Complex valued data for ai / ml

Abstract

According to some embodiments, a method is performed by a data measurement node (e.g., network node or wireless device). The method comprises obtaining measurement data for a reference signal received on an antenna port. The measurement data comprises a sequence of complex-valued samples. The method further comprises modifying the measurement data to remove random initial phase effects and transmitting the modified measurement data to a data collection node.

Description

Complex Valued Data for AI / ML TECHNICAL FIELD

[0001] The present disclosure generally relates to communication networks, and morespecifically to complex valued data reporting, signaling, and collection for artificial intelligence / machine learning (AI / ML). BACKGROUND

[0002] Artificial intelligence (AI) and machine learning (ML) have been investigated, both inacademia and industry, as promising tools to optimize the design of the air interface in wireless communication networks. Example use cases include using autoencoders for channel state information (CSI) compression to reduce the feedback overhead and improve channel prediction accuracy; using deep neural networks for classifying line-of-sight (LOS) and non-LOS (NLOS) conditions to enhance positioning accuracy; and using reinforcement learning for beam selection at the network side and / or the user equipment (UE) side to reduce the signaling overhead and beam alignment latency; and using deep reinforcement learning to learn an optimal precoding policy for complex multiple input multiple output (MIMO) precoding problems.

[0003] The Third Generation Partnership Project (3GPP) New Radio (NR) standardizationwork for release 18 (Rel. 18) included a study item (SI) on AI / ML for the NR air interface. The work explores the benefits of augmenting the air interface with features enabling improved support of AI / ML-based algorithms for enhanced performance and / or reduced complexity / overhead. Through studying and specifying a few selected use cases (CSI feedback, beam management, and positioning), the works aim to design the mechanisms to accommodate AI / ML into the 3GPP standard.

[0004] Building an AI / ML model includes several development steps where the actualtraining of the AI model is just one step in a training pipeline. An important part in AI / ML development is the AI / ML model lifecycle management (LCM). This is illustrated in Figure 1.

[0005] Figure 1 is a flow diagram illustrating training and inference pipelines, and theirinteractions within a model lifecycle management procedure. The AI model lifecycle management typically consists of a training (re-training) pipeline. Data ingestion refers to gathering raw (training) data from a data storage. After data ingestion, there may also be a step that controls the validity of the gathered data. Data pre-processing refers to feature engineering applied to thegathered data, e.g., it may include data normalization and possibly a data transformation required for the input data to the AI / ML model.

[0006] The actual model training steps is where a model is obtained using the training dataset.

[0007] Model evaluation refers to benchmarking the performance to a baseline. The iterativesteps of model training and model evaluation continues until the acceptable level of performance is achieved.

[0008] Model registration refers to registering the AI / ML model, including any correspondingAI / ML-meta data that provides information on how the AI / ML model was developed, and possibly AI / ML model evaluations performance outcomes.

[0009] The AI model lifecycle management also typically consists of a deployment stage tomake the trained (or re-trained) AI / ML model part of the inference pipeline.

[0010] The AI model lifecycle management also typically consists of an inference pipeline.Data ingestion refers to gathering raw (inference) data from a data storage. The data pre-processing stage is typically identical to corresponding processing that occurs in the training pipeline.

[0011] Model operational refers to using the trained and deployed model in an operationalmode. Data and model monitoring refers to validating that the inference data are from a distribution that aligns well with the training data, as well as monitoring model outputs for detecting any performance, or operational, drifts.

[0012] The AI model lifecycle management also typically consists of a drift detection stagethat informs about any drifts in the model operations.

[0013] A first scenario assumes that AI / ML models operating with the existing standard air-interface are placed at the UE side. A UE uses the AI / ML models to generate output that is reported to a centralized node in the network for positioning the UE location.

[0014] A second scenario assumes AI / ML models operating with the existing standard air-interface are placed at different transmit / receive points (TRPs). A TRP uses the AI / ML models to generate output that is reported to a centralized node in the network for positioning the UE location.

[0015] UE positioning is an example of complex-valued data for AI / ML models. UEpositioning is the core of location-based services, which has a variety of commercial applications such as entertainment, healthcare, geo-targeting advertisement, and smart factories and smart warehouses. Moreover, with the advent of extended reality (XR), UE positioning becomes more important. The requirement for positioning accuracy varies among different applications, for example, from centimeter level for industrial applications to a few meters accuracy for emergency calls.

[0016] Current signal processing techniques can generally be applied by the UE, base stationor TRP to generate positioning related reports in operating environments with enough line-of-sight (LoS) links. A typical system setup is illustrated below in Figure 2.

[0017] Figure 2 illustrates conventional positioning system setup for environments withenough line-of-sight links. In this system, a positioning related report generation node (such as a UE or a gNB) measures the radio link conditions and provides one or more positioning related reports to a node capable of determining the UE position based on the reports.

[0018] For example, a UE can measure on the positioning reference signals (PRS) transmittedfrom the transmit and receive points of a gNB and provide time of arrival (ToA) estimates as positioning related reports. Similarly, a gNB can measure on the sounding reference signals (SRS) transmitted from a UE to generate positioning related reports. The location management function (LMF) can use trilateration algorithms based on the ToA estimate reports to determine the UE positions.

[0019] Such conventional positioning systems rely on the existence of enough line-of-sightlinks in the radio environment for the trilateration algorithms to work correctly. More specifically, using the illustration in Figure 3 as an example, between the UE and the TRP A, there is an unblocked direct path (i.e., a LoS link) for the radio signal to travel between the two radio nodes. Between the UE and the TRP B, there is no unblocked direct path between the two nodes. The radio signals from one node will need to be reflected off other surfaces to reach the other node. The radio link the UE and the TRP B is then a non-line-of-sight (NLoS) link.

[0020] Figure 3 illustrates multipath radio environment between a UE and two TRPs. For TRPA, a LoS path exists between the UE’s transmitter and TRP A’s receiver. For TRP B, however, only NLoS paths exist between the UE’s transmitter and TRP B’s receiver because of the blockers in the environment.

[0021] To improve positioning accuracy, AI / ML deep learning models have been introducedto use richer radio channel conditions than current positioning related reports. For example, during the 3GPP Rel-18 AI / ML for Physical Layer Study Item, the time domain (TD) channel impulse response (CIR) measurement data as AI / ML model inputs have been studied extensively. The study has found such AI / ML positioning approach can achieve high UE positioning accuracy even in environments with no LoS links.

[0022] Figure 4 illustrates AI / ML model positioning system setup for environments with orwithout enough line-of-sight links. The measurement reports may also be forward to a ML measurement data collection node.

[0023] The ML measurement data collection node may perform a multitude of functions. Forexample, the ML measurement data collection node may collect the measurements and other related information to compile an AI / ML model training data set, which can be used by AI / ML training node to train or finetune AI / ML models. The ML measurement data collection node may perform model monitoring functions to ensure the AI / ML models are operating within prescribed performance targets. The ML measurement data collection node can be: a UE, a gNB, a LMF, another radio network node, or a node outside of the radio network.

[0024] Figure 5 illustrates AI / ML model positioning system that includes at least a MLmeasurement data collection node. One TD CIR example is shown in Figure 5 for a radio with two receive antenna ports. The time domain (TD) channel impulse response (CIR) samples are complex values. It can generally be represented in two equivalent forms.

[0025] A complex sample is represented by its real and imaginary parts (each of which is areal value). This is shown in Figure 6.

[0026] Figure 6 is a graph illustrating an example of TD CIR measurement data for tworeceive antenna ports represented by the real and imaginary parts.

[0027] A complex sample can be represented by its magnitudes and phases (each of which isa real value). This is shown in Figure 7.

[0028] Figure 7 is a graph illustrating an example of TD CIR measurement data for tworeceive antenna ports represented by the magnitudes and phases.

[0029] Different sub-sampling modifications to the TD CIR measurement data had beeninvestigated in the 3GPP Study. The TD CIR can be truncated to a shorter length. The TD CIR can be sub-sampled by keeping only a certain number of CIR samples (e.g., with strongest powers) and setting other samples to zero. An example of subsampling to the 9 strongest samples is illustrated in Figure 8.

[0030] Figure 8 is graph illustrating an example of TD CIR measurement data for two receiveantenna ports sub-sampled to 9 strongest samples.

[0031] One skilled in the art to may apply the disclosed embodiments to ML measurementdata with these or other similar modifications.

[0032] There currently exist certain challenges. For example, due to the random states ofclock / frequency generation circuits in the measurement radio node, the phase of the ML measurement data such as the time domain channel impulse responses may contain random and unhelpful information to AI / ML models. Using the AI / ML positioning application as a nonlimiting example, the measured phases of the TD CIR can be affected by the random states of clock / frequency generation circuits even if the UE moved and returned to the exact location. AnAI / ML model inference or training without using the disclosed embodiments will have degraded performance.

[0033] Using the AI / ML positioning application as a nonlimiting example, compare thefollowing two assumptions for AI / ML models operating in the 3GPP indoor factory scenario with dense clutters. With a setting of 60% clutter density of 6m height clutters, the environment has a LoS link probability of less than 1%. This is essentially a completely NLoS environment.

[0034] In a first assumption, the AI / ML measurement data is not affected by the random statesof clock / frequency generation circuits such that the measured complex-valued TD CIR samples always have the same phases when the UE moved and returned to the same location. In this hypothetical scenario, an AI / ML model can achieve a 90 percentile two-dimensional (2D) position error of 38 cm.

[0035] In a second assumption, the AI / ML measurement data is affected by the random statesof clock / frequency generation circuits. In this real-world scenario, the same AI / ML model will be confused by the random phases and the 90 percentile 2D position error is increased by more than 20% to 46 cm. SUMMARY

[0036] As described above, certain challenges currently exist with complex valued datareporting, signaling, and collection for artificial intelligence / machine learning (AI / ML). Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. For example, disclosed herein are a multitude of embodiments in improved complex-valued ML measurement data reporting or signaling and improved ML model training approaches with or without the improved reporting or signaling.

