Patent Description:
Aspects of the present disclosure generally relate to wireless communications, and more particularly to techniques and apparatuses for federated channel state information (CSI) learning.

Wireless communications systems are widely deployed to provide various telecommunications services such as telephony, video, data, messaging, and broadcasts. Typical wireless communications systems may employ multiple-access technologies capable of supporting communications with multiple users by sharing available system resources (e.g., bandwidth, transmit power, and/or the like). Examples of such multiple-access technologies include code division multiple access (CDMA) systems, time division multiple access (TDMA) systems, frequency-division multiple access (FDMA) systems, orthogonal frequency-division multiple access (OFDMA) systems, single-carrier frequency-division multiple access (SC-FDMA) systems, time division synchronous code division multiple access (TD-SCDMA) systems, and long term evolution (LTE). LTE/LTE-Advanced is a set of enhancements to the universal mobile telecommunications system (UMTS) mobile standard promulgated by the Third Generation Partnership Project (3GPP).

A wireless communications network may include a number of base stations (BSs) that can support communications for a number of user equipment (UEs). The downlink (or forward link) refers to the communications link from the BS to the UE, and the uplink (or reverse link) refers to the communications link from the UE to the BS.

The above multiple access technologies have been adopted in various telecommunications standards to provide a common protocol that enables different user equipment to communicate on a municipal, national, regional, and even global level. However, as the demand for mobile broadband access continues to increase, there exists a need for further improvements in NR and LTE technologies. Preferably, these improvements should be applicable to other multiple access technologies and the telecommunications standards that employ these technologies.

Artificial neural networks may comprise interconnected groups of artificial neurons (e.g., neuron models). The artificial neural network may be a computational device or represented as a method to be performed by a computational device. Convolutional neural networks, such as deep convolutional neural networks, are a type of feed-forward artificial neural network. Convolutional neural networks may include layers of neurons that may be configured in a tiled receptive field. It would be desirable to apply neural network processing to wireless communications to achieve greater efficiencies. <CIT> discloses a system to train and update parameters of machine learning algorithm at a transmitter and a receiver in a MIMO system. In a pre-deployment scenario, a central entity distributes machine learning parameters to the transmitters and receivers connected by a channel model or the real channel. The transmitter and receivers send performance metrics or copies of the transmitted or received signals to the central entity which updates the machine learning parameters. <NPL>), discloses a Machine Learning Approach for Hybrid Beamforming/Combining for Millimeter Wave MIMO. In order to train the neural network in an end-to-end style, it is suggested to transmit the neural network parameters between the two sides of the system. <NPL>, discloses CSI Feedback Based on Deep Learning for Massive MIMO Systems. In that case it is suggested that the training of the deep learning model is performed offline in a simulator.

According to the invention as claimed in independent claim <NUM>, a method of wireless communication by a user equipment (UE) includes receiving a channel state information (CSI) decoder and CSI encoder from a base station on a physical downlink shared channel (PDSCH) or a media access control-control element (MAC-CE). The method also includes training the CSI decoder and CSI encoder based on observed channel and interference conditions to obtain updated decoder coefficients and updated encoder coefficients. The method further includes receiving an indication of resources for transmission of the updated encoder coefficients and updated decoder coefficients. The method includes transmitting the updated decoder coefficients and updated encoder coefficients to the base station in accordance with the indication of resources. Further, the method includes receiving an updated CSI decoder and updated CSI encoder from the base station for further training.

According to the invention as claimed in independent claim <NUM>, a method of wireless communication by a base station includes transmitting a channel state information (CSI) decoder and a CSI encoder to multiple user equipments (UEs) via a physical downlink shared channel (PSDCH) or a media access control-control element (MAC-CE). The method also includes assigning resources for receiving updated CSI coefficients for the CSI decoder and CSI encoder. The method also includes receiving updated CSI coefficients from the plurality of UEs in accordance with the assigned resources. The method further includes extracting weights of common layers associated with the updated coefficients for a subset of UEs. The method generates updated common weights for the CSI encoder and CSI decoder based on the extracted weights. Further, the method also distributes the updated common weights to the subset of UEs.

