Patent Description:
<CIT> discloses a channel state information processing method and an apparatus thereof. The method includes the following steps: configuring, by a network device, at least one piece of channel state information CSI reporting configuration information and resource configuration information corresponding to the at least one piece of CSI reporting configuration information, where one piece of the CSI reporting configuration information corresponds to at least two pieces of resource configuration information, one of the at least two pieces of resource configuration information is used for interference measurement and includes a channel state information-reference signal CSI-RS resource set, one of the at least two pieces of resource configuration information is used for channel measurement and includes a non-zero power channel state information-reference signal NZP CSI-RS resource, and one CSI-RS resource set corresponds to at least one CSI report; and sending, by the network device, the at least one piece of CSI reporting configuration information and the resource configuration information corresponding to the at least one piece of CSI reporting configuration information to a terminal device.

<CIT> discloses a system and method for providing an eNodeB with the flexibility to configure a Channel State Information (CSI) report to match a specific Coordinated Multipoint (CoMP) transmission hypothesis, which is a candidate for a downlink transmission to a User Equipment (UE) is disclosed. A UE receives, from the eNodeB, a configuration message that specifies a CSI report. The CSI report is specified by a particular interference hypothesis and a particular desired signal hypothesis corresponding to data transmission over at least one effective channel characterized by a specific reference signal. The UE estimates interference according to the interference hypothesis, and/or estimates at least one effective channel by performing measurements on the specific reference signal, and determines a CSI report based on the interference estimation and on the estimated effective channel. The UE also transmits the CSI report to the eNodeB.

Preferred embodiments of the invention are stipulated in the dependent claims.

Aspects of the present disclosure provide apparatus, methods, processing systems, and computer readable mediums for enhanced channel state information (CSI) reporting.

A CSI report is based on a channel and interference estimate. Generally, the time and frequency resource locations from which the channel and interference estimates are controlled by a base station (BS) configuration. While channel estimates are generally stable over time, interference estimates may change rapidly. As channel conditions between a user equipment (UE) and a BS change, it is important for the UE to report certain CSI parameters (e.g., channel quality indicator (CQI), precoding matrix index (PMI), and rank indicator (RI)) about the latest channel conditions to the BS (e.g., BS 110a).

However, the rapid interference changes may reduce the usefulness of CQI reports in general, as the CQI value has little correlation to the expected CQI in the upcoming slots. Aspects of the present disclosure generally include techniques that may be considered enhancements to interference estimates for CSI reporting, by modeling and predicting candidate interference levels for upcoming slots.

The following description provides examples of improved interference estimates for CSI reporting in communication systems, and is not limiting of the scope, applicability, or examples set forth in the claims.

The techniques described herein may be used for various wireless networks and radio technologies. While aspects may be described herein using terminology commonly associated with <NUM>, <NUM>, and/or new radio (e.g., <NUM> NR) wireless technologies, aspects of the present disclosure can be applied in other generation-based communication systems.

NR access may support various wireless communication services, such as enhanced mobile broadband (eMBB) targeting wide bandwidth, millimeter wave mmW, massive machine type communications MTC (mMTC) targeting non-backward compatible MTC techniques, and/or mission critical targeting ultra-reliable low-latency communications (URLLC).

NR supports beamforming and beam direction may be dynamically configured.

The wireless communication network <NUM> may be an NR system (e.g., a <NUM> NR network). As shown in <FIG>, the wireless communication network <NUM> may be in communication with a core network <NUM>. The core network <NUM> may in communication with one or more base station (BSs) 110a-z (each also individually referred to herein as BS <NUM> or collectively as BSs <NUM>) and/or user equipment (UE) 120a-y (each also individually referred to herein as UE <NUM> or collectively as UEs <NUM>) in the wireless communication network <NUM> via one or more interfaces.

As shown, the BS 110a may include a CSI processing module <NUM> that may be configured to perform (or cause BS 110a to perform) operations <NUM> of <FIG>. Similarly, UE 120a may include an interference model and prediction module <NUM> that may be configured to perform (or cause UE 120a to perform) operations <NUM> of <FIG>.

Wireless communication network <NUM> may also include relay stations (e.g., relay station 11or), also referred to as relays or the like, that receive a transmission of data and/or other information from an upstream station (e.g., a BS 110a or a UE 120r) and sends a transmission of the data and/or other information to a downstream station (e.g., a UE <NUM> or a BS <NUM>), or that relays transmissions between UEs <NUM>, to facilitate communication between devices.

At the BS 110a, a transmit processor <NUM> may receive data from a data source <NUM> and control information from a controller/processor <NUM>.

The transmit processor <NUM> may also generate reference symbols, such as for the primary synchronization signal (PSS), secondary synchronization signal (SSS), and channel state information reference signal (CSI-RS). A transmit (TX) multiple-input multiple-output (MIMO) processor <NUM> may perform spatial processing (e.g., precoding) on the data symbols, the control symbols, and/or the reference symbols, if applicable, and may provide output symbol streams to the modulators (MODs) in transceivers 232a-232t. Downlink signals from modulators in transceivers 232a-232t may be transmitted via the antennas 234a-234t, respectively.

