WO2023158363A1 - Évaluation de la performance d'un codeur ae - Google Patents

Évaluation de la performance d'un codeur ae Download PDF

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WO2023158363A1
WO2023158363A1 PCT/SE2023/050146 SE2023050146W WO2023158363A1 WO 2023158363 A1 WO2023158363 A1 WO 2023158363A1 SE 2023050146 W SE2023050146 W SE 2023050146W WO 2023158363 A1 WO2023158363 A1 WO 2023158363A1
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encoder
wireless communications
network
communications device
trained
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PCT/SE2023/050146
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Daniel CHEN LARSSON
Lars Lindbom
Emil RINGH
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Telefonaktiebolaget Lm Ericsson (Publ)
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G06N3/0455—Auto-encoder networks; Encoder-decoder networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/088—Non-supervised learning, e.g. competitive learning
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W8/00—Network data management
    • H04W8/22—Processing or transfer of terminal data, e.g. status or physical capabilities
    • H04W8/24—Transfer of terminal data

Definitions

  • the present disclosure relates to nodes and methods for supporting evaluation of performance of an AE-encoder.
  • wireless devices also known as wireless communication devices, mobile stations, stations (STA) and/or User Equipments (UE), communicate via a Local Area Network such as a Wi-Fi network or a Radio Access Network (RAN) to one or more core networks (CN).
  • the RAN covers a geographical area which is divided into service areas or cell areas. Each service area or cell area may provide radio coverage via a beam or a beam group.
  • Each service area or cell area is typically served by a radio access node such as a radio access node e.g., a Wi-Fi access point or a radio base station (RBS), which in some networks may also be denoted, for example, a NodeB, eNodeB (eNB), or gNB as denoted in 5G.
  • a radio access node such as a radio access node e.g., a Wi-Fi access point or a radio base station (RBS), which in some networks may also be denoted, for example, a NodeB, eNodeB (eNB), or gNB as denoted in 5G.
  • a service area or cell area is a geographical area where radio coverage is provided by the radio access node.
  • the radio access node communicates over an air interface operating on radio frequencies with the wireless device within range of the radio access node.
  • Evolved Packet System also called a Fourth Generation (4G) network
  • 4G Fourth Generation
  • 3GPP 3rd Generation Partnership Project
  • 5G Fifth Generation
  • NR 5G New Radio
  • the EPS comprises the Evolved Universal Terrestrial Radio Access Network (E-UTRAN), also known as the Long Term Evolution (LTE) radio access network, and the Evolved Packet Core (EPC), also known as System Architecture Evolution (SAE) core network.
  • E-UTRAN/LTE is a variant of a 3GPP radio access network wherein the radio access nodes are directly connected to the EPC core network rather than to RNCs used in 3G networks.
  • SAE System Architecture Evolution
  • E-UTRAN/LTE is a variant of a 3GPP radio access network wherein the radio access nodes are directly connected to the EPC core network rather than to RNCs used in 3G networks.
  • the functions of a 3G RNC are distributed between the radio access nodes, e.g. eNodeBs in LTE, and the core network.
  • the RAN of an EPS has an essentially “flat” architecture comprising radio access nodes connected directly to one or more core networks, i.e. they are not connected to RNCs.
  • the E-UTRAN specification defines a direct interface between the radio access nodes, this interface being denoted the X2 interface.
  • Figure 1 illustrates a simplified wireless communication system.
  • a UE 12 which communicates with one or multiple access nodes 103-104, which in turn is connected to a network node 106.
  • the access nodes 103-104 are part of the radio access network 10.
  • the access nodes 103-104 corresponds typically to Evolved NodeBs (eNBs) and the network node 106 corresponds typically to either a Mobility Management Entity (MME) and/or a Serving Gateway (SGW).
  • MME Mobility Management Entity
  • SGW Serving Gateway
  • the eNB is part of the radio access network 10, which in this case is the E-UTRAN (Evolved Universal Terrestrial Radio Access Network), while the MME and SGW are both part of the EPC (Evolved Packet Core network).
  • the eNBs are inter-connected via the X2 interface, and connected to EPC via the S1 interface, more specifically via S1-C to the MME and S1-U to the SGW.
  • the access nodes 103-104 corresponds typically to an 5G NodeB (gNB) and the network node 106 corresponds typically to either an Access and Mobility Management Function (AMF) and/or a User Plane Function (UPF).
  • the gNB is part of the radio access network 10, which in this case is the NG-RAN (Next Generation Radio Access Network), while the AMF and UPF are both part of the 5G Core Network (5GC).
  • the gNBs are inter-connected via the Xn interface, and connected to 5GC via the NG interface, more specifically via NG-C to the AMF and NG-U to the UPF.
  • LTE eNBs may also be connected to the 5G-CN via NG-U/NG-C and support the Xn interface.
  • An eNB connected to 5GC is called a next generation eNB (ng-eNB) and is considered part of the NG-RAN.
  • LTE connected to 5GC will not be discussed further in this document; however, it should be noted that most of the solutions/features described for LTE and NR in this document also apply to LTE connected to 5GC. In this document, when the term LTE is used without further specification it refers to LTE-EPC.
  • NR uses Orthogonal Frequency Division Multiplexing (OFDM) with configurable bandwidths and subcarrier spacing to efficiently support a diverse set of use-cases and deployment scenarios.
  • OFDM Orthogonal Frequency Division Multiplexing
  • NR improves deployment flexibility, user throughputs, latency, and reliability.
  • the throughput performance gains are enabled, in part, by enhanced support for Multi-User Multiple-Input Multiple-Output (MU-MIMO) transmission strategies, where two or more UEs receive data on the same time frequency resources, i.e. , by spatially separated transmissions.
  • MU-MIMO Multi-User Multiple-Input Multiple-Output
  • FIG. 2 illustrates an example transmission and reception chain for MU-MIMO operations. Note that the order of modulation and precoding, or demodulation and combining respectively, may differ depending on the implementation of MU-MIMO transmission.
  • a multi-antenna base station with N x antenna ports is simultaneously, e.g., on the same OFDM time-frequency resources, transmitting information to several UEs: a sequence S (1) is transmitted to is transmitted to UE(2), and so on.
  • An antenna port may be a logical unit which may comprise one or more antenna elements. Before modulation and transmission, precoding is applied to each sequence to mitigate multiplexing interference - the transmissions are spatially separated.
  • Each UE demodulates its received signal and combines receiver antenna signals to obtain an estimate of the transmitted sequence.
  • This estimate for UE / may be expressed as (neglecting other interference and noise sources except the MU-MIMO interference)
  • the second term represents the spatial multiplexing interference, due to MU-MIMO transmission, seen by UE(j).
  • a goal for a wireless communication network may be to construct a set of precoders to meet a given target.
  • One such target may be to make the norm
  • ,j # i small (this norm represents the interference of user i’s transmission received by user j).
  • the precoder Wy 1 - 1 shall correlate well with the channel observed by UE(j) whereas it shall correlate poorly with the channels observed by other UEs.
  • UL uplink
  • SRS Sounding Reference Signals
  • the wireless communication network may directly estimate the uplink channel from SRS and, therefore (by reciprocity), the downlink channel Full channel reciprocity may be obtained in time division duplex (TDD) deployments for UEs with same number of transmitters (TX chains) as receive branches (RX chains).
  • TDD time division duplex
  • RX chains receive branches
  • TX chains transmitters
  • RX chains receive branches
  • TX chains transmitters
  • RX chains receive branches
  • a typical scenario is that UEs have fewer TX chains than RX chains, so the radio access network may only be able to estimate part of the uplink channel using SRS (in which case only certain columns of a precoding matrix may be estimated using SRS). This situation is known as partial channel knowledge.
  • the wireless communication network cannot always accurately estimate the downlink channel from uplink reference signals.
  • the uplink and downlink channels use different carriers and, therefore, the uplink channel may not provide enough information about the downlink channel to enable MU-MIMO precoding.
  • FDD frequency division duplex
  • the wireless communication network may only be able to estimate part of the uplink channel using SRS because UEs typically have fewer TX branches than RX branches (in which case only certain columns of the precoding matrix may be estimated using SRS). This situation is known as partial channel knowledge.
  • the wireless communication network cannot accurately estimate the full downlink channel from uplink transmissions, then active UEs may report channel information to the wireless communication network over the uplink control or data channels.
  • this feedback is achieved by the following signalling protocol:
  • the radio access network configures a UE to report CSI in a certain way.
  • the wireless communication network transmits Channel State Information reference signals (CSI-RS) over the downlink, e.g., using N ports.
  • CSI-RS Channel State Information reference signals
  • the UE estimates the downlink channel (or important features thereof, such as eigenvectors of the channel or the Gram matrix of the channel, one or more eigenvectors that correspond to the largest eigenvalues of an estimated channel covariance matrix, one or more Discrete Fourier Transform (DFT) base vectors (described on the next page), or orthogonal vectors from any other suitable and defined vector space, that best correlates with an estimated channel matrix, or an estimated channel covariance matrix, the channel delay profile), e.g., for each of the N antenna ports from the transmitted CSI-RS.
  • DFT Discrete Fourier Transform
  • the UE reports CSI (e.g., channel quality index (CQI), precoding matrix indicator (PMI), rank indicator (Rl)) to the wireless communication network over an uplink control channel and/or over a data channel.
  • CSI e.g., channel quality index (CQI), precoding matrix indicator (PMI), rank indicator (Rl)
  • the wireless communication network uses the UE’s feedback, e.g., the CSI reported from the UE, for downlink user scheduling and precoding, such as MIMO precoding.
  • both Type I and Type II reporting is configurable, where the CSI Type II reporting protocol has been specifically designed to enable MU -Ml MO operations from uplink UE reports, such as the CSI reports.
  • the CSI Type II may be configured in a normal reporting mode or in a port selection reporting mode.
  • the CSI Type II normal reporting mode is based on the specification of sets of Discrete Fourier Transform (DFT) basis functions in a precoder codebook.
  • the UE selects and reports L DFT vectors from the codebook that best match its channel conditions (like the classical codebook precoding matrix indicator (PMI) from earlier 3GPP releases).
  • the number of DFT vectors L is typically 2 or 4 and it is configurable by the wireless communication network.
  • the UE reports how the L DFT vectors should be combined in terms of relative amplitude scaling and co-phasing.
  • Algorithms to select L, the L DFT vectors, and co-phasing coefficients are outside the specification scope -- left to UE and network implementation. Or, put another way, the 3GPP Rel. 16 specification only defines signaling protocols to enable the above message exchanges.
  • DFT beams will be used interchangeably with DFT vectors. This slight shift of terminology is for example appropriate whenever the base station has a uniform planar array with antenna elements separated by half of the carrier wavelength.
  • the CSI type II normal reporting mode is illustrated in Figure 3 and described in 3GPP TS 38.214 “Physical layer procedures for data” (Release 16).
  • the selection and reporting of the L DFT vectors b n and their relative amplitudes a n is done in a wideband manner; that is, the same beams are used for both polarizations over the entire transmission frequency band.
  • the selection and reporting of the DFT vector co-phasing coefficients are done in a subband manner; that is, DFT vector co-phasing parameters are determined for each of multiple subsets of contiguous subcarriers.
  • the co-phasing parameters are quantized such that e jdn is taken from either a Quadrature phase-shift keying (QPSK) or 8-Phase Shift Keying (8PSK) signal constellation.
  • QPSK Quadrature phase-shift keying
  • 8PSK 8-Phase Shift Keying
  • the precoder W v [k] reported by the UE to the network can be expressed as follows:
  • the Type II CSI report may be used by the network to co-schedule multiple UEs on the same OFDM time-frequency resources. For example, the network can select UEs that have reported different sets of DFT vectors with weak correlations.
