tracking of a channel, for example, in scenarios associated with increased variability of a channel, may be transmitted in the reverse direction from the UE to the network node.

In some examples, a CSI feedback rate (e.g., performing a CSI measurement and reporting the CSI measurement) may be adjusted at the UE based on a channel coherence time. The channel coherence time may refer to a period during which the channel is assumed to be quasi-static. In such scenarios, the UE may send a CSI report once during the period. When the channel variation and the mobility of the UE is low, the channel coherence time may be large, which may allow the CSI feedback rate to be low. That is, the update rate may be a function of the channel coherence time. A channel that is fast varying may correspond to an increased CSI feedback rate. If the channel coherence time is too short for highly mobile network entities (e.g., UEs) associated with a fast/frequent CSI feedback rate, an adaptive approach based on the channel coherence time may still result in significant signaling configuration (or reconfiguration) overhead, such as when the mobility of the UE is non-uniform. For example, as the mobility changes, multiple configurations and signaling updates may be used to indicate information to the UE and receive CSI feedback from the UE indicative of the mobility of the UE and/or parameters to use for measuring and reporting the CSI feedback. Thus, while such an adaptive approach may avoid or reduce channel tracking being outdated, the configurations needed for providing CSI feedback may need to be dynamically updated frequently, thereby increasing overhead.

In some examples, Doppler domain information associated with the CSI feedback may be used to indicate timing information for frequency domain (FD) codebooks and/or spatial domain (SD) codebooks. For example, Doppler domain compression may be based on a channel correlation time. For mmW communications and higher bands, such as FR2, FR4, etc., the CSI feedback may be compressed in the Doppler domain in addition to the frequency domain and/or the spatial domain. The CSI feedback may be indicative of precoder entries in a Type-II codebook. While such techniques may provide improved channel tracking with less frequent CSI reporting occasions, the codebook size and the reporting overhead may be increased. Additionally, a CSI processing time for each report at the UE may be higher than a processing time for Type-II CSI reporting procedures and/or eType-II CSI reporting procedures.

In some examples, DM-RS based CSI feedback adjustments may be performed based on a PMI and/or an RI/CQI. In addition to tracking the channel based on CSI resources, the DM-RS of scheduled downlink transmissions may be used for adjusting a previous CSI feedback report from the UE. Additional signaling may be performed with the downlink traffic to associate DM-RS resources with previous CSI resources/reports. The channel may be modeled as a time-varying, multi-path complex channel based on a linear combination of narrowband, time-invariant components, with CSI feedback via the PMI, RI, layer indicator (LI), CQI, etc.

While the channel may be modeled in some scenarios as a time-varying wideband channel, a model-based representation of the channel may also be configured to track the channel variation with reduced overhead. In some examples, the model may be updated periodically at a transmitting node and the receiving node such that a same model may be used to predict a future CSI without performing a corresponding measurement. For example, the model may be based on a state-space channel profile where each state may correspond to a sparse representation of the channel in a multi-dimensional space. Procedures associated with the model-based representation of the channel may be similar to a delta CSI update for tracking the channel for CSI feedback, but may be further compressed based on a model configuration communicated between the transmitting node and the receiving node.

FIG.4is a call flow diagram400illustrating a model-based channel representation to more efficiently track channel variation between a first network entity402(e.g., a UE) and a second network entity404(e.g., a base station) with less overhead. While the channel may be modeled in some cases as a time-varying wideband channel, a model-based representation of the channel may also be configured to track the channel variation with reduced overhead. In some aspects, the model may be updated periodically at the Tx node (e.g., second network entity404) and the Rx node (e.g., first network entity402), such that a same model may be used to predict a future CSI without performing a corresponding measurement. For instance, the model may be based on a state-space channel profile where each state may correspond to a sparse representation of the channel in a multi-dimensional space. Procedures associated with the model-based representation of the channel may be similar to a delta CSI update for tracking the channel for CSI feedback, but may be further compressed based on the model configuration communicated, at420, between the second network entity404and the first network entity402.

The first network entity402and the second network entity404, such as a UE and a base station, may exchange common model information during an initial setup procedure for tracking the channel. For example, at406, the second network entity404(e.g., base station) may transmit/receive a channel model configuration and initial parameters to/from the first network entity402(e.g., UE). The channel model configuration may be used by the first network entity402and the second network entity404to ensure that both nodes are using a same model to predict the CSI.

The first network entity402may measure the channel based on a reference signal, e.g.,412and428, received from the second network entity404. The reference signal and corresponding measurement may occur before or after the channel model configuration and initial parameters are communicated, at420, between the second network entity404and the first network entity402. The reference signal may correspond to periodic CSI-RS, aperiodic CSI-RS, or semi-persistent CSI-RS. In an example, the first network entity402may receive, at428, the periodic/aperiodic/semi-persistent CSI-RS for measuring the channel after reception/transmission, at420, of the channel model configuration and initial parameters.

Based on receiving the periodic/aperiodic/semi-persistent CSI-RS from the second network entity404, the first network entity402may execute, at426, a channel tracking and channel measurement algorithm. The second network entity404may likewise execute, at424, a channel tracking algorithm after transmission, at428, of the periodic/aperiodic/semi-persistent CSI-RS to the first network entity402. Based on a channel measurement by the first network entity402indicative of a change in a condition of the channel, the first network entity402may transmit, at430, a model parameter update to the second network entity404via a CSI feedback procedure. For example, a state-space model including complex and/or vector weights, a measured noise variance, etc., may be signaled, at430, from the first network entity402to the second network entity404. Each of the nodes (e.g., UE and base station) may be configured to further track the state-space variation. In some implementations, the nodes may determine a mapping between the physical channel and a state vector, and/or a relation between PMI/RI/LI/CQI and the state vector. The mapping/relationship may be incorporated in an exchange of common model information between the nodes.

Each of the nodes may be configured to update/track the channel variation/state-space variation based on one or more filtering operations for the state-space model. For example, one of the nodes may execute an adaptive CSI update algorithm, such as a Kalman filtering algorithm, to track the channel variation, where the update may be transmitted to the other node based on a measurement performed by a measuring node. A CSI report of model parameters (e.g., at430) may include a Kalman gain update, an indication of non-measuring node updates for the model/channel information, etc. The update, at430, may be performed by a measuring entity (e.g., the first network entity402) using a compressed CSI report in which the report may include the model parameter updates for the non-measuring entity (e.g., second network entity404) to update the model information.

When a state change occurs (e.g., due to a mobility change), the first network entity402(e.g., UE) may sparsely update the model parameter to reduce signaling/resource overhead while also ensuring reliable and efficient tracking of the channel variation. In examples, the first network entity402may reset a tracking session to ensure that both network entities are tracking the channel variation based on a same state-space model. When an update occurs, at430, based on a measurement by the first network entity402or when a change to the physical channel is detected, a compressed CSI report may be transmitted, at430, to the second network entity404or the tracking session may be reset at the first network entity402. Such techniques may reduce the signaling overhead and increase the throughput of the system. Accordingly, a model-based representation of the channel may provide both an overhead reduction and more efficient tracking of the channel via a decreased amount of CSI feedback and measurements by the first network entity402.

Model-based channel compression techniques may be based on the first network entity402performing a channel measurement and transmitting, at430, the compressed CSI feedback report to the second network entity404. However, model-based channel compression may also be performed for uplink and/or sidelink communications. For instance, the first network entity402may indicate the channel model configuration and initial parameters in the reverse direction to the second network entity404, which may track the channel based on the state-space model for the channel model configuration associated with the communication link between the first network entity402and the second network entity404. Channel measurements by the second network entity404may be based on an uplink reference signal, such as SRS, such that the second network entity404may transmit model parameter updates in the reverse direction to the first network entity402.

After an initial setup procedure between the first network entity402and the second network entity404, a reference signal for channel measurement, such as the periodic/aperiodic/semi-persistent CSI-RS or SRS, may be communicated between the nodes. Feedback may be transmitted from the measuring node to the non-measuring node, such that both nodes may determine an initial state of the system. One node may transmit the feedback to the other node in some cases before receiving the model configuration from the other node. For example, CSI feedback may correspond to transmissions of CSI-RS and CSI feedback that are also used for non-model based tracking (e.g., which may be referred to as legacy CSI-RS transmissions/feedback), which may be used by the second network entity404to configure the model and the initial parameters transmitted to the first network entity402.

Both nodes may track the channel variation based on the state-space model. If the first network entity402is the node that is performing the channel measurement, the first network entity402may provide the update, at430, in addition to the channel tracking, at426, to recommend a state-space change to the second network entity404. That is, the measuring node may perform both the tracking procedure and the channel measurement for updating, at430, the model parameters.

The first network entity402may indicate a delta change to the second network entity404via compressed CSI feedback, which may be used for updating the state-space model at the second network entity404. Channel tracking procedures may be respectively executed at both nodes, at424and426, but when a measurement and update occur at one of the nodes, the measurement and update may be indicated to the other node via a feedback procedure. Frequent CSI transmissions and reporting, signaling overhead, and power consumption may be reduced via model-based channel compression techniques. Likewise, uplink reference signals may be used to perform the procedure in the reverse direction where the second network entity404may update the model/states and signal the updated model/states to the first network entity402.

In some aspects, as shown at416, the first network entity402may provide initial feedback, such as initial CSI, for the channel. In some aspects, the second network entity404may send an indication410to the first network entity to start channel tracking. The second network entity404may transmit a reference signal412such as a periodic CSI-RS, a semi-persistent CSI-RS, an aperiodic CSI-RS, or another reference signal. The first network entity402may measure the reference signal, at414, to obtain an initial assessment, or measurement of the channel, e.g., h(0). The first network entity402may transmit feedback416to the second network entity based on the measurement of the CSI-RS. The feedback416may indicate the estimation of the channel h(0). In some aspects, the second network entity404may indicate an ACK or a NACK, e.g.,418, for the channel, e.g., h(0), indicated by the first network entity402. The response from the network, at420may include one or more model parameters, e.g., F and/or Q, in addition to an ACK/NACK418for h(0). For example, before the channel model configuration and initial parameters are communicated, at406, the first network entity402may send feedback416, which the second network entity404may use to determine the channel model configuration and/or initial parameters to send to the first network entity. Additionally, or alternatively, the first network entity402may send initial feedback416that assists the first network entity402and/or the second network entity404in tracking the channel at424and/or426.