According to the invention as claimed in independent claim <NUM>, an apparatus for a user equipment (UE) for wireless communication includes a memory and one or more processors operatively coupled to the memory. The memory and the processor(s) receive a channel state information (CSI) decoder and CSI encoder from a base station on a physical downlink shared channel (PDSCH) or a media access control-control element (MAC-CE). The UE trains the CSI decoder and CSI encoder based on observed channel and interference conditions to obtain updated decoder coefficients and updated encoder coefficients. The UE also receives an indication of resources for transmission of the updated encoder coefficients and updated decoder coefficients. The UE further transmits the updated decoder coefficients and updated encoder coefficients to the base station in accordance with the indication of resources. The UE also receives an updated CSI decoder and updated CSI encoder from the base station for further training.

According to the invention as claimed in independent claim <NUM>, an apparatus for a base station for wireless communication includes a memory and one or more processors operatively coupled to the memory. The memory and the processor(s) transmit a channel state information (CSI) decoder and a CSI encoder to multiple user equipments (UEs) via a physical downlink shared channel (PSDCH) or a media access control-control element (MAC-CE). The base station assigns assign resources for receiving updated CSI coefficients for the CSI decoder and CSI encoder. The base station receives updated CSI coefficients from the UEs in accordance with the assigned resources. The base station also extracts weights of common layers associated with the updated coefficients for a subset of UEs. The base station further generates updated common weights for the CSI encoder and CSI decoder based on the extracted weights. The base station also distributes the updated common weights to the subset of UEs.

A user equipment (UE) for wireless communication may include means for receiving a channel state information (CSI) decoder and CSI encoder from a base station on a physical downlink shared channel (PDSCH) or a media access control-control element (MAC-CE). The UE may also include means for training the CSI decoder and CSI encoder based on observed channel and interference conditions to obtain updated decoder coefficients and updated encoder coefficients. The UE may further include means for receiving an indication of resources for transmission of the updated encoder coefficients and updated decoder coefficients. The UE includes means for transmitting the updated decoder coefficients and updated encoder coefficients to the base station in accordance with the indication of resources. Further, the UE includes means for receiving an updated CSI decoder and updated CSI encoder from the base station for further training.

A base station for wireless communication may include means for transmitting a channel state information (CSI) decoder and a CSI encoder to multiple user equipments (UEs) via a physical downlink shared channel (PSDCH) or a media access control-control element (MAC-CE). The base station may also include means for assigning resources for receiving updated CSI coefficients for the CSI decoder and CSI encoder. The base station also includes means for receiving updated CSI coefficients from the plurality of UEs in accordance with the assigned resources. The base station further includes means for extracting weights of common layers associated with the updated coefficients for a subset of UEs. The base station includes means for generating updated common weights for the CSI encoder and CSI decoder based on the extracted weights. Further, the UE includes means for distributing the updated common weights to the subset of UEs.

A non-transitory computer-readable medium may include program code executed by a user equipment processor. The medium may include program code to transmit a channel state information (CSI) decoder and a CSI encoder to multiple user equipments (UEs) via a physical downlink shared channel (PSDCH) or a media access control-control element (MAC-CE).

The medium may include program code to receive a channel state information (CSI) decoder and CSI encoder from a base station on a physical downlink shared channel (PDSCH) or a media access control-control element (MAC-CE). The medium may include program code to train the CSI decoder and CSI encoder based on observed channel and interference conditions to obtain updated decoder coefficients and updated encoder coefficients. The medium may also include program code to receive an indication of resources for transmission of the updated encoder coefficients and updated decoder coefficients. The medium may further include program code to transmit the updated decoder coefficients and updated encoder coefficients to the base station in accordance with the indication of resources. The medium may also include program code to receive an updated CSI decoder and updated CSI encoder from the base station for further training.

A non-transitory computer-readable medium may include program code executed by a base station. The medium may include program code to assign resources for receiving updated CSI coefficients for the CSI decoder and CSI encoder. The medium may include program code to receive updated CSI coefficients from the UEs in accordance with the assigned resources. The medium may also include program code to extract weights of common layers associated with the updated coefficients for a subset of UEs. The medium may further include program code to generate updated common weights for the CSI encoder and CSI decoder based on the extracted weights. The medium may also include program code to distribute the updated common weights to the subset of UEs.

Aspects generally include a method, apparatus, system, computer program product, non-transitory computer-readable medium, user equipment, base station, wireless communications device, and processing system as substantially described with reference to and as illustrated by the accompanying drawings and specification.