[0037] Some embodiments include improved complex-valued ML measurement datareporting or signaling. In a first embodiment, the ML measurement data provided by the ML data measurement node to an ML inference node or a ML measurement data collection node contains at least a combination of the magnitude and relative phase sequences.

[0038] The relative phase may be the difference between a sample phase and the initial samplephase. The relative phase may be the difference between two consecutive sample phases.

[0039] The provided ML measurement data may not contain the phase of the initial sample.The provided ML measurement data may contain the phase of the initial sample at a fixed value of 0.

[0040] The provided complex-valued ML measurement data for multiple receive antennaports may have zero initial sample phase for at least one antenna port. The provided complex-valued ML measurement data for multiple receive antenna ports may have zero initial sample phase for all antenna ports.

[0041] In a second embodiment, the complex-valued ML measurement data provided by theML data measurement node to an ML inference node or a ML measurement data collection node is based on real and imaginary parts representation of the complex-valued ML measurement data where at least one sample of the provided complex-valued ML measurement data is real-valued.

[0042] The ML data measurement node applies a common phase rotation to all measuredsamples of the complex-valued ML measurement data. The common phase rotation may be the negative of the phase of the first sample of the ML measurement data.

[0043] The ML data measurement node may apply differential phase rotations to all measuredsamples of the complex-valued ML measurement data.

[0044] The at least one real-valued sample of the provided complex-valued ML measurementdata may be the first reported measurement sample. The at least one real-valued sample of the provided complex-valued ML measurement data may be the first reported measurement sample.

[0045] The provided complex-valued ML measurement data for multiple receive antennaports may have a real-valued sample for at least one antenna port. The provided complex-valued ML measurement data for multiple receive antenna ports may have a real-valued sample for all antenna ports.

[0046] In a third embodiment, smaller ML measurement data reporting or signaling may bedesigned using the above embodiments.

[0047] The reporting or signaling format may be designed to not include the phase of the firstreported sample. The reporting or signaling format may be designed such that the imaginary part of the first reported sample is not included.

[0048] One skilled in the art understands that a teaching of the above embodiments is toremove effects on the measured data that is not representative of the radio channel conditions before the data is signaled, reported or collected. Specific nonlimiting examples of such effects include the random initial phase and frequency drift. However, the teaching may be practiced by one skilled in the art to remove other non-radio-channel related effects from the measurement data before signaling, reporting and collection.

[0049] Some embodiments include improved ML model training approaches withoutimproved reporting or signaling. In a first embodiment, an AI / ML model training node applies a common phase rotation to a complex-valued ML measurement training data before using the training data as inputs to the AI / ML model under training.

[0050] The application of a common phase rotation may be the negative of the phase of thefirst sample of the ML measurement training data. The application of a common phase rotation may be a phase randomly generated by the node performing AI / ML model training.

[0051] In another embodiment, an AI / ML model training node converts the complex-valuedML measurement training data samples to a magnitude and relative phase representation before using the training data as inputs to the AI / ML model under training.

[0052] The relative phase may be the difference between a sample phase and the initial samplephase. The relative phase may be the difference between two consecutive sample phases.

[0053] According to some embodiments, a method is performed by a data measurement node(e.g., UE or network node). The method comprises obtaining measurement data for a reference signal received on an antenna port. The measurement data comprises a sequence of complex- valued samples. The method further comprises modifying the measurement data to remove random initial phase effects and transmitting the modified measurement data to a data collection node.

[0054] In particular embodiments, modifying the measurement data to remove random initialphase effects comprises representing the sequence of complex-valued samples as a combination of a magnitude sequence and a relative phase sequence. A relative phase of the relative phase sequence may comprise a difference between a sample phase and an initial sample phase. A relative phase of the relative phase sequence may comprise a difference between two consecutive sample phases.

[0055] In particular embodiments, the modified measurement data excludes a phase of aninitial sample or includes a phase of an initial sample at a fixed value of 0.

[0056] In particular embodiments, modifying the measurement data to remove random initialphase effects comprises representing the sequence of complex-valued samples as a real and imaginary parts representation where at least one sample of the provided complex-valued measurement data is real-valued.

[0057] In particular embodiments, modifying the measurement data further comprisesapplying a common phase rotation to all measured samples of the complex-valued sample data. The common phase rotation may be a negative of a phase of a first sample of the complex-valued sample data.

[0058] In particular embodiments, modifying the measurement data further comprisesapplying differential phase rotations to all measured samples of the complex-valued sample data.

[0059] In particular embodiments, at least one real-valued sample of the complex-valuedsample data is a first reported measurement sample.

[0060] According to some embodiments, a data measurement node comprises processingcircuitry operable to perform any of the data measurement node methods described above.

[0061] According to some embodiments, a method is performed by a data collection node(e.g., network node, inference node, LMF, etc.). The method comprises receiving measurement data for a reference signal received on an antenna port from a data measurement node. The measurement data comprises a sequence of complex-valued samples. The method further comprises modifying the measurement data to remove random initial phase effects and inputting the modified measurement data to a machine learning model (e.g., for training, inference, performance monitoring, etc.).

[0062] In particular embodiments, the data collection node may modify the measurement dataaccording to the any of the modifications performed by the data measurement node described above.

[0063] According to some embodiments, a data collection node comprises processingcircuitry operable to perform any of the data collection node methods described above.

[0064] Also disclosed is a computer program product comprising a non-transitory computerreadable medium storing computer readable program code, the computer readable program code operable, when executed by processing circuitry to perform any of the methods performed by the data measurement node described above.

[0065] Another computer program product comprises a non-transitory computer readablemedium storing computer readable program code, the computer readable program code operable, when executed by processing circuitry to perform any of the methods performed by the data collection node described above.

[0066] Certain embodiments may provide one or more of the following technical advantages.For example, particular embodiments may ensure consistent ML model performance during training and inference phases and may improve the ML model performance. Particular embodiments may reduce ML measurement data reporting or signaling sizes.

[0067] Continuing the AI / ML positioning application as a nonlimiting example, a comparisonof the following two assumptions for AI / ML models operating in the Third Generation Partnership Project (3GPP) indoor factory scenario with dense clutters illustrates benefits. With a setting of 60% clutter density of 6m height clutters, the environment has a line-of-sight (LoS) link probability of less than 1%. This is essentially a completely non line-of-sight (NLoS) environment.

[0068] In a first assumption, the AI / ML measurement data is not affected by the random statesof clock / frequency generation circuits such that the measured complex-valued time domain (TD) channel impulse response (CIR) samples always have the same phases when the user equipment(UE) moved and returned to the same location. In this hypothetical scenario, an AI / ML model can achieve a 90 percentile two-dimensional (2D) position error of 38 cm.

[0069] In a second assumption, the AI / ML measurement data is affected by the random statesof clock / frequency generation circuits. In this real-world scenario, the same AI / ML model will be confused by the random phases and the 90 percentile 2D position error is increased by more than 20% to 46 cm.

[0070] Keeping the same second assumption, the AI / ML model using the disclosed MLmeasurement data reporting or signaling embodiments during the training and / or the inference phases may achieve improved performance. In this real-world scenario using the disclosed embodiments, the AI / ML model can achieve a 90 percentile 2D position error of 39 cm. (The 1 cm difference from the first scenario is within evaluation precision.) That is, the negative impacts of the random states of clock / frequency generation circuits on the complex-valued ML measurement data are removed by the disclosed embodiments.

[0071] BRIEF DESCRIPTION OF THE DRAWINGS

[0072] The present disclosure may be best understood by way of example with reference tothe following description and accompanying drawings that are used to illustrate embodiments of the present disclosure. In the drawings: Figure 1 is a flow diagram illustrating training and inference pipelines, and their interactions within a model lifecycle management procedure; Figure 2 illustrates conventional positioning system setup for environments with enough line-of-sight links; Figure 3 illustrates multipath radio environment between a user equipment (UE) and two transmission / reception points (TRPs); Figure 4 illustrates artificial intelligence / machine learning (AI / ML) model positioning system setup for environments with or without enough line-of-sight links; Figure 5 illustrates AI / ML model positioning system that includes at least a ML measurement data collection node; Figure 6 is a graph illustrating an example of time domain (TD) channel impulse response (CIR) measurement data for two receive antenna ports represented by the real and imaginary parts; Figure 7 is a graph illustrating an example of TD CIR measurement data for two receive antenna ports represented by the magnitudes and phases; Figure 8 is graph illustrating an example of TD CIR measurement data for two receive antenna ports sub-sampled to 9 strongest samples;Figure 9 is a graph illustrating an example of TD CIR measurement samples at integer- valued sampling time locations and non-integer-valued sampling time locations; Figure 10 shows an example of a communication system, according to certain embodiments; Figure 11 shows a user equipment (UE), according to certain embodiments; Figure 12 shows a network node, according to certain embodiments; Figure 13 is a block diagram of a host, according to certain embodiments; Figure 14 is a block diagram illustrating a virtualization environment in which functions implemented by some embodiments may be virtualized; Figure 15 shows a communication diagram of a host communicating via a network node with a UE over a partially wireless connection in accordance with some embodiments; Figure 16 is a flowchart illustrating an example method in a data measurement node, according to certain embodiments; and Figure 17 is a flowchart illustrating an example method in a data collection node, according to certain embodiments. DETAILED DESCRIPTION

[0073] As described above, certain challenges currently exist with complex valued datareporting, signaling, and collection for artificial intelligence / machine learning (AI / ML). Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. For example, disclosed herein are a multitude of embodiments in improved complex-valued ML measurement data reporting or signaling and improved ML model training approaches with or without the improved reporting or signaling.

[0074] Particular embodiments are described more fully with reference to the accompanyingdrawings. Embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art.

[0075] The artificial intelligence / machine learning (AI / ML) positioning use case is usedherein as a nonlimiting example to teach the embodiments to address the issues causes by random phases in complex-valued ML measurement data. The time domain channel impulse response (TD CIR) samples are used as a nonlimiting example disclosing the necessary details. One skilled in the art may apply the disclosed teachings to other AI / ML inference solutions where complex- valued data are used as input for the AI / ML models.