In some aspects, the first network entity402(e.g., UE) may detect a model change based on a local event at the first network entity402, e.g., at432. The local event may include a mobility change of the first network entity402, a change in channel conditions (e.g., noise, interference, blockage), or a change of the physical device (e.g., battery life, power usage, device heating, etc.).

After the first network entity402detects, at406, a change to the model/states, the first network entity402may trigger, at a second network entity404(e.g., base station), a switch of the channel or an update to the model. For example, the first network entity402may transmit, at434, a request for an updated channel and/or an updated set of recommended initial parameters. In cases where the first network entity402performs the measurement, the first network entity402may switch a Tx/Rx configuration based on detected changes to the mobility of the first network entity402, channel conditions, device conditions, etc. The switch of the Tx/Rx configuration may impact parameters of the channel model configuration.

If the first network entity402detects, at432, a model change based on a local event at the first network entity402, the first network entity402may indicate to the second network entity404that the nodes may no longer use a current channel model configuration, e.g., at434. For example, the first network entity402may transmit (e.g., in a PUSCH) the request, at434, for the updated channel and/or the updated set of recommended initial parameters. Based on a report from the first network entity402, the second network entity404may respond/transmit, at436, to the first network entity402with a confirmation message to the request and/or an updated channel model configuration and parameters.

As both nodes may be tracking the channel based on a common model, e.g., as shown at426and424, whenever a change is detected by one of the nodes, the detecting node may indicate the change to the other node (e.g., non-detection node). In some examples, a different beam pair may be used for communications between the first network entity402and the second network entity404. If a different beam is used by one of the nodes to perform the communication, the different beam may have different properties based on the channel parameters. For instance, the different beam may have a different delay spread, Doppler spread, etc. The parameters may be beam-specific. Hence, if a different tracking procedure is to be performed for the different beam or the different CSI-RS resource, or if the number of ports or the rank associated with the transmission has changed, a new tracking session may have to be initiated.

The channel model may be in a discrete time domain, with a sampling duration as an adjustable parameter. For example, the sampling duration may be one of the parameters indicated to the first network entity (e.g., such as a UE) by the second network entity (e.g., such as a base station) as part of or in connection with a model configuration.

An example state-space channel model may correspond to h(n)=Fh(n−1)+w(n), and an example observation model may correspond to z(n)=h(n)+v(n), where h(n) corresponds to the channel at time n, F corresponds to a state transition matrix, w(n) corresponds to process noise, which may be modeled as a circular symmetric complex Gaussian random variable denoted by CN(0, Q), where CN is indicative of a complex normal distribution, and v(n) corresponds to a measurement noise, which may be modeled as a circular symmetric complex Gaussian random variable denoted by CN(0, R). F, Q, and R may correspond to portions of the model configuration that are commonly known, or otherwise agreed, among the nodes. The state-space model may be indicative of the channel to be measured, whereas the state transition matrix may represent part of the model configuration between the first network entity402and the second network entity404. At each observation instance, the nodes may apply the transition matrix to a previous observation to determine a current state. At least one of the nodes may measure the channel, which may include the state. The measurement process may be noisy in some cases. Thus, covariance in the system may be associated with unknown variables.

An estimate of the channel at a time n, e.g., h(n) given observations until z(n−1) may be indicated as ĥ(n|n−1), where:
ĥ(n|n−1)=Fĥ(n−1|n−1)
with a covariance matrix for time n given n−1 being Pn|n−1=FPn−1|n−1FH+Q. In some examples, rather than reporting a differential channel state (e.g., based on Δh(n)=h(n)−h(n−1)), a model-based update (e.g., based on Kny(n), where K n corresponds to a Kalman gain/filter coefficient at time n, and y(n) corresponds to a signal at time n based on the observation model and the state-space model) may be reported from the first network entity402to the second network entity404. The same state-space model and Kalman filtering procedure may be used at both nodes to predict a future channel corresponding to ĥ(n+k|n). Instead of applying the state-space model to determine the channel h(n), similar state-space models may also be applied to other channel state feedback (CSF) metrics, such as CQI, PMI, etc., to determine channel information. The Kalman filtering procedure may be represented as:
y(n)=z(n)−ĥ(n|n−1)
Sn=Pn|n−1+R
Kn=Pn|n−1Sn−1
ĥ(n|n)=ĥ(n|n−1)+Kny(n)
Pn|n=(I−Kn)Pn|n−1
Where Snrepresents the covariance of y(n).

Irrespective of an observation (e.g., a CSI transmission or measurement), both network entities (for example, a UE and a base station) and UE can track h using the state transition model using ĥ(n|n−1)=Fĥ(n−1|n−1).

When an observation is available at a time instance n, the measuring entity, e.g., the first network entity402, can provide the (Kalman) update. A sampling duration may be the same as the CSI-RS periodicity, in which case tracking may be based on measurement of the CSI-RS. In other aspects, the tracking or sampling duration may be different than the CSI-RS periodicity. When an observation is not available at an instance n, the estimate may be indicated as:
ĥ(n|n):=ĥ(n|n−1)
Pn|n:=Pn|n−1

As an example, when an observation is 0, it may be treated as a missed observation. In some aspects, the time stamp of updating the model for the channel may be decoupled from the measurement of the channel, and the tracking rate and the channel measurement rate may be signaled between the network entities, e.g., between the UE and the base station. As an example, when a tracking periodicity is to be faster than a CSI-RS periodicity, then the tracking can still work by treating instances as missing observation, e.g., z(n)=0.

A beamforming technology (e.g., 5G NR mmW technology) may use beam management procedures, such as beam measurements and beam switches, to maintain a quality of a link between a first network entity and a second network entity (e.g., an access link between a base station and a UE or a sidelink communication link between a first UE and a second UE) at a sufficient level. Beam management procedures aim to support mobility and the selection of the best beam pairing (or beam pair link (BPL)) between the first network entity and the second network entity. Beam selection may be based on a number of considerations including logical state, power saving, robustness, mobility, throughput, etc. For example, wide beams may be used for initial connection and for coverage/mobility and narrow beams may be used for high throughput scenarios with low mobility.

FIG.5A,FIG.5B, andFIG.5Cillustrate an example of beam pair link (BPL) discovery and refinement for a second network entity504(“NE2”) and a first network entity502(“NE1”). A beam pair link may also be referred to as a “beam pair” or a CSI-RS resource indicator (CRI). In 5G NR, P1, P2, and P3 procedures are used for beam pair discovery and refinement.

A P1 procedure enables the discovery of new BPLs. Referring toFIG.5A, in a P1 procedure500, the second network entity504transmits different symbols of a reference signal (e.g., a P1 signal), each beamformed in a different spatial direction. Stated otherwise, the second network entity504transmits beams using different transmit beams (e.g., transmit beams510a,510b,510c,510d,510e,510f) over time in different directions. For successful reception of at least a symbol of the P1 signal, the first network entity502searches for an appropriate receive beam. The first network entity502searches using available receive beams (e.g., receive beams512a,512b,512c,512d,512e,5120and applying a different receive beam during each occurrence of the periodic P1 signal.

Once the first network entity502has succeeded in receiving a symbol of the P1 signal, the first network entity502has discovered a BPL. In some aspects, the first network entity502may not want to wait until it has found the best receive beam, since this may delay further actions. The first network entity502may measure a signal strength (e.g., a reference signal receive power (RSRP)) and report the symbol index together with the RSRP to the second network entity504. Such a report may contain the findings of one or more BPLs. In an example, the first network entity502may determine a received signal having a high RSRP. The first network entity502may not know which transmit beam the second network entity504used to transmit. However, the first network entity502may report to the second network entity504the time at which the signal having a high RSRP was observed. The second network entity504may receive this report and may determine which transmit beam the second network entity504used at the given time.

The second network entity504may then offer P2 and P3 procedures to refine an individual BPL. Referring toFIG.5B, a P2 procedure520refines the beam (transmit beam) of a BPL at the second network entity504. The second network entity504may transmit a set of symbols of a reference signal with different beams that are spatially close to the beam of the BPL (e.g., the second network entity504may perform a sweep using neighboring beams around the selected beam). For example, the second network entity504may transmit a plurality of transmit beams (e.g., transmit beams522a,522b, and522c) over a consecutive sequence of symbols, with a different beam per symbol. In the P2 procedure520, the first network entity502keeps its receive beam (e.g., a receive beam524a) constant. Thus, the first network entity502uses the same beam as in the BPL. The beams used by the second network entity504for the P2 procedure520may be different from those used for the P1 procedure in that they may be spaced closer together or they may be more focused. The first network entity502may measure the signal strength (e.g., RSRP) for the various beams (e.g., the transmit beams522a,522b, and522c) and indicate the strongest beam and/or the highest RSRP to the second network entity504. Additionally, or alternatively, the first network entity502may indicate all RSRPs measured for the beams. The first network entity502may indicate such information via a CSI-RS resource indicator feedback message, which may contain the RSRPs of the received beams (e.g., the transmit beams522a,522b,522c) in a sorted manner. The second network entity504may switch an active beam to the strongest beam reported, thus keeping the RSRP of the BPL at a highest level and supporting low mobility. If the transmit beams used for the P2 procedure520are spatially close (or even partially overlapped), no beam switch notification may be sent to the first network entity502.

Referring toFIG.5C, a P3 procedure540refines the beam (receive beam) of a BPL at the first network entity502. In this example, the second network entity504keeps it transmit beam (e.g., a transmit beam542a) constant over a consecutive sequence of symbols. The first network entity502may use this opportunity to refine the receive beam by checking a strength of multiple receive beams (from the same or different panels). That is, while the transmit beam stays constant, the first network entity502may scan using different receive beams (e.g., the first network entity502performs a sweep using neighboring beams (e.g., receive beams544a,544b, and544c)). The first network entity502may measure the RSRP of each receive beam and identify the best beam. Afterwards, the first network entity502may use the best beam for the BPL. The first network entity502may or may not send a report of RSRP(s) of the receive beam to the second network entity504. By the end of the P2 and P3 procedures, the refined transmit beam at the second network entity504and the refined receive beam at the first network entity502maximize the RSRP of the BPL.