At the UE 120a, the antennas 252a-252r may receive the downlink signals from the BS 110a and may provide received signals to the demodulators (DEMODs) in transceivers 254a-254r, respectively. A MIMO detector <NUM> may obtain received symbols from all the demodulators in transceivers 254a-254r, perform MIMO detection on the received symbols if applicable, and provide detected symbols. A receive processor <NUM> may process (e.g., demodulate, deinterleave, and decode) the detected symbols, provide decoded data for the UE 120a to a data sink <NUM>, and provide decoded control information to a controller/processor <NUM>.

On the uplink, at UE 120a, a transmit processor <NUM> may receive and process data (e.g., for the physical uplink shared channel (PUSCH)) from a data source <NUM> and control information (e.g., for the physical uplink control channel (PUCCH) from the controller/processor <NUM>. The symbols from the transmit processor <NUM> may be precoded by a TX MIMO processor <NUM> if applicable, further processed by the modulators in transceivers 254a-254r (e.g., for SC-FDM, etc.), and transmitted to the BS 110a. At the BS 110a, the uplink signals from the UE 120a may be received by the antennas <NUM>, processed by the demodulators in transceivers 232a-232t, detected by a MIMO detector <NUM> if applicable, and further processed by a receive processor <NUM> to obtain decoded data and control information sent by the UE 120a.

Antennas <NUM>, processors <NUM>, <NUM>, <NUM>, and/or controller/processor <NUM> of the UE 120a and/or antennas <NUM>, processors <NUM>, <NUM>, <NUM>, and/or controller/processor <NUM> of the BS 110a may be used to perform the various techniques and methods described herein. As shown in <FIG>, the controller/processor <NUM> of the BS 110a has a CSI processing module <NUM> that may be configured to perform operations <NUM> of <FIG>. As shown in <FIG>, the controller/processor <NUM> of the UE 120a has an interference model and prediction module <NUM> that may be configured to perform operations <NUM> of <FIG>. Although shown at the Controller/Processor, other components of the UE 120a and BS 110a may be used performing the operations described herein.

Each subframe may include a variable number of slots (e.g., <NUM>, <NUM>, <NUM>, <NUM>, <NUM>,. slots) depending on the SCS. Each slot may include a variable number of symbol periods (e.g., <NUM>, <NUM>, or <NUM> symbols) depending on the SCS. A sub-slot structure may refer to a transmit time interval having a duration less than a slot (e.g., <NUM>, <NUM>, or <NUM> symbols). Each symbol in a slot may be configured for a link direction (e.g., DL, UL, or flexible) for data transmission and the link direction for each subframe may be dynamically switched.

In NR, a synchronization signal block (SSB) is transmitted. In certain aspects, SSBs may be transmitted in a burst where each SSB in the burst corresponds to a different beam direction for UE-side beam management (e.g., including beam selection and/or beam refinement). The SSB includes a PSS, a SSS, and a two symbol PBCH. The SSB can be transmitted in a fixed slot location, such as the symbols <NUM>-<NUM> as shown in <FIG>. The PBCH carries some basic system information, such as downlink system bandwidth, timing information within radio frame, SS burst set periodicity, system frame number, etc. The SSBs may be organized into SS bursts to support beam sweeping. The SSB can be transmitted up to sixty-four times, for example, with up to sixty-four different beam directions for mmWave. The multiple transmissions of the SSB are referred to as a SS burst set. SSBs in an SS burst set may be transmitted in the same frequency region, while SSBs in different SS bursts sets can be transmitted at different frequency regions.

CSI may refer to channel properties of a communication link. The CSI may represent the combined effects of, for example, scattering, fading, and power decay with a distance between a transmitter and a receiver. Channel estimation using pilots, such as CSI reference signals (CSI-RS), may be performed to determine these effects on a channel. The CSI may be used to adapt transmissions based on current channel conditions, which is useful for achieving reliable communication, in particular, with high data rates in multi-antenna systems. The CSI is typically estimated at the receiver, quantized, and fed back to the transmitter.

A UE (e.g., such as a UE 120a) may be configured by a BS (e.g., such as a BS <NUM>) for CSI reporting. The BS may configure the UE with a CSI reporting configuration or with multiple CSI report configurations. The BS may provide the CSI reporting configuration to the UE via higher layer signaling, such as radio resource control (RRC) signaling (e.g., via a CSI-ReportConfig information element (IE)).