[0076] In the following, a sequence of ^^^^ complex-valued TD CIR samples measured onreceive antenna port ^^ is denoted by {ℎ^^[^^0], ℎ^^[^^1], … , ℎ^^[^^^^^^−1]} where ^^0, ^^1, … , ^^^^^^−1 arethe sampling time locations of the TD CIR samples. The sampling time locations may be integer valued or may be non-integer valued.

[0077] Examples of integer-valued sampling time locations and non-integer-valued samplingtime locations are illustrated in Figure 9.

[0078] Figure 9 is a graph illustrating an example of time domain (TD) channel impulseresponse (CIR) measurement samples at integer-valued sampling time locations and non-integer- valued sampling time locations.

[0079] The complex-valued sequence can be equivalently represented by two real-valuedsequences: Magnitude sequence {^^^^[^^0],^^^^[^^1], … ,^^^^[^^^^^^−1]} where ^^^^[^^] = |ℎ^^[^^]| and ^^ =^^0,^^1, … ,^^^^^^−1 ; and phase sequence {^^^^[^^0],^^^^[^^1], … , ^^^^[^^^^^^−1]} where ℎ^^[^^] = ^^^^[^^] ⋅^^are components in moderndigital wireless communications systems. The circuits are used to drive reference waveform to demodulate receive signals received over the air. The implementations range from low-cost (LC) oscillators for very low-cost and low data rate systems to high quality crystal oscillators for modern high data rate systems such as the fourth generation (4G) Long-Term Evolution (LTE) and the fifth generation (5G) New Radio (NR) systems.

[0081] As described above, for a UE revisiting the same location, the TD CIR samplesequences may still differ because the state of the clock / frequency generation circuits may be at different state. A TD CIR sample ℎ(1) ^^[^^] measured at a first visit and ℎ(2) ^^[^^] measured at a second visit may be different becausethe different states ofgeneration circuits: ℎ(1) ^^ [^^] ≠ ℎ(2) ^^ [^^] for ^^ = ^^0,^^1, … ,^^^^^^−1.observing the same channel at exactly same time andlocation, the time-domain CIR sample sequences observed by the two UEs are likely different due to their different receiver implementation.

[0083] Apart from the receiver circuit effect, the transmitter circuit affects the waveform ofthe transmitted reference signal, e.g., the transmitter initial phase error. In fact, the observed channel impulse response is the composite channel including transmitter effect, wireless channel, and receiver effect. The following are the typical error sources that exist for carrier phase estimation at a receiver: phase noise (for frequency range two (FR2)); carrier frequency offset (CFO) and Doppler effect; oscillator-drift; transmitter / receiver antenna reference point location errors; transmitter / receiver initial phase error; and / or phase center offset.

[0084] Thus, without careful calibration, the observed initial phase ^^^^[^^0] of a given wirelesschannel may be considered random when observed by the same UE at different times, or different UEs at the same time, or both.

[0085] However, given the same UE locations at the first and second visits, it can be deducedthat the radio channel conditions should not be different and the impact of the random states of clock / frequency generation circuits on the channel condition measurements should manifest in specific ways.

[0086] This can be illustrated by considering an alternative representation of the phasesequence:

[0087] Relative phase sequence (with an initial phase) {^^^^[^^0], ∆^^^^[^^1], … , ∆^^^^[^^^^^^−1]}where ∆^^^^[^^] = ^^^^[^^] − ^^^^[^^0] and ^^ = ^^1,^^2, … ,^^^^^^−1. That is, ∆^^^^[^^] is the differencebetween a later sample phase and the initial sample phase.

[0088] Given the radio channel conditions are identical because the UE revisits the samelocation, it can be concluded that the initial phase ^^^^[^^0] can be affected by the random state of the clock / frequency generation circuits both at the transmitter and the receiver. An initial phase ^^(1) ^^ [^^0] measured at a first visit and ^^(2) ^^ [^^0] measured at a second visit can be different:^^(1) ^^ [^^0] ≠ ^^(2) ^^ [^^0] . However,phase sequence depends on the radio channelis not affected by the random state of the clock / frequency generation circuits. Thus, a relative initial phase ∆^^(1) (2 ^^[^^] measured at a first visit and ∆^^ ) ^^[^^] measured at a second visit will be identical when not considering the phase noise: ∆^^(1) ^^ [^^] = ∆^^(2) ^^ [^^] where ^^ =^^1,^^2, … ,^^^^^^−1.

[0089] The above analysis and conclusions can be applied to other types of relative phaserepresentation for the complex-valued measurement data. One skilled in the art may apply the disclosed teachings to other types of relative phase representations.

[0090] For example, another possible relative representation of the phase sequence isDifferential phase sequence (with an initial phase) {^^^^[^^0], ∆^^^^[^^1], … , ∆^^^^[^^^^^^−1]} where∆^^^^[^^^^] = ^^^^[^^^^] − ^^^^[^^^^−1] and ^^ = 1,2, … ,^^^^ − 1. That is, ∆^^^^[^^] is the difference betweentwo phases.

[0091] Given the radio channel conditions are identical because the UE revisits the samelocation, it can be concluded that the initial phases can be different, but the differential phases should remain identical when not considering the phase noise: ^^(1) ^^ [^^0] ≠ ^^(2) ^^[^^0], ∆^^(1) ^^ [^^] =∆^^(2) ^^ [^^] where ^^ = ^^1,^^2, … ,^^^^^^−1.

[0092] Some embodiments include improved complex-valued ML measurement datareporting or signaling.

[0093] In a first embodiment, the complex-valued ML measurement data provided by the MLdata measurement node to an ML inference node or a ML measurement data collection node is based on a relative phase representation of the complex-valued ML measurement data. The relative phase representation contains at least one sample with zero phase. The sample with zero phase may be the first sample.

[0094] As a nonlimiting example embodiment, the complex-valued ML measurement data isprovided as at least a combination of the magnitude and relative phase sequences: length ^^^^magnitude sequence {^^^^[^^0], ^^^^[^^1], … , ^^^^[^^^^^^−1]}; and length (^^^^ − 1) relative phase sequence{∆^^^^[^^1], ∆^^^^[^^2], … , ∆^^^^[^^^^^^−1]}.embodiment, the relative phase is the difference betweenaphase: ∆^^^^[^^] = ^^^^[^^] − ^^^^[0] for ^^ = ^^1,^^2, … ,^^^^^^−1.

[0096] In another nonlimiting example embodiment, the relative phase is the differencebetween two consecutive sample phases: ∆^^^^[^^^^] = ^^^^[^^^^] − ^^^^[^^^^−1] for ^^ = 1,2, … ,^^^^ − 1.

[0097] In a further nonlimiting example embodiment, the initial phase is provided by the MLdata measurement node at a fixed value. As a nonlimiting example, the provided initial phase has a fixed value of 0.

[0098] In another nonlimiting example embodiment, the initial phase is not provided by theML data measurement node.

[0099] In one further nonlimiting example embodiment, the provided complex-valued MLmeasurement data for multiple receive antenna ports have zero initial sample phase for at least one antenna port.

[0100] In yet a further nonlimiting example embodiment, the provided complex-valued MLmeasurement data for multiple receive antenna ports have zero initial sample phase for all antenna ports.

[0101] In a second embodiment, the complex-valued ML measurement data provided by theML data measurement node to an ML inference node or a ML measurement data collection node is based on real and imaginary parts representation of the complex-valued ML measurement data where at least one sample of the provided complex-valued ML measurement data is real-valued.

[0102] As a nonlimiting example embodiment, the data measurement node applies a commonphase rotation to all measured samples of the complex-valued ML measurement data. The common phase rotation may be the negative of the phase of the first sample of the ML measurement data.That is, the at least one real-valued sample of the provided complex-valued ML measurement data is the first reported measurement sample.

[0103] Commonly rotated measurement data {ℎ ′^^ [^^0], ℎ^ ′^ [^^1], … , ℎ′^^[^^^^ ′^^−1]} where ℎ^^ [^^] =ℎ^^[^^] ⋅ exp(−^^^^^^[^^0]) for ^^ = ^^0,^^1, … ,^^^^^^−1 . Note the first sample ℎ ′^^ [^^0] =^^ [^^0] = |ℎ^^[^^0]|^^ is real-valued.

[0104] In another nonlimiting example embodiment, the ML data measurement node appliesdifferential phase rotations to all measured samples of the complex-valued ML measurement data. The at least one real-valued sample of the provided complex-valued ML measurement data is the first reported measurement sample.

[0105] Differentially rotated measurement data {^^^^[^^0], ℎ ′^^ [^^1], … , ℎ ′^^ [^^^^^^−1]} whereℎ′^^ [^^^^] = ℎ^^[^^^^] ⋅ exp(−^^^^^^[^^^^−1]) for ^^ = 1,2, … sample^^ ^^one further nonlimiting example embodiment, the provided complex-valued MLmeasurement data for multiple receive antenna ports have a real-valued sample for at least one antenna port.

[0107] In yet a further nonlimiting example embodiment, the provided complex-valued MLmeasurement data for multiple receive antenna ports have one real-valued sample for all antenna ports.

[0108] In a third embodiment, smaller ML measurement data reporting or signaling may bedesigned using the above embodiments. In one nonlimiting example embodiment, the reporting or signaling format is designed to not include the phase of the first reported sample. In another nonlimiting example embodiment, the reporting or signaling format is designed such that the imaginary part of the first reported sample is not included.

[0109] In another embodiment, the complex-valued ML measurement data provided by theML data measurement node to an ML inference node or a ML measurement data collection node is based on modified phase representation of the complex-valued ML measurement data, where the additional phase changes in the ML data measurement node are corrected.

[0110] As a nonlimiting example embodiment, the complex-valued ML measurement data isprovided as at least a combination of the magnitude and modified phase sequences: magnitude sequence {^^^^[^^0], ^^^^[^^1], … ,^^^^[^^^^^^−1]} ; modified phase sequencethe additional phase changes may be causedby the frequency drifts in the ML data measurement node.