Although the examples ofFIG.5A,FIG.5B, andFIG.5Cdescribe measuring and reporting RSRP, in other examples, the first network entity502may measure and/or report additional or alternate measurements, such as a signal to interference and noise ratio (SINR).

In the example ofFIG.4, the channel tracking is described in connection with a single Tx-Rx beam pair, which may also be referred to as a “beam pair” herein. However, as described in connection withFIG.5A,FIG.5B, andFIG.5C, network entities may use beam management procedures, such as beam measurements and beam switches, to maintain a quality of a link between the respective network entities.

Aspects disclosed herein provide techniques for multiple beam tracking and beam management techniques with model-based channel tracking. For example, disclosed techniques may facilitate tracking performance of multiple beam pairs. In some examples, disclosed techniques may facilitate predicting future beam pair performance, such as predicting a beam failure.

FIG.6illustrates an example communication flow600between a second network entity604(“NE2”) and a first network entity602(“NE1”), as presented herein. In some aspects, the second network entity604may be a base station or a component of a base station (e.g., a CU, a DU, and/or an RU) and the first network entity602may be UE. Although not shown in the illustrated example ofFIG.6, in additional or alternate examples, the second network entity604and/or the first network entity602may be in communication with one or more other network entities, such as one or more other base stations or UEs.

In the illustrated example, the communication flow600facilitates tracking of multiple beam pairs based on a model-based channel representation. In some examples, the communication flow600may enable tracking the performance of multiple beam pairs by the second network entity604and/or the first network entity602. Although the example ofFIG.6is directed to tracking the multiple beam pairs based on downlink signaling (e.g., a CSI-RS) from the second network entity604to the first network entity602, the concepts described may be applicable to tracking multiple beam pairs based on uplink signaling (e.g., a sounding reference signal (SRS)) from the first network entity602to the second network entity604, or may be applicable to tracking multiple beam pairs based on downlink signaling and uplink signaling.

In the example ofFIG.6, the first network entity602transmits capability information610that is received by the second network entity604. In some examples, the first network entity602may transmit the capability information610while performing a connection establishment procedure with the second network entity604. The capability information610may include an indicator612indicating a maximum quantity of tracking sessions (K) that the first network entity602is capable of simultaneously maintaining. For example, for each beam pair that the first network entity602is tracking, the first network entity602may be configured to maintain a separate tracking session. In some examples, the indicator612may include a maximum quantity of downlink tracking sessions (K1) and a maximum quantity of uplink tracking sessions (K2). In some examples, the indicator612may include a combination of one or more of the maximum quantity of tracking sessions (K), the maximum quantity of downlink tracking sessions (K1), and the maximum quantity of uplink tracking sessions (K2).

In the example ofFIG.6, the second network entity604and the first network entity602perform beam pair procedures614to facilitate beam pair discovery and refinement. The beam pair procedures614may enable the second network entity604and the first network entity602to select one or more beam pairs. Aspects of the beam pair procedures614are described in connection withFIG.5A,FIG.5B, andFIG.5C.

As shown inFIG.6, the first network entity602may transmit a beam report616that is received by the second network entity604. The beam report616may indicate beams pairs with the highest (or best) quality. The quality of a beam pair may be based on an RSRP measurement (“cri-RSRP”) and/or an SINR measurement (“cri-SINR”). In the example ofFIG.6, the beam report616includes a quantity of beam pairs (L). That is, the beam report616includes measurement information for the top L beam pairs on which the first network entity602performed measurements. In some examples, the quantity of beam pairs (L) may be the same or less than the maximum quantity of training sessions (K). In other examples, the quantity of beam pairs (L) may be more than the maximum quantity of training sessions (K).

After receiving the beam report616, the second network entity604may begin transmitting measurement resources618associated with different beam pairs. The different beam pairs may be based on the beam pairs with the highest (or best) quality. In the example ofFIG.6, the second network entity604selects K beam pairs for tracking. That is, the second network entity604selects the K beam pairs with the highest (or best) quality of the L beam pairs indicated by the beam report616on which to transmit the measurement resources618. For example, the second network entity604may transmit a first measurement resource618afor a first beam pair (“CRI(i)”), . . . and may transmit a second measurement resource618bfor a second beam pair (“CRI(i+k−1)”). The measurement resources618may include an interference measurement resource (IMR) or a channel measurement resource (CMR). The measurement resources618may include a periodic (“P”) CSI-RS, a semi-persistent (“SP”) CSI-RS, and/or an aperiodic (“A”) CSI-RS.

In some examples, the transmissions associated with the measurement resources618may include an indication to start tracking of the respective beam pair at the first network entity602. That is, the transmissions may include an indicator to initiate a tracking session with the first network entity602for each of the respective beam pairs. For example, the transmission of the first measurement resource618afor the first beam pair (CRI(i)) may include an indication indicating to the first network entity602to start a tracking algorithm for the first beam pair (CRI(i)). In a similar manner, the transmission of the second measurement resource618bfor the second beam pair (CRI(i+k−1)) may include an indication indicating to the first network entity602to start a tracking algorithm for the second beam pair (CRI(i+k−1)).

In some examples, the transmission associated with the measurement resources618may include an indication for the tracking of the respective beam pair to be based on downlink signaling (e.g., a CSI-RS) or to be based on an uplink signal (e.g., an SRS). For example, of the K beam pairs that the second network entity604selects for tracking, the second network entity604may indicate a first subset of the K beam pairs to be tracked via downlink signaling and may indicate a second subset of the K beam pairs to be tracked via uplink signaling. The quantity of beam pairs of the first subset of the K beam pairs may be based on the maximum quantity of downlink tracking sessions (K1) indicated by the first network entity602via capability information610. The quantity of beam pairs of the second subset of the K beam pairs may be based on the maximum quantity of uplink tracking sessions (K2) indicated by the first network entity602via the capability information610.

Although not shown in the example ofFIG.6, in other examples, the second network entity604may transmit an indication indicating the one or more beam pairs on which the first network entity602is to start tracking. For example, the second network entity604may transmit a configuration indicating the one or more beam pairs and indication to initiate tracking for the indicated one or more beam pairs. Additionally, the second network entity604may transmit an indication indicating whether the tracking of each of the respective beam pairs is based on downlink signaling (e.g., the first network entity602is to perform channel tracking for the beam pair based on CSI-RS) or based on uplink signaling (e.g., the second network entity604is to perform channel tracking for the bema pair based on SRS).

As shown inFIG.6, the first network entity602estimates initial channel states620for each of the measurement resources618. For example, the first network entity602may estimate a first initial channel state620a(hi(0)) based on the first measurement resource618a, . . . , and may estimate a second initial channel state620bhi+k−1(0) based on the second measurement resource618b. Thus, the first network entity602may estimate an initial channel state for each beam pair. Aspects of estimating the initial channel state are described in connection with414ofFIG.4.

The first network entity602may provide feedback622that is received by the second network entity604. The feedback may be based on an estimate of an initial channel state associated with a beam pair. For example, the feedback622may include an initial CSI for the channel and the associated beam pair. In the example ofFIG.6, the feedback622indicates a j-th initial channel state (hj(0)) for a j-th beam pair (“CRI(j)”).

The second network entity604may transmit a response624based on the feedback622. The response624may include an ACK or a NACK indicating a successful or unsuccessful, respectively, receiving of the feedback622. In some examples, the response624may include the initial channel state for the channel and beam pair (e.g., the hj(0)) for the j-th beam pair (CRI(j))). The second network entity604may include the initial channel state for the channel and beam pair (e.g., the hj(0)) for the j-th beam pair (CRI(j))) so that the second network entity604and the first network entity602are using the same model configuration when tracking the respective channel and beam pair. In some examples, the response624may include one or more model parameters, such as the state transmission matrix Fjand the process noise covariance Qjassociated with the j-th beam pair and the corresponding channel. Aspects of the response624are described in connection with418and/or420ofFIG.4.

Based on transmitting the response624, the second network entity604may execute tracking algorithms626. As shown inFIG.6, the second network entity604may execute a separate tracking algorithm for each of the beam pairs being tracked by the second network entity604and the first network entity602. For example, the second network entity604may execute a first tracking algorithm626aassociated with the first beam pair (CRI(i)), . . . , and may execute a second tracking algorithm626bassociated with the second beam pair (CRI(i+k−1)). Aspects of the tracking algorithms626are described in connection with424and426ofFIG.4.

The first network entity602may likewise execute channel tracking algorithms628and channel measurement algorithms629. As shown inFIG.6, the first network entity602may execute a separate channel tracking algorithm for each of the beam pairs being tracked by the second network entity604and the first network entity602. For example, the first network entity602may execute a first channel tracking algorithm628aassociated with the first beam pair (CRI(i)), . . . , and may execute a second channel tracking algorithm628bassociated with the second beam pair (CRI(i+k−1)). The first network entity602may also execute channel measurement algorithms629to perform measurements for the corresponding channels. Aspects of the channel tracking algorithms628are described in connection with424and426ofFIG.4.

The first network entity602may transmit a model parameter update632that is received by the second network entity604. The model parameter update632may be based on a channel measurement by the first network entity602indicative of a change in a condition of the channel. The first network entity602may transmit the model parameter update632via a CSI feedback procedure. The model parameter update632may include a Kalman gain update. In some examples, the first network entity602may provide the model parameter update632using a compressed CSI report in which the report may include the model parameter updates for the non-measuring entity (e.g., the second network entity604) to update the model configuration. Aspects of the model parameter update632are described in connection with430ofFIG.4.