Various aspects of the disclosure are described more fully below with reference to the accompanying drawings. For example, an apparatus may be implemented or a method may be practiced using any number of the aspects set forth. In addition, the scope of the disclosure is intended to cover such an apparatus or method, which is practiced using other structure, functionality, or structure and functionality in addition to or other than the various aspects of the disclosure set forth. It should be understood that any aspect of the disclosure disclosed may be embodied by one or more elements of a claim.

Several aspects of telecommunications systems will now be presented with reference to various apparatuses and techniques.

It should be noted that while aspects may be described using terminology commonly associated with <NUM> and later wireless technologies, aspects of the present disclosure can be applied in other generation-based communications systems, such as and including <NUM> and/or <NUM> technologies.

The network <NUM> may be a <NUM> or NR network or some other wireless network, such as an LTE network. Each BS may provide communications coverage for a particular geographic area.

A BS may provide communications coverage for a macro cell, a pico cell, a femto cell, and/or another type of cell. The terms "eNB," "base station," "NR BS," "gNB," "TRP," "AP," "node B," "5GNB," and "cell" may be used interchangeably herein.

In the example shown in <FIG>, a relay station 110d may communicate with macro BS 110a and a UE 120d in order to facilitate communications between BS 110a and UE 120d.

A UE may be a cellular phone (e.g., a smart phone), a personal digital assistant (PDA), a wireless modem, a wireless communications device, a handheld device, a laptop computer, a cordless phone, a wireless local loop (WLL) station, a tablet, a camera, a gaming device, a netbook, a smartbook, an ultrabook, a medical device or equipment, biometric sensors/devices, wearable devices (smart watches, smart clothing, smart glasses, smart wrist bands, smart jewelry (e.g., smart ring, smart bracelet)), an entertainment device (e.g., a music or video device, or a satellite radio), a vehicular component or sensor, smart meters/sensors, industrial manufacturing equipment, a global positioning system device, or any other suitable device that is configured to communicate via a wireless or wired medium.

Some UEs may be considered machine-type communications (MTC) or evolved or enhanced machine-type communications (eMTC) UEs. A wireless node may provide, for example, connectivity for or to a network (e.g., a wide area network such as Internet or a cellular network) via a wired or wireless communications link. Some UEs may be considered a customer premises equipment (CPE).

Base station <NUM> may include communications unit <NUM> and communicate to network controller <NUM> via communications unit <NUM>. Network controller <NUM> may include communications unit <NUM>, controller/processor <NUM>, and memory <NUM>.

Controller/processor <NUM> of base station <NUM>, controller/processor <NUM> of UE <NUM>, and/or any other component(s) of <FIG> may perform one or more techniques associated with federated CSI learning, as described in more detail elsewhere. For example, controller/processor <NUM> of base station <NUM>, controller/processor <NUM> of UE <NUM>, and/or any other component(s) of <FIG> may perform or direct operations of, for example, the processes <NUM>, <NUM> of <FIG> and <FIG> and/or other processes as described. Memories <NUM> and <NUM> may store data and program codes for base station <NUM> and UE <NUM>, respectively.

In some aspects, UE <NUM> may include means for receiving, means for training, means for transmitting, means for assigning, means for extracting weights, means for generating updated common weights, and means for distributing the updated common weights. Such means may include one or more components of the UE <NUM> or base station <NUM> described in connection with <FIG>.

In some cases, different types of devices supporting different types of applications and/or services may coexist in a cell. Examples of different types of devices include UE handsets, customer premises equipment (CPEs), vehicles, Internet of Things (IoT) devices, and/or the like. Examples of different types of applications include ultra-reliable low-latency communications (URLLC) applications, massive machine-type communications (mMTC) applications, enhanced mobile broadband (eMBB) applications, vehicle-to-anything (V2X) applications, and/or the like. Furthermore, in some cases, a single device may support different applications or services simultaneously.