Each CSI report configuration may be associated with a single downlink bandwidth part (BWP). The CSI report setting configuration may define a CSI reporting band as a subset of subbands of the BWP. The associated DL BWP may indicated by a higher layer parameter (e.g., bwp-Id) in the CSI report configuration for channel measurement and contains parameter(s) for one CSI reporting band, such as codebook configuration, time-domain behavior, frequency granularity for CSI, measurement restriction configurations, and the CSI-related quantities to be reported by the UE. Each CSI resource setting may be located in the DL BWP identified by the higher layer parameter, and all CSI resource settings may be linked to a CSI report setting have the same DL BWP.

The CSI report configuration may configure time and frequency resources used by the UE to report the CSI. For example, the CSI report configuration may be associated with CSI-RS resources for channel measurement (CM), interference measurement (IM), or both. The CSI report configuration may configure the CSI-RS resources for measurement (e.g., via a CSI-ResourceConfig IE). The CSI-RS resources provide the UE with the configuration of CSI-RS ports, or CSI-RS port groups, mapped to time and frequency resources (e.g., resource elements (REs)). The CSI-RS resources can be zero power (ZP) or non-zero power (NZP) resources. At least one NZP CSI-RS resource may be configured for CM. For interference measurement, it can be NZP CSI-RS or zero power CSI-RS, which is known as CSI-IM (note, if NZP CSI-RS, it is called NZP CSI-RS for interference measurement, if zero power, it is called CSI-IM).

The CSI report configuration may configure the UE for aperiodic, periodic, or semi-persistent CSI reporting. For periodic CSI, the UE may be configured with periodic CSI-RS resources. Periodic CSI and semi-persistent CSI report on physical uplink control channel (PUCCH) may be triggered via RRC or a medium access control (MAC) control element (CE). For aperiodic and semi-persistent CSI on the physical uplink shared channel (PUSCH), the BS may signal the UE a CSI report trigger indicating for the UE to send a CSI report for one or more CSI-RS resources, or configuring the CSI-RS report trigger state (e.g., CSI-AperiodicTriggerStateList and CSI-SemiPersistentOnPUSCH-TriggerStateList). The CSI report trigger for aperiodic CSI and semi-persistent CSI on PUSCH may be provided via downlink control information (DCI). The CSI-RS trigger may be signaling indicating to the UE that CSI-RS will be transmitted for the CSI-RS resource. The UE may report the CSI feedback based on the CSI report configuration and the CSI report trigger. For example, the UE may measure the channel associated with CSI for the triggered CSI-RS resources. Based on the measurements, the UE may select a preferred CSI-RS resource. The UE reports the CSI feedback for the selected CSI-RS resource.

The CSI report configuration can also configure the CSI parameters (sometimes referred to as quantities) to be reported. Codebooks may include Type I single panel, Type I multi-panel, and Type II single panel. Regardless which codebook is used, the CSI report may include at least the channel quality indicator (CQI), precoding matrix indicator (PMI), CSI-RS resource indicator (CRI), and rank indicator (RI). The structure of the PMI may vary based on the codebook. The CRI, RI, and CQI may be in a first part (Part I) and the PMI may be in a second part (Part II) of the CSI report.

For the Type I single panel codebook, the PMI may include a W1 matrix (e.g., subest of beams) and a W2 matrix (e.g., phase for cross polarization combination and beam selection). For the Type I multi-panel codebook, compared to type I single panel codebook, the PMI further comprises a phase for cross panel combination. The BS may have a plurality of transmit (TX) beams. The UE can feed back to the BS an index of a preferred beam, or beams, of the candidate beams. For example, the UE may feed back the precoding vector w for the l-th layer: <MAT> , where b represents the oversampled beam (e.g., discrete Fourier transform (DFT) beam), for both polarizations, and ϕ is the co-phasing.

For the Type II codebook (e.g., which may be designed for single panel), the PMI is a linear combination of beams; it has a subset of orthogonal beams to be used for linear combination and has per layer, per polarization, amplitude and phase for each beam. The preferred precoder for a layer can be a combination of beams and associated quantized coefficients, and the UE can feedback the selected beams and the coefficients to the BS.

The UE may report the CSI feedback based on the CSI report configuration and the CSI report trigger. For example, the UE may measure the channel associated with CSI for the triggered CSI-RS resources. Based on the measurements, the UE may select a preferred CSI-RS resource. The UE reports the CSI feedback for the selected CSI-RS resource. LI may be calculated conditioned on the reported CQI, PMI, RI and CRI; CQI may be calculated conditioned on the reported PMI, RI and CRI; PMI may be calculated conditioned on the reported RI and CRI; and RI may be calculated conditioned on the reported CRI.

As channel conditions between a UE and a BS (e.g., such as between the UE 120a and the BS 110a) change, it is important for the UE to report certain CSI parameters (e.g., CQI, PMI, and RI) about the latest channel conditions to the BS. In certain aspects, the UE transmits a CSI report to the BS to indicate channel conditions to the BS. The BS then utilizes the received CSI report to improve communications with the UE. For example, a CSI report may be used to improve a modulating and coding scheme (MCS), precoder, rank, beam selection, etc..