In the example ofFIG.6, the first network entity602transmits the model parameter update632associated with a k-th channel tracking algorithm. For example, the second network entity604may transmit a k-th measurement resource630that is received by the first network entity602. Aspects of the k-th measurement resource630may be similar to the measurement resources associated with the measurement resources618. For example, the k-th measurement resource630may include a periodic CSI-RS, a semi-persistent CSI-RS, or an aperiodic CSI-RS. When an update occurs based on a measurement by the first network entity602, the first network entity602may transmit the model parameter update632.

Although the example ofFIG.6describes the response624being associated with the j-th beam pair (CRI(j)), it may be appreciated that the second network entity604may transmit a response for each of the beam pairs based on their respective feedback. Thus, the second network entity604may provide an initial channel state for each of the beam pairs being tracked. The second network entity604may also provide one or more parameters (e.g., a state transmission matrix F, a process noise covariance Q, etc.) associated with each of the beam pairs being tracked. That is, each of the tracking algorithms of the tracking algorithms626and the channel tracking algorithms of the channel tracking algorithms628may be associated with their own associated state model parameters (e.g., Fj, Qj, etc.).

In the example ofFIG.6, the second network entity604may provide the state model parameters to the first network entity602via the response624. In other examples, the first network entity602may be preconfigured with the model state parameters so that the second network entity604and the first network entity602know what model to use for each measurement resource and/or reporting occasion. For example, the second network entity604may configure the first network entity602with the state model parameters before tracking is started for a beam pair.

FIG.7illustrates an example communication flow700between a second network entity704(“NE2”) and a first network entity702(“NE1”), as presented herein. The communication flow700ofFIG.7facilitates tracking of multiple beam pairs based on a model-based channel representation and replacing a beam pair that is unusable. In some aspects, the second network entity704may be a base station or a component of a base station (e.g., a CU, a DU, and/or an RU) and the first network entity702may be UE. Although not shown in the illustrated example ofFIG.7, in additional or alternate examples, the second network entity704and/or the first network entity702may be in communication with one or more other network entities, such as one or more other base stations or UEs.

In the illustrated example ofFIG.7, the second network entity704and the first network entity702are tracking k beam pairs710. The second network entity704and the first network entity702may be tracking the k beam pairs710based on a state space update model, as described in connection with the examples ofFIG.4andFIG.6. In the illustrated example ofFIG.7, the k beam pairs710includes a first beam pair712(“BP(i)”) and a second beam pair714(“BP(j)”). However, other examples of k beam pairs may include any suitable quantity of beam pairs.

In some examples, tracking the k beam pairs710may include executing multiple tracking sessions. For example, the second network entity704and the first network entity702may execute k tracking sessions to track the k beam pairs710. Aspects of the k tracking sessions are described in connection with the tracking algorithms626, the channel tracking algorithms628, and the channel measurement algorithms629ofFIG.6.

Each tracking session of the multiple tracking sessions may be associated with its own state model parameters. For example, a first tracking session may be associated with a first state model set including one or more state model parameters, a second tracking session may be associated with a second state model set including one or more state model parameters, etc. Examples of state model parameters for a j-th beam pair (CRI(j)) include an initial channel state (hj(0)), a state transmission matrix (Fj), a process noise covariance (Qj), as described in connection with the response624ofFIG.6. The state model parameters may also include a reporting configuration, a thresholds configuration, and/or a periodicity configuration. The reporting configuration may indicate what information to report when a network entity is providing channel model updates. The thresholds configuration may configure one or more thresholds used by a network entity when assessing a beam pair. The periodicity configuration may configure a rate or frequency at which a network entity is providing channel model updates.

In the illustrated example ofFIG.7, the second network entity704may transmit a state model configuration720that is received by the first network entity702. The state model configuration720may provide the one or more state model parameters associated with one or more state model sets. In the example ofFIG.7, the state model configuration720includes a first state model set722and a second state model set724. The first state model set722may include one or more parameters associated with a first tracking session associated with the first beam pair712and the second state model set724may include one or more parameters associated with a second tracking sessions associated with second beam pair714. As shown inFIG.7, the first state model set722includes multiple state model parameters including an initial channel state722a(hj(0)), a state transmission matrix722b(Fj), a process noise covariance722c(Qj), a reporting configuration722d, a thresholds configuration722e, and a periodicity configuration722f. Although not shown in the example ofFIG.7, it may be appreciated that the second state model set724may include one or more state model parameters associated with the second beam pair714.

In some examples, the second network entity704may output the state model configuration720including one or more state model sets to the first network entity702to facilitate tracking of the k beam pairs710. For example, the state model configuration720may correspond to the response624ofFIG.6.

In some examples, the one or more state model parameters of a state model set may be preconfigured at a network entity. For example, each beam pair of the k beam pairs710may be associated with a corresponding measurement resource, such as a CSI-RS resource or an SRS resource. The second network entity704and the first network entity702may exchange RRC signaling that configures the measurement resource at the respective network entities. For example, the RRC signaling may indicate time-frequency resources associated with the measurement resource. The RRC signaling may also include the one or more state model parameters associated with the measurement resource and, thus, the corresponding beam pair. For example, the first beam pair712may be associated with a first measurement resource. The RRC signaling associated with the first measurement resource may indicate the time-frequency resources associated with the first measurement resource and the one or more state model parameters associated with tracking the first beam pair712via the first measurement resource. In some examples, the RRC signaling associated with a measurement resource may include an RRC information element (IE).

In some examples, each beam pair may be associated with a respective set of one or more state model parameters. Thus, the configuration associated with two or more of the beam pairs may be different. For example, the reporting configuration722dassociated with the first beam pair712may be different than the corresponding reporting configuration associated with the second beam pair714, the thresholds configuration722eassociated with the first beam pair712may be different than the corresponding thresholds configuration associated with the second beam pair714, and/or the periodicity configuration722fassociated with the first beam pair712may be different than the corresponding periodicity configuration associated with the second beam pair714.

In the example ofFIG.7, the second network entity704may transmit a measurement resource on the first beam pair712at different times. For example, the second network entity704may transmit a first measurement resource730on the first beam pair712at a time n. The first measurement resource730may include an IMR or a CMR. The first measurement resource730may include a periodic CSI-RS, a semi-persistent CSI-RS, or an aperiodic CSI-RS. Aspects of the first measurement resource730are described in connection with the measurement resources618ofFIG.6.

The first network entity702may receive the first measurement resource730and, based on a measurement of the first measurement resource730, may transmit (e.g., output or communicate) a model parameter update736that is received by the second network entity704. The model parameter update736may correspond to compressed delta CSI feedback, as described in connection with430ofFIG.4and/or the model parameter update632ofFIG.6.

It may be appreciated that one or more of the beam pairs may become unusable and may be detected based on tracking of the k beam pairs710. In some examples, a beam pair replacement event740amay be detected by the second network entity704. In some examples, a beam pair replacement event740bmay be detected by the first network entity702. An occurrence of a beam pair replacement event associated with a beam pair may be referred to as an “event T.” For example, a beam pair replacement event associated with the first beam pair712may be referred to as an event Ti. Likewise, the occurrence of a beam pair replacement event associated with the second beam pair714may be referred to as an event Tj.

The occurrence of an event T may be determined based on a quality metric associated with the beam pair. For example, the first network entity702may detect the beam pair replacement event740bwhen the quality of a beam pair fails to satisfy a quality threshold. For example, and with respect to the first beam pair712, the quality threshold may be configured via the thresholds configuration722eof the state model configuration720. The quality metric may include an RSRP (e.g., a layer 1 RSRP (L1-RSRP)) and/or an SINR measurement associated with the corresponding measurement resource. In some examples, the occurrence of an event T may be determined based on Equation 1 (below).
∥ĥi(n)∥<Qout,iEquation 1:

In Equation 1, the term “ĥi(n)” refers to an estimate of the channel associated with the first beam pair712at a time n, and the term “Qout” refers to a quality threshold at which a downlink radio signal cannot be reliability received. Thus, when a magnitude of the estimate of the channel is less than the Qout,ithreshold configured for the first beam pair712, an occurrence of an event Timay be detected.

In some examples, the occurrence of an event T may be based on a sequence of states failing to satisfy a quality threshold. For example, a network entity may detect an occurrence of an event T when M states out of a last L states fail to satisfy a quality threshold. In some examples, the occurrence of an event T may be determined based on Equation 2 (below).
{ĥi(n−L), . . . ,ĥi(n−1),ĥi(n)}<Qout,iEquation 2:

Based on Equation 2, a network entity may perform measurements on the last L measurement resources for the first beam pair712. For each measurement, the network entity may determine whether the measurement fails to satisfy the quality threshold (e.g., the Qoutthreshold). When a quantity M of the last L measurements fail to satisfy the quality threshold, the network entity may detect the occurrence of the event T. For example, and referring to the example ofFIG.7, the second network entity704may transmit the first measurement resource730at the time n, may transmit a second measurement resource732at a time n−1, and may transmit a third measurement resource734at a time n−L. In the example ofFIG.7, the time n−1 may occur before the time n in the time domain, and the time n−L may occur before the time n−1 in the time domain. The first network entity702may perform a number of quality determinations based on the received measurement resources. For example, the first network entity702may perform a first quality determination based on a measurement of the third measurement resource734and the quality threshold (e.g., the Qoutthreshold), may perform a second quality determination based on a measurement of the second measurement resource732and the quality threshold, and may perform a third quality determination based on a measurement of the first measurement resource730and the quality threshold. When a quantity M of the L quality determinations indicate unsatisfied qualities, the first network entity702may detect the occurrence of the beam pair replacement event740b.

In some examples, the state model configuration720may configure the quantity M, the quantity L, and the Qoutthreshold. Additionally, the quantity M, the quantity L, and the Qoutthreshold may be different for one or more of the k beam pairs710. For example, the thresholds configuration722emay include a quantity Mi, a quantity Li, and a Qout,ithreshold associated with the first beam pair712, and a corresponding thresholds configuration of the second state model set724may include a quantity a quantity Lj, and a Qout,ithreshold associated with the second beam pair714.