<FIG> illustrates an example implementation of a system-on-a-chip (SOC) <NUM>, which may include a central processing unit (CPU) <NUM> or a multi-core CPU configured for federated CSI learning, in accordance with certain aspects of the present disclosure. The SOC <NUM> may be included in the base station <NUM> or UE <NUM>. Variables (e.g., neural signals and synaptic weights), system parameters associated with a computational device (e.g., neural network with weights), delays, frequency bin information, and task information may be stored in a memory block associated with a neural processing unit (NPU) <NUM>, in a memory block associated with a CPU <NUM>, in a memory block associated with a graphics processing unit (GPU) <NUM>, in a memory block associated with a digital signal processor (DSP) <NUM>, in a memory block <NUM>, or may be distributed across multiple blocks. Instructions executed at the CPU <NUM> may be loaded from a program memory associated with the CPU <NUM> or may be loaded from a memory block <NUM>.

The SOC <NUM> may also include additional processing blocks tailored to specific functions, such as a GPU <NUM>, a DSP <NUM>, a connectivity block <NUM>, which may include fifth generation (<NUM>) connectivity, fourth generation long term evolution (<NUM> LTE) connectivity, Wi-Fi connectivity, USB connectivity, Bluetooth connectivity, and the like, and a multimedia processor <NUM> that may, for example, detect and recognize gestures. In one implementation, the NPU is implemented in the CPU, DSP, and/or GPU. The SOC <NUM> may also include a sensor processor <NUM>, image signal processors (ISPs) <NUM>, and/or navigation module <NUM>, which may include a global positioning system.

The SOC <NUM> may be based on an ARM instruction set. In an aspect of the present disclosure, the instructions loaded into the general-purpose processor <NUM> may comprise code to receive a channel state information (CSI) decoder and CSI encoder from a base station on physical downlink shared channel (PDSCH) or a media access control-control element (MAC-CE). The instructions loaded into the general-purpose processor <NUM> may also include code to train the CSI decoder and CSI encoder based on observed channel and interference conditions to obtain updated decoder coefficients and updated encoder coefficients. The instructions loaded into the general-purpose processor <NUM> may also include code to receive an indication of resources for transmission of the updated encoder coefficients and updated decoder coefficients; and code to transmit the updated decoder coefficients and updated encoder coefficients to the base station in accordance with the indication of resources. The instructions loaded into the general-purpose processor <NUM> may also include code to receive an updated CSI decoder and updated CSI encoder from the base station, for further training.

In another aspect, the instructions loaded into the general-purpose processor <NUM> may also include code to assign resources for receiving updated CSI coefficients for the CSI decoder and CSI encoder; code to receive updated CSI coefficients from the plurality of UEs, in accordance with the assigned resources; and code to extract weights of common layers associated with the updated coefficients for a subset of UEs. The instructions loaded into the general-purpose processor <NUM> may also include code to generate updated common weights for the CSI encoder and CSI decoder based on the extracted weights; and code to distribute the updated common weights to the subset of UEs.

Deep learning architectures may perform an object recognition task by learning to represent inputs at successively higher levels of abstraction in each layer, thereby building up a useful feature representation of the input data. In this way, deep learning addresses a major bottleneck of traditional machine learning. Prior to the advent of deep learning, a machine learning approach to an object recognition problem may have relied heavily on human engineered features, perhaps in combination with a shallow classifier. A shallow classifier may be a two-class linear classifier, for example, in which a weighted sum of the feature vector components may be compared with a threshold to predict to which class the input belongs. Human engineered features may be templates or kernels tailored to a specific problem domain by engineers with domain expertise. Deep learning architectures, in contrast, may learn to represent features that are similar to what a human engineer might design, but through training. Furthermore, a deep network may learn to represent and recognize new types of features that a human might not have considered.

A deep learning architecture may learn a hierarchy of features. If presented with visual data, for example, the first layer may learn to recognize relatively simple features, such as edges, in the input stream. In another example, if presented with auditory data, the first layer may learn to recognize spectral power in specific frequencies. The second layer, taking the output of the first layer as input, may learn to recognize combinations of features, such as simple shapes for visual data or combinations of sounds for auditory data. For instance, higher layers may learn to represent complex shapes in visual data or words in auditory data. Still higher layers may learn to recognize common visual objects or spoken phrases.

Deep learning architectures may perform especially well when applied to problems that have a natural hierarchical structure. For example, the classification of motorized vehicles may benefit from first learning to recognize wheels, windshields, and other features. These features may be combined at higher layers in different ways to recognize cars, trucks, and airplanes.