[0112] In another nonlimiting example embodiment, the modified phase is the differencebetween a sample phase and the frequency drift at that sample: ^^^ ′^ [^^^^] = ^^^^[^^^^] − Δ^^. (^^^^ −^^^^−1 ) for ^^ = 1,2, … ,^^^^ − 1 , where Δ^^ is frequency drift of the local oscillator that causesadditional phase changes between different samples that are not caused by theconditions. In 5G NR, the frequency drift may be estimated from, for example, phase tracking reference signal (PTRS), which was introduced for tracking the phase of the local oscillator at the receiver and transmitter to suppress phase noise and common phase errors.

[0113] One skilled in the art will understand that a teaching of the above embodiments is toremove effects on the measured data that is not representative of the radio channel conditions before the data is signaled, reported or collected. Specific nonlimiting examples of such effects include the random initial phase and frequency drift. However, the teaching may be practiced by one skilled in the art to remove other non-radio-channel related effects from the measurement data before signaling, reporting and collection.

[0114] Some embodiments include measurement details for improved complex-valued MLmeasurement data reporting. In the following, as a representative example, measurement reporting details are provided for the relative phase sequences for complex-valued measurement data. The following is used as an example:

[0115] Length ( ^^^^ − 1) relative phase sequence {∆^^^^[^^1], ∆^^^^[^^2], … , ∆^^^^[^^^^^^−1]} isreported. The relative phase is the difference between a sample phase and the initial sample phase:∆^^^^[^^] = ^^^^[^^] − ^^^^[0] for ^^ = ^^1,^^2, … , ^^^^^^−1. The initial phase ^^^^[0] is not reported, becauseit tends to be a random value even for the measurement of a same radio channel.

[0116] When the measurement is performed using an uplink reference signal such as asounding reference signal (SRS), the following measurement may be used - uplink reference signal relative carrier phase (UL RSRCP):Definition UL reference signal relative carrier phase (RSRCP) is defined as a sequence of relative phase values of the channel response derived from the resource elements carrying sounding reference signals (SRS) configured for the measurement. For the j-th path, the relative phase is the difference between the phase of the j-th path and the phase of the first path. UL RSRCP is associated with the center frequency of the transmission bandwidth of the SRS for positioning purposes configured for the measurement. The reference point for UL RSRCP shall be: - for type 1-C base station TS 38.104: the Rx antenna connector, - for type 1-O or 2-O base station TS 38.104: the Rx antenna (i.e., the center location of the radiating region of the Rx antenna), - for type 1-H base station TS 38.104: the Rx Transceiver Array Boundary connector. Applicable for RRC_CONNECTED, RRC_INACTIVE, RRC_IDLE

[0117] When the measurement is performed using a downlink reference signal such as apositioning reference signal (PRS), the following measurement can be used - downlink reference signal relative carrier phase (DL RSRCP):Definition DL reference signal relative carrier phase (RSRCP) is defined as a sequence of relative phase values of the channel response derived from the resource elements carrying DL PRS configured for the measurement. For the j-th path, the relative phase is the difference between the phase of the j-th path and the phase of the first path. DL RSRCP is associated with the center frequency of the DL positioning frequency layer (PFL) configured for the measurement for RRC_CONNECTED, RRC_INACTIVE, and RRC_IDLE modes. For frequency range 1, the reference point for the DL RSRCP shall be the antenna connector of the UE. For frequency range 2, the reference point for the DL RSRCP shall be the antenna of the UE. Applicable for RRC_CONNECTED, RRC_INACTIVE, RRC_IDLE

[0118] For DL RSRCP reporting, the following can be used:reference signal relative carrier phase(DL RSRCP). The parameter nr-PhaseQuality reports the uncertainty in the reported DL RSRCP, and the resolution of the uncertainty.

[0120] The above example additional path signaling format for complex-valued data may beused in various positioning measurement information. Using DL TDOA measurement information as an example, the signaling of complex-valued data can be as follows: NR-DL-TDOA-MeasElement-r16 ::= SEQUENCE { ..., nr-RSTD-r16 CHOICE { k0-r16 INTEGER (0..1970049), k1-r16 INTEGER (0..985025), k2-r16 INTEGER (0..492513),k3-r16 INTEGER (0..246257), k4-r16 INTEGER (0..123129), k5-r16 INTEGER (0..61565), …, kMinus1-r18 INTEGER (0..3940097), kMinus2-r18 INTEGER (0..7880193) }, nr-AdditionalPathList-r19 NR-AdditionalPathList-r19 OPTIONAL, …, }

[0121] According to particular embodiments, no phase information for the first sample needsto be signaled. This is reflected in the above signaling format that no additional phase related element (which corresponds to ^^^^[^^0]) is added for the first sample reported by nr-RSTD-r16 in the example. The phase information is added only to the signaling format for the nr- AdditionalPathList-r19, which corresponds to the relative phase sequence {∆^^^^[^^1], ∆^^^^[^^2], … , ∆^^^^[^^^^^^−1]}.may be defined, including the sequence of measured ULreported relative phase.

[0123] The above measurement report details are used to illustrate the design principle. It isunderstood that the same may be applied to other variants without losing generality.

[0124] The measurement reporting discussed above applies to both (a) channel measurementcollected as training data for an AI / ML model; and (b) channel measurement taken as model input for performing model inference.

[0125] Some embodiments include improved ML model training approaches withoutimproved complex-valued ML measurement data reporting / signaling. In a first embodiment, an AI / ML model training node applies a common phase rotation to a complex-valued ML measurement training data before using the training data as input to the AI / ML model under training.

[0126] The application of a common phase rotation may be the negative of the phase of thefirst sample of the ML measurement training data. The application of a common phase rotation may be a phase randomly generated by the node performing AI / ML model training.

[0127] In another embodiment, an AI / ML model training node converts the complex-valuedML measurement training data samples to a magnitude and relative phase representation before using the training data as inputs to the AI / ML model under training.

[0128] The relative phase may be the difference between a sample phase and the initial samplephase. The relative phase may be the difference between two consecutive sample phases.

[0129] Figure 10 shows an example of a communication system 100 in accordance with someembodiments. In the example, the communication system 100 includes a telecommunication network 102 that includes an access network 104, such as a radio access network (RAN), and acore network 106, which includes one or more core network nodes 108. The access network 104 includes one or more access network nodes, such as network nodes 110a and 110b (one or more of which may be generally referred to as network nodes 110), or any other similar 3rd Generation Partnership Project (3GPP) access node or non-3GPP access point. The network nodes 110 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs 112a, 112b, 112c, and 112d (one or more of which may be generally referred to as UEs 112) to the core network 106 over one or more wireless connections.

[0130] Example wireless communications over a wireless connection include transmittingand / or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and / or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system 100 may include any number of wired or wireless networks, network nodes, UEs, and / or any other components or systems that may facilitate or participate in the communication of data and / or signals whether via wired or wireless connections. The communication system 100 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0131] The UEs 112 may be any of a wide variety of communication devices, includingwireless devices arranged, configured, and / or operable to communicate wirelessly with the network nodes 110 and other communication devices. Similarly, the network nodes 110 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs 112 and / or with other network nodes or equipment in the telecommunication network 102 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in the telecommunication network 102.

[0132] In the depicted example, the core network 106 connects the network nodes 110 to oneor more hosts, such as host 116. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network 106 includes one more core network nodes (e.g., core network node 108) that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 108. Example core network nodes include functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier De-concealing function (SIDF),Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).

[0133] The host 116 may be under the ownership or control of a service provider other thanan operator or provider of the access network 104 and / or the telecommunication network 102 and may be operated by the service provider or on behalf of the service provider. The host 116 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio / video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.

[0134] As a whole, the communication system 100 of Figure 10 enables connectivity betweenthe UEs, network nodes, and hosts. In that sense, the communication system may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM); Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and / or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standard (e.g., 6G); wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (WiFi); and / or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, LiFi, and / or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox.

[0135] In some examples, the telecommunication network 102 is a cellular network thatimplements 3GPP standardized features. Accordingly, the telecommunications network 102 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 102. For example, the telecommunications network 102 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and / or Massive Machine Type Communication (mMTC) / Massive IoT services to yet further UEs.

[0136] In some examples, the UEs 112 are configured to transmit and / or receive informationwithout direct human interaction. For instance, a UE may be designed to transmit information to the access network 104 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 104. Additionally, a UE may be configured for operating in single- or multi-RAT or multi-standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e. being configuredfor multi-radio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio – Dual Connectivity (EN-DC).

[0137] In the example, the hub 114 communicates with the access network 104 to facilitateindirect communication between one or more UEs (e.g., UE 112c and / or 112d) and network nodes (e.g., network node 110b). In some examples, the hub 114 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 114 may be a broadband router enabling access to the core network 106 for the UEs. As another example, the hub 114 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes 110, or by executable code, script, process, or other instructions in the hub 114. As another example, the hub 114 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub 114 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 114 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 114 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 114 acts as a proxy server or orchestrator for the UEs, in particular in if one or more of the UEs are low energy IoT devices.

[0138] The hub 114 may have a constant / persistent or intermittent connection to the networknode 110b. The hub 114 may also allow for a different communication scheme and / or schedule between the hub 114 and UEs (e.g., UE 112c and / or 112d), and between the hub 114 and the core network 106. In other examples, the hub 114 is connected to the core network 106 and / or one or more UEs via a wired connection. Moreover, the hub 114 may be configured to connect to an M2M service provider over the access network 104 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 110 while still connected via the hub 114 via a wired or wireless connection. In some embodiments, the hub 114 may be a dedicated hub – that is, a hub whose primary function is to route communications to / from the UEs from / to the network node 110b. In other embodiments, the hub 114 may be a non- dedicated hub – that is, a device which is capable of operating to route communications between the UEs and network node 110b, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0139] Figure 11 shows a UE 200 in accordance with some embodiments. As used herein, aUE refers to a device capable, configured, arranged and / or operable to communicate wirelessly with network nodes and / or other UEs. Examples of a UE include, but are not limited to, a smartphone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), smart device, wireless customer-premise equipment (CPE), vehicle-mounted or vehicle embedded / integrated wireless device, etc. Other examples include any UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-IoT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0140] A UE may support device-to-device (D2D) communication, for example byimplementing a 3GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle-to- everything (V2X). In other examples, a UE may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, a UE may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter).