  • the CSI Type II report enables the UE to report a precoder hypothesis that trades CSI resolution against uplink transmission overhead.
  • NR 3GPP Release 15 supports Type II CSI feedback using port selection mode, in addition to the above normal reporting mode. In this case,
  • the base station transmits a CSI-RS port in each one of the beam directions.
  • the UE does not use a codebook to select a DFT vector (a beam), instead the UE selects one or multiple antenna ports from the CSI-RS resource of multiple ports.
  • Type II CSI feedback using port selection gives the base station some flexibility to use non-standardized precoders that are transparent to the UE.
  • the precoder reported by the UE can be described as follows
  • the vector e is a unit vector with only one non-zero element, which can be viewed as a selection vector that selects a port from the set of ports in the measured CSI- RS resource.
  • the UE thus feeds back which ports it has selected, the amplitude factors and the co-phasing factors.
  • AEs neural network based autoencoders
  • the AEs may be used to compress downlink MIMO channel estimates.
  • the compressed output of the AE may then be used as uplink feedback.
  • the use of the AE is here in the context of CSI compressing where a UE provides CSI feedback to a radio access network node by sending a CSI report that include a compressed and encoded version of the estimated downlink channel, or of important features thereof.
  • An AE is a type of Neural Network (NN), e.g., a type of machine learning algorithm, that may be used to compress and decompress data in an unsupervised manner.
  • NN Neural Network
  • Unsupervised learning is a type of machine learning in which the algorithm is not provided with any pre-assigned labels or scores for the training data.
  • unsupervised learning algorithms may first self-discover any naturally occurring patterns in that training data set. Common examples include clustering, where the algorithm automatically groups its training examples into categories with similar features, and principal component analysis, where the algorithm finds ways to compress the training data set by identifying which features are most useful for discriminating between different training examples and discarding the rest. This contrasts with supervised learning in which the training data include pre-assigned category labels, often by a human, or from the output of non-learning classification algorithm.
  • Figure 4a illustrates a fully connected (dense) AE (fully connected layers).
  • the AE may be divided into two parts: an encoder (used to compress the input data ), and a decoder (used to recover important features of the input data).
  • the encoder and decoder are separated by a bottleneck layer that holds a compressed representation, Y in Figure 4a, of the input data X.
  • the variable Y is sometimes called the latent representation of the input X. More specifically,
  • the size of the bottleneck (latent representation) Y is smaller than the size of the input data X.
  • the AE encoder thus compresses the input features X to Y.
  • the decoder part of the AE tries to invert the encoder’s compression and reconstruct X with minimal error, according to some predefined loss function.
  • AEs may have different architectures.
  • AEs may be based on dense NNs (like Figure 4a), multi-dimensional convolution NNs, recurrent NNs, transformer NNs, or any combination thereof.
  • dense NNs like Figure 4a
  • multi-dimensional convolution NNs like Figure 4a
  • recurrent NNs like the one presented in Figure 4a
  • transformer NNs or any combination thereof.
  • all AEs architectures possess an encoder- bottleneck-decoder structure, like the one presented in Figure 4a.
  • Figure 4b illustrates how an AE may be used for Al-enhanced CSI reporting in NR during an inference phase (that is, during live network operation).
  • the UE estimates the downlink channel (or important features thereof) using configured downlink reference signal(s), e.g., CSI-RS.
  • important features of the channel may be eigenvectors of the channel or the Gram matrix of the channel, one or more eigenvectors that correspond to the largest eigenvalues of an estimated channel covariance matrix, one or more Discrete Fourier Transform (DFT) base vectors (described on the next page), or orthogonal vectors from any other suitable and defined vector space, that best correlates with an estimated channel matrix, or an estimated channel covariance matrix, the channel delay profile).
  • DFT Discrete Fourier Transform
  • the UE estimates the downlink channel as a 3D complex-valued tensor, with dimensions defined by the gNB’s Tx-antenna ports, the UE’s Rx antenna ports, and frequency units (the granularity of which is configurable, e.g., subcarrier or subband).
  • the UE uses a trained AE encoder to compress the estimated channel or important features thereof down to a binary codeword.
  • the binary codeword is reported to the network over an uplink control channel and/or data channel. In practice, this codeword will likely form one part of a channel state information (CSI) report that might also include rank, channel quality, and interference information.
  • CSI channel state information
  • the network uses a trained AE decoder to reconstruct the estimated channel or the important features thereof.
  • the decompressed output of the AE decoder is used by the network in, for example, MIMO precoding, scheduling, and link adaption.
  • the architecture of an AE may need to be tailored for each particular use case. For example, properties of the data (e.g., CSI-RS channel estimates), the channel size, uplink feedback rate, and hardware limitations of the encoder and decoder may all need to be considered when designing the AE’s architecture.
  • the AE’s architecture After the AE’s architecture is fixed, it needs to be trained on one or more datasets. To achieve good performance during live operation in a network (the so-called inference phase), the training datasets need to be representative of the actual data the AE will encounter during live operation in the network.
  • the training process involves numerically tuning the AE’s trainable parameters (e.g., the weights and biases of the underlying NN) to minimize a loss function on the training datasets.
  • the loss function may be, for example, the Mean Squared Error (MSE) loss calculated as the average of the squared error between the UE’s downlink channel estimate H and the network’s reconstruction H, i.e., ⁇ H - H ⁇ 2 .
  • MSE Mean Squared Error
  • the training process is typically based on some variant of the gradient descent algorithm, which, at its core, comprises three components: a feedforward step, a back propagation step, and a parameter optimization step.
  • a dense AE i.e., a dense NN with a bottleneck layer, see Figure 4a
  • Feedforward A batch of training data, such as a mini-batch, (e.g., several downlink-channel estimates) is pushed through the AE, from the input to the output.
  • the loss function is used to compute the reconstruction loss for all training samples in the batch.
  • the reconstruction loss may be an average reconstruction loss for all training samples in the batch.
  • W and are the trainable weights and biases of layer n, respectively, and g is an activation function applied elementwise (for example, a rectified linear unit).
  • BP Back propagation
  • the gradients (partial derivatives of the loss function, L, with respect to each trainable parameter in the AE) are computed.
  • the back propagation algorithm sequentially works backwards from the AE output, layer-by-layer, back through the AE to the input.
  • the back propagation algorithm is built around the chain rule for differentiation: When computing the gradients for layer n in the AE, it uses the gradients for layer n + 1.
  • a core idea here is to make small adjustments to each parameter with the aim of reducing the average loss over the (mini) batch. It is common to use special optimizers to update the AE’s trainable parameters using gradient information. The following optimizers are widely used to reduce training time and improving overall performance: adaptive subgradient methods (AdaGrad), RMSProp, and adaptive moment estimation (ADAM).
  • AdaGrad adaptive subgradient methods
  • RMSProp RMSProp
  • ADAM adaptive moment estimation
  • An acceptable level of performance may refer to the AE achieving a pre-defined average reconstruction error over the training dataset (e.g., normalized MSE of the reconstruction error over the training dataset is less than, say, 0.1).
  • it may refer to the AE achieving a pre-defined user data throughput gain with respect to a baseline CSI reporting method (e.g., a MIMO precoding method is selected, and user throughputs are separately estimated for the baseline and the AE CSI reporting methods).
  • the above actions use numerical methods (e.g., gradient descent) to optimize the AE’s trainable parameters (e.g., weights and biases).
  • the training process typically involves optimizing many other parameters (e.g., higher-level hyperparameters that define the model or the training process).
  • Some example hyperparameters are as follows:
  • the architecture of the AE e.g., dense, convolutional, transformer.
  • Architecture-specific parameters e.g., the number of nodes per layer in a dense network, or the kernel sizes of a convolutional network.
  • the depth or size of the AE e.g., number of layers.
  • the mini-batch size (e.g., the number of channel samples fed into each iteration of the above training steps).
  • the regularization method e.g., weight regularization or dropout
  • Additional validation datasets may be used to tune the AE’s architecture and other hyperparameters.
  • a first aspect provides embodiments of a method, performed by a wireless communications device operating in a wireless communications network, for supporting evaluation of a Neural Network, NN, -based Auto Encoder, AE, -encoder.
  • the NN-based AE-encoder is trained to provide encoded data to a compatible trained NN-based AE- decoder of a network node of the wireless communications network.
  • the method comprises transmitting, to the network node, first capability information indicating support for a specific trained NN-based AE-encoder.
  • the method comprises transmitting, to the network node, second capability information indicating support for testing the specific trained NN-based AE-encoder.
  • the method comprises receiving, from the network node, a request to evaluate the specific trained NN-based AE-encoder.
  • Embodiments of a corresponding wireless communications device are also provided.
  • a second aspect provides embodiments of a method, performed by a network node for supporting evaluation of a NN-based AE-encoder of a wireless communications device.
  • the NN-based AE-encoder is trained to provide encoded data to a compatible trained NN-based AE-decoder of the network node.
  • the method comprises receiving, from the wireless communications device, first capability information indicating support for a specific trained NN-based AE-encoder.
  • the method comprises receiving, from the wireless communications device, second capability information indicating support for evaluating the specific trained NN-based AE-encoder.
  • the method comprises transmitting, to the wireless communications device, a request to evaluate the specific trained NN-based AE-encoder.
  • Embodiments of a corresponding network node are also provided.
  • Figure 1 illustrates a simplified wireless communication system.
  • Figure 2 illustrates an example transmission and reception chain for MU-MIMO operations.
  • Figure 3 illustrates a CSI type II normal reporting mode.
  • Figure 4a illustrates a fully connected (dense) autoencoder (fully connected layers).
  • Figure 4b illustrates how an autoencoder (AE) may be used for Al-enhanced CSI reporting in NR during an inference phase (that is, during live network operation).
  • AE autoencoder
  • Figure 4c illustrates training and inference pipelines, and their interactions within a model lifecycle management procedure.
  • Figure 5 provides a schematic overview of a wireless communications network.
  • FIGS 6aa, 6ab and 6b illustrate wireless communication devices and network nodes, according to some embodiments.
  • Figure 7a is a signaling diagram, according to some embodiments.
  • Figure 7b is a flow chart of methods performed at a wireless communication device, according to some embodiments.
  • Figure 7c is a signaling diagram, according to some embodiments.
  • Figure 7d is a flow chart of a method performed at a wireless communication device, using a digital twin, according to some embodiments.
  • Figure 8 is a flow chart of a method performed at a wireless communication device, according to some embodiments.
  • Figure 9a is a flow chart of a method performed at a network node, according to some embodiments.
  • Figure 9b illustrates band combinations linked to feature set combinations, according to some embodiments.
  • Figure 10 shows an example of a wireless communications device, according to some embodiments.
  • Figure 11 shows an example of a network node, according to some embodiments.
  • Figures 12-13 illustrate communication systems, according to some embodiments.
  • Figures 14-17 are flowcharts of methods implemented in a communication system, according to some embodiments.
  • the AE training process is a highly iterative process that may be expensive - consuming significant time, compute, memory, and power resources. Therefore, it may be expected that AE architecture design and training will largely be performed offline, e.g., in a development environment, using appropriate compute infrastructure, training data, validation data, and test data.
  • a development-domain may refer to a software/simulation-based environment used by a vendor to develop algorithms and functionality to be implemented in a product.
  • Data for training, validation, and testing may be collected from one or more of the following examples: real measurements recorded in live networks, synthetic radio channel data from, e.g., 3GPP channel models or ray tracing models and/or digital twins, and mobile drive tests.
  • Validation data may be part of the development and tuning of the NN, whereas the test data may be applied to the final NN.
  • a “validation dataset” may be used to optimize AE hyperparameters (like its architecture).
  • two different AE architectures may be trained on the same training dataset. Then the performance of the two trained AE architectures may be validated on the validation dataset. The architecture with the best performance on the validation dataset may be kept for the inference phase.