In some examples, the occurrence of an event T may be determined based on a change in quality of a channel associated with a beam pair. For example, the occurrence of an event T may be determined based on Equation 3 (below).
∥ĥi(n)−ĥi(n−1)∥>QiEquation 3:

Based on Equation 3, a network entity may compare the estimate of the channel state at time n (e.g., the ĥi(n)) to the estimate of the channel state at time n−1 (e.g., ĥi(n−1)) and when the magnitude of the change is greater than a quality threshold (e.g., a Qi), the network entity may detect the occurrence of an event T.

In some examples, the state model configuration720may configure the Q threshold. Additionally, the Q threshold may be different for one or more of the k beam pairs710. For example, the thresholds configuration722emay include a Qithreshold associated with the first beam pair712, and a corresponding thresholds configuration of the second state model set724may include a Qithreshold associated with the second beam pair714.

In the examples of Equation 1, Equation 2, and Equation 3, the detecting of an occurrence of an event T is associated with the first beam pair712(e.g., an event Ti). However, the respective equations may be modified for detecting an occurrence of an event T associated with any other beam pair of the k beam pairs710.

It may be appreciated that the detection of an event T (e.g., the beam pair replacement event740aat the second network entity704and/or the beam pair replacement event740band the first network entity702) may be different than a beam failure detection (BFD). For example, for BFD, a hypothetical block error rate (BLER) is used, which is a measurement known to a network entity (e.g., a UE) receiving a measurement resource (e.g., the first network entity702). In contrast, the occurrence of an event T may be detected by a network entity receiving a measurement resource (e.g., the first network entity702) or a network entity transmitting the measurement resource (e.g., the second network entity704).

In some examples, the occurrence of an event T (e.g., the beam pair replacement event740aand/or the beam pair replacement event740b) may trigger signaling between the second network entity704and the first network entity702related to the corresponding beam pair. For example, the occurrence of the event Timay trigger the second network entity704and the first network entity702to exchange signaling related to replacing the first beam pair712.

In the example ofFIG.7, the second network entity704may transmit a beam pair replacement communication750to signal to the first network entity702to replace a “poor” beam pair. As used herein, a poor beam pair refers to a beam pair for which the event T is detected. For example, after the occurrence of the event Tiis detected, the second network entity704may transmit the beam pair replacement communication750to indicate to the first network entity702that a replacement beam pair for the first beam pair712is needed.

In some examples, when at least one event T is detected among the k beam pairs710being tracked, the second network entity704may request the first network entity702provide a beam report including a top L beam pairs. For example, the second network entity704may transmit a beam report request760that is received by the first network entity702. The beam report request760may request that the first network entity702provide a beam report including measurement information for a top L beam pairs. As shown inFIG.7, the first network entity702may transmit a beam report762that is received by the second network entity704. Aspects of the beam report762may be similar to the beam report616ofFIG.6. For example, the beam report762may include measurement information for the top L beam pairs on which the first network entity702performed measurements. In such examples, the second network entity704and the first network entity702may exchange signaling to reset the tracking sessions and to start new tracking sessions for multiple beam pairs, as described in connection with the example communication flow600ofFIG.6.

In some examples, when an event T is detected, the second network entity704may signal a new beam pair716(“BP(x)”). For example, the second network entity704may transmit a communication770that is received by the first network entity702. The communication770may include an indicator associated with the new beam pair716. In some such examples, the first network entity702may, at772, replace the poorest beam pair with the new beam pair716. For example, the first network entity702may determine which of the k beam pairs710has the poorest beam quality and replace the determined beam with the new beam pair716. The first network entity702and the second network entity704may then begin a tracking session associated with the new beam pair716.

In some examples, when an event T is detected, the second network entity704may indicate a new beam pair to replace an old beam pair. For example, the second network entity704may transmit a communication780that is received by the first network entity702. The communication780may include a first indicator indicating a new beam pair (e.g., the new beam pair716) for which the first network entity702is to start a new tracking session. The communication780may also include a second indicator indicating an old beam pair (e.g., the first beam pair712) that the first network entity702is to stop tracking. That is, based on the communication780, the first network entity702may, at782, replace the old beam pair (e.g., the first beam pair712) with the new beam pair (e.g., the new beam pair716). The second network entity704and the first network entity702may also start a tracking session associated with the new beam pair.

As shown inFIG.7, the occurrence of an event T may be detected at the second network entity704and/or the first network entity702. In some examples, the occurrence of an event T may be erroneously detected. For example, an occurrence of an event T may be due to a tracking algorithm error at the second network entity704and/or the first network entity702. In some examples, an occurrence of an event T may be detected at the second network entity704or the first network entity702and not by both network entities. In some examples, an occurrence of an event T may be detected by a network entity, or both network entities, without a recent transmission of a measurement resource on the corresponding beam pair. In some examples, an occurrence of an event T may be detected by a network entity, or both network entities, without receiving measurement of a measurement resource on the corresponding beam pair.

In some examples, to prevent the replacement of a beam pair based on an erroneous occurrence of an event T, the second network entity704and/or the first network entity702may perform a confirmation procedure. The network entities may perform the confirmation procedure to confirm that the assessment of an occurrence of an event T is accurate. In some examples, the network entity detecting the occurrence of an event T may initiate the confirmation procedure. For example, if the second network entity704detects the beam pair replacement event740a, then the second network entity704may initiate a confirmation procedure742ato confirm that the detection of the beam pair replacement event740ais accurate. In a similar manner, if the first network entity702detects the beam pair replacement event740b, then the704/may indicate a confirmation procedure742bto confirm that the detection of the beam pair replacement event740bis accurate. Aspects of the confirmation procedures are described in connection withFIG.8A,FIG.8B, andFIG.8C.

FIG.8A,FIG.8B, andFIG.8Cillustrate example communication flows to perform respective confirmation procedures between a second network entity804(“NE2”) and a first network entity802(“NE1”), as presented herein. In the illustrated examples, the second network entity804may be a network entity transmitting a measurement resource and the first network entity802may be a network entity receiving the measurement resource. For example, the second network entity804may correspond to the second network entity604ofFIG.6and/or the second network entity704ofFIG.7, and the first network entity802may correspond to the first network entity602ofFIG.6and/or the first network entity702ofFIG.7.

The example communication flows ofFIG.8A,FIG.8B, andFIG.8Cmay enable a detecting network entity (e.g., a network entity that detects the occurrence of an event T) to verify that the detection of the event T is accurate and, thus, to avoid replacing a beam pair that may have a satisfactory quality. In the illustrated examples, the event T may be detected on a first beam pair812(“BP(i)”).

FIG.8Aillustrates an example communication flow800in which the second network entity804detects the occurrence of an event T (e.g., the beam pair replacement event740aofFIG.7). For example, the second network entity804may detect a beam pair replacement event810. Aspects of detecting the beam pair replacement event810are described in connection with the beam pair replacement event740aofFIG.7.

After detecting the beam pair replacement event810, the second network entity804may transmit a measurement resource816using the first beam pair812. The measurement resource816may be an event-triggered measurement resource. That is, the transmission of the measurement resource816is based on the occurrence of the beam pair replacement event810. The second network entity804may use the first beam pair812to transmit the measurement resource816so that a new measurement for the first beam pair812may be reported. For example, the first network entity802may perform a measurement818on the measurement resource816. The measurement818may include an RSRP (e.g., an L1-RSRP) and/or an SINR. The first network entity802may also transmit a report820that is received by the second network entity804. The report820may include the measurement818on the measurement resource816. The second network entity804may then verify that the first beam pair812is unusable beam pair based on the report820from the first network entity802. If the second network entity804determines that the first beam pair812is a poor beam pair (e.g., based on the report820), the second network entity804may signal beam pair replacement, as described in connection with the beam pair replacement communication750ofFIG.7.

In the example ofFIG.8A, the transmitting of the measurement resource816and report820are different from beam management procedures in that the second network entity804and the first network entity802are already communicating via the first beam pair812. Additionally, the first network entity802is transmitting compressed CSI reports (e.g., model parameter updates) to the second network entity804for the first beam pair812during the tracking session associated with the first beam pair812, as described in connection with the channel tracking algorithms628ofFIG.6.

However, to verify the assertion that the first beam pair812is an unusable beam pair (e.g., a poor quality beam pair), the second network entity804may request that the first network entity802include a measurement quantity with the report820. For example, the second network entity804may transmit a communication814signaling an upcoming event-triggered measurement resource. The communication814may configure the first network entity802to receive the measurement resource816. For example, the communication814may indicate time resources and/or frequency resources that the first network entity802may use to locate the measurement resource816. In the example ofFIG.8A, the communication814includes an indicator822that indicates to the first network entity802to include the measurement quantity (e.g., the measurement818) with the report820. The second network entity804may transmit the communication814and the indicator822via control signaling, such as DCI and/or a MAC-CE.

In some examples, the first network entity802may detect the occurrence of the event T. For example,FIG.8Billustrates an example communication flow830in which the first network entity802detects the occurrence of an event T (e.g., the beam pair replacement event740bofFIG.7). For example, the first network entity802may detect a beam pair replacement event834. Aspects of detecting the beam pair replacement event834are described in connection with the beam pair replacement event740bofFIG.7.

In the example ofFIG.8B, the first network entity802detects the beam pair replacement event834after performing a measurement832on a beam pair (e.g., the first beam pair812). For example, the first network entity802may determine that the measurement832on the first beam pair812fails to satisfy a quality threshold or a change in quality of the first beam pair812fails to satisfy a quality threshold.

After detecting the beam pair replacement event834, the first network entity802may transmit a report836that is received by the second network entity804. Aspects of the report836may be similar to the report820ofFIG.8A. For example, the report836may include a measurement quantity (e.g., an L1-RSRP and/or an SINR) of a measurement resource on which the assertion of the beam pair replacement event834is based. The report836and the measurement quantity may enable the second network entity804to verify that the quality of the first beam pair812fails to satisfy a threshold quality. In some such examples, the second network entity804may signal beam pair replacement, as described in connection with the beam pair replacement communication750ofFIG.7.