[0141] The UE 200 includes processing circuitry 202 that is operatively coupled via a bus 204to an input / output interface 206, a power source 208, a memory 210, a communication interface 212, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Figure 2. The level of integration between the components may vary from one UE to another UE. Further, certain UEs may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.

[0142] The processing circuitry 202 is configured to process instructions and data and may beconfigured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs in the memory 210. The processing circuitry 202 may be implemented as one or more hardware-implemented state machines (e.g., in discrete logic, field- programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs, general-purpose processors, such as a microprocessor or digital signal processor (DSP), together with appropriate software; or any combination of the above. For example, the processing circuitry 202 may include multiple central processing units (CPUs).

[0143] In the example, the input / output interface 206 may be configured to provide aninterface or interfaces to an input device, output device, or one or more input and / or output devices.Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into the UE 200. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device.

[0144] In some embodiments, the power source 208 is structured as a battery or battery pack.Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used. The power source 208 may further include power circuitry for delivering power from the power source 208 itself, and / or an external power source, to the various parts of the UE 200 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 208. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 208 to make the power suitable for the respective components of the UE 200 to which power is supplied.

[0145] The memory 210 may be or be configured to include memory such as random accessmemory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory 210 includes one or more application programs 214, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 216. The memory 210 may store, for use by the UE 200, any of a variety of various operating systems or combinations of operating systems.

[0146] The memory 210 may be configured to include a number of physical drive units, suchas redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memorysuch as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and / or ISIM, other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory 210 may allow the UE 200 to access instructions, application programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory 210, which may be or comprise a device-readable storage medium.

[0147] The processing circuitry 202 may be configured to communicate with an accessnetwork or other network using the communication interface 212. The communication interface 212 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 222. The communication interface 212 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another UE or a network node in an access network). Each transceiver may include a transmitter 218 and / or a receiver 220 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 218 and receiver 220 may be coupled to one or more antennas (e.g., antenna 222) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0148] In the illustrated embodiment, communication functions of the communicationinterface 212 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short- range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented in according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol / internet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0149] Regardless of the type of sensor, a UE may provide an output of data captured by itssensors, through its communication interface 212, via a wireless connection to a network node. Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE. The output may be periodic (e.g., once every 15 minutes if it reports thesensed temperature), random (e.g., to even out the load from reporting from several sensors), in response to a triggering event (e.g., when moisture is detected an alert is sent), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient).

[0150] As another example, a UE comprises an actuator, a motor, or a switch, related to acommunication interface configured to receive wireless input from a network node via a wireless connection. In response to the received wireless input the states of the actuator, the motor, or the switch may change. For example, the UE may comprise a motor that adjusts the control surfaces or rotors of a drone in flight according to the received input or to a robotic arm performing a medical procedure according to the received input.

[0151] A UE, when in the form of an Internet of Things (IoT) device, may be a device for usein one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application and healthcare. Non-limiting examples of such an IoT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / window sensor, a flood / moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a head-mounted display for Augmented Reality (AR) or Virtual Reality (VR), a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item- tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an IoT device comprises circuitry and / or software in dependence of the intended application of the IoT device in addition to other components as described in relation to the UE 200 shown in Figure 2.

[0152] As yet another specific example, in an IoT scenario, a UE may represent a machine orother device that performs monitoring and / or measurements and transmits the results of such monitoring and / or measurements to another UE and / or a network node. The UE may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, the UE may implement the 3GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation.

[0153] In practice, any number of UEs may be used together with respect to a single use case.For example, a first UE might be or be integrated in a drone and provide the drone’s speed information (obtained through a speed sensor) to a second UE that is a remote controller operating the drone. When the user makes changes from the remote controller, the first UE may adjust the throttle on the drone (e.g. by controlling an actuator) to increase or decrease the drone’s speed. The first and / or the second UE can also include more than one of the functionalities described above. For example, a UE might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators.

[0154] Figure 12 shows a network node 300 in accordance with some embodiments. As usedherein, network node refers to equipment capable, configured, arranged and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment, in a telecommunication network. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NR NodeBs (gNBs)).

[0155] Base stations may be categorized based on the amount of coverage they provide (or,stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. A base station may be a relay node or a relay donor node controlling a relay. A network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units and / or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).

[0156] Other examples of network nodes include multiple transmission point (multi-TRP) 5Gaccess nodes, multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi-cell / multicast coordination entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and / or Minimization of Drive Tests (MDTs).

[0157] The network node 300 includes a processing circuitry 302, a memory 304, acommunication interface 306, and a power source 308. The network node 300 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respectivecomponents. In certain scenarios in which the network node 300 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node 300 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory 304 for different RATs) and some components may be reused (e.g., a same antenna 310 may be shared by different RATs). The network node 300 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 300, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node 300.

[0158] The processing circuitry 302 may comprise a combination of one or more of amicroprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and / or encoded logic operable to provide, either alone or in conjunction with other network node 300 components, such as the memory 304, to provide network node 300 functionality.

[0159] In some embodiments, the processing circuitry 302 includes a system on a chip (SOC).In some embodiments, the processing circuitry 302 includes one or more of radio frequency (RF) transceiver circuitry 312 and baseband processing circuitry 314. In some embodiments, the radio frequency (RF) transceiver circuitry 312 and the baseband processing circuitry 314 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry 312 and baseband processing circuitry 314 may be on the same chip or set of chips, boards, or units.

[0160] The memory 304 may comprise any form of volatile or non-volatile computer-readablememory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and / or any other volatile or non-volatile, non-transitory device-readable and / or computer-executable memory devices that store information, data, and / or instructions that may be used by the processing circuitry 302. The memory 304 may store any suitable instructions, data, or information, including a computerprogram, software, an application including one or more of logic, rules, code, tables, and / or other instructions capable of being executed by the processing circuitry 302 and utilized by the network node 300. The memory 304 may be used to store any calculations made by the processing circuitry 302 and / or any data received via the communication interface 306. In some embodiments, the processing circuitry 302 and memory 304 is integrated.

[0161] The communication interface 306 is used in wired or wireless communication ofsignaling and / or data between a network node, access network, and / or UE. As illustrated, the communication interface 306 comprises port(s) / terminal(s) 316 to send and receive data, for example to and from a network over a wired connection. The communication interface 306 also includes radio front-end circuitry 318 that may be coupled to, or in certain embodiments a part of, the antenna 310. Radio front-end circuitry 318 comprises filters 320 and amplifiers 322. The radio front-end circuitry 318 may be connected to an antenna 310 and processing circuitry 302. The radio front-end circuitry may be configured to condition signals communicated between antenna 310 and processing circuitry 302. The radio front-end circuitry 318 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry 318 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 320 and / or amplifiers 322. The radio signal may then be transmitted via the antenna 310. Similarly, when receiving data, the antenna 310 may collect radio signals which are then converted into digital data by the radio front-end circuitry 318. The digital data may be passed to the processing circuitry 302. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0162] In certain alternative embodiments, the network node 300 does not include separateradio front-end circuitry 318, instead, the processing circuitry 302 includes radio front-end circuitry and is connected to the antenna 310. Similarly, in some embodiments, all or some of the RF transceiver circuitry 312 is part of the communication interface 306. In still other embodiments, the communication interface 306 includes one or more ports or terminals 316, the radio front-end circuitry 318, and the RF transceiver circuitry 312, as part of a radio unit (not shown), and the communication interface 306 communicates with the baseband processing circuitry 314, which is part of a digital unit (not shown).

[0163] The antenna 310 may include one or more antennas, or antenna arrays, configured tosend and / or receive wireless signals. The antenna 310 may be coupled to the radio front-end circuitry 318 and may be any type of antenna capable of transmitting and receiving data and / orsignals wirelessly. In certain embodiments, the antenna 310 is separate from the network node 300 and connectable to the network node 300 through an interface or port.

[0164] The antenna 310, communication interface 306, and / or the processing circuitry 302may be configured to perform any receiving operations and / or certain obtaining operations described herein as being performed by the network node. Any information, data and / or signals may be received from a UE, another network node and / or any other network equipment. Similarly, the antenna 310, the communication interface 306, and / or the processing circuitry 302 may be configured to perform any transmitting operations described herein as being performed by the network node. Any information, data and / or signals may be transmitted to a UE, another network node and / or any other network equipment.

[0165] The power source 308 provides power to the various components of network node 300in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 308 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 300 with power for performing the functionality described herein. For example, the network node 300 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source 308. As a further example, the power source 308 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail.

[0166] Embodiments of the network node 300 may include additional components beyondthose shown in Figure 12 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and / or any functionality necessary to support the subject matter described herein. For example, the network node 300 may include user interface equipment to allow input of information into the network node 300 and to allow output of information from the network node 300. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 300.

[0167] Figure 13 is a block diagram of a host 400, which may be an embodiment of the host116 of Figure 1, in accordance with various aspects described herein. As used herein, the host 400 may be or comprise various combinations hardware and / or software, including a standalone server, a blade server, a cloud-implemented server, a distributed server, a virtual machine, container, or processing resources in a server farm. The host 400 may provide one or more services to one or more UEs.

[0168] The host 400 includes processing circuitry 402 that is operatively coupled via a bus404 to an input / output interface 406, a network interface 408, a power source 410, and a memory 412. Other components may be included in other embodiments. Features of these components may be substantially similar to those described with respect to the devices of previous figures, such as Figures 10 and 3, such that the descriptions thereof are generally applicable to the corresponding components of host 400.

[0169] The memory 412 may include one or more computer programs including one or morehost application programs 414 and data 416, which may include user data, e.g., data generated by a UE for the host 400 or data generated by the host 400 for a UE. Embodiments of the host 400 may utilize only a subset or all of the components shown. The host application programs 414 may be implemented in a container-based architecture and may provide support for video codecs (e.g., Versatile Video Coding (VVC), High Efficiency Video Coding (HEVC), Advanced Video Coding (AVC), MPEG, VP9) and audio codecs (e.g., FLAC, Advanced Audio Coding (AAC), MPEG, G.711), including transcoding for multiple different classes, types, or implementations of UEs (e.g., handsets, desktop computers, wearable display systems, heads-up display systems). The host application programs 414 may also provide for user authentication and licensing checks and may periodically report health, routes, and content availability to a central node, such as a device in or on the edge of a core network. Accordingly, the host 400 may select and / or indicate a different host for over-the-top services for a UE. The host application programs 414 may support various protocols, such as the HTTP Live Streaming (HLS) protocol, Real-Time Messaging Protocol (RTMP), Real-Time Streaming Protocol (RTSP), Dynamic Adaptive Streaming over HTTP (MPEG-DASH), etc.