  • validation may be performed on the same data set as the training, but on “unseen” data samples (e.g. taken from the same source). Test may be performed on a new data set, usually from another source and it tests the NN ability to generalize.
  • This AE model represents the outcome of an iterative training process, or training pipeline, from which the AE model is ready to be deployed and used to perform inference.
  • AE model lifecycle management illustrates training and inference pipelines, and their interactions within a model lifecycle management procedure.
  • the AE model lifecycle management typically consists of
  • a training pipeline which may also be a re-training pipeline o
  • data ingestion referring to gathering raw (training) input data, like wireless channel data, from a data storage. After data ingestion, there may also be an action that controls the validity of the gathered data.
  • data pre-processing referring to some feature engineering applied to the gathered data. For example, it may include data normalization and possibly a data transformation required for the input data to the AE.
  • model evaluation referring to benchmarking the performance to some AE baseline. The iterative steps of model training and model evaluation may continue until the acceptable level of performance (as previously exemplified) is achieved.
  • model registration referring to register the AE model, including any corresponding AE-meta data that provides information on how the AE model was developed, and possibly performance outcomes of the AE model evaluations.
  • An inference pipeline o
  • data ingestion referring to gathering raw (inference) input data from a data storage
  • the AE means gathering raw channel data from a data buffer storage of a UE.
  • data pre-processing stage that is typically identical to corresponding processing that occurs in the training pipeline.
  • model operational referring to using the trained and deployed model in an operational mode, which for the AE may mean that the UE feedforwards the input data through its AE-encoder, sends the output data of the AE-encoder to the radio access network, which uses the received output data as input to its AE- decoder.
  • a drift detection stage that informs about any drifts in the model operations.
  • One problem with existing AE solutions is that they require a very tight cooperation between the UE (AE-encoder) and the base station (AE-decoder) to conduct performance validation in a test environment setup. Firstly, this may not always be possible since not all UE manufacturers may have direct contacts with the different base station manufacturers in question. Secondly, the AE-encoder may have been tested in a lab setting with limited sets of test data, or perhaps more importantly the lab test may have occurred before new relevant deployment scenarios were considered e.g., in terms of new radio propagations and antenna configurations, and thereby not captured in the lab test. The performance may then need to be verified or validated for UEs in the field, e.g., operating in a wireless communications system, directly rather than in the lab environment.
  • AE-encoder or AE-decoder
  • the ML research area is advancing so rapidly that there is a large risk that the design available in the field, e.g. in nodes operating in the wireless communication network, will be outdated quickly in terms of performance. Therefore, new designs need to be considered.
  • An object of embodiments herein may be to obviate some of the problems related to evaluation of performance of AEs in wireless communication networks.
  • the object is achieved by a method, performed by a wireless communications device operating in a wireless communication network, for supporting evaluation of an NN-based AE-encoder trained to provide encoded data to a compatible trained NN-based AE-decoder of a network node of the wireless communications network.
  • An object of embodiments herein is therefore to improve encoded reporting in communications networks.
  • embodiments herein disclose improved evaluation of performance of AE-encoders and/or AE-decoders in a wireless communication network.
  • Embodiments herein are mainly described from an CSI reporting use case when using AE-encoder. However, embodiments are not limited to that use case given that the AE-encoder may be used in other use cases as well, such as for positioning, data transfer and so on.
  • AEs may for example be used to compress a channel that is decompressed at the network side.
  • a base station may be able to derive PM I from the decompressed channel. It is believed that this is a more efficient manner of representing the channel for PM I reporting than the hand-designed/theoretical approach that is currently used within LTE and NR.
  • For positioning AE may be used to derive a more efficient representation of the reference signal time difference (RSTD) measurement, i.e., the channel impulse response (channel delay profile).
  • RSTD reference signal time difference
  • For data transfer AE may be used for transferring the data in an efficient manner instead as current standards wherein the components are designed theoretically.
  • Embodiments herein relate to communication networks in general, and specifically to wireless communication networks.
  • Figure 5 is a schematic overview depicting a wireless communications network 100 wherein embodiments herein may be implemented.
  • the wireless communications network 100 comprises one or more RANs and one or more CNs.
  • the wireless communications network 100 may use a number of different technologies, such as Wi-Fi, Long Term Evolution (LTE), LTE-Advanced, 5G, New Radio (NR), Wideband Code Division Multiple Access (WCDMA), Global System for Mobile communications/enhanced Data rate for GSM Evolution (GSM/EDGE), Worldwide Interoperability for Microwave Access (WiMax), or Ultra Mobile Broadband (UMB), just to mention a few possible implementations.
  • LTE Long Term Evolution
  • NR New Radio
  • WCDMA Wideband Code Division Multiple Access
  • GSM/EDGE Global System for Mobile communications/enhanced Data rate for GSM Evolution
  • WiMax Worldwide Interoperability for Microwave Access
  • UMB Ultra Mobile Broadband
  • Access nodes operate in the wireless communications network 100 such as a radio access node 111.
  • the radio access node 111 provides radio coverage over a geographical area, a service area referred to as a cell 115, which may also be referred to as a beam or a beam group of a first radio access technology (RAT), such as 5G, LTE, Wi-Fi or similar.
  • the radio access node 111 may be a NR-RAN node, transmission and reception point e.g. a base station, a radio access node such as a Wireless Local Area Network (WLAN) access point or an Access Point Station (AP STA), an access controller, a base station, e.g.
  • WLAN Wireless Local Area Network
  • AP STA Access Point Station
  • a radio base station such as a NodeB, an evolved Node B (eNB, eNode B), a gNB, a base transceiver station, a radio remote unit, an Access Point Base Station, a base station router, a transmission arrangement of a radio base station, a stand-alone access point or any other network unit capable of communicating with a wireless device within the service area depending e.g. on the radio access technology and terminology used.
  • the respective radio access node 111 may be referred to as a serving radio access node and communicates with a UE with Downlink (DL) transmissions on a DL channel 123-DL to the UE and Uplink (UL) transmissions on an UL channel 123-UL from the UE.
  • DL Downlink
  • UL Uplink
  • a number of wireless communications devices operate in the wireless communication network 100, such as a UE 121.
  • the UE 121 may be a mobile station, a non-access point (non-AP) STA, a STA, a user equipment and/or a wireless terminal, that communicate via one or more Access Networks (AN), e.g. RAN, e.g. via the radio access node 111 to one or more core networks (CN) e.g. comprising a CN node 130, for example comprising an Access Management Function (AMF).
  • AN Access Networks
  • CN core networks
  • AMF Access Management Function
  • UE is a non-limiting term which means any terminal, wireless communication terminal, user equipment, Machine Type Communication (MTC) device, Device to Device (D2D) terminal, or node e.g. smart phone, laptop, mobile phone, sensor, relay, mobile tablets or even a small base station communicating within a cell.
  • MTC Machine Type Communication
  • D2D Device to Device
  • Figures 6aa, 6ab and 6b all illustrate a wireless communications device 601 comprising hardware and/or software implementing a trained Neural Network, NN,- based Auto Encoder, AE, -encoder 601-1.
  • a wireless communications device 601 comprising hardware and/or software implementing a trained Neural Network, NN,- based Auto Encoder, AE, -encoder 601-1.
  • the trainable parameters are fixed for a specific version of the AE-encoder.
  • the trained AE-encoder 601-1 has been trained to provide compressed encoded output data, such as coded CSI, based on input data, such as channel measurements.
  • the compressed coded output data is sent from the wireless communications device 601 to one or more network nodes, such as the radio access node 111, over a communications channel, such as the UL channel 123-LIL, in a communications network, such as the wireless communications network 100.
  • the compressed coded output data is then processed by an AE-decoder of the one or more network nodes to produce an uncompressed output.
  • the wireless communications device 601 may further implement a reference AE- encoder 601-2 illustrated in Figure 6aa, which may be used to produce reference reports to the network.
  • Figures 6aa, 6ab and 6b further illustrates a network node 602 comprising hardware and/or software implementing a trained NN-based AE-decoder 602-1.
  • the AE-decoder 602-1 is compatible with the AE-encoder 601-1 of the wireless communications device 601.
  • the NN-based AE-decoder 602-1 may comprise a same number of input nodes as a number of output nodes of the AE-encoder 601-1.
  • the network node 602 may correspond to a radio access network node, such as the radio access node 111 in Figure 5.
  • the network node 602 may also correspond to a relay node, a donor base station, or a gNB distributed unit or central unit, or similar entities.
  • the network node 602 may also refer to multiple RAN nodes and units, including those previously mentioned single nodes and units, as well as including core network nodes and cloud servers, and other network architecture entities.
  • the network node 602 and/or the wireless communications device 601 may have access to test data, such as channel data.
  • Figures 6aa and 6ab illustrate a scenario in which a test data source 602-2 is located in the network node 602.
  • the network node 602 may provide the test data to the wireless communications device 601 or the network node 602 may provide the wireless communications device 601 with a network address of the test data source 602-2 or some other indication of how the wireless communications device 601 may retrieve the test data itself.
  • Figure 6b illustrates a scenario in which a second test data source 601-3 is located in the wireless communications device 601.
  • the wireless communications device 601 may provide the test data to the network node 602 or the wireless communications device 601 may provide the network node 602 with a network address of the second test data source 601-3 or some other indication of how the network node 602 may retrieve the test data itself.
  • the wireless communications device 601 may have access to one or more trained NN-based AE-encoder models, e.g., for encoding CSI.
  • the network node 602 may have access to one or more trained NN-based AE-decoder models for decoding the encoded CSI provided by the wireless communications device 601.
  • the network node 602 may further comprise a reference AE encoder
  • the reference AE encoder 602-3 may be used together with a reference AE decoder 602-4 also comprised in the network node 602.
  • the network node 602 may indicate to the wireless communications device 601 that the performance is good enough.
  • Figures 6ab and 6b further illustrate a further node 603 comprising a digital twin
  • the digital twin 603-1 may be a virtual representation of the physical wireless communications device 601 , or of some objects of the physical wireless communications device 601, or of some functionality that may be processed by the physical wireless communications device 601, within the digital domain.
  • the object may be the AE-encoder 601-1 and the functionality may be the functionality of the AE-encoder 601-1.
  • the digital twin 603-1 may yield the same response (output) as the corresponding function/object of the physical wireless communications device 601 given the same input. With that said, the digital twin 603-1 representation may thus not be a full virtual representation of the complete physical wireless communications device 601 in the sense that it may have support for only a subset of the physical functionality of the wireless communications device 601.
  • the digital twin 603-1 may have some additional functionality implemented that is not supported by the physical wireless communications device 601.
  • the reason for this may be that the digital twin 603-1 may in part be another software implementation and hence may have a slightly different set of supported features.
  • the digital twin may support functionality that is not yet released into the wireless communication device. In this way the new functionality may be tested before releasing it in the wireless communication device. If the functionality passes one or several tests the functionality may then be made available within the wireless device. Further the digital twin 603-1 of the function and/or object may not be constrained by the processing and/or memory constrains of the physical wireless communications device 601 since it may be hosted on a computer, cloud server or similar entity.
  • the digital twin 603-1 may therefore be able to process more data or process the data quicker or both in comparison to the physical wireless communications device 601.
  • the digital twin 603-1 may have the possibility to run multiple (including two or more) functions at the same time while the wireless communications device 601 will not. For example, creating a reference report and the AE-encoder report with the same input data while the wireless communications device 601 may not have that possibility.
  • each node 601, 602, 603 may be implemented as a Distributed Node (DN) and functionality, e.g. comprised in a cloud 140 as shown in Figures 6aa, 6ab and 6b, may be used for performing or partly performing the methods. There may be a respective cloud for each node.