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

The connections between layers of a neural network may be fully connected or locally connected. <FIG> illustrates an example of a fully connected neural network <NUM>. In a fully connected neural network <NUM>, a neuron in a first layer may communicate its output to every neuron in a second layer, so that each neuron in the second layer will receive input from every neuron in the first layer. <FIG> illustrates an example of a locally connected neural network <NUM>. In a locally connected neural network <NUM>, a neuron in a first layer may be connected to a limited number of neurons in the second layer. More generally, a locally connected layer of the locally connected neural network <NUM> may be configured so that each neuron in a layer will have the same or a similar connectivity pattern, but with connections strengths that may have different values (e.g., <NUM>, <NUM>, <NUM>, and <NUM>). The locally connected connectivity pattern may give rise to spatially distinct receptive fields in a higher layer, because the higher layer neurons in a given region may receive inputs that are tuned through training to the properties of a restricted portion of the total input to the network.

One example of a locally connected neural network is a convolutional neural network. <FIG> illustrates an example of a convolutional neural network <NUM>. The convolutional neural network <NUM> may be configured such that the connection strengths associated with the inputs for each neuron in the second layer are shared (e.g., <NUM>). Convolutional neural networks may be well suited to problems in which the spatial location of inputs is meaningful.

One type of convolutional neural network is a deep convolutional network (DCN). <FIG> illustrates a detailed example of a DCN <NUM> designed to recognize visual features from an image <NUM> input from an image capturing device <NUM>, such as a car-mounted camera. The DCN <NUM> of the current example may be trained to identify traffic signs and a number provided on the traffic sign. Of course, the DCN <NUM> may be trained for other tasks, such as identifying lane markings or identifying traffic lights.

The DCN <NUM> may be trained with supervised learning. During training, the DCN <NUM> may be presented with an image, such as the image <NUM> of a speed limit sign, and a forward pass may then be computed to produce an output <NUM>. The DCN <NUM> may include a feature extraction section and a classification section. Upon receiving the image <NUM>, a convolutional layer <NUM> may apply convolutional kernels (not shown) to the image <NUM> to generate a first set of feature maps <NUM>. As an example, the convolutional kernel for the convolutional layer <NUM> may be a 5x5 kernel that generates 28x28 feature maps. In the present example, because four different feature maps are generated in the first set of feature maps <NUM>, four different convolutional kernels were applied to the image <NUM> at the convolutional layer <NUM>. The convolutional kernels may also be referred to as filters or convolutional filters.

The first set of feature maps <NUM> may be subsampled by a max pooling layer (not shown) to generate a second set of feature maps <NUM>. The max pooling layer reduces the size of the first set of feature maps <NUM>. That is, a size of the second set of feature maps <NUM>, such as 14x14, is less than the size of the first set of feature maps <NUM>, such as 28x28. The reduced size provides similar information to a subsequent layer while reducing memory consumption. The second set of feature maps <NUM> may be further convolved via one or more subsequent convolutional layers (not shown) to generate one or more subsequent sets of feature maps (not shown).

In the example of <FIG>, the second set of feature maps <NUM> is convolved to generate a first feature vector <NUM>. Furthermore, the first feature vector <NUM> is further convolved to generate a second feature vector <NUM>. Each feature of the second feature vector <NUM> may include a number that corresponds to a possible feature of the image <NUM>, such as "sign," "<NUM>," and "<NUM>. " A softmax function (not shown) may convert the numbers in the second feature vector <NUM> to a probability. As such, an output <NUM> of the DCN <NUM> is a probability of the image <NUM> including one or more features.

In the present example, the probabilities in the output <NUM> for "sign" and "<NUM>" are higher than the probabilities of the others of the output <NUM>, such as "<NUM>," "<NUM>," "<NUM>," "<NUM>," "<NUM>," "<NUM>," and "<NUM>". Before training, the output <NUM> produced by the DCN <NUM> is likely to be incorrect. Thus, an error may be calculated between the output <NUM> and a target output. The target output is the ground truth of the image <NUM> (e.g., "sign" and "<NUM>"). The weights of the DCN <NUM> may then be adjusted so the output <NUM> of the DCN <NUM> is more closely aligned with the target output.

To adjust the weights, a learning algorithm may compute a gradient vector for the weights. The gradient may indicate an amount that an error would increase or decrease if the weight were adjusted. At the top layer, the gradient may correspond directly to the value of a weight connecting an activated neuron in the penultimate layer and a neuron in the output layer. In lower layers, the gradient may depend on the value of the weights and on the computed error gradients of the higher layers. The weights may then be adjusted to reduce the error. This manner of adjusting the weights may be referred to as "back propagation" as it involves a "backward pass" through the neural network.