Typically, a CSI report is based on a single channel and interference estimate. Generally, the time and frequency resource locations from which the channel and interference estimates are based (e.g., such as the CSI-RS resources sets in new radio (NR), or subframe sets in LTE) are controlled by the configuration from the BS. The BS may also control the periodicity and averaging of the interference estimates. For example, the BS may configure the UE with periodic, semi-periodic, or aperiodic configurations for sending CSI reports. The BS may also configure infinite impulse response (IIR) filtering of channel and interference values. The BS may also configure the resources to share spatial relationships (e.g., so reference signals sent on these resources are received by the same receiver beam), for example, via quasi co-location (QCL) indications.

While channel estimates are generally stable over time, interference estimates may change rapidly for each scheduling unit (e.g., slot). For example, the UE may detect strong interference or no interference, depending on the beam from the BS, causing swings in the detected interference estimates/levels. The differences in interference estimates for each scheduling slot can depend on the scheduling strategy of the interfering scheduler. These rapid interference changes may reduce the usefulness of CQI reports in general, as the CQI value has little correlation to the expected CQI in in the upcoming slots. With increasing spatially directed transmissions in NR, the swings in interference levels between two interference measurement resources may be quite large, which may increase this problem significantly.

In order to address the swings in interference estimates, the BS may average the interference estimates in multiple CSI reports to schedule and use hybrid automatic repeat request (HARQ) retransmissions to correct any errors in the interference estimates. Alternatively, the UE may average interference estimates to send in a CSI report, and then the BS again relies on HARQ retransmissions to correct any errors. Generally, when a BS averages the interference estimates, the BS has more information to make scheduling decisions. However, if the BS does not average the interference estimates, then the BS still needs to be able to determine which CQI reports are valid to make scheduling decisions. Accordingly, what is needed are techniques and apparatus for improving interference estimates for CSI reporting.

Aspects of the present disclosure generally include techniques that may be considered enhancements to interference estimates for channel state information (CSI) reporting, by modeling and predicting candidate interference levels for upcoming slots. The modeling may be trained with actual observed interference values.

By training with actual observed interference values from previous slots, the modeling may recognize a recurring structure to the interference pattern (e.g., round robin, group round robin, minimum number of slots, maximum number of slots) and use this information to predict multiple candidate values of interference levels in upcoming slots. In some cases, modeling interference may detect a temporary pattern recognition.

The candidate interference levels generally refer to predicted interference levels based on previously observed interference levels used to train a predictive model. The predicted interference levels may be also based on an analysis of the previously observed interference levels used to train the predictive model. Each candidate interference level is associated with a probability of occurrence. In some cases, the candidate interference levels may be mapped or associated to a transmission from one or more neighboring cells. The candidate interference levels and corresponding probability of occurrence in a CSI report may be used by a base station (BS) improve on the interference estimates in CSI reporting as the BS has more information on channel condition to use to improve communication with the user equipment (UE).

<FIG> is a flow diagram illustrating example operations <NUM> for wireless communication, in accordance with certain aspects of the present disclosure. The operations <NUM> is by a UE (e.g., such as the UE 120a in the wireless communication network <NUM>). The operations <NUM> may be implemented as software components that are executed and run on one or more processors (e.g., controller/processor <NUM> of <FIG>). Further, the transmission and reception of signals by the UE in operations <NUM> may be enabled, for example, by one or more antennas (e.g., antennas <NUM> of <FIG>). In certain aspects, the transmission and/or reception of signals by the UE may be implemented via a bus interface of one or more processors (e.g., controller/processor <NUM>) obtaining and/or outputting signals.

Operations <NUM> begin, at <NUM>, by obtaining, from a network entity, a CSI reporting configuration for reporting CSI for a channel. For example, the UE may be configured with resources for the CSI reporting (e.g., such CSI reference signal (CSI-RS) resource sets, CSI interference measurement (CSI-IM) resource sets, etc.), quantities to report, etc..

In some aspects, at <NUM>, the UE may receive signaling indicating a number, K, of upcoming slots for which the UE may predict candidate interference levels and a number, N, of previously observed interference levels. The UE may be configured to predict candidate interference levels for K upcoming slots, using observed interference levels for N prior slots as input to the model. In some cases, the UE may determine the last N slots to be used for this computation based on the CSI-RS configuration, physical downlink shared channel (PDSCH) received in slots quasi-colocated (QCLed) with the CSI-RS.

At <NUM>, the UE generates a model of an interference pattern for the channel. The model of the interference pattern may take into account a history of the observed interference values. The model of the interference pattern may be generated using a neural network, such as a recurrent neural network (RNN), a long short-term memory (LSTM) neural network, and a deep recurrent neural network. The model of the interference pattern may be trained in real-time and using observed interference values.