As shown inFIG.8B, the first network entity802may also transmit a model parameter update838that is received by the second network entity804. The model parameter update838may include a compressed CSI report that the first network entity802is expected to transmit when performing a tracking session associated with the first beam pair812.

In some examples, the first network entity802may detect the occurrence of the event T without performing a measurement on the first beam pair812. For example,FIG.8Cillustrates an example communication flow850in which the first network entity802detects the occurrence of an event T (e.g., the beam pair replacement event740bofFIG.7). For example, the first network entity802may detect a beam pair replacement event852. Aspects of detecting the beam pair replacement event852are described in connection with the beam pair replacement event740bofFIG.7.

In the example ofFIG.8C, the first network entity802detects the beam pair replacement event852without a measurement. For example, the detecting of the beam pair replacement event852may be due to an error in the tracking algorithm associated with the first beam pair812. Additionally, or alternatively, the detection of the beam pair replacement event852may be due to the tracking rate being different from the measurement rate, among other examples of beam pair replacement events.

After the first network entity802detects the beam pair replacement event852, the first network entity802may request that the second network entity804transmit a measurement resource on the first beam pair812so that the first network entity802may obtain a measurement858for the first beam pair812that is “fresh” or new. For example, the first network entity802may transmit a request854that is received by the second network entity804. The request854may request the second network entity804to output a transmission of a measurement resource856on the first beam pair812. As shown inFIG.8C, the second network entity804may output the measurement resource856that is received by the first network entity802. The first network entity802may then perform a measurement on the measurement resource856to obtain the measurement858. The first network entity802may also transmit a report860that is received by the second network entity804. Aspects of the report860may be similar to the report820ofFIG.8A. For example, the report860may include a measurement quantity (e.g., an L1-RSRP and/or an SINR) of the measurement resource856for the first beam pair812. The report860and the measurement858may enable the second network entity804to verify that the quality of the first beam pair812fails to satisfy a threshold quality. In some such examples, the second network entity804may signal beam pair replacement, as described in connection with the beam pair replacement communication750ofFIG.7.

FIG.9is a flowchart900of a method of wireless communication. The method may be performed by a first network entity (e.g., the UE104; the first network entity402,502,602,702,802; and/or an apparatus1004ofFIG.10). In some aspects, the first network entity may be a UE and the method of the flowchart900may be performed by a cellular RF transceiver1022and/or the UE channel tracking component198of the apparatus1004ofFIG.10. In some aspects, the first network entity may be a UE, and the second network entity may be a network node, such as a base station or a device or component implementing base station functionality. In some aspects, the first network entity may be a first UE, and the second network entity may be a second UE. In some aspects, the first network entity may be a first network node, and the second network entity may be a second network node. The method may facilitate improving channel tracking and reducing signaling overhead for beam management associated with multiple beam pairs.

At902, the first network entity receives, from a second network entity, a model configuration indicative of a model condition of a channel between the first network entity and the second network entity for multiple beam pairs, as described in connection with the channel model configuration420ofFIG.4and/or the response624ofFIG.6. For example, the first network entity may receive one or more model configurations for the multiple beam pairs, such as receiving a model configuration for each of the beam pairs. The receiving of the model configuration indicative of the model condition may be performed by the cellular RF transceiver1022and/or the UE channel tracking component198ofFIG.10.

At904, the first network entity tracks a variation in a channel condition relative to a model condition of a channel based on the model configuration for each of multiple beam pairs separately in multiple tracking sessions that overlap in time, each beam pair including a transmission beam and a reception beam, as described in connection with the channel tracking algorithms628ofFIG.6. The tracking of the variations in the channel conditions for each of the multiple beam pairs may be performed by the UE channel tracking component198ofFIG.10.

In some examples, the first network entity may transmit, to the second network entity, one or more updated parameters for the model configuration for one of the multiple beam pairs based on the variation of the channel condition in a corresponding tracking session of the multiple tracking sessions, as described in connection with the model parameter update632ofFIG.6and/or the model parameter update736ofFIG.7.

In some examples, the first network entity may transmit an indication of support for a maximum number of simultaneous tracking sessions, as described in connection with the indicator612ofFIG.6. The first network entity may also receive a configuration to perform a number of the multiple tracking sessions that is within the maximum number of simultaneous tracking sessions supported by the first network entity, as described in connection with the state model configuration720, the first state model set722, and the second state model set724ofFIG.7.

In some examples, tracking the variation in the channel condition relative to the model configuration separately for each of the multiple beam pairs (e.g., at904) may include tracking, in a first tracking session, a first variation in the channel condition relative to the model configuration for a first beam pair, as described in connection with the first channel tracking algorithm628aofFIG.6. Tracking the variation in the channel condition relative to the model configuration separately for each of the multiple beam pairs (e.g., at904) may also include tracking, in a second tracking session, a second variation in the channel condition relative to the model configuration for a second beam pair, as described in connection with the second channel tracking algorithm628bofFIG.6.

In some examples, the first tracking session may be based on a first state model set and the second tracking session may be based on a second state model set, the first state model set and the second state model set each including one or more state model parameters, as described in connection with the first state model set722and the second state model set724ofFIG.7.

In some examples, the first network entity may receive a configuration of the first state model set for the first tracking session and the second state model set for the second tracking session, as described in connection with the state model configuration720, the first state model set722, and the second state model set724ofFIG.7. In some examples, the first state model set and the second state model set may be known to the first network entity (e.g., the first state model set and the second state model set may be preconfigured at the first network entity.

In some examples, the first network entity may receive, for different tracking sessions in the multiple tracking sessions, one or more of: a different report configuration, a different threshold, or a different tracking rate for determining the variation in the channel condition relative to the model configuration, as described in connection with the reporting configuration722d, the thresholds configuration722e, and/or the periodicity configuration722fassociated with the first state model set722and the corresponding reporting configuration, the threshold configuration, and/or the periodicity configuration associated with the second state model set724.

In some examples, the first network entity may determine that a beam pair of the multiple beam pairs has an occurrence of an event in which the channel condition for the beam pair is below a threshold for one or more instances, as described in connection with the beam pair replacement event740bofFIG.7.

In some examples, after the determination of the occurrence of the event, the first network entity may receive a request from the second network entity for a set of beam reports based on at least a subset of the multiple beam pairs in response to the event at the beam pair, as described in connection with the beam report request760ofFIG.7.

In some examples, after the determination of the occurrence of the event, the first network entity may receive an indication of a new beam pair, the indication of the new beam pair indicating a replacement of the beam pair having a lowest beam quality metric, as described in connection with communication770and the new beam pair716ofFIG.7. The first network entity may also track the variation in the channel condition relative to the model configuration for the new beam pair, as described in connection with772ofFIG.7.

In some examples, after the determination of the occurrence of the event, the first network entity may receive a first indication of a new beam pair and a second indication of a first beam pair of the multiple beam pairs being replaced by the new beam pair, as described in connection with the communication780including a first indicator indicating a new beam pair (e.g., the new beam pair716) for which the first network entity is to start a new tracking session, and a second indicator indicating an old beam pair (e.g., the first beam pair712) that the first network entity is to stop tracking. The first network entity may also start tracking the variation in the channel condition relative to the model configuration for the new beam pair, as described in connection with782ofFIG.7.

In some examples, the first network entity may receive a request for a measurement report for the beam pair for which the event is detected to have occurred, as described in connection with the indicator822ofFIG.8A. The first network may also transmit the measurement report in response to the request, as described in connection with the report820ofFIG.8A. The measurement report may include at least one of an L1-RSRP or an SINR.

In some examples, the first network entity may transmit, in response to the occurrence of the event, a measurement report for at least the beam pair in addition to an indication of the variation for the channel condition relative to the model configuration, as described in connection with the report836and the model parameter update838ofFIG.8B.

In some examples, the first network entity may transmit, in response to the occurrence of the event, a request for a transmission on the beam pair, as described in connection with the request854ofFIG.8C.

FIG.10is a diagram1000illustrating an example of a hardware implementation for an apparatus1004. The apparatus1004may be a UE, a component of a UE, or may implement UE functionality. In some aspects, the apparatus1004may include a cellular baseband processor1024(also referred to as a modem) coupled to one or more transceivers (e.g., a cellular RF transceiver1022). The cellular baseband processor1024may include on-chip memory1024′. In some aspects, the apparatus1004may further include one or more subscriber identity modules (SIM) cards1020and an application processor1006coupled to a secure digital (SD) card1008and a screen1010. The application processor1006may include on-chip memory1006′. In some aspects, the apparatus1004may further include a Bluetooth module1012, a WLAN module1014, an SPS module1016(e.g., GNSS module), one or more sensor modules1018(e.g., barometric pressure sensor/altimeter; motion sensor such as inertial management unit (IMU), gyroscope, and/or accelerometer(s); light detection and ranging (LIDAR), radio assisted detection and ranging (RADAR), sound navigation and ranging (SONAR), magnetometer, audio and/or other technologies used for positioning), additional memory modules1026, a power supply1030, and/or a camera1032. The Bluetooth module1012, the WLAN module1014, and the SPS module1016may include an on-chip transceiver (TRX) (or in some cases, just a receiver (RX)). The Bluetooth module1012, the WLAN module1014, and the SPS module1016may include their own dedicated antennas and/or utilize one or more antennas1080for communication. The cellular baseband processor1024communicates through transceiver(s) (e.g., the cellular RF transceiver1022) via one or more antennas1080with the UE104and/or with an RU associated with a network entity1002. The cellular baseband processor1024and the application processor1006may each include a computer-readable medium/memory, such as the on-chip memory1024′, and the on-chip memory1006′, respectively. The additional memory modules1026may also be considered a computer-readable medium/memory. Each computer-readable medium/memory (e.g., the on-chip memory1024′, the on-chip memory1006′, and/or the additional memory modules1026) may be non-transitory. The cellular baseband processor1024and the application processor1006are each responsible for general processing, including the execution of software stored on the computer-readable medium/memory. The software, when executed by the cellular baseband processor1024/application processor1006, causes the cellular baseband processor1024/application processor1006to perform the various functions described supra. The computer-readable medium/memory may also be used for storing data that is manipulated by the cellular baseband processor1024/application processor1006when executing software. The cellular baseband processor1024/application processor1006may be a component of the UE350and may include the memory360and/or at least one of the TX processor368, the RX processor356, and the controller/processor359. In one configuration, the apparatus1004may be a processor chip (modem and/or application) and include just the cellular baseband processor1024and/or the application processor1006, and in another configuration, the apparatus1004may be the entire UE (e.g., see the UE350ofFIG.3) and include the additional modules of the apparatus1004.