In practice, the error gradient of weights may be calculated over a small number of examples, so that the calculated gradient approximates the true error gradient. This approximation method may be referred to as stochastic gradient descent. Stochastic gradient descent may be repeated until the achievable error rate of the entire system has stopped decreasing or until the error rate has reached a target level. After learning, the DCN may be presented with new images (e.g., the speed limit sign of the image <NUM>) and a forward pass through the network may yield an output <NUM> that may be considered an inference or a prediction of the DCN.

Deep belief networks (DBNs) are probabilistic models comprising multiple layers of hidden nodes. DBNs may be used to extract a hierarchical representation of training data sets. A DBN may be obtained by stacking up layers of Restricted Boltzmann Machines (RBMs). An RBM is a type of artificial neural network that can learn a probability distribution over a set of inputs. Because RBMs can learn a probability distribution in the absence of information about the class to which each input should be categorized, RBMs are often used in unsupervised learning. Using a hybrid unsupervised and supervised paradigm, the bottom RBMs of a DBN may be trained in an unsupervised manner and may serve as feature extractors, and the top RBM may be trained in a supervised manner (on a joint distribution of inputs from the previous layer and target classes) and may serve as a classifier.

Deep convolutional networks (DCNs) are networks of convolutional networks, configured with additional pooling and normalization layers. DCNs have achieved state-of-the-art performance on many tasks. DCNs can be trained using supervised learning in which both the input and output targets are known for many exemplars and are used to modify the weights of the network by use of gradient descent methods.

DCNs may be feed-forward networks. In addition, as described above, the connections from a neuron in a first layer of a DCN to a group of neurons in the next higher layer are shared across the neurons in the first layer. The feed-forward and shared connections of DCNs may be exploited for fast processing. The computational burden of a DCN may be much less, for example, than that of a similarly sized neural network that comprises recurrent or feedback connections.

The processing of each layer of a convolutional network may be considered a spatially invariant template or basis projection. If the input is first decomposed into multiple channels, such as the red, green, and blue channels of a color image, then the convolutional network trained on that input may be considered three-dimensional, with two spatial dimensions along the axes of the image and a third dimension capturing color information. The outputs of the convolutional connections may be considered to form a feature map in the subsequent layer, with each element of the feature map (e.g., <NUM>) receiving input from a range of neurons in the previous layer (e.g., feature maps <NUM>) and from each of the multiple channels. The values in the feature map may be further processed with a non-linearity, such as a rectification, max(<NUM>, x). Values from adjacent neurons may be further pooled, which corresponds to down sampling, and may provide additional local invariance and dimensionality reduction. Normalization, which corresponds to whitening, may also be applied through lateral inhibition between neurons in the feature map.

The performance of deep learning architectures may increase as more labeled data points become available or as computational power increases. Modern deep neural networks are routinely trained with computing resources that are thousands of times greater than what was available to a typical researcher just fifteen years ago. New architectures and training paradigms may further boost the performance of deep learning. Rectified linear units may reduce a training issue known as vanishing gradients. New training techniques may reduce over-fitting and thus enable larger models to achieve better generalization. Encapsulation techniques may abstract data in a given receptive field and further boost overall performance.

<FIG> is a block diagram illustrating a deep convolutional network <NUM>. The deep convolutional network <NUM> may include multiple different types of layers based on connectivity and weight sharing. As shown in <FIG>, the deep convolutional network <NUM> includes the convolution blocks 554A, 554B. Each of the convolution blocks 554A, 554B may be configured with a convolution layer (CONV) <NUM>, a normalization layer (LNorm) <NUM>, and a max pooling layer (MAX POOL) <NUM>.

The convolution layers <NUM> may include one or more convolutional filters, which may be applied to the input data to generate a feature map. Although only two of the convolution blocks 554A, 554B are shown, the present disclosure is not so limiting, and instead, any number of the convolution blocks 554A, 554B may be included in the deep convolutional network <NUM> according to design preference. The normalization layer <NUM> may normalize the output of the convolution filters. For example, the normalization layer <NUM> may provide whitening or lateral inhibition. The max pooling layer <NUM> may provide down sampling aggregation over space for local invariance and dimensionality reduction.