[0170] Figure 14 is a block diagram illustrating a virtualization environment 500 in whichfunctions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments 500 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, UE, core network node, or host. Further, in embodiments in which the virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized.

[0171] Applications 502 (which may alternatively be called software instances, virtualappliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment Q400 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0172] Hardware 504 includes processing circuitry, memory that stores software and / orinstructions executable by hardware processing circuitry, and / or other hardware devices as described herein, such as a network interface, input / output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers 506 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs 508a and 508b (one or more of which may be generally referred to as VMs 508), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 506 may present a virtual operating platform that appears like networking hardware to the VMs 508.

[0173] The VMs 508 comprise virtual processing, virtual memory, virtual networking orinterface and virtual storage, and may be run by a corresponding virtualization layer 506. Different embodiments of the instance of a virtual appliance 502 may be implemented on one or more of VMs 508, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV). NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers, and customer premise equipment.

[0174] In the context of NFV, a VM 508 may be a software implementation of a physicalmachine that runs programs as if they were executing on a physical, non-virtualized machine. Each of the VMs 508, and that part of hardware 504 that executes that VM, be it hardware dedicated to that VM and / or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more VMs 508 on top of the hardware 504 and corresponds to the application 502.

[0175] Hardware 504 may be implemented in a standalone network node with generic orspecific components. Hardware 504 may implement some functions via virtualization. Alternatively, hardware 504 may be part of a larger cluster of hardware (e.g. such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration 510, which, among others, oversees lifecycle management of applications 502. In some embodiments, hardware 504 is coupled to one or more radio units that each include one ormore transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system 512 which may alternatively be used for communication between hardware nodes and radio units.

[0176] Figure 15 shows a communication diagram of a host 602 communicating via a networknode 604 with a UE 606 over a partially wireless connection in accordance with some embodiments. Example implementations, in accordance with various embodiments, of the UE (such as a UE 112a of Figure 10 and / or UE 200 of Figure 2), network node (such as network node 110a of Figure 10 and / or network node 300 of Figure 3), and host (such as host 116 of Figure 10 and / or host 400 of Figure 4) discussed in the preceding paragraphs will now be described with reference to Figure 6.

[0177] Like host 400, embodiments of host 602 include hardware, such as a communicationinterface, processing circuitry, and memory. The host 602 also includes software, which is stored in or accessible by the host 602 and executable by the processing circuitry. The software includes a host application that may be operable to provide a service to a remote user, such as the UE 606 connecting via an over-the-top (OTT) connection 650 extending between the UE 606 and host 602. In providing the service to the remote user, a host application may provide user data which is transmitted using the OTT connection 650.

[0178] The network node 604 includes hardware enabling it to communicate with the host602 and UE 606. The connection 660 may be direct or pass through a core network (like core network 106 of Figure 1) and / or one or more other intermediate networks, such as one or more public, private, or hosted networks. For example, an intermediate network may be a backbone network or the Internet.

[0179] The UE 606 includes hardware and software, which is stored in or accessible by UE606 and executable by the UE’s processing circuitry. The software includes a client application, such as a web browser or operator-specific “app” that may be operable to provide a service to a human or non-human user via UE 606 with the support of the host 602. In the host 602, an executing host application may communicate with the executing client application via the OTT connection 650 terminating at the UE 606 and host 602. In providing the service to the user, the UE's client application may receive request data from the host's host application and provide user data in response to the request data. The OTT connection 650 may transfer both the request dataand the user data. The UE's client application may interact with the user to generate the user data that it provides to the host application through the OTT connection 650.

[0180] The OTT connection 650 may extend via a connection 660 between the host 602 andthe network node 604 and via a wireless connection 670 between the network node 604 and the UE 606 to provide the connection between the host 602 and the UE 606. The connection 660 and wireless connection 670, over which the OTT connection 650 may be provided, have been drawn abstractly to illustrate the communication between the host 602 and the UE 606 via the network node 604, without explicit reference to any intermediary devices and the precise routing of messages via these devices.

[0181] As an example of transmitting data via the OTT connection 650, in step 608, the host602 provides user data, which may be performed by executing a host application. In some embodiments, the user data is associated with a particular human user interacting with the UE 606. In other embodiments, the user data is associated with a UE 606 that shares data with the host 602 without explicit human interaction. In step 610, the host 602 initiates a transmission carrying the user data towards the UE 606. The host 602 may initiate the transmission responsive to a request transmitted by the UE 606. The request may be caused by human interaction with the UE 606 or by operation of the client application executing on the UE 606. The transmission may pass via the network node 604, in accordance with the teachings of the embodiments described throughout this disclosure. Accordingly, in step 612, the network node 604 transmits to the UE 606 the user data that was carried in the transmission that the host 602 initiated, in accordance with the teachings of the embodiments described throughout this disclosure. In step 614, the UE 606 receives the user data carried in the transmission, which may be performed by a client application executed on the UE 606 associated with the host application executed by the host 602.

[0182] In some examples, the UE 606 executes a client application which provides user datato the host 602. The user data may be provided in reaction or response to the data received from the host 602. Accordingly, in step 616, the UE 606 may provide user data, which may be performed by executing the client application. In providing the user data, the client application may further consider user input received from the user via an input / output interface of the UE 606. Regardless of the specific manner in which the user data was provided, the UE 606 initiates, in step 618, transmission of the user data towards the host 602 via the network node 604. In step 620, in accordance with the teachings of the embodiments described throughout this disclosure, the network node 604 receives user data from the UE 606 and initiates transmission of the received user data towards the host 602. In step 622, the host 602 receives the user data carried in the transmission initiated by the UE 606.

[0183] One or more of the various embodiments improve the performance of OTT servicesprovided to the UE 606 using the OTT connection 650, in which the wireless connection 670 forms the last segment. More precisely, the teachings of these embodiments may improve the data rate and latency and thereby provide benefits such as reduced user waiting time, better responsiveness, and better QoE.

[0184] In an example scenario, factory status information may be collected and analyzed bythe host 602. As another example, the host 602 may process audio and video data which may have been retrieved from a UE for use in creating maps. As another example, the host 602 may collect and analyze real-time data to assist in controlling vehicle congestion (e.g., controlling traffic lights). As another example, the host 602 may store surveillance video uploaded by a UE. As another example, the host 602 may store or control access to media content such as video, audio, VR or AR which it can broadcast, multicast or unicast to UEs. As other examples, the host 602 may be used for energy pricing, remote control of non-time critical electrical load to balance power generation needs, location services, presentation services (such as compiling diagrams etc. from data collected from remote devices), or any other function of collecting, retrieving, storing, analyzing and / or transmitting data.

[0185] In some examples, a measurement procedure may be provided for the purpose ofmonitoring data rate, latency and other factors on which the one or more embodiments improve. There may further be an optional network functionality for reconfiguring the OTT connection 650 between the host 602 and UE 606, in response to variations in the measurement results. The measurement procedure and / or the network functionality for reconfiguring the OTT connection may be implemented in software and hardware of the host 602 and / or UE 606. In some embodiments, sensors (not shown) may be deployed in or in association with other devices through which the OTT connection 650 passes; the sensors may participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or supplying values of other physical quantities from which software may compute or estimate the monitored quantities. The reconfiguring of the OTT connection 650 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not directly alter the operation of the network node 604. Such procedures and functionalities may be known and practiced in the art. In certain embodiments, measurements may involve proprietary UE signaling that facilitates measurements of throughput, propagation times, latency and the like, by the host 602. The measurements may be implemented in that software causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection 650 while monitoring propagation times, errors, etc.

[0186] Although the computing devices described herein (e.g., UEs, network nodes, hosts)may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and / or software needed to perform the tasks, features, functions and methods disclosed herein. Determining, calculating, obtaining or similar operations described herein may be performed by processing circuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and / or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination. Moreover, while components are depicted as single boxes located within a larger box, or nested within multiple boxes, in practice, computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitioned between separate components. For example, a communication interface may be configured to include any of the components described herein, and / or the functionality of the components may be partitioned between the processing circuitry and the communication interface. In another example, non-computationally intensive functions of any of such components may be implemented in software or firmware and computationally intensive functions may be implemented in hardware.

[0187] In certain embodiments, some or all of the functionality described herein may beprovided by processing circuitry executing instructions stored on in memory, which in certain embodiments may be a computer program product in the form of a non-transitory computer- readable storage medium. In alternative embodiments, some or all of the functionality may be provided by the processing circuitry without executing instructions stored on a separate or discrete device-readable storage medium, such as in a hard-wired manner. In any of those particular embodiments, whether executing instructions stored on a non-transitory computer-readable storage medium or not, the processing circuitry can be configured to perform the described functionality. The benefits provided by such functionality are not limited to the processing circuitry alone or to other components of the computing device, but are enjoyed by the computing device as a whole, and / or by end users and a wireless network generally.

[0188] FIGURE 16 is a flowchart illustrating an example method 1600 in a data measurementnode, according to certain embodiments. In particular embodiments, one or more steps of FIGURE 16 may be performed by UE 200 or network node 300 described with respect to FIGURE 11.

[0189] The method begins at step 1612, where the data measurement node (e.g., network node300, UE 200) obtains measurement data for a reference signal received on an antenna port. The measurement data comprises a sequence of complex-valued samples.

[0190] For example, the data measurement node may measure a positioning reference signal,or any other suitable reference signal.

[0191] In particular embodiments, the data measurement node may obtain the measurementdata according to any of the embodiments and examples described herein.

[0192] At step 1614, the data measurement node modifies the measurement data to removerandom initial phase effects.