At <NUM>, the UE predicts one or more candidate interference levels for one or more upcoming slots using the model. As mentioned, the candidate interference levels generally refer to predicted interference levels based on previously observed interference levels used to train a predictive model. The predicted interference levels may be also based on an analysis of the previously observed interference levels used to train the predictive model. Each candidate interference level may be associated with a probability of occurrence. In some cases, the candidate interference levels may be mapped or associated to a transmission from one or more neighboring cells.

At <NUM>, the UE reports information regarding the candidate interference levels to the network entity. The UE may provide the information regarding the candidate interference level via a CSI report. The candidate interference levels and corresponding probability of occurrence in a CSI report may be used by a network entity improve on the interference estimates in CSI reporting as the network entity has more information on channel condition to use to improve communication with the UE.

<FIG> is a flow diagram illustrating example operations <NUM> for wireless communication, in accordance with certain aspects of the present disclosure. The operations <NUM> may be performed, for example, by a BS (e.g., such as the BS 110a in the wireless communication network <NUM>). The operations <NUM> may be complementary to the operations <NUM> performed by the UE. The operations <NUM> may be implemented as software components that are executed and run on one or more processors (e.g., controller/processor <NUM> of <FIG>). Further, the transmission and reception of signals by the BS in operations <NUM> may be enabled, for example, by one or more antennas (e.g., antennas <NUM> of <FIG>). In certain aspects, the transmission and/or reception of signals by the BS may be implemented via a bus interface of one or more processors (e.g., controller/processor <NUM>) obtaining and/or outputting signals.

Operations <NUM> begin, at <NUM>, by transmitting, to a UE, a CSI reporting configuration for reporting a CSI for a channel.

In some aspects, at <NUM>, the network entity may transmit signaling indicating a number, K, of upcoming slots for which the UE may predict candidate interference levels and a number, N, of previously observed interference levels. The UE may be configured to predict candidate interference levels for K upcoming slots, using observed interference levels for N prior slots as input to the model. In some cases, the network entity may configure the UE with values of K and N (e.g., via the CSI reporting configuration).

At <NUM>, the network entity receives, from the UE, as a part of the CSI reporting, information regarding one or more interference levels for one or more upcoming slots. The network entity may receive the information regarding the candidate interference level via a CSI report. The candidate interference levels and corresponding probability of occurrence in a CSI report may be used by a network entity improve on the interference estimates in CSI reporting as the network entity has more information on channel condition to use to improve communication with the UE.

<FIG> is a call flow diagram that illustrates how a UE may generate and train an interference pattern model and use this model to predict interference levels for upcoming slots. The UE can report the predicted interference levels to a BS.

As illustrated, at <NUM>, the gNB 110a configures the UE 120a with a CSI reporting configuration. The CSI reporting configuration may indicate time and/or frequency resources for CSI-RS transmission from the gNB 110a.

At <NUM>, the UE may observe the CSI-RS transmission(s) sent over multiple slots. When observing the CSI-RS transmission(s), the UE may observe the interference levels from the CSI-RS transmission(s).

At <NUM>, the UE may use the observed interference levels from the CSI-RS transmission(s) to generate and train the predictive model. For example, the model may be trained using machine learning. The model may be implemented using an artificial neural network. The machine learning and/or artificial neural network can perform modeling of time series data to obtain a probability density function of expected interference in upcoming slots. In some cases, the model may be generated by modeling time series data involving the observed interference levels. The model may be generated/trained by taking into account a history of the observed interference values. A history of the observed interference values may involve a windowed version of the observed interference values, or a finite history of the observed interference values. In some cases, the model may be updated and/or refined with a history of the observed interference values. The model may be trained in real time or offline (e.g., using stored observed values). The UE use any suitable algorithm (not necessarily a neural network) to model and predict the interference.

Once trained, the UE may use the model to predict candidate interference levels (and corresponding probabilities) for upcoming slots, based on observed interference levels for previous slots.

For example, as illustrated in <FIG>, the UE may be configured to use a model to predict candidate interference levels for K upcoming slots, using observed interference levels for N prior slots as input to the model. In some cases, the gNB may configure the UE with values of K and N (e.g., via the CSI reporting configuration). In some cases, the UE may determine the last N slots to be used for this computation based on the CSI-RS configuration, PDSCH received in slots QCLed with the CSI-RS.

As illustrated in <FIG>, each slot N can have a candidate interference level. Each of the candidate interference levels may have a corresponding probability of occurrence (noted as probability % or P%). In some aspects, the UE may generate a probability density function (PDF) of the candidate interference levels and the corresponding probabilities of occurrence. For example, the UE can generate the probability using the machine learning algorithm, artificial neural network, etc..

Returning to <FIG>, the UE may report the predicted interference level candidates and probabilities for the K slots, for example, using a CSI reporting framework. For example, the UE may report the information regarding the candidate interference levels to the network entity via a CSI report. The UE may report a single interference level and probability per slot, may report multiple interference levels and probabilities per slot, or could report interference level and probability over multiple slots.