  • DN Distributed Node
  • Details of the network node 602 and the NN-based AE-decoder 602-1, such as a reconstructed channel H, a loss function, and a method to compute gradients may be transparent to the wireless communications device 601.
  • a test may be initiated whenever there has been an update of the AE encoder or/and the AE decoder (e.g., after a re-training of the AE encoder or/and the AE decoder), or that the characteristics of the inference data (i.e. , the input data to a deployed AE encoder) may have changed with respect to the data used in a predeployment training.
  • One reason for initiating a performance evaluation may be that the neural network AE encoder of the UE model has been re-trained, fully, or partly, meaning that some or all AE encoder trainable parameters have been changed after that the UE model was released at the first time.
  • the UE’s AE-encoder model may first be tested in a lab environment together with a network vendor’s AE-decoder. Later the UE’s AE- encoder has been re-trained.
  • the radio access capability signalling may include one or more parameters indicating a re-training status of the UE model, or more specifically of the AE encoder version. For example, this may be signalled by a version number of the AE-encoder.
  • the radio access network may access the retraining status of the AE encoder from a repository when a UE is connecting to the network. This type of information may be used by the network to conclude whether to initiate a test.
  • Another reason for initiating the performance evaluation may be that the lab test, or offline testing, was limited to testing the AE encoder as part of a software module rather than then testing the UE with the software being implemented in a final product.
  • the UE and/or chipset vendor may have tested its AE encoder software with a network vendor’s AE decoder software in e.g., a lab or in a development domain, while the final performance test of the UE, with the AE encoder being implemented, may be done in a live network.
  • some UE testing in between may be needed to ensure a minimum AE encoder performance before a certain UE model is released.
  • the AE-decoder in the network may have been retrained without considering all the AE-encoders on the market and hence needs to be tested with the AE-encoders in UEs on the market that it was not able to be tested on within a lab-environment.
  • the signaling diagram illustrates a method for supporting evaluation of performance of the trained AE-encoder 601-1 in the wireless communications network 100.
  • the wireless communications device 121 operates in the wireless communication network 100.
  • the AE-encoder 601-1 is trained to provide encoded data to the compatible trained NN-based AE-decoder 602-1 comprised in one or more network nodes, such as the network node 602, of the wireless communication network 100.
  • the wireless communications device 601 communicates with the wireless communications network 100.
  • the wireless communications device 601 may communicate with any network node of the wireless communications network 100, such as the network node 602.
  • the wireless communications device 601 may further communicate with the node 603 in which the digital twin 603-1 is implemented.
  • the wireless communications device 601 sends an indication that it supports an AE-encoder framework and/or version of an AE-encoder and/or a version of the UE model that indicates the AE-encoder framework and/or version of the AE-encoder to the network.
  • a UE connecting to a wireless network may report its UE radio access capability to the network. Therefore, the indication of the AE-encoder framework and/or version of the AE-encoder and/or the version of the UE model may be sent as capability information, e.g., as UE capability information.
  • the wireless communications device 601 may indicate its capabilities with respect to a specific version of a trained AE-encoder, e.g., in a UE capability report.
  • the UE may further report some specific details around its UE model to the network, such as the number of receive antennas, supported number of downlink transmission layers, supported number of CSI-RS antenna ports, chipset model or other unique parameters that could be used to identify a specific UE model.
  • the unique information may also be secondary type information that may be used by the network to identify a specific UE, for example, a full list of UE radio access capabilities.
  • the reported UE radio access capability parameters is one or more parameters indicating support of an AE-based framework potentially for a given purpose, including the support of one or more AE-encoder versions.
  • the purpose may for example be CSI reporting, positioning, data transfer, beam management.
  • the framework may consist of a type of formatting that is defined as the output of the AE-encoder, e.g. number of bits, bit representation (e.g. several bits together represent a number how this is done), use case for the AE-encoder (as there may be separate capabilities for separate use case (e.g. CSI reporting, positioning, data transfer and soon, in addition there may be multiple frameworks within the same use case).
  • a type of formatting that is defined as the output of the AE-encoder, e.g. number of bits, bit representation (e.g. several bits together represent a number how this is done), use case for the AE-encoder (as there may be separate capabilities for separate use case (e.g. CSI reporting, positioning, data transfer and soon, in addition there may be multiple frameworks within the same use case).
  • the one or more AE encoder versions may further be identified either via specification or via a pre-registered (unique) identity that have been stored in some repository which may either be part of the network or accessible by the network.
  • a specific AE-encoder version is considered with the understanding that the UE may support more than one AE-encoder version.
  • the indicated support may be split up into two parts, wherein one capability signalling is for indicating support for a specific type of AE-encoder framework and the second part is a version number for that AE-encoder.
  • the UE may or may not support multiple versions. If the UE supports multiple versions the UE may then need to indicate which version numbers the UE supports.
  • the version number may be used to indicate if the UE has modified its implementation of the AE-encoder in some form that may warrant to indicate it as a new version. In practise the version number may be implemented as a counting value that is increased, but other options are also possible.
  • the specific AE-encoder design used in the UE may not be known to the network. Instead, the specific AE-encoder design may be identified by a version. Alternatively, parts of the AE-encoder design may be known by the network for example to aid the design of an AE-decoder. The specific design details may be given by either the AE-encoder framework capability or the AE-encoder version. Alternatively, both together.
  • the UE may further report a parameter that indicates its capability for conducting a certain performance test.
  • a parameter that indicates its capability for conducting a certain performance test.
  • the UE may support performance test based on a given AE-encoder framework and AE-encoder version. Alternatively, it may be based on only the AE- encoder framework.
  • additional parameters may be provided among the UE capability parameters. These may be a parameter related to test buffer capacity of the UE, whether or not the UE may sample test data by itself for the test, receive test data, etc.
  • the test buffer capacity may be two different parameters or a single parameter and covers the aspects of how much test data samples the UE may measure of the channel and/or how much output from the AE-encoder the UE may store.
  • This UE capability may be needed to be indicated by the UE independently if the UE may measure its own test data or not. Since if the UE cannot measure its own test data the UE will be provided with test data and the capability may then indicate the amount of test data it may be provided with. On the other hand, if the UE measures its own test data this parameter may set a limit on how many measurements the UE is able to make.
  • the parameter of whether or not the UE may sample test data by itself, is whether the UE can measure and/or generate data that results in input data to the AE-encoder.
  • the parameter of receiving test data is whether the UE is able to receive test data from the network.
  • the UE may in addition provide a capability signalling indicating a passed test status of the UE.
  • a capability signalling indicating a passed test status of the UE.
  • it may mean that the UE’s AE-encoder has been tested by one or multiple network’(s) AE-decoder(s) and not by only a development test towards a reference AE-encoder.
  • the network may use this info to select which AE-encoders to test.
  • the passed test status may be sent before the other capabilities.
  • An additional capability may be whether the UE is able to feedback additional information with the output of the AE-encoder results. Such additional information may be used as a reference of the performance of the AE-encoder.
  • this may be for example another CSI report that is for example based on CSI report type II and uses the similar or same input data as the AE-encoder.
  • the input data may vary slightly depending on how the algorithms are designed. It may also vary depending on if some form of filtering such as averaging is done on the input data for one of the reports but not the other one. Taking the use case of positioning as another example, the measurements described above may be a representation of the impulse response.
  • the reference report that is indicated to be supported may be an RSTD measurement.
  • the receiving network may compare the performance between the two reporting modes and the network based on that may conclude whether the AE-encoder is functioning sufficiently good.
  • the other reporting mode may also be another AE-encoder that the UE supports.
  • one additional capability is the number of AE-encoders the UE may test at once since multiple AE-encoders may be tested at the same time within the UE.
  • the capability may indicate how many AE-encoders the UE may test simultaneously, and it may further identify these AE-encoders that may be tested simultaneously. This may be advantageous since the AE-encoders may then be tested with the same input data which may provide a better comparison. Further, using the same input data may reduce memory requirements.
  • One or more of the AE-encoders running in parallel may be used as a reference.
  • the test may be initiated by the network in that the UE first reports the abovedescribed UE capabilities, after which the UE receives a request from the network to perform a test of one or multiple AE-encoder(s) in accordance with the sent UE capabilities (i.e. , the UE has indicated support for the AE-encoders it is being tested for).
  • the request will be described further below in action 703.
  • the network may determine, based on the indication of the AE- encoder framework and/or version of the AE-encoder and/or the version of the UE model whether or not it is required to evaluate the performance of the AE-encoder 601-1.
  • the network may conclude from the UE capability report that the UE is of a specific UE model, or has a particular AE-encoder, that needs to undergo a performance evaluation, such as a validation or a test, before it may be used to provide AE-based encoded data in an operational mode.
  • the network may retrieve information associated with received AE-encoder framework and/or version of the AE-encoder and/or version of the UE mode from a memory in the network or from a repository in a cloud.
  • the operational mode may for example be implemented for CSI reporting and the UE may in such case construct a CSI report or parts of a CSI report based on an AE- encoder.
  • other operational modes of the AE-encoder are also possible such as using it for data transfer, positioning and so on.
  • One possibility for the network for validating the AE-encoder is to configure the UE to report test output from the encoder but not use the output from the AE-encoder for other purposes than testing the AE-encoder. That is, the network doesn’t need to take the test information into account when scheduling the UE.
  • the network may use other methods such as CSI type II reporting. This is here exemplified for CSI feedback.
  • the UE receives a configuration from the network to provide AE-based CSI feedback with the AE-encoder, but the network may not use that CSI feedback other than for the purpose of testing the AE-encoder. Whether or not the CSI report based on the AE-encoder is used for something else than testing may be unknown to the UE. To conduct the testing the UE may need to be configured with some intermediate, or additional, CSI reporting at least until the UE has passed the performance test of the specific AE encoder version to be tested. The network may then compare the difference between the additional CSI report and the tested AE-encoder CSI report.
  • One reason for initiating a performance test may be that the neural network AE encoder of the UE model has been re-trained, fully, or partly, meaning that some or all AE encoder trainable parameters have been changed after that the UE model was released at the first time.
  • the UE’s AE-encoder model may first be tested in a lab environment together with a network vendor’s AE-decoder. Later the UE’s AE-encoder has been re-trained.
  • the radio access capability signalling may include one or more parameters indicating a re-training status of the UE model, or more specifically of the AE encoder version. For example, this may be signalled by a version number of the AE-encoder.
  • the radio access network may access the retraining status of the AE encoder from a repository when a UE is connecting to the network. This type of information may be used by the network to conclude whether to initiate a test.
  • Another reason for initiating the performance test may be that the lab test, or offline testing, was limited to testing the AE encoder as part of a software module rather than then testing the UE with the software being implemented in a final product.
  • the UE and/or chipset vendor may have tested its AE encoder software with a network vendor’s AE decoder software in e.g., a lab or in a development domain, while the final performance test of the UE, with the AE encoder being implemented, may be done in a live network.
  • some UE testing in between may be needed to ensure a minimum AE encoder performance before a certain UE model is released.
  • the AE-decoder in the network may have been retrained without considering all the AE-encoders on the market and hence needs to be tested with the AE-encoders in UEs on the market that it was not able to be tested on within a lab-environment.
  • the wireless communications device 601 receives a request from the network to perform a test of one or multiple AE-encoder(s) in accordance with the sent UE capabilities.
  • the request may also be to let the digital twin 603-1 perform the test.
  • the UE may then perform the test and report the result of the test, i.e. the output, to the network.
  • the request may further include more details related to the UE capabilities, e.g., if the UE should measure its own test data or if the test data is provided by the network. The amount of test data the UE should collect if the UE is requested to collect test data and so on.
  • the request may further include information of any reference format (e.g., reference AE-encoder(s) or CSI reporting modes) that may have its results jointly reported with the tested AE-encoder(s).
  • the instruction may include any of the below examples which all are related to the generation of the test data set, e.g.