[0193] In particular embodiments, modifying the measurement data to remove random initialphase effects comprises representing the sequence of complex-valued samples as a combination of a magnitude sequence and a relative phase sequence. A relative phase of the relative phase sequence may comprise a difference between a sample phase and an initial sample phase. A relative phase of the relative phase sequence may comprise a difference between two consecutive sample phases.

[0194] In particular embodiments, the modified measurement data excludes a phase of aninitial sample or includes a phase of an initial sample at a fixed value of 0.

[0195] In particular embodiments, modifying the measurement data to remove random initialphase effects comprises representing the sequence of complex-valued samples as a real and imaginary parts representation where at least one sample of the provided complex-valued measurement data is real-valued.

[0196] In particular embodiments, modifying the measurement data further comprisesapplying a common phase rotation to all measured samples of the complex-valued sample data. The common phase rotation may be a negative of a phase of a first sample of the complex-valued sample data.

[0197] In particular embodiments, modifying the measurement data further comprisesapplying differential phase rotations to all measured samples of the complex-valued sample data.

[0198] In particular embodiments, at least one real-valued sample of the complex-valuedsample data is a first reported measurement sample.

[0199] In particular embodiments, the data measurement node may modify the measurementdata according to any of the embodiments and examples described herein.

[0200] At step 1616, the data measurement node transmits the modified measurement data toa data collection node. The data collection node may comprise an AI / ML inference or trainingnode. In particular embodiments, the data measurement node may transmit the modified measurement data according to any of the embodiments and examples described herein.

[0201] Modifications, additions, or omissions may be made to method 1600 of FIGURE 16.Additionally, one or more steps in the method of FIGURE 16 may be performed in parallel or in any suitable order.

[0202] FIGURE 17 is a flowchart illustrating an example method 1700 in a data collectionnode, according to certain embodiments. In particular embodiments, one or more steps of FIGURE 17 may be performed by network node 300 described with respect to FIGURE 12.

[0203] The method begins at step 1712, where the data collection node (e.g., network node300, inference node, LMF, etc.) receiving measurement data for a reference signal received on an antenna port from a data measurement node. The measurement data comprises a sequence of complex-valued samples.

[0204] At step 1714, the data collection node modifies the measurement data to removerandom initial phase effects. The data collection node may modify the measurement data in the same manner as the data collection node described with respect to FIGURE 16. In particular embodiments, the data collection node may modify the measurement data according to any of the embodiments and examples described herein.

[0205] At step 1716, the data collection node inputs the modified measurement data to amachine learning model (e.g., for training, inference, performance monitoring, etc.). The data collection node may input the modified measurement data locally, or may transmit the modified measurement data to another network node for input to the machine learning model.

[0206] Modifications, additions, or omissions may be made to method 1700 of FIGURE 17.Additionally, one or more steps in the method of FIGURE 17 may be performed in parallel or in any suitable order.

[0207] The foregoing description sets forth numerous specific details. It is understood,however, that embodiments may be practiced without these specific details. In other instances, well-known circuits, structures and techniques have not been shown in detail in order not to obscure the understanding of this description. Those of ordinary skill in the art, with the included descriptions, will be able to implement appropriate functionality without undue experimentation.

[0208] References in the specification to “one embodiment,” “an embodiment,” “an exampleembodiment,” etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described inconnection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to implement such feature, structure, or characteristic in connection with other embodiments, whether or not explicitly described.

[0209] Although this disclosure has been described in terms of certain embodiments,alterations and permutations of the embodiments will be apparent to those skilled in the art. Accordingly, the above description of the embodiments does not constrain this disclosure. Other changes, substitutions, and alterations are possible without departing from the scope of this disclosure, as defined by the claims below.

[0210] Some example embodiments are described below.EMBODIMENTS 1. A method performed by a AI / ML measurement data collection node, the methodcomprising: − obtaining measurement data for a reference signal that contains at least a combination of a magnitude and relative phase sequences; and − transmitting the measurement data to an AI / ML inference node. 2. The method of the previous embodiment, wherein the relative phase is a difference betweena sample phase and an initial sample phase. 3. The method of embodiment 1, wherein the relative phase is a difference between twoconsecutive sample phases. 4. The method of embodiment 1, wherein the measurement data does not contain a phase ofan initial sample. 5. The method of embodiment 1, wherein the measurement data contains a phase of the initialsample at a fixed value of 0. 6. The method of embodiment 1, wherein the measurement data for multiple receive antennaports have zero initial sample phase for at least one antenna port. 7. The method of embodiment 1, wherein the measurement data for multiple receive antennaports have zero initial sample phase for all antenna ports.A method performed by a AI / ML measurement data collection node, the methodcomprising: − obtaining measurement data for a reference signal that is based on real and imaginary parts representation of a complex-valued measurement data where at least one sample of the provided complex-valued measurement data is real-valued; and − transmitting the measurement data to an AI / ML inference node.The method of the previous embodiment, further comprising applying a common phaserotation to all measured samples of the complex-valued measurement data.The method of the previous embodiment, wherein the common phase rotation is a negativeof a phase of a first sample of the measurement data.The method of the embodiment 8, further comprising applying differential phase rotationsto all measured samples of the complex-valued measurement data.The method of the previous embodiment, wherein at least one real-valued sample of thecomplex-valued measurement data is a first reported measurement sample.The method of embodiment 8, wherein the at least one real-valued sample of the providedcomplex-valued measurement data is a first reported measurement sample.The method of embodiment 8, wherein the provided complex-valued measurement datafor multiple receive antenna ports have a real-valued sample for at least one antenna port.The method of embodiment 8, wherein the provided complex-valued measurement datafor multiple receive antenna ports have a real-valued sample for all antenna ports.A method performed by a AI / ML measurement data collection node, the methodcomprising: − any of the AI / ML measurement data collection node steps, features, or functions described above, either alone or in combination with other steps, features, or functions described above.17. A method performed by a AI / ML inference node, the method comprising:− receiving any of the measurement data from embodiments 1-16. 18. A method performed by a AI / ML inference node, the method comprising:− applying a common phase rotation to a complex-valued ML measurement training data before using the training data as inputs to an AI / ML model under training. 19. The method of the previous embodiment, wherein application of a common phase rotationis the negative of a phase of a first sample of the ML measurement training data. 20. The method of embodiment 18, wherein application of a common phase rotation is a phaserandomly generated by a node performing AI / ML model training. 21. A method performed by a AI / ML inference node, the method comprising:− converting complex-valued ML measurement training data samples to a magnitude and relative phase representation before using the training data as inputs to an AI / ML model under training. 22. The method of the previous embodiment, wherein the relative phase is a difference betweena sample phase and an initial sample phase. 23. The method of embodiment 18, wherein the relative phase is the difference between twoconsecutive sample phases. 24. A method performed by a AI / ML inference node, the method comprising:− any of the AI / ML inference node steps, features, or functions described above, either alone or in combination with other steps, features, or functions described above. Group A Embodiments 25. A method performed by a wireless device, the method comprising:− any of the wireless device steps, features, or functions described above, either aloneor in combination with other steps, features, or functions described above. 26. The method of the previous embodiment, further comprising one or more additionalwireless device steps, features or functions described above. 27. The method of any of the previous two embodiments, further comprising:− providing user data; and − forwarding the user data to a host computer via the transmission to the base station. Group B Embodiments 28. A method performed by a base station, the method comprising:− any of the steps, features, or functions described above with respect to base stations, either alone or in combination with other steps, features, or functions described above. 29. The method of the previous embodiment, further comprising one or more additional basestation steps, features or functions described above. 30. The method of any of the previous embodiments, further comprising:− obtaining user data; and − forwarding the user data to a host computer or a wireless device. Group C Embodiments 31. A mobile terminal comprising:− processing circuitry configured to perform any of the steps of any of the Group A embodiments; and − power supply circuitry configured to supply power to the wireless device. 32. A base station comprising:− processing circuitry configured to perform any of the steps of any of the Group B embodiments; − power supply circuitry configured to supply power to the wireless device.A user equipment (UE) comprising:− an antenna configured to send and receive wireless signals; − radio front-end circuitry connected to the antenna and to processing circuitry, and configured to condition signals communicated between the antenna and the processing circuitry; − the processing circuitry being configured to perform any of the steps of any of the Group A embodiments; − an input interface connected to the processing circuitry and configured to allow input of information into the UE to be processed by the processing circuitry; − an output interface connected to the processing circuitry and configured to output information from the UE that has been processed by the processing circuitry; and − a battery connected to the processing circuitry and configured to supply power to the UE.A communication system including a host computer comprising:− processing circuitry configured to provide user data; and − a communication interface configured to forward the user data to a cellular network for transmission to a user equipment (UE), − wherein the cellular network comprises a base station having a radio interface and processing circuitry, the base station’s processing circuitry configured to perform any of the steps of any of the Group B embodiments.The communication system of the pervious embodiment further including the base station.The communication system of the previous 2 embodiments, further including the UE,wherein the UE is configured to communicate with the base station.The communication system of the previous 3 embodiments, wherein:− the processing circuitry of the host computer is configured to execute a host application, thereby providing the user data; and − the UE comprises processing circuitry configured to execute a client application associated with the host application.A method implemented in a communication system including a host computer, a basestation and a user equipment (UE), the method comprising: − at the host computer, providing user data; and − at the host computer, initiating a transmission carrying the user data to the UE via a cellular network comprising the base station, wherein the base station performs any of the steps of any of the Group B embodiments.The method of the previous embodiment, further comprising, at the base station,transmitting the user data.The method of the previous 2 embodiments, wherein the user data is provided at the hostcomputer by executing a host application, the method further comprising, at the UE, executing a client application associated with the host application.A user equipment (UE) configured to communicate with a base station, the UE comprisinga radio interface and processing circuitry configured to performs any of the previous 3 embodiments.A communication system including a host computer comprising:− processing circuitry configured to provide user data; and − a communication interface configured to forward user data to a cellular network for transmission to a user equipment (UE), − wherein the UE comprises a radio interface and processing circuitry, the UE’s components configured to perform any of the steps of any of the Group A embodiments.The communication system of the previous embodiment, wherein the cellular networkfurther includes a base station configured to communicate with the UE.The communication system of the previous 2 embodiments, wherein:− the processing circuitry of the host computer is configured to execute a host application, thereby providing the user data; and − the UE’s processing circuitry is configured to execute a client application associated with the host application.A method implemented in a communication system including a host computer, a basestation and a user equipment (UE), the method comprising: − at the host computer, providing user data; and − at the host computer, initiating a transmission carrying the user data to the UE via a cellular network comprising the base station, wherein the UE performs any of the steps of any of the Group A embodiments.The method of the previous embodiment, further comprising at the UE, receiving the userdata from the base station.A communication system including a host computer comprising:− communication interface configured to receive user data originating from a transmission from a user equipment (UE) to a base station, − wherein the UE comprises a radio interface and processing circuitry, the UE’s processing circuitry configured to perform any of the steps of any of the Group A embodiments.The communication system of the previous embodiment, further including the UE.The communication system of the previous 2 embodiments, further including the basestation, wherein the base station comprises a radio interface configured to communicate with the UE and a communication interface configured to forward to the host computer the user data carried by a transmission from the UE to the base station.The communication system of the previous 3 embodiments, wherein:− the processing circuitry of the host computer is configured to execute a host application; and − the UE’s processing circuitry is configured to execute a client application associated with the host application, thereby providing the user data.The communication system of the previous 4 embodiments, wherein:− the processing circuitry of the host computer is configured to execute a host application, thereby providing request data; and − the UE’s processing circuitry is configured to execute a client applicationassociated with the host application, thereby providing the user data in response to the request data.A method implemented in a communication system including a host computer, a basestation and a user equipment (UE), the method comprising: − at the host computer, receiving user data transmitted to the base station from the UE, wherein the UE performs any of the steps of any of the Group A embodiments.The method of the previous embodiment, further comprising, at the UE, providing the userdata to the base station.The method of the previous 2 embodiments, further comprising:− at the UE, executing a client application, thereby providing the user data to be transmitted; and − at the host computer, executing a host application associated with the client application.The method of the previous 3 embodiments, further comprising:− at the UE, executing a client application; and − at the UE, receiving input data to the client application, the input data being provided at the host computer by executing a host application associated with the client application, − wherein the user data to be transmitted is provided by the client application in response to the input data.A communication system including a host computer comprising a communication interfaceconfigured to receive user data originating from a transmission from a user equipment (UE) to a base station, wherein the base station comprises a radio interface and processing circuitry, the base station’s processing circuitry configured to perform any of the steps of any of the Group B embodiments.The communication system of the previous embodiment further including the base station.The communication system of the previous 2 embodiments, further including the UE,wherein the UE is configured to communicate with the base station.The communication system of the previous 3 embodiments, wherein:− the processing circuitry of the host computer is configured to execute a host application; − the UE is configured to execute a client application associated with the host application, thereby providing the user data to be received by the host computer.A method implemented in a communication system including a host computer, a basestation and a user equipment (UE), the method comprising: − at the host computer, receiving, from the base station, user data originating from a transmission which the base station has received from the UE, wherein the UE performs any of the steps of any of the Group A embodiments.The method of the previous embodiment, further comprising at the base station, receivingthe user data from the UE.The method of the previous 2 embodiments, further comprising at the base station,initiating a transmission of the received user data to the host computer.