There are various options for the quantity and content of the reported information. The information to the network entity may include pre-configured candidate interference levels and the corresponding probability of occurrence the pre-configured candidate interference levels. For example, the UE may be configured to report the probability of occurrence of a pre-configured interference level (e.g., of <NUM> decibels). In some aspects, the information to the network entity includes information from a PDF generated by the UE, the PDF including information of the candidate interference levels and corresponding probabilities of occurrence. In some aspects, the information to the network entity includes information regarding a subset of the candidate interference levels selected based on their probability of occurrence. The subset of candidate interference levels may have P candidate interference levels, and these candidate interference levels may have the highest probability of occurrence among the predicted candidate interference levels. For example, the subset includes <NUM> candidate interference levels with the highest probabilities of "<NUM>%" and "<NUM>%" from the candidate interference levels. In some examples, the information regarding the neural network model may include weights of the neural network model after training the model.

While the examples above have assumed prediction performed at the UE side, in some cases, with sufficient reporting, the prediction may be implemented at the network (gNB) side. Prediction may make sense at the UE side, particularly if interference is measured on resources other than CSI-RS and CSI-IM reference signals, such as Demodulation Reference Signal (DMRS) or PDSCH.

As mentioned above, the UE may use machine learning for predicting the interference levels and probabilities. In some examples, the UE may use a machine learning (ML) algorithm to form the prediction(s) discussed above and/or for reporting CSI to the BS based on the prediction(s).

In some examples, machine learning involves training a model, such as a predictive model. The model may be used to predict a feasible duration a missing packet may be received. The model may be used to perform the prediction(s) discussed above and/or other factors. The model may be trained based on training data (e.g., training information), which may include parameters discussed above, such as interference history, etc., and/or other training information.

<FIG> illustrates an example networked environment <NUM> in which a packet buffering duration manager <NUM> of a node <NUM> uses a predictive model <NUM> for dynamic determination of a packet buffering duration, according to certain aspects of the present disclosure. As shown in <FIG>, networked environment <NUM> includes a node <NUM>, a training system <NUM>, and a training repository <NUM>, communicatively connected via network(s) <NUM>. The node <NUM> may be a UE (e.g., such as the UE 120a in the wireless communication network <NUM>). The network(s) <NUM> may include a wireless network such as the wireless communication network <NUM>, which may be a <NUM> NR network and/or an LTE network. While the training system <NUM>, node <NUM>, and training repository <NUM> are illustrated as separate components in <FIG>, the training system <NUM>, node <NUM>, and training repository8415 may be implemented on any number of computing systems, either as one or more standalone systems or in a distributed environment.

The training system <NUM> generally includes a predictive model training manager <NUM> that uses training data to generate the predictive model <NUM> for candidate interference level predictions. The predictive model <NUM> may be determined based, at least in part, on the information in the training repository <NUM>.

The training repository <NUM> may include training data obtained before and/or after deployment of the node <NUM>. The node <NUM> may be trained in a simulated communication environment (e.g., in field testing, drive testing) prior to deployment of the node <NUM>. For example, various history information can be stored to obtain training information related to the estimates, predictions, etc..

This information can be stored in the training repository <NUM>. After deployment, the training repository <NUM> can be updated to include feedback associated with packet buffering durations used by the node <NUM>. The training repository can also be updated with information from other BSs and/or other UEs, for example, based on learned experience by those BSs and UEs, which may be associated with interference levels observed by those BSs and/or UEs.

The predictive model training manager <NUM> may use the information in the training repository <NUM> to determine the predictive model <NUM> (e.g., algorithm) used for candidate interference level prediction. The predictive model training manager <NUM> may use various different types of machine learning algorithms to form the predictive model <NUM>. The training system <NUM> may be located on the node <NUM>, on a BS in the network <NUM>, or on a different entity that determines the predictive model <NUM>. If located on a different entity, then the predictive model <NUM> is provided to the node <NUM>. The training repository <NUM> may be a storage device, such as a memory. The training repository <NUM> may be located on the node <NUM>, the training system <NUM>, or another entity in the network <NUM>. The training repository <NUM> may be in cloud storage. The training repository <NUM> may receive training information from the node <NUM>, entities in the network <NUM> (e.g., BSs or UEs in the network <NUM>), the cloud, or other sources.

The machine learning may use any appropriate machine learning algorithm. In some non-limiting examples, the machine learning algorithm is a reinforcement learning algorithm, a value reinforcement algorithm, a supervised learning algorithm, an unsupervised learning algorithm, a deep learning algorithm, an artificial neural network algorithm, a Q-learning algorithm, a polar reinforcement algorithm, or other type of machine learning algorithm.