  • a time period during which the input data shall be generated e.g., a time period for collecting the test data, or a start and stop time • geographical area the test data set should be associated with
  • a cell or group of cells for collecting the test data e.g., cells the input data shall be generated from
  • the type may be based for example on another AE-encoder or CSI report type that is not AE-encoder based, e.g. CSI report II
  • the request may include a request to calculate one or more reference reports on the test data set.
  • the reference reports may be derived based on reference AE-encoder(s) or on one or multiple CSI reporting types, e.g. CSI report type II.
  • Doppler ranges different encoders/decoders may be used for different Doppler ranges. This may make sure the UE does not collect data under unfavourable conditions. If the encoder is only tested with low Doppler then the network may want to selectively target high-Doppler scenarios for further tests.
  • the network node 602 may transmit a test data set, or a test data segment, (e.g., including multiple H’s) to one or more wireless communication devices, such as the wireless communication device 601.
  • the test data set may comprise input data for the test, such as channel data.
  • the network node 602 transmits a network address from where the test data may be downloaded by one or more UEs.
  • the wireless communications device 601 may also receive or retrieve the test data H and processes each H through its AE-encoder which results in a set of encoded and compressed output data Y that is derived based on the input test data set H.
  • Each H being representative of each individual input data vector H.
  • Each such input produces one output vector Y. If one takes the example of CSI estimation each H would be the one single channel measurement represented in some format.
  • the test input data H may not necessarily be a representation of channels observed by the UE when estimating channels from CSI-RS. It may for example be a representation of uplink channels estimated by base stations from uplink transmissions such as from SRS transmissions. It may also be a representation of channels in some other manners, such as being synthetically generated channels with or without noise, or impairments, included.
  • the test input data H may also be a representation that corresponds to some stage of a CSI report, such as a NR CSI type II report.
  • the wireless communications device 601 may process the test input data set H to produce the output data Y.
  • test data set may be done within the UE as a background process over a certain time as it is not too time critical.
  • the output Y of the AE-encoder is sent to the network node 602 in action 705. If there are several outputs, e.g., several Ys based on several Hs, then the outputs may be sent in a big batch rather than being sent individually.
  • the UE may further report other information together with the output data for the network to be able to process the output data.
  • Such information may be the time period when the test data set, H, was collected (e.g., some form of time stamp), the approximate location where the UE collected the test data set, information about the cell the UE was connected to when collecting the test data, neighbour cell that the UE detects, SS/PBCH indices, CSI-RS indices, RSRP/RSRQ/SINR levels for the connected cell(s) and neighbour cell(s), CSI report(s) for the channel, etc.
  • the additional information reported by the UE may further be on request by the network or it may not be.
  • the network node 602 may then process the test output data from the AE encoder test to evaluate the performance of the AE-encoder.
  • the network node 602 may for example
  • the network may input the compressed channel data Y into an AE- decoder and compare the output of the AE-decoder H, to some form reference for the output Href or the test input data set H.
  • the reference may either be a fixed value or based on a reference AE-encoder together with the AE-decoder.
  • the network node 602 may record this for future use in action 707.
  • the network node 602 may record that the specific AE-encoder functions sufficiently well and may be used within the deployments that are deemed representative of the test data. This information may in the future be shared among network nodes within the whole operator’s deployment or between a limited set of base stations within an operator’s deployment. The information may also be shared across different operators.
  • the network node 602 may in the future use this information to check that a specific AE-encoder works with its AE-decoder implementation. Hence if a UE reports that it supports a specific AE-encoder or AE-encoder version in its UE capability and the network is able to identify that it has previously tested that AE-encoder via the test data described earlier, then the network may configure the UE to use that AE-encoder.
  • the network may, based on comparing an AE-encoder ID reported in the UE capabilitiesites with the AE-encoder IDs of stored AE-encoders that have passed testing of the AE-encoder (fulfils requirement) configure the UE to use the AE-encoder that matches the AE-encoder ID.
  • the network node 602 may indicate to the UE that the AE-encoder’s performance is deemed sufficient.
  • This indication may be in the form of a pass/fail indication (e.g. in terms of loss), a relative value of the loss (compared to a reference model AE-encoder), an absolute loss value, a single bit indication, etc.
  • the network node 602 may also indicate the performance by configuring the UE to use the AE-encoder 601- 2 that has been tested according to the above description. For example, the network node 602 may configure CSI reporting with the tested AE-encoder or indicate a test failure.
  • the wireless communications device 601 may determine and set a test status indication, e.g. register AE test status with tested AE-encoder.
  • the test status may be any one or more of: test started, test passed and test failed.
  • the wireless communications device 601 may determine the test status based on the indication received from the network node 602 in action 708, e.g., based on the configuration or lack of configuration (e.g., within a certain time).
  • the wireless communications device 601 may transmit the test status indication to the network node 602.
  • the flowchart of Figure 7b illustrates a specific embodiment of a method, performed by the UE, for supporting evaluation of performance of the AE-encoder.
  • FIG. 7c a first signaling diagram in Figure 7c and a flowchart in Figure 7d and with continued reference to Figures 5 and 6aa, 6ab and 6b.
  • the signaling diagram illustrates a method for supporting evaluation of performance of the trained AE-encoder 601-1 in the wireless communications network 100.
  • Figure 7c and 7d illustrate a case where the test is performed in the digital twin.
  • the UE capability includes information about a digital twin representation.
  • the digital twin may be used for testing purposes. The testing is then conducted in the digital twin rather than in the UE.
  • the digital twin may for example be implemented within a cloud server.
  • the network may or may not be able to communicate directly with the digital twin. If the network is not able to communicate directly with the digital twin then the network may communicate with the digital twin via the UE.
  • the test of the AE-encoder may be initiated by the network communicating with the UE.
  • the UE may forward the information to its digital twin.
  • the information that the UE forwards may be information on how to setup a test.
  • the actual transfer of the test data set and the results of the test may be sent directly between the network and the digital twin. It may be so that the digital twin retrieves the test data set and uploads the results.
  • An alternative is that the UE indicates that it has a digital twin and further provides the accessible network address of the digital twin to the network.
  • the network may then communicate input and output test data directly with the digital twin.
  • the network may initiate the test in the digital twin and the digital twin may provide the result of the test to the network.
  • the test initiated to be conducted by the digital twin is handled from the network side via communication with the UE. For example, other communication than communication of test data may be provided via the UE.
  • the wireless communications device 601 may send an indication to the network that it supports evaluation or testing of at least one AE-encoder framework and/or version of an AE-encoder by the digital twin 603-1.
  • the wireless communications device 601 may further report some specific details around the model of the wireless communications device 601 to the network, such as the parameters mentioned above in action 701 , which may be unique for the model of the wireless communications device 601 , that may be used to identify a specific model of the wireless communications device 601.
  • the specific model of the wireless communications device 601 may in turn identify the version of the AE-encoder which may have been updated.
  • the information may also be secondary type information that may be used by the network to identify a specific UE, for example, a full list of UE radio access capabilities.
  • the indication that it supports evaluation or testing of at least one AE-encoder framework and/or version of an AE-encoder by the digital twin 603-1 may be sent as capability information, e.g., as UE capability information.
  • the wireless communications device 601 may indicate its capabilities with respect to a specific version of a trained AE-encoder, e.g., in a UE capability report.
  • the digital twin has a separate set of UE capabilities then the UE has.
  • not all the features may be supported by a UE, or more features may be represented in the digital twin than may be configured for live operation in the network by the UE. All features that are associated with the digital twin may be indicated separately, such as for example the test buffer capacity, whether or not the UE may sample/collect test data by itself for the test, receive test data, reference of the performance of the AE-encoder or reference CSI report, number of AE- encoders possible to test at once, etc.
  • An example framework for signalling UE capabilities will be given below in the section “Further detailed example embodiments” with NR as the example radio access technology.
  • the network may determine, based on the received indication that the wireless communications device 601 supports evaluation or testing of at least one AE- encoder framework and/or version of an AE-encoder by the digital twin 603-1, whether or not it is required to evaluate the performance of the AE-encoder 601-1.
  • the network may identify based on the AE-encoder framework and/or AE-encoder version together with the implemented AE-decoder(s) the network has implemented that it is deemed necessary to test the performance of the UEs AE-encoder implementation.
  • the network may conclude from the UE capability report that the UE is of a specific UE model, or has a particular AE-encoder, that needs to undergo a performance evaluation, such as a validation or a test before it may be used to provide AE-based encoded data in an operational mode.
  • the network may retrieve information associated with the received AE-encoder framework and/or version of the AE-encoder and/or version of the UE mode from a memory in the network or from a repository in a cloud.
  • the network node 602 In response to determining that it is required to evaluate the performance of the AE- encoder 601-1 , the network node 602 transmits a request to the wireless communication device 601 to initiate a test of one or multiple AE-encoder(s) within the digital twin 603-1.
  • the UE receives the request to initiate the test of the one or multiple AE-encoder(s) within the digital twin 603-1 from the network node 602.
  • the request may or may not include a network address together with access information where test data may be retrieved by the further node 603, such as a cloud server, wherein the digital twin 603-1 is operated.
  • the access information may include information needed to setup a secure connection.
  • the UE In response to receiving the request the UE sends a request to the further node 603, such as a cloud server, and/or the digital twin 603-1 to test one or multiple AE- encoder(s) in action 724. With the request, the UE may forward the information on where the test data may be retrieved, if it has received such information from the network together with the access information.
  • the further node 603 such as a cloud server, and/or the digital twin 603-1 to test one or multiple AE- encoder(s) in action 724.
  • the UE may forward the information on where the test data may be retrieved, if it has received such information from the network together with the access information.
  • the further node 603, such as the cloud server, retrieves the test data in action 725.
  • the network node 602 transmits a test data set, or a test data segment, (e.g., including multiple H’s) to the wireless communication device 601 or directly to the further node 603.
  • the test data set comprises input data for the test, such as channel data.
  • the network node may 602 transmit a network address from where the test data may be downloaded by one or more UEs.
  • test input data H may not necessarily be a representation of channels observed by the UE when estimating channels from CSI-RS.
  • the digital twin 603-1 may process the test input data set H to produce the output data Y.
  • the cloud server may process the test data through the AE-encoder(s).
  • the digital twin 603-1 may process each H through its AE-encoder which results in a set of encoded and compressed output data Y that is derived based on the input test data set H.
  • the UE may receive a complete message, indicating that the AE- encoder of the digital twin 603-1 has finished processing the data and produced an output, from the cloud server.
  • the complete message may potentially comprise a network address at which the processed test data may be located with the associated access information.
  • the UE may then send a message to the network node 602 in action 727b indicating that the AE-encoder(s) test is completed with the network address where the network may retrieve the output data of the test.
  • the UE either knows the network address before or has received the network address from the cloud server with associated access information.
  • the output Y of the AE-encoder within the digital twin 603-1 is then retrieved or received by the network node 602 in action 728. If there are several outputs, e.g., several Ys based on several Hs, then the outputs may be sent in a big batch rather than being sent individually.
  • the network node 602 may then process the test output data from the AE encoder test to evaluate the performance of the AE-encoder. This action corresponds to action 706 above.
  • the network node 602 may record this for future use, e.g., in action 730. This action corresponds to action 707 above.
  • the network node 602 may record that the specific AE-encoder functions sufficiently well and may be used within the deployments that are deemed representative of the test data.
  • the network node 602 may indicate to the UE that the AE-encoder’s performance is deemed sufficient. This indication may be in the form of a pass/fail indication (e.g. in terms of loss), a relative value of the loss (compared to a reference model AE-encoder), an absolute loss value, a single bit indication, etc.
  • the network node 602 may also indicate the performance by configuring the UE to use the AE-encoder 601- 2 that has been tested according to the above description. For example, the network node 602 may configure CSI reporting with the tested AE-encoder or indicate a test failure.