As discussed supra, the UE channel tracking component198is configured to receive, from a second network entity, a model configuration indicative of a model condition of a channel between the first network entity and the second network entity; and track a variation in a channel condition relative to the model condition of the channel based on the model configuration for each of multiple beam pairs separately in multiple tracking sessions that overlap in time, each beam pair including a transmission beam and a reception beam.

The UE channel tracking component198may be within the cellular baseband processor1024, the application processor1006, or both the cellular baseband processor1024and the application processor1006. The UE channel tracking component198may be one or more hardware components specifically configured to carry out the stated processes/algorithm, implemented by one or more processors configured to perform the stated processes/algorithm, stored within a computer-readable medium for implementation by one or more processors, or some combination thereof.

As shown, the apparatus1004may include a variety of components configured for various functions. For example, the UE channel tracking component198may include one or more hardware components that perform each of the blocks of the algorithm in the flowchart ofFIG.9and/or any of the aspects performed by the first network entity in any ofFIGS.4-8.

In one configuration, the apparatus1004, and in particular the cellular baseband processor1024and/or the application processor1006, includes means for performing any of the aspects of the method ofFIG.9and/or any of the aspects performed by the first network entity in any ofFIGS.4-8.

The means may be the UE channel tracking component198of the apparatus1004configured to perform the functions recited by the means. As described supra, the apparatus1004may include the TX processor368, the RX processor356, and the controller/processor359. As such, in one configuration, the means may be the TX processor368, the RX processor356, and/or the controller/processor359configured to perform the functions recited by the means.

FIG.11is a flowchart1100of a method of wireless communication. The method may be performed by a second network entity (e.g., the base station102; the second network entity404,504,604,704, or804; and/or a network entity1202ofFIG.12). In some aspects, the first network entity may be a UE, and the second network entity may be a network node, such as a base station or a device or component implementing base station functionality. In some aspects, the first network entity may be a first UE, and the second network entity may be a second UE. In some aspects, the first network entity may be a first network node, and the second network entity may be a second network node. The method may facilitate improving channel tracking and reducing signaling overhead for beam management associated with multiple beam pairs.

At1102, the second network entity outputs for transmission, one or more model configurations indicative of a model condition of a channel between a first network entity and the second network entity for multiple beam pairs separately in multiple tracking sessions, as described in connection with the state model configuration720ofFIG.7. As an example, the second network entity may transmit to the first network entity one or more model configurations indicative of a model condition of a channel between a first network entity and the second network entity for multiple beam pairs separately in multiple tracking sessions. In some examples, each beam pair of the multiple beam pairs may have a separate tracking session of the multiple tracking sessions, as described in connection with the tracking algorithms626ofFIG.6. In some examples, the multiple tracking sessions for the multiple beam pairs may be overlapping in time. The outputting for transmission of the model configuration may be performed by the BS channel tracking component199ofFIG.12.

At1104, the second network entity obtains one or more updated parameters for the one or more model configuration for one of the multiple beam pairs based on a variation, observed at the first network entity, of a channel condition in a corresponding tracking session of the multiple tracking sessions, as described in connection with the model parameter update632ofFIG.6and/or the model parameter update736ofFIG.7. For example, the second network entity may receive, e.g., from the first network entity, one or more updated parameters for the one or more model configuration for one of the multiple beam pairs based on a variation, observed at the first network entity, of a channel condition in a corresponding tracking session of the multiple tracking sessions. The obtaining of the one or more updated parameters for the model configuration(s) may be performed by the BS channel tracking component199ofFIG.12.

In some examples, the second network entity may obtain an indication of support of the first network entity for a maximum number of simultaneous tracking sessions, as described in connection with the indicator612ofFIG.6. The second network entity may also output for transmission a configuration to perform a number of the multiple tracking sessions that is within the maximum number of simultaneous tracking sessions supported by the first network entity, as described in connection with the k beam pairs and the state model configuration720ofFIG.7.

In some examples, a first tracking session for a first beam pair of the multiple beam pairs may be based on a first state model set and a second tracking session for a second beam pair of the multiple beam pairs may be based on a second state model set, the first state model set and the second state model set each including one or more state model parameters, as described in connection with the first tracking algorithm626aand the second tracking algorithm626bofFIG.6. The second network entity may also output for transmission a configuration of the first state model set for the first tracking session and the second state model set for the second tracking session, as described in connection with the first state model set722and the second state model set724ofFIG.7.

In some examples, the second network entity may output for transmission, for different tracking sessions in the multiple tracking sessions, one or more of: a different report configuration, a different threshold, or a different tracking rate for determining the variation in the channel condition relative to the one or more model configurations, as described in connection with the reporting configuration722d, the thresholds configuration722e, and/or the periodicity configuration722fassociated with the first state model set722and the corresponding reporting configuration, the threshold configuration, and/or the periodicity configuration associated with the second state model set724.

In some examples, the second network entity may output for transmission, in response to an occurrence of an event in which the channel condition for at least one beam pair of the multiple beam pairs is below a threshold for one or more instances, a request from the second network entity for a set of beam reports based on at least a subset of the multiple beam pairs in response to the event at the at least one beam pair, as described in connection with the beam report request760ofFIG.7.

In some examples, the second network entity may output for transmission, in response to an occurrence of an event in which the channel condition for at least one beam pair of the multiple beam pairs is below a threshold for one or more instances, an indication of a new beam pair, the indication of the new beam pair indicating a replacement of a beam pair having a lowest beam quality metric, as described in connection with the communication770and the indication of the new beam pair716ofFIG.7.

In some examples, the second network entity may output for transmission, in response to an occurrence of an event in which the channel condition for at least one beam pair of the multiple beam pairs is below a threshold for one or more instances, a first indication of a new beam pair and a second indication of a first beam pair of the multiple beam pairs, the new beam pair being a replacement for the first beam pair, as described in connection with the communication780including a first indicator indicating a new beam pair (e.g., the new beam pair716) for which the first network entity is to start a new tracking session, and a second indicator indicating an old beam pair (e.g., the first beam pair712) that the first network entity is to stop tracking.

In some examples, the second network entity may output for transmission, in response to an occurrence of an event in which the channel condition for at least one beam pair of the multiple beam pairs is below a threshold for one or more instances, a request for a measurement report for the at least one beam pair for which the event is detected to have occurred, as described in connection with the indicator822ofFIG.8A. The second network entity may also obtain the measurement report in response to the request, as described in connection with the report820ofFIG.8A. The measurement report may include at least one of an L1-RSRP or an SINR.

In some examples, the second network entity may obtain, in response to an occurrence of an event in which the channel condition for at least one beam pair of the multiple beam pairs is below a threshold for one or more instances, a measurement report for the at least one beam pair in addition to an indication of the variation for the channel condition relative to the one or more model configurations, as described in connection with the report836and the model parameter update838ofFIG.8B.

In some examples, the second network entity may obtain, in response to an occurrence of an event in which the channel condition for a beam pair of the multiple beam pairs is below a threshold for one or more instances, a request for a transmission on the beam pair, as described in connection with the request854ofFIG.8C.

FIG.12is a diagram1200illustrating an example of a hardware implementation for a network entity1202. The network entity1202may be a BS, a component of a BS, or may implement BS functionality. The network entity1202may include at least one of a CU1210, a DU1230, or an RU1240. For example, depending on the layer functionality handled by the BS channel tracking component199, the network entity1202may include the CU1210; both the CU1210and the DU1230; each of the CU1210, the DU1230, and the RU1240; the DU1230; both the DU1230and the RU1240; or the RU1240. The CU1210may include a CU processor1212. The CU processor1212may include on-chip memory1212′. In some aspects, may further include additional memory modules1214and a communications interface1218. The CU1210communicates with the DU1230through a midhaul link, such as an F1 interface. The DU1230may include a DU processor1232. The DU processor1232may include on-chip memory1232′. In some aspects, the DU1230may further include additional memory modules1234and a communications interface1238. The DU1230communicates with the RU1240through a fronthaul link. The RU1240may include an RU processor1242. The RU processor1242may include on-chip memory1242′. In some aspects, the RU1240may further include additional memory modules1244, one or more transceivers1246, antennas1280, and a communications interface1248. The RU1240communicates with the UE104. The on-chip memories (e.g., the on-chip memory1212′, the on-chip memory1232′, and/or the on-chip memory1242′) and/or the additional memory modules (e.g., the additional memory modules1214, the additional memory modules1234, and/or the additional memory modules1244) may each be considered a computer-readable medium/memory. Each computer-readable medium/memory may be non-transitory. Each of the CU processor1212, the DU processor1232, the RU processor1242is responsible for general processing, including the execution of software stored on the computer-readable medium/memory. The software, when executed by the corresponding processor(s) causes the processor(s) to perform the various functions described supra. The computer-readable medium/memory may also be used for storing data that is manipulated by the processor(s) when executing software.

As discussed supra, the BS channel tracking component199is configured to output for transmission, a model configuration indicative of a model condition of a channel between a first network entity and the second network entity for multiple beam pairs separately in multiple tracking sessions, each beam pair of the multiple beam pairs having a separate tracking session of the multiple tracking sessions, the multiple tracking sessions for the multiple beam pairs overlapping in time; and obtain one or more updated parameters for the one or more model configurations for one of the multiple beam pairs based on a variation, observed at the first network entity, of a channel condition in a corresponding tracking session of the multiple tracking sessions.