In some examples, the machine learning (e.g., used by the training system <NUM>) is performed using a deep convolutional network (DCN). DCNs are networks of convolutional networks, configured with additional pooling and normalization layers. DCNs have achieved state-of-the-art performance on many tasks. DCNs can be trained using supervised learning in which both the input and output targets are known for many exemplars and are used to modify the weights of the network by use of gradient descent methods. DCNs may be feed-forward networks. In addition, as described above, the connections from a neuron in a first layer of a DCN to a group of neurons in the next higher layer are shared across the neurons in the first layer. The feed-forward and shared connections of DCNs may be exploited for fast processing. The computational burden of a DCN may be much less, for example, than that of a similarly sized neural network that comprises recurrent or feedback connections.

In some examples, the machine learning (e.g., used by the training system <NUM>) is performed using a neural network. Neural networks may be designed with a variety of connectivity patterns. In feed-forward networks, information is passed from lower to higher layers, with each neuron in a given layer communicating to neurons in higher layers. A hierarchical representation may be built up in successive layers of a feed-forward network. Neural networks may also have recurrent or feedback (also called top-down) connections. In a recurrent connection, the output from a neuron in a given layer may be communicated to another neuron in the same layer. A recurrent architecture may be helpful in recognizing patterns that span more than one of the input data chunks that are delivered to the neural network in a sequence. A connection from a neuron in a given layer to a neuron in a lower layer is called a feedback (or top-down) connection. A network with many feedback connections may be helpful when the recognition of a high-level concept may aid in discriminating the particular low-level features of an input.

An artificial neural network, which may be composed of an interconnected group of artificial neurons (e.g., neuron models), is a computational device or represents a method performed by a computational device. These neural networks may be used for various applications and/or devices, such as Internet Protocol (IP) cameras, Internet of Things (IoT) devices, autonomous vehicles, and/or service robots. Individual nodes in the artificial neural network may emulate biological neurons by taking input data and performing simple operations on the data. The results of the simple operations performed on the input data are selectively passed on to other neurons. Weight values are associated with each vector and node in the network, and these values constrain how input data is related to output data. For example, the input data of each node may be multiplied by a corresponding weight value, and the products may be summed. The sum of the products may be adjusted by an optional bias, and an activation function may be applied to the result, yielding the node's output signal or "output activation. " The weight values may initially be determined by an iterative flow of training data through the network (e.g., weight values are established during a training phase in which the network learns how to identify particular classes by their typical input data characteristics).

Different types of artificial neural networks can be used to implement machine learning (e.g., used by the training system <NUM>), such as recurrent neural networks (RNNs), multilayer perceptron (MLP) neural networks, convolutional neural networks (CNNs), and the like. RNNs work on the principle of saving the output of a layer and feeding this output back to the input to help in predicting an outcome of the layer. In MLP neural networks, data may be fed into an input layer, and one or more hidden layers provide levels of abstraction to the data. Predictions may then be made on an output layer based on the abstracted data. MLPs may be particularly suitable for classification prediction problems where inputs are assigned a class or label. Convolutional neural networks (CNNs) are a type of feed-forward artificial neural network. Convolutional neural networks may include collections of artificial neurons that each has a receptive field (e.g., a spatially localized region of an input space) and that collectively tile an input space. Convolutional neural networks have numerous applications. In particular, CNNs have broadly been used in the area of pattern recognition and classification. In layered neural network architectures, the output of a first layer of artificial neurons becomes an input to a second layer of artificial neurons, the output of a second layer of artificial neurons becomes an input to a third layer of artificial neurons, and so on. Convolutional neural networks may be trained to recognize a hierarchy of features. Computation in convolutional neural network architectures may be distributed over a population of processing nodes, which may be configured in one or more computational chains. These multi-layered architectures may be trained one layer at a time and may be fine-tuned using back propagation.

In some examples, when using a machine learning algorithm, the training system <NUM> generates vectors from the information in the training repository <NUM>. In some examples, the training repository <NUM> stores vectors. In some examples, the vectors map one or more features to a label. For example, the features may correspond to various training parameters and/or upcoming slots and/or other factors discussed above. The label may correspond to the predicted likelihoods of candidate interference levels. The predictive model training manager <NUM> may use the vectors to train the predictive model <NUM> for the node <NUM>. As discussed above, the vectors may be associated with weights in the machine learning algorithm.

The processing system <NUM> includes a processor <NUM> coupled to a computer-readable medium/memory <NUM> via a bus <NUM>. In certain aspects, the computer-readable medium/memory <NUM> is configured to store instructions (e.g., computer-executable code) that when executed by the processor <NUM>, cause the processor <NUM> to perform the operations illustrated in <FIG>, or other operations for performing the various techniques discussed herein for improving interference estimates for CSI reporting. In certain aspects, computer-readable medium/memory <NUM> stores code <NUM> for obtaining, from a network entity, a CSI reporting configuration for reporting CSI for a channel; code <NUM> for generating a model of an interference pattern for the channel; code <NUM> for predicting one or more candidate interference levels for one or more upcoming slots using the model; and code <NUM> for reporting information regarding the candidate interference levels to the network entity. In certain aspects, the processor <NUM> has circuitry configured to implement the code stored in the computer-readable medium/memory <NUM>. In certain aspects, computer-readable medium/memory <NUM> may store code <NUM> for receiving signaling indicating a number, K, of upcoming slots for which the UE may predict candidate interference levels and a number, N, of previously observed interference levels. The processor <NUM> includes circuitry <NUM> for obtaining, from a network entity, a CSI reporting configuration for reporting CSI for a channel; circuitry <NUM> for generating a model of an interference pattern for the channel; circuitry <NUM> for predicting one or more candidate interference levels for one or more upcoming slots using the model; and circuitry <NUM> for reporting information regarding the candidate interference levels to the network entity. In certain aspects, processor <NUM> may include circuitry <NUM> for receiving signaling indicating a number, K, of upcoming slots for which the UE may predict candidate interference levels and a number, N, of previously observed interference levels.