  • the wireless communications device 601 may determine and set a test status indication, e.g. register AE test status with tested AE-encoder.
  • the test status may be any one or more of: test started, test passed and test failed.
  • the wireless communications device 601 may determine the test status based on the indication received from the network node 602 in action 728, e.g., based on the configuration or lack of configuration (e.g., within a certain time).
  • the UE may further report the AE-encoder’s performance to a manufacturer of the UE to indicate a test status of its AE-encoder, such as the performance status of its AE- encoder.
  • the information that is reported to the manufacturer may further include information about the network operator and similar information as the UE would report in case it measured the test data set by itself. This is particularly true if a fails indication was received by the UE from the network.
  • the UE may determine a test indication status as described above in the optional action 709. The UE may determine this status either by receiving signalling indicating the test status from the network or detecting an absence of receiving such signalling from the network.
  • the UE receives signalling indicating the test status from the network.
  • This may in its simplest form be an indication of pass, alternatively either pass or fail.
  • the signalling from the network may include more information such as how good the AE-encoder performance was, what type of AE-decoder the AE-encoder was used together with, the version of the AE-decoder.
  • the performance of the AE-encoder may for example be provided in relation to a reference AE-encoder that is specified, in relation to a type of CSI report (e.g. CSI report type II), or another AE-encoder the UE supports and was simultaneously tested with.
  • the specific results may be indicated as a relative value of the loss (compared to a reference model AE-encoder), an absolute loss value, a single bit indication, etc.
  • the UE may for such a case detect that the AE-encoder test was successful if the UE receives a configuration message configuring the AE-encoder. If the UE is not configured with the AE-encoder the UE may detect that the test of the AE-encoder has failed.
  • the flowchart of Figure 7d illustrates some specific embodiments of methods, performed by the UE, for supporting evaluation of performance of the AE-encoder using the digital twin.
  • the network does not provide test data. Instead, the network includes a message to the UE that may trigger the digital twin to initiate a procedure to collect test data from one or multiple UE(s).
  • the network message may indicate that test data should be collected, and/or it may include details on how the collection of test data should be collected. That request may in such a case include one or more of the following information:
  • the type may be based for example on another AE-encoder or CSI report type that is not AE-encoder based, e.g. CSI report II
  • the request may include a request to calculate one or more reference reports on the test data set.
  • the reference reports may be reports calculated on the test data set but the reference reports may be derived within the digital twin rather than in the UE.
  • the reference reports may be derived based on reference AE-encoder(s) or on one or multiple CSI reporting types, e.g. CSI report type II.
  • Doppler ranges different encoders/decoders may be used for different Doppler ranges. This may make sure the UE does not collect data under unfavourable conditions. If the encoder is only tested with low Doppler then the network may want to selectively target high-Doppler scenarios for further tests.
  • the UE may indicate to the digital twin that test data should be collected. If the network has indicated details on how the test data should be collected the UE may include such information in the request to the digital twin.
  • the digital twin may initiate a test data collection procedure asking either one or multiple UEs to collect test data and provide it to the digital twin.
  • the request to collect test data may include one or more of the information provided as examples for the request received by the UE above from the network to collect test data.
  • One or multiple UEs may then start to collect test data according to the instructions and provide it to the digital twin.
  • the digital twin processes the test data in some form. After enough test data is collected processing of the test data is complete.
  • the digital twin either indicates to a UE that the test is complete or connects to the network directly and indicates that the test is complete.
  • the UE that receives this indication may indicate that to the network and the UE and the network may follow a similar procedure as when the test data was not collected by the UE(s).
  • the collected test data set may further be provided to the network in a similar manner as the output of the AE-encoder(s).
  • the other information mentioned above defining the collection of the data may be retrieved or received by the network together with the output data for the network to be able to process the output data.
  • such information may be a time period when the test data set, H, was collected (e.g., some form of time stamp), an approximate location where the UE collected the test data set, information about the cell the UE was connected to when collecting the test data, neighbour cell that the UE detects, SS/PBCH indices, CSI-RS indices, RSRP/RSRQ/SINR levels for the connected cell(s) and neighbour cell(s), CSI report(s) for the channel, etc.
  • the additional information may further be on request by the network or it may not be.
  • the network may further save the test data set H collected by the one or multiple UEs for future use together with the additional information collected with it.
  • the test data set H and its additional information may however be anonymized, so it is not possible to derive a specific user from it, to retain privacy.
  • the collected test data set H by a set of UEs may later be used to evaluate yet another UE model’s AE-encoder without requiring that UE model to measure the test data itself.
  • the request from the network to the UE may also include information that the test data (or reformatted version of the test data) should be processed by another AE-encoder, such as reference AE-encoder, or a non-AE-encoder based CSI report.
  • another AE-encoder such as reference AE-encoder, or a non-AE-encoder based CSI report.
  • the UE may indicate this information to the digital twin.
  • the digital twin may derive the additional AE-encoder and/or a non-AE-encoder based CSI report, e.g. CSI report II on the same test data set as the AE-encoder(s) are tested.
  • the output from these reports may be provided in a similar manner as the output of the AE-encoder(s) described above.
  • the network may for example use the CSI report(s) from the UE to calculate a relative difference between the CSI report(s) and the AE-decoder output the network calculates based on a specific AE-encoder.
  • the AE-decoder together with the AE-encoder may be aiming to represent the phase and amplitude of each CSI-RS.
  • the network may use this to derive the corresponding PM I to use and compare this to the UE reported PM I value from the CSI report of type II.
  • a similar example for positioning is that the AE-encoder together with AE-decoder aim to represent taps (or some taps) of the channel (measured a set of reference signals from a cell) with their strength and difference to each other in connection to a reference time that may be retrieved from a cell.
  • the network may then compare this to a RSTD measurement report wherein the UE reports the time difference of arrival between two cells.
  • a report may include the first occasion of the cell of interest to measure on or multiple occasion.
  • the occasion represents here taps in the channel.
  • the network may further only deem the performance of the AE-encoder to be sufficient if that relative difference is sufficiently large or positive in favour of the AE- encoder.
  • the network has an implementation of an AE-encoder, such as the reference AE-encoder 602-3 of the network node 602. Then, based on the test data set H received from the UE the network may generate an output Y from the reference AE-encoder 602-3 of the network node 602. The network may then compare performance between the network’s AE-encoder and the UE’s AE-encoder to determine if the performance of the UE’s AE-encoder is sufficient.
  • an AE-encoder such as the reference AE-encoder 602-3 of the network node 602.
  • the network node may configure the UE with an alternative reporting mechanism for the data that is encoded by the AE-encoder.
  • an alternative reporting mechanism may use another supported AE-encoder that has a pass indication.
  • the alternative reporting mechanism may also be based on a CSI report not based on an AE-encoder, such as a CSI type II report. Exemplifying methods according to embodiments herein will now be described with reference to a flow chart in Figure 8 and with continued reference to Figures 5 and 6aa, 6ab and 6b.
  • the flow chart illustrates a method, performed by the wireless communications device 121 , 601 , for supporting evaluation of performance of the trained NN-based AE-encoder 601-1.
  • the trained NN-based AE-encoder may be implemented in the wireless communications device 121, 601. Evaluation may for example mean validation or testing.
  • the wireless communications device 121, 601 transmits, to the network node 111 , 130, 602, first capability information indicating support for a specific trained NN-based AE-encoder 601-1.
  • the specific trained AE-encoder may be defined by an AE-encoder framework and/or a specific version of the AE-encoder framework and/or a model version of the wireless communications device 121.
  • a model version of the wireless communications device 121 may also include the indication of which software version the wireless communication device 121 is running, for example as the version of the operating system.
  • version 1 may implicitly mean a first original version, such as version 1 or version 0 depending on where the counting starts. Thus, this may indicate a version, although it is not signalled separately as a parameter.
  • the wireless communications device 121, 601 transmits, to the network node 111 , 130, 602, second capability information indicating support for testing the specific trained AE-encoder.
  • the second capability information may include one or more of the following: a. a buffer size of input data for evaluation of the trained AE-encoder; b. a buffer size of output data for evaluation of the trained AE-encoder; c. an indication of whether or not the wireless communications device 121 is able to collect the test input data by itself based on measurements of the wireless communication channel (the network may want to know if it may ask the UE to do collect the data); d. an indication of whether or not the wireless communications device 121 is able to receive the input data for evaluation from the wireless communications network 100; e. how many and/or which AE-encoders supported by the wireless communications device 121 that may be evaluated at the same time; f. a reference AE-encoder or a reference report type supported, for which the same input data is used and for which a reference output data is reported jointly with the output data of the specific trained AE-encoder to the network node 111.
  • the wireless communication device 121 is configured to collect test input data by itself based on measurements of the wireless communication channel for the purpose of doing Reference signal time difference for positioning purposes. For which the relative timing difference between the neighbour cell j and the reference cell i is captured within the measurement for a collection of taps (or possible starting points of a subframe/slot) within the channel.
  • the second capability information comprises a first indication indicating support for evaluating the specific trained AE-encoder in the wireless communications device 121 and/or a second indication indicating support for evaluating the specific trained AE-encoder based on the digital twin 603-1.
  • the capability information comprises: a first capability set associated with the specific trained AE-encoder as implemented in the wireless communications device 121, and/or a second capability set associated with the digital twin 603-1.
  • the wireless communications device 121, 601 receives, from the network node 111 , a request to evaluate the specific trained AE-encoder, e.g. to evaluate the performance of the AE-encoder 601-1.
  • the flow chart illustrates a method, performed by the network node 111 of the wireless communication network 100 for supporting evaluation of the performance of the trained NN-based AE-encoder 601-1 implemented in the wireless communications device 121.
  • the network node 602 receives, from the wireless communications device 121, first capability information indicating support for a specific trained NN-based AE-encoder 601-1.
  • the network node 602 receives, from the wireless communications device 121, second capability information indicating support for evaluating the specific trained AE-encoder.
  • the network node 602 transmits, to the wireless communications device 121, a request to evaluate the specific trained AE-encoder.
  • the request to evaluate may include configuration information of the evaluation of the specific trained AE-encoder.
  • the request to evaluate includes one or more information indicating: a. whether or not the wireless communications device 121 is configured to collect input data for evaluation of the specific trained AE-encoder by itself based on measurements of the wireless communication channel; and/or b. the wireless communications device 121 is configured to receive the input data from the wireless communications network 100; c. an amount of input data the wireless communications device 121 shall collect if the wireless communications device 121 is configured to collect the input data; d. a reference AE-encoder 601-2 or a reference report type, for which the same input data is to be used and for which a reference output data is to be reported jointly with output data of the specific trained AE-encoder to the network node 111.
  • the proposed solution enables AE-encoders to be verified in the field. For example, it is possible to verify the AE-encoder 601-1 implemented in the wireless communications device 121 operating in the wireless communications network 100. This enables the AE- encoders to be
  • the UE Since the UE provides the UE capabilities for the testing the network will know what is supported by the UE.
  • the UE may also provide capability signalling for retraining purposes.
  • the retraining is to retrain the AE-encoder in order to adjust the trainable AE-encoder parameters.
  • the retraining may either be done within the UE or within a digital twin or a combination of both.
  • the UE may provide a set of additional parameters such as the buffer sizes for the batches supported, retraining based on measurement data (by the UE) or data sets provided by the network.
  • the UE either measures the training data, does batch operation on it or receives training data from the network.
  • the training data is either provided by the network, generated by the digital twin or collected by one or multiple UEs and provided to the digital twin. This in collaboration with network’s AE-decoder or a digital twin of the network’s AE-decoder.
  • the above-described UE capability signalling may be implemented specific per feature set, frequency range FR1/FR2, TDD/FDD band specific or band combinations, alternatively common for all the mentioned aspects or parts of them.