The channel tracking component199may be within one or more processors of one or more of the CU1210, DU1230, and the RU1240. The BS channel tracking component199may be one or more hardware components specifically configured to carry out the stated processes/algorithm, implemented by one or more processors configured to perform the stated processes/algorithm, stored within a computer-readable medium for implementation by one or more processors, or some combination thereof.

The network entity1002may include a variety of components configured for various functions. For example, the BS channel tracking component199may include one or more hardware components that perform each of the blocks of the algorithm in the flowchart ofFIG.11and/or the aspects performed by the second network entity in any ofFIGS.4-8.

In one configuration, the network entity1002includes means for performing any of the aspects of the method ofFIG.11and/or the aspects performed by the second network entity in any ofFIGS.4-8.

The means may be the BS channel tracking component199of the network entity1202configured to perform the functions recited by the means. As described supra, the network entity1202may include the TX processor316, the RX processor370, and the controller/processor375. As such, in one configuration, the means may be the TX processor316, the RX processor370, and/or the controller/processor375configured to perform the functions recited by the means.

Aspects disclosed herein provide techniques for multiple beam tracking and beam management techniques with model-based channel tracking. For example, disclosed techniques may facilitate tracking performance of multiple beam pairs. In some examples, disclosed techniques may facilitate predicting future beam pair performance, such as predicting a beam failure.

Aspect 1 is a method of wireless communication at a first network entity, comprising: receiving from a second network entity, a model configuration indicative of a model condition of a channel between the first network entity and the second network entity for multiple beam pairs; and tracking a variation in a channel condition relative to the model condition of the channel based on the model configuration for each of the multiple beam pairs separately in multiple tracking sessions that overlap in time, each beam pair including a transmission beam and a reception beam.

Aspect 2 is the method of aspect 1, further including: transmitting, to the second network entity, one or more updated parameters for the model configuration for one of the multiple beam pairs based on the variation of the channel condition in a corresponding tracking session of the multiple tracking sessions.

Aspect 3 is the method of any of aspects 1 and 2, further including: transmitting an indication of support for a maximum number of simultaneous tracking sessions; and receiving a configuration to perform a number of the multiple tracking sessions that is within the maximum number of simultaneous tracking sessions supported by the first network entity.

Aspect 4 is the method of any of aspects 1 to 3, further including that tracking the variation in the channel condition relative to the model configuration separately for each of the multiple beam pairs includes: tracking, in a first tracking session, a first variation in the channel condition relative to the model configuration for a first beam pair; and tracking, in a second tracking session, a second variation in the channel condition relative to the model configuration for a second beam pair.

Aspect 5 is the method of any of aspects 1 to 4, further including that the first tracking session is based on a first state model set and the second tracking session is based on a second state model set, the first state model set and the second state model set each including one or more state model parameters.

Aspect 6 is the method of any of aspects 1 to 5, further including: receiving a configuration of the first state model set for the first tracking session and the second state model set for the second tracking session.

Aspect 7 is the method of any of aspects 1 to 5, further including that the first state model set and the second state model set are known to the first network entity.

Aspect 8 is the method of any of aspects 1 to 7, further including: receiving, for different tracking sessions in the multiple tracking sessions, one or more of: a different report configuration, a different threshold, or a different tracking rate for determining the variation in the channel condition relative to the model configuration.

Aspect 9 is the method of any of aspects 1 to 8, further including: determining that a beam pair of the multiple beam pairs has an occurrence of an event in which the channel condition for the beam pair is below a threshold for one or more instances.

Aspect 10 is the method of any of aspects 1 to 9, further including: receiving a request from the second network entity for a set of beam reports based on at least a subset of the multiple beam pairs in response to the event at the beam pair.

Aspect 11 is the method of any of aspects 1 to 9, further including: receiving an indication of a new beam pair, the indication of the new beam pair indicating a replacement of the beam pair having a lowest beam quality metric; and tracking the variation in the channel condition relative to the model configuration for the new beam pair.

Aspect 12 is the method of any of aspects 1 to 9, further including: receiving a first indication of a new beam pair and a second indication of a first beam pair of the multiple beam pairs being replaced by the new beam pair; and tracking the variation in the channel condition relative to the model configuration for the new beam pair.

Aspect 13 is the method of any of aspects 1 to 9, further including: receiving a request for a measurement report for the beam pair for which the event is detected to have occurred; and transmitting the measurement report in response to the request.

Aspect 14 is the method of any of aspects 1 to 13, further including that the measurement report includes at least one of a layer 1 reference signal received power (L1 RSRP) or a signal to interference and noise ratio (SINR).

Aspect 15 is the method of any of aspects 1 to 9, further including: transmitting, in response to the occurrence of the event, a measurement report for at least the beam pair in addition to an indication of the variation for the channel condition relative to the model configuration.

Aspect 16 is the method of any of aspects 1 to 15, further including: transmitting, in response to the occurrence of the event, a request for a transmission on the beam pair.

Aspect 17 is an apparatus for wireless communication at a first network entity including at least one processor coupled to a memory and configured to implement any of aspects 1 to 16.

In aspect 18, the apparatus of aspect 17 further includes at least one antenna coupled to the at least one processor.

In aspect 19, the apparatus of aspect 17 or 18 further includes a transceiver coupled to the at least one processor.

Aspect 20 is an apparatus for wireless communication at a first network entity including means for implementing any of aspects 1 to 16.

In aspect 21, the apparatus of aspect 20 further includes at least one antenna coupled to the means to perform the method of any of aspects 1 to 16.

In aspect 22, the apparatus of aspect 20 or 21 further includes a transceiver coupled to the means to perform the method of any of aspects 1 to 16.

Aspect 23 is a non-transitory computer-readable storage medium storing computer executable code, where the code, when executed, causes a processor to implement any of aspects 1 to 16.

Aspect 24 is a method of wireless communication at a second network entity, comprising outputting for transmission, one or more model configurations indicative of a model condition of a channel between a first network entity and the second network entity for multiple beam pairs separately in multiple tracking sessions, each beam pair of the multiple beam pairs having a separate tracking session of the multiple tracking sessions, the multiple tracking sessions for the multiple beam pairs overlapping in time; and obtaining one or more updated parameters for the one or more model configurations for one of the multiple beam pairs based on a variation, observed at the first network entity, of a channel condition in a corresponding tracking session of the multiple tracking sessions.

Aspect 25 is the method of aspect 24, further including: obtaining an indication of support of the first network entity for a maximum number of simultaneous tracking sessions; and outputting for transmission a configuration to perform a number of the multiple tracking sessions that is within the maximum number of simultaneous tracking sessions supported by the first network entity.

Aspect 26 is the method of any of aspects 24 and 25, further including that a first tracking session for a first beam pair of the multiple beam pairs is based on a first state model set and a second tracking session for a second beam pair of the multiple beam pairs is based on a second state model set, the first state model set and the second state model set each including one or more state model parameters.

Aspect 27 is the method of any of aspects 24 to 26, further including: outputting for transmission a configuration of the first state model set for the first tracking session and the second state model set for the second tracking session.

Aspect 28 is the method of any of aspects 24 to 27, further including: outputting for transmission, for different tracking sessions in the multiple tracking sessions, one or more of: a different report configuration, a different threshold, or a different tracking rate for determining the variation in the channel condition relative to the one or more model configurations.

Aspect 29 is the method of any of aspects 24 to 27, further including: outputting for transmission, in response to an occurrence of an event in which the channel condition for at least one beam pair of the multiple beam pairs is below a threshold for one or more instances, a request from the second network entity for a set of beam reports based on at least a subset of the multiple beam pairs in response to the event at the at least one beam pair.

Aspect 30 is the method of any of aspects 24 to 27, further including: outputting for transmission, in response to an occurrence of an event in which the channel condition for at least one beam pair of the multiple beam pairs is below a threshold for one or more instances, an indication of a new beam pair, the indication of the new beam pair indicating a replacement of a beam pair having a lowest beam quality metric.

Aspect 31 is the method of any of aspects 24 to 27, further including: outputting for transmission, in response to an occurrence of an event in which the channel condition for at least one beam pair of the multiple beam pairs is below a threshold for one or more instances, a first indication of a new beam pair and a second indication of a first beam pair of the multiple beam pairs, the new beam pair being a replacement for the first beam pair.

Aspect 32 is the method of any of aspects 24 to 27, further including: outputting for transmission, in response to an occurrence of an event in which the channel condition for at least one beam pair of the multiple beam pairs is below a threshold for one or more instances, a request for a measurement report for the at least one beam pair for which the event is detected to have occurred; and obtaining the measurement report in response to the request.

Aspect 33 is the method of any of aspects 24 to 32, further including that the measurement report includes at least one of a layer 1 reference signal received power (L1 RSRP) or a signal to interference and noise ratio (SINR).

Aspect 34 is the method of any of aspects 24 to 27, further including: obtaining, in response to an occurrence of an event in which the channel condition for at least one beam pair of the multiple beam pairs is below a threshold for one or more instances, a measurement report for the at least one beam pair in addition to an indication of the variation for the channel condition relative to the one or more model configurations.

Aspect 35 is the method of any of aspects 24 to 34, further including: obtaining, in response to an occurrence of an event in which the channel condition for a beam pair of the multiple beam pairs is below a threshold for one or more instances, a request for the transmission on the beam pair.

Aspect 36 is an apparatus for wireless communication at a second network entity including at least one processor coupled to a memory and configured to implement any of aspects 24 to 35.

In aspect 37, the apparatus of aspect 36 further includes at least one antenna coupled to the at least one processor.

In aspect 38, the apparatus of aspect 36 or 37 further includes a transceiver coupled to the at least one processor.

Aspect 39 is an apparatus for wireless communication at a second network entity including means for implementing any of aspects 24 to 35.

In aspect 40, the apparatus of aspect 39 further includes at least one antenna coupled to the means to perform the method of any of aspects 24 to 35.

In aspect 41, the apparatus of aspect 39 or 40 further includes a transceiver coupled to the means to perform the method of any of aspects 24 to 35.

Aspect 42 is a non-transitory computer-readable storage medium storing computer executable code, where the code, when executed, causes a processor to implement any of aspects 24 to 35.