For example, means for transmitting (or means for outputting for transmission) may include the transmitter unit <NUM> and/or antenna(s) <NUM> of the UE 120a illustrated in <FIG> and/or circuitry <NUM> of the communication device <NUM> in <FIG>. Means for receiving (or means for obtaining) may include a receiver and/or antenna(s) <NUM> of the UE 120a illustrated in <FIG> and/or circuitry <NUM> of the communication device <NUM> in <FIG>. Means for communicating may include a transmitter, a receiver or both. Means for generating, means for performing, means for determining, means for taking action, means for determining, means for coordinating may include a processing system, which may include one or more processors, such as the receive processor <NUM>, the transmit processor <NUM>, the TX MIMO processor <NUM>, and/or the controller/processor <NUM> of the UE 120a illustrated in <FIG> and/or the processing system <NUM> of the communication device <NUM> in <FIG>.

The processing system <NUM> includes a processor <NUM> coupled to a computer-readable medium/memory <NUM> via a bus <NUM>. In certain aspects, the computer-readable medium/memory <NUM> is configured to store instructions (e.g., computer-executable code) that when executed by the processor <NUM>, cause the processor <NUM> to perform the operations illustrated in <FIG>, or other operations for performing the various techniques discussed herein for improving interference estimates for CSI reporting. In certain aspects, computer-readable medium/memory <NUM> stores code <NUM> for transmitting, to a UE, a CSI reporting configuration for reporting CSI for a channel; and code <NUM> for receiving from the UE, as a part of the CSI reporting, information regarding candidate interference levels for one or more upcoming slots. In certain aspects, computer-readable medium/memory <NUM> may store code <NUM> for transmitting signaling indicating a number, K, of upcoming slots for which the UE may predict candidate interference levels and a number, N, of previously observed interference levels. In certain aspects, the processor <NUM> has circuitry configured to implement the code stored in the computer-readable medium/memory <NUM>. The processor <NUM> includes circuitry <NUM> for transmitting, to a UE, a CSI reporting configuration for reporting CSI for a channel; and circuitry <NUM> for receiving from the UE, as a part of the CSI reporting, information regarding candidate interference levels for one or more upcoming slots. In certain aspects, processor <NUM> may include circuitry <NUM> for transmitting signaling indicating a number, K, of upcoming slots for which the UE may predict candidate interference levels and a number, N, of previously observed interference levels.

For example, means for transmitting (or means for outputting for transmission) may include a transmitter and/or an antenna(s) <NUM> or the BS 110a or the transmitter unit <NUM> illustrated in <FIG> and/or circuitry <NUM> of the communication device <NUM> in <FIG>. Means for receiving (or means for obtaining) may include a receiver and/or an antenna(s) <NUM> of the BS 110a illustrated in <FIG> and/or circuitry <NUM> of the communication device <NUM> in <FIG>. Means for communicating may include a transmitter, a receiver or both. Means for generating, means for configuring means for performing, means for determining, means for taking action, means for determining, means for coordinating may include a processing system, which may include one or more processors, such as the transmit processor <NUM>, the TX MIMO processor <NUM>, the receive processor <NUM>, and/or the controller/processor <NUM> of the BS 110a illustrated in <FIG> and/or the processing system <NUM> of the communication device <NUM> in <FIG>.

For example, such a computer program product may comprise a computer-readable medium having instructions stored (and/or encoded) thereon, the instructions being executable by one or more processors to perform the operations described herein, for example, instructions for performing the operations described herein and illustrated in <FIG> and/or <FIG>.

Claim 1:
A method for wireless communications by a user equipment, UE (120a), the method
comprising:
obtaining (<NUM>), from a network entity (110a), a channel state indicator, CSI, reporting configuration for reporting CSI for a channel;
generating (<NUM>) a model of an interference pattern for the channel;
predicting (<NUM>) one or more candidate interference levels for one or more upcoming slots using the model; and
reporting (<NUM>) information regarding the one or more candidate interference levels to the network entity, wherein the information regarding the candidate interference levels includes information regarding a subset of the candidate interference levels selected based on their probability of occurrence.