  • FR1/FR2, TDD/FDD band specific or band combinations see 38.306 V 16.6.0 (https://www.3gpp.org/ftp/Specs/archive/38 series/38.306/38306-g60.zip).
  • the capability may be a separate reporting for the case the digital twin is supported or not, i.e., one setup for the digital twin and one without a digital twin.
  • a UE that needs to evaluate the performance of a supported AE-encoder may first be added to a list of UEs that share the same AE-encoder version before the actual test is initiated.
  • the network may then decide whether or not to initiate an evaluation of the performance of the AE-encoder with the UE based on the number of active UEs that are of the same UE model or have the same AE-encoder version. For example, a testing may be initiated first after that the number of such active UEs is above a threshold value that has been set by the network to control evaluation conditions when performing the performance test. With such an approach, it will be possible to push a test to many UEs that are in the same test status with respect to a specific UE model or AE-encoder version.
  • test data may be partitioned into segments in which one or a few segments are handled by a single UE. This will lower the requirements on the UE’s capability to buffer evaluation data (both input and output), and thus lower the memory requirements for the UE. This also leads to a lower energy consumption by the UE as less amount of test data needs to be processed.
  • each UE may contribute to an aggregated evaluation result from which the network may determine the AE performance.
  • One may group UEs to be tested into evaluation groups such that one evaluation group of UEs process the same evaluation data segment(s).
  • the network may have multiple AE-decoder implemented and may perform the network related functions described above once per AE-decoder.
  • the network may afterwards rank the AE-decoders after the performance they have with a specific AE- encoder.
  • the network may for example identify that a specific AE-decoder should be used with the specific AE-encoder for a specific UE.
  • the network may rank an AE- decoder in some other manner further based on additional parameters. For example, if not only pure performance is considered but also other aspects such as required processing power in the network node is considered then the network may rank the AE- decoder also based on required processing power in the network node.
  • the UE may indicate support for multiple AE-encoders in its UE capabilities to the network.
  • the network may request the UE to test a specific or multiple, or all AE-encoders with the same test data set.
  • the UE then applies the abovedescribed features on all the applicable AE-encoders.
  • the UE may further send one response message per AE-encoder with the output of the AE-encoder or a single message with all the outputs of all request AE-encoders.
  • the network will then apply the above steps for all the AE-encoders.
  • the UE sends UE capability information to the network indicating that UE supports an AE-encoder framework and/or AE-encoder version.
  • the UE capability information may comprise radio access capability information.
  • the UE capabilities for the AE-encoder may indicate that the UE supports for the AE-encoder to be tested or retrained. The testing or retraining may either occur within the UE or within a digital twin, e.g., as indicated by a UE capability support.
  • Based on the sent UE capabilities the UE receives a request from the network to conduct a test of an AE-encoder or to retrain an AE-encoder.
  • the UE may in response perform the test or retrain the AE-encoder. If the UE receives a request to perform an AE-encoder test or retrain the AE-encoder within a digital twin the UE initiates the test or retraining process within the digital twin.
  • the UE capabilities of the UE may be separately indicated for the UE and for the digital twin. For example, it is not necessary that the same set of UE features are supported by the UE and by the digital twin.
  • the proposed solution comprises a signalling framework that enables AE-encoders of different UE models to be tested and validated with the network AE-decoder within the field. The signalling involves indicating to the UE from the network that a test of the AE-encoder will occur.
  • the operational mode may for example be implemented for CSI reporting and the UE may in such case construct a CSI report or parts of a CSI report based on an AE- encoder.
  • other operational modes of the AE-encoder are also possible such as using it for data transfer, positioning and so on.
  • One possibility for the network for evaluating the AE-encoder is to configure the UE to report test output from the encoder but not use the output from the AE-encoder for other purposes than evaluating the AE-encoder. That is, the network doesn’t need to take the evaluation information into account when scheduling the UE.
  • the network may use other methods such as CSI type II reporting. This is here exemplified for CSI feedback.
  • the UE receives a configuration from the network to provide AE-based CSI feedback with the AE- encoder, but the network may not use that CSI feedback other than for the purpose of evaluating the AE-encoder. Whether or not the CSI report based on the AE-encoder is used for something else than evaluation may be unknown to the UE. To conduct the evaluation the UE may need to be configured with some intermediate, or additional, CSI reporting at least until the UE has passed the performance evaluation of the specific AE encoder version to be tested, e.g. based on the digital twin. The network may then compare the difference between the additional CSI report and the tested AE-encoder CSI report.
  • the UE capabilities in NR rely on a hierarchical structure where each capability parameter is defined per UE, per duplex mode (FDD/TDD), per frequency range (FR1/FR2), per band, per band combinations, ... as the UE may support different functionalities depending on those (see TS 38.306 [11]).
  • SDAP Secure Digital Protocol
  • PDCP Packet Control Protocol
  • RLC Radio Link Control Protocol
  • some other not always e.g. MAC and Physical Layer Parameters
  • the UE capabilities in NR do not rely on UE categories: UE categories associated to fixed peak data rates are only defined for marketing purposes and not signalled to the network. Instead, the peak data rate for a given set of aggregated carriers in a band or band combination is the sum of the peak data rates of each individual carrier in that band or band combination, where the peak data rate of each individual carrier is computed according to the capabilities supported for that carrier in the corresponding band or band combination.
  • Feature Set For each block of contiguous serving cells in a band, the set of features supported thereon is defined in a Feature Set (FS).
  • the UE may indicate several Feature Sets for a band (also known as feature sets per band) to advertise different alternative features for the associated block of contiguous serving cells in that band.
  • the two-dimensional matrix of feature sets for all the bands of a band combination i.e. all the feature sets per band
  • a feature set combination the number of feature sets per band is equal to the number of band entries in the corresponding band combination, and all feature sets per band have the same number of feature sets.
  • Each band combination is linked to one feature set combination. This is depicted in Figure 9b.
  • the UE reports its capabilities individually per carrier. Those capability parameters are sent in feature set per component carrier and they are signalled in the corresponding FSs (per Band) i.e. for the corresponding block of contiguous serving cells in a band. The capability applied to each individual carrier in a block is agnostic to the order in which they are signalled in the corresponding FS.
  • the gNB can request the UE to provide NR capabilities for a restricted set of bands. When responding, the UE can skip a subset of the requested band combinations when the corresponding UE capabilities are the same.
  • the UE may provide an ID in NAS signalling that represents its radio capabilities for one or more RATs in order to reduce signalling overhead.
  • the ID may be assigned either by the manufacturer or by the serving PLMN.
  • the manufacturer-assigned ID corresponds to a pre-provisioned set of capabilities. In the case of the PLMN-assigned ID, assignment takes place in NAS signalling.
  • the AMF stores the UE Radio Capability uploaded by the gNB as specified in TS 23.501.
  • the gNB can request the UE capabilities for RAT-Types NR, EUTRA, UTRA-FDD.
  • the UTRAN capabilities i.e. the INTER RAT HANDOVER INFO, include START-CS, START-PS and "predefined configurations", which are "dynamic" lEs.
  • the gNB In order to avoid the START values desynchronisation and the key replaying issue, the gNB always requests the UE UTRA-FDD capabilities before handover to UTRA-FDD. The gNB does not upload the UE UTRA-FDD capabilities to the AMF.
  • Figure 10 shows an example of the wireless communications device 601 and Figure 11 shows an example of the network node 602.
  • the wireless communications device 601 may be configured to perform the method actions of Figure 8 above.
  • the network node 602 may be configured to perform the method actions of Figure 9a above.
  • the wireless communications device 601 and the network node 602 may comprise a respective input and output interface, IF, 1006, 1106 configured to communicate with each other, see Figures 10-11.
  • the input and output interface may comprise a wireless receiver (not shown) and a wireless transmitter (not shown).
  • the wireless communications device 601 and the network node 602 may comprise a respective processing unit 1001, 1101 for performing the above method actions.
  • the respective processing unit 1001 , 1101 may comprise further sub-units which will be described below.
  • the wireless communications device 601 and the network node 602 may further comprise a respective receiving unit 1020, 1110, and transmitting unit 1010, 1120, see Figure 10 and 11 which may receive and transmit messages and/or signals.
  • the network node 602 may further comprise an evaluating unit 1130 which for example may evaluate the performance of the AE-encoder 601-1 based on the received output data.
  • the network node 602 may further comprise a determining unit 1140 which for example may determine whether or not to request the evaluation of the performance of the AE-encoder 601-1.
  • the embodiments herein may be implemented through a respective processor or one or more processors, such as the respective processor 1004, and 1104, of a processing circuitry in the wireless communications device 601 and the network node 602, and depicted in Figures 10-11 together with computer program code for performing the functions and actions of the embodiments herein.
  • the program code mentioned above may also be provided as a computer program product, for instance in the form of a data carrier carrying computer program code for performing the embodiments herein when being loaded into the respective wireless communications device 601 and network node 602.
  • One such carrier may be in the form of a CD ROM disc. It is however feasible with other data carriers such as a memory stick.
  • the computer program code may furthermore be provided as pure program code on a server and downloaded to the respective wireless communications device 601 and network node 602.
  • the wireless communications device 601 and the network node 602 may further comprise a respective memory 1002, and 1102 comprising one or more memory units.
  • the memory comprises instructions executable by the processor in the wireless communications device 601 and network node 602.
  • Each respective memory 1002 and 1102 is arranged to be used to store e.g. information, data, configurations, and applications to perform the methods herein when being executed in the respective wireless communications device 601 and network node 602.
  • a respective computer program 1003 and 1103 comprises instructions, which when executed by the at least one processor, cause the at least one processor of the respective wireless communications device 601 and network node 602 to perform the actions above.
  • a respective carrier 1005 and 1105 comprises the respective computer program, wherein the carrier is one of an electronic signal, an optical signal, an electromagnetic signal, a magnetic signal, an electric signal, a radio signal, a microwave signal, or a computer-readable storage medium.
  • the units described above may refer to a combination of analog and digital circuits, and/or one or more processors configured with software and/or firmware, e.g. stored in the respective wireless communications device 601 and network node 602, that when executed by the respective one or more processors such as the processors described above.
  • processors as well as the other digital hardware, may be included in a single Application-Specific Integrated Circuitry (ASIC), or several processors and various digital hardware may be distributed among several separate components, whether individually packaged or assembled into a system-on-a-chip (SoC).
  • ASIC Application-Specific Integrated Circuitry
  • SoC system-on-a-chip
  • the second capability information includes one or more of the following: a. a buffer size of input data for evaluation of the trained AE-encoder; b. a buffer size of output data for evaluation of the trained AE-encoder; c. an indication of whether or not the wireless communications device (121) is able to collect the test input data by itself based on measurements of the wireless communication channel; d. an indication of whether or not the wireless communications device (121) is able to receive the input data for evaluation from the wireless communications network (100); e. how many and/or which AE-encoders supported by the wireless communications device (121) that may be evaluated at the same time; f. a reference AE-encoder () or a reference report type supported, for which the same input data is used and for which a reference output data is reported jointly with the output data of the specific trained AE-encoder to the network node (111).
  • the second capability information comprises a first indication indicating support for evaluating the specific trained AE-encoder in the wireless communications device (121) and/or a second indication indicating support for evaluating the specific trained AE-encoder based on a digital twin (603-1).
  • the capability information comprises: a first capability set associated with the specific trained AE-encoder as implemented in the wireless communications device (121), and/or a second capability set associated with the digital twin (603-1).