Claims

CLAIMS:

1. A method (1600) performed by a data measurement node, the method comprising: obtaining (1612) measurement data for a reference signal received on an antenna port, the measurement data comprising a sequence of complex-valued samples; modifying (1614) the measurement data to remove random initial phase effects; and transmitting (1616) the modified measurement data to a data collection node.

2. The method of claim 1, wherein modifying the measurement data to remove random initial phase effects comprises representing the sequence of complex-valued samples as a combination of a magnitude sequence and a relative phase sequence.

3. The method of claim 2, wherein a relative phase of the relative phase sequence is a difference between a sample phase and an initial sample phase.

4. The method of claim 2, wherein a relative phase of the relative phase sequence is a difference between two consecutive sample phases.

5. The method of any one of claims 2-4, wherein the modified measurement data excludes a phase of an initial sample.

6. The method of any one of claims 2-4, wherein the modified measurement data includes a phase of an initial sample at a fixed value of 0.

7. The method of claim 1, wherein modifying the measurement data to remove random initial phase effects comprises representing the sequence of complex-valued samples as a real and imaginary parts representation where at least one sample of the provided complex-valued measurement data is real-valued.

8. The method of claim 7, wherein modifying the measurement data further comprises applying a common phase rotation to all measured samples of the complex-valued sample data.

9. The method of claim 8, wherein the common phase rotation is a negative of a phase of a first sample of the complex-valued sample data.

10. The method of claim 7, wherein modifying the measurement data further comprises applying differential phase rotations to all measured samples of the complex-valued sample data.

11. The method of claim 10, wherein at least one real-valued sample of the complex- valued sample data is a first reported measurement sample.

12. A data measurement node (200, 300) comprising processing circuitry (202, 302), the processing circuitry operable to: obtain measurement data for a reference signal received on an antenna port, the measurement data comprising a sequence of complex-valued samples; modify the measurement data to remove random initial phase effects; and transmit the modified measurement data to a data collection node.

13. The data measurement node of claim 12, wherein the processing circuitry is operable to modify the measurement data to remove random initial phase effects by representing the sequence of complex-valued samples as a combination of a magnitude sequence and a relative phase sequence.

14. The data measurement node of claim 13, wherein a relative phase of the relative phase sequence is a difference between a sample phase and an initial sample phase.

15. The data measurement node of claim 13, wherein a relative phase of the relative phase sequence is a difference between two consecutive sample phases.

16. The data measurement node of any one of claims 13-15, wherein the modified measurement data excludes a phase of an initial sample.

17. The data measurement node of any one of claims 13-15, wherein the modified measurement data includes a phase of an initial sample at a fixed value of 0.

18. The data measurement node of claim 12, wherein the processing circuitry is operable to modify the measurement data to remove random initial phase effects by representing the sequence of complex-valued samples as a real and imaginary parts representation where at leastone sample of the provided complex-valued measurement data is real-valued.

19. The data measurement node of claim 18, wherein the processing circuitry is further operable to modify the measurement data by applying a common phase rotation to all measured samples of the complex-valued sample data.

20. The data measurement node of claim 19, wherein the common phase rotation is a negative of a phase of a first sample of the complex-valued sample data.

21. The data measurement node of claim 18, wherein the processing circuitry is further operable to modify the measurement data by applying differential phase rotations to all measured samples of the complex-valued sample data.

22. The data measurement node of claim 21, wherein at least one real-valued sample of the complex-valued sample data is a first reported measurement sample.

23. A method (1700) performed by a data collection node, the method comprising: receiving (1712) measurement data for a reference signal received on an antenna port from a data measurement node, the measurement data comprising a sequence of complex-valued samples; modifying (1714) the measurement data to remove random initial phase effects; and inputting (1616) the modified measurement data to a machine learning model.

24. The method of claim 23, wherein modifying the measurement data to remove random initial phase effects comprises representing the sequence of complex-valued samples as a combination of a magnitude sequence and a relative phase sequence.

25. The method of claim 24, wherein a relative phase of the relative phase sequence is a difference between a sample phase and an initial sample phase.

26. The method of claim 24, wherein a relative phase of the relative phase sequence is a difference between two consecutive sample phases.

27. The method of any one of claims 24-26, wherein the modified measurement dataexcludes a phase of an initial sample.

28. The method of any one of claims 24-26, wherein the modified measurement data includes a phase of an initial sample at a fixed value of 0.

29. The method of claim 23, wherein modifying the measurement data to remove random initial phase effects comprises representing the sequence of complex-valued samples as a real and imaginary parts representation where at least one sample of the provided complex-valued measurement data is real-valued.

30. The method of claim 29, wherein modifying the measurement data further comprises applying a common phase rotation to all measured samples of the complex-valued sample data.

31. The method of claim 30, wherein the common phase rotation is a negative of a phase of a first sample of the complex-valued sample data.

32. The method of claim 29, wherein modifying the measurement data further comprises applying differential phase rotations to all measured samples of the complex-valued sample data.

33. The method of claim 32, wherein at least one real-valued sample of the complex- valued sample data is a first reported measurement sample.

34. A data collection node (300) comprising processing circuitry (302), the processing circuitry operable to: receive measurement data for a reference signal received on an antenna port from a data measurement node, the measurement data comprising a sequence of complex-valued samples; modify the measurement data to remove random initial phase effects; and input the modified measurement data to a machine learning model.

35. The data collection node of claim 34, wherein the processing circuitry is operable to modify the measurement data to remove random initial phase effects by representing the sequence of complex-valued samples as a combination of a magnitude sequence and a relative phase sequence.

36. The data collection node of claim 35, wherein a relative phase of the relative phase sequence is a difference between a sample phase and an initial sample phase.

37. The data collection node of claim 35, wherein a relative phase of the relative phase sequence is a difference between two consecutive sample phases.

38. The data collection node of any one of claims 35-37, wherein the modified measurement data excludes a phase of an initial sample.

39. The data collection node of any one of claims 35-37, wherein the modified measurement data includes a phase of an initial sample at a fixed value of 0.

40. The data collection node of claim 34, wherein the processing circuitry is operable to modify the measurement data to remove random initial phase effects by representing the sequence of complex-valued samples as a real and imaginary parts representation where at least one sample of the provided complex-valued measurement data is real-valued.

41. The data collection node of claim 40, wherein the processing circuitry is operable to further modify the measurement data by applying a common phase rotation to all measured samples of the complex-valued sample data.

42. The data collection node of claim 41, wherein the common phase rotation is a negative of a phase of a first sample of the complex-valued sample data.

43. The data collection node of claim 40, wherein the processing circuitry is operable to further modify the measurement data by applying differential phase rotations to all measured samples of the complex-valued sample data.

44. The data collection node of claim 43, wherein at least one real-valued sample of the complex-valued sample data is a first reported measurement sample.

Images