  • the request to evaluate includes one or more information indicating: a. whether or not the wireless communications device (121) is configured to collect input data for evaluation of the specific trained AE-encoder by itself based on measurements of the wireless communication channel; and/or b. the wireless communications device (121) is configured to receive the input data from the wireless communications network (100); c. an amount of input data the wireless communications device (121) shall collect if the wireless communications device (121) is configured to collect the input data; d. a reference AE-encoder (601-2) or a reference report type, for which the same input data is to be used and for which a reference output data is to be reported jointly with output data of the specific trained AE-encoder to the network node (111).
  • a communication system includes a telecommunication network 3210, such as a 3GPP-type cellular network, which comprises an access network 3211 , such as a radio access network, and a core network 3214.
  • the access network 3211 comprises a plurality of base stations 3212a, 3212b, 3212c, such as the source and target access node 111, 112, AP STAs NBs, eNBs, gNBs or other types of wireless access points, each defining a corresponding coverage area 3213a, 3213b, 3213c.
  • Each base station 3212a, 3212b, 3212c is connectable to the core network 3214 over a wired or wireless connection 3215.
  • a first user equipment (UE) such as a Non-AP STA 3291 located in coverage area 3213c is configured to wirelessly connect to, or be paged by, the corresponding base station 3212c.
  • a second UE 3292 such as a Non-AP STA in coverage area 3213a is wirelessly connectable to the corresponding base station 3212a. While a plurality of UEs 3291, 3292 are illustrated in this example, the disclosed embodiments are equally applicable to a situation where a sole UE is in the coverage area or where a sole UE is connecting to the corresponding base station 3212.
  • the telecommunication network 3210 is itself connected to a host computer 3230, which may be embodied in the hardware and/or software of a standalone server, a cloud- implemented server, a distributed server or as processing resources in a server farm.
  • the host computer 3230 may be under the ownership or control of a service provider, or may be operated by the service provider or on behalf of the service provider.
  • the connections 3221, 3222 between the telecommunication network 3210 and the host computer 3230 may extend directly from the core network 3214 to the host computer 3230 or may go via an optional intermediate network 3220.
  • the intermediate network 3220 may be one of, or a combination of more than one of, a public, private or hosted network; the intermediate network 3220, if any, may be a backbone network or the Internet; in particular, the intermediate network 3220 may comprise two or more sub-networks (not shown).
  • the communication system of Figure 12 as a whole enables connectivity between one of the connected UEs 3291, 3292 such as e.g. the UE 121, and the host computer 3230.
  • the connectivity may be described as an over-the-top (OTT) connection 3250.
  • the host computer 3230 and the connected UEs 3291 , 3292 are configured to communicate data and/or signaling via the OTT connection 3250, using the access network 3211 , the core network 3214, any intermediate network 3220 and possible further infrastructure (not shown) as intermediaries.
  • the OTT connection 3250 may be transparent in the sense that the participating communication devices through which the OTT connection 3250 passes are unaware of routing of uplink and downlink communications.
  • a base station 3212 may not or need not be informed about the past routing of an incoming downlink communication with data originating from a host computer 3230 to be forwarded (e.g., handed over) to a connected UE 3291. Similarly, the base station 3212 need not be aware of the future routing of an outgoing uplink communication originating from the UE 3291 towards the host computer 3230.
  • a host computer 3310 comprises hardware 3315 including a communication interface 3316 configured to set up and maintain a wired or wireless connection with an interface of a different communication device of the communication system 3300.
  • the host computer 3310 further comprises processing circuitry 3318, which may have storage and/or processing capabilities.
  • the processing circuitry 3318 may comprise one or more programmable processors, application-specific integrated circuits, field programmable gate arrays or combinations of these (not shown) adapted to execute instructions.
  • the host computer 3310 further comprises software 3311, which is stored in or accessible by the host computer 3310 and executable by the processing circuitry 3318.
  • the software 3311 includes a host application 3312.
  • the host application 3312 may be operable to provide a service to a remote user, such as a UE 3330 connecting via an OTT connection 3350 terminating at the UE 3330 and the host computer 3310. In providing the service to the remote user, the host application 3312 may provide user data which is transmitted using the OTT connection 3350.
  • the communication system 3300 further includes a base station 3320 provided in a telecommunication system and comprising hardware 3325 enabling it to communicate with the host computer 3310 and with the UE 3330.
  • the hardware 3325 may include a communication interface 3326 for setting up and maintaining a wired or wireless connection with an interface of a different communication device of the communication system 3300, as well as a radio interface 3327 for setting up and maintaining at least a wireless connection 3370 with a UE 3330 located in a coverage area (not shown in Figure 13) served by the base station 3320.
  • the communication interface 3326 may be configured to facilitate a connection 3360 to the host computer 3310.
  • connection 3360 may be direct or it may pass through a core network (not shown in Figure 13) of the telecommunication system and/or through one or more intermediate networks outside the telecommunication system.
  • the hardware 3325 of the base station 3320 further includes processing circuitry 3328, which may comprise one or more programmable processors, application-specific integrated circuits, field programmable gate arrays or combinations of these (not shown) adapted to execute instructions.
  • the base station 3320 further has software 3321 stored internally or accessible via an external connection.
  • the communication system 3300 further includes the UE 3330 already referred to.
  • Its hardware 3335 may include a radio interface 3337 configured to set up and maintain a wireless connection 3370 with a base station serving a coverage area in which the UE 3330 is currently located.
  • the hardware 3335 of the UE 3330 further includes processing circuitry 3338, which may comprise one or more programmable processors, applicationspecific integrated circuits, field programmable gate arrays or combinations of these (not shown) adapted to execute instructions.
  • the UE 3330 further comprises software 3331, which is stored in or accessible by the UE 3330 and executable by the processing circuitry 3338.
  • the software 3331 includes a client application 3332.
  • the client application 3332 may be operable to provide a service to a human or non-human user via the UE 3330, with the support of the host computer 3310.
  • an executing host application 3312 may communicate with the executing client application 3332 via the OTT connection 3350 terminating at the UE 3330 and the host computer 3310.
  • the client application 3332 may receive request data from the host application 3312 and provide user data in response to the request data.
  • the OTT connection 3350 may transfer both the request data and the user data.
  • the client application 3332 may interact with the user to generate the user data that it provides.
  • the host computer 3310, base station 3320 and UE 3330 illustrated in Figure 13 may be identical to the host computer 3230, one of the base stations 3212a, 3212b, 3212c and one of the UEs 3291, 3292 of Figure 12, respectively.
  • the inner workings of these entities may be as shown in Figure 13 and independently, the surrounding network topology may be that of Figure 12.
  • the OTT connection 3350 has been drawn abstractly to illustrate the communication between the host computer 3310 and the use equipment 3330 via the base station 3320, without explicit reference to any intermediary devices and the precise routing of messages via these devices.
  • Network infrastructure may determine the routing, which it may be configured to hide from the UE 3330 or from the service provider operating the host computer 3310, or both. While the OTT connection 3350 is active, the network infrastructure may further take decisions by which it dynamically changes the routing (e.g., on the basis of load balancing consideration or reconfiguration of the network).
  • the wireless connection 3370 between the UE 3330 and the base station 3320 is in accordance with the teachings of the embodiments described throughout this disclosure.
  • One or more of the various embodiments improve the performance of OTT services provided to the UE 3330 using the OTT connection 3350, in which the wireless connection 3370 forms the last segment. More precisely, the teachings of these embodiments may improve the data rate, latency, power consumption and thereby provide benefits such as reduced user waiting time, relaxed restriction on file size, better responsiveness, extended battery lifetime.
  • a measurement procedure may be provided for the purpose of monitoring data rate, latency and other factors on which the one or more embodiments improve.
  • the measurement procedure and/or the network functionality for reconfiguring the OTT connection 3350 may be implemented in the software 3311 of the host computer 3310 or in the software 3331 of the UE 3330, or both.
  • sensors (not shown) may be deployed in or in association with communication devices through which the OTT connection 3350 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 3311 , 3331 may compute or estimate the monitored quantities.
  • the reconfiguring of the OTT connection 3350 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not affect the base station 3320, and it may be unknown or imperceptible to the base station 3320. Such procedures and functionalities may be known and practiced in the art.
  • measurements may involve proprietary UE signaling facilitating the host computer’s 3310 measurements of throughput, propagation times, latency and the like.
  • the measurements may be implemented in that the software 3311, 3331 causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection 3350 while it monitors propagation times, errors etc.
  • FIG 14 is a flowchart illustrating a method implemented in a communication system, in accordance with one embodiment.
  • the communication system includes a host computer, a base station such as a AP STA, and a UE such as a Non-AP STA which may be those described with reference to Figure 12 and Figure 13. For simplicity of the present disclosure, only drawing references to Figure 14 will be included in this section.
  • the host computer provides user data.
  • the host computer provides the user data by executing a host application.
  • the host computer initiates a transmission carrying the user data to the UE.
  • the base station transmits to the UE the user data which was carried in the transmission that the host computer initiated, in accordance with the teachings of the embodiments described throughout this disclosure.
  • the UE executes a client application associated with the host application executed by the host computer.
  • FIG. 15 is a flowchart illustrating a method implemented in a communication system, in accordance with one embodiment.
  • the communication system includes a host computer, a base station such as a AP STA, and a UE such as a Non-AP STA which may be those described with reference to Figure 12 and Figure 13. For simplicity of the present disclosure, only drawing references to Figure 15 will be included in this section.
  • the host computer provides user data.
  • the host computer provides the user data by executing a host application.
  • the host computer initiates a transmission carrying the user data to the UE. The transmission may pass via the base station, in accordance with the teachings of the embodiments described throughout this disclosure.
  • the UE receives the user data carried in the transmission.
  • FIG 16 is a flowchart illustrating a method implemented in a communication system, in accordance with one embodiment.
  • the communication system includes a host computer, a base station such as a AP STA, and a UE such as a Non-AP STA which may be those described with reference to Figure 12 and Figure 13.
  • a first action 3610 of the method the UE receives input data provided by the host computer.
  • the UE provides user data.
  • the UE provides the user data by executing a client application.
  • the UE executes a client application which provides the user data in reaction to the received input data provided by the host computer.
  • the executed client application may further consider user input received from the user.
  • the UE initiates, in an optional third subaction 3630, transmission of the user data to the host computer.
  • the host computer receives the user data transmitted from the UE, in accordance with the teachings of the embodiments described throughout this disclosure.
  • FIG 17 is a flowchart illustrating a method implemented in a communication system, in accordance with one embodiment.
  • the communication system includes a host computer, a base station such as a AP STA, and a UE such as a Non-AP STA which may be those described with reference to Figures 32 and 33.
  • a first action 3710 of the method in accordance with the teachings of the embodiments described throughout this disclosure, the base station receives user data from the UE.
  • the base station initiates transmission of the received user data to the host computer.
  • the host computer receives the user data carried in the transmission initiated by the base station.

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Abstract

L'invention concerne un dispositif de communication sans fil (601) qui fonctionne dans un réseau de communication sans fil pour prendre en charge l'évaluation d'un codeur automatique, AE, à base de réseau neuronal NN (601-1). Le codeur AE à base de NN est entraîné pour fournir des données codées à un décodeur AE à base de NN entraîné (602-1) compatible d'un nœud de réseau (602) du réseau de communication sans fil. Le dispositif de communication sans fil transmet, au nœud de réseau, des premières informations de capacité indiquant une prise en charge pour un codeur AE basé sur NN entraîné (601-1) spécifique. Le dispositif de communication sans fil transmet, au nœud de réseau, des secondes informations de capacité indiquant une prise en charge pour tester le codeur AE basé sur NN entraîné spécifique. Le dispositif de communication sans fil reçoit, en provenance du nœud de réseau, une demande d'évaluation du codeur AE basé sur NN entraîné spécifique.
PCT/SE2023/050146 2022-02-18 2023-02-17 Évaluation de la performance d'un codeur ae WO2023158363A1 (fr)

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Citations (2)

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