The parallel filter banks, for example, of a deep convolutional network may be loaded on a CPU <NUM> or GPU <NUM> of an SOC <NUM> to achieve high performance and low power consumption. In alternative embodiments, the parallel filter banks may be loaded on the DSP <NUM> or an ISP <NUM> of an SOC <NUM>. In addition, the deep convolutional network <NUM> may access other processing blocks that may be present on the SOC <NUM>, such as sensor processor <NUM> and navigation module <NUM>, dedicated, respectively, to sensors and navigation.

The deep convolutional network <NUM> may also include one or more fully connected layers <NUM> (FC1 and FC2). The deep convolutional network <NUM> may further include a logistic regression (LR) layer <NUM>. Between each layer <NUM>, <NUM>, <NUM>, <NUM>, <NUM> of the deep convolutional network <NUM> are weights (not shown) that are to be updated. The output of each of the layers (e.g., <NUM>, <NUM>, <NUM>, <NUM>, <NUM>) may serve as an input of a succeeding one of the layers (e.g., <NUM>, <NUM>, <NUM>, <NUM>, <NUM>) in the deep convolutional network <NUM> to learn hierarchical feature representations from input data <NUM> (e.g., images, audio, video, sensor data and/or other input data) supplied at the first of the convolution blocks 554A. The output of the deep convolutional network <NUM> is a classification score <NUM> for the input data <NUM>. The classification score <NUM> may be a set of probabilities, where each probability is the probability of the input data, including a feature from a set of features.

Artificial intelligence (AI)/machine learning (ML) algorithms can improve wireless communications. An AI/ML module can run at the UE, the base station or in the case of distributed algorithms, jointly across the UE and base station. In an auto-encoder scenario, joint training may occur across the UE and the base station.

Massive multiple-input multiple-output (MIMO) systems are an important area for <NUM> and later systems. To implement massive MIMO, downlink channel state information (CSI) is analyzed by a base station, having hundreds or even thousands of centralized or distributed antennas, to address inter-user interference and to increase channel capacity. CSI measurements are made at the UE based on signals, such as CSI-RS, received from the base station. The downlink CSI measurements are fed back from the UEs to the base station for processing.

The large amount of CSI feedback can be compressed with neural network processing, for example, with an auto-encoder at the UE. The UE can encode the channel state feedback and transmit the encoded feedback over the air to the base station. The channel state feedback can be sent from the UE in accordance with timelines configured by radio resource control (RRC) signaling. Upon receiving the information, the base station feeds the received compressed channel state feedback values into the decoder to approximate the channel state feedback.

<FIG> is a block diagram illustrating an exemplary auto-encoder <NUM>, in accordance with aspects of the present disclosure. The auto-encoder <NUM> includes an encoder <NUM> having a convolutional layer (Conv) and a fully connected layer (FC). The encoder <NUM> receives the channel realization and/or interference realization as an input and compresses the channel/interference realization. The channel realization can also be referred to as a channel estimate. The interference realization can also be referred to as an interference estimate. Interference depends upon the environment and can address uplink interference or inter-stream interference in MIMO scenarios.

The compressed channel state feedback is output from the encoder <NUM>. The auto-encoder <NUM> also has a decoder <NUM> that receives the compressed channel state feedback output from the encoder <NUM>. The decoder <NUM> passes the received information through a fully connected layer and a series of convolutional layers to recover the channel state (e.g., approximate channel state).

The UE trains the encoder <NUM> and decoder <NUM> and occasionally transmits the decoder coefficients to the base station. At a higher frequency, the UE sends the outputs of the encoder <NUM> (e.g., channel state feedback or compressed output of the encoder <NUM>) to the base station. As the UE moves from location to location, the weights of the decoder <NUM> may change. That is, when the channel environment changes, the decoder weights (e.g., coefficients) may change. Updated decoder coefficients can thus be fed back to the base station from the UE to reflect the changing environment. In other words, the UE can train the decoder, and not just the encoder, based on the existing environment. The coefficients can be sent from the UE in accordance with timelines configured by RRC signaling. In one configuration, the coefficients are sent less frequently than the channel state feedback is sent.

Each UE sends not only the decoder coefficients, but also the encoder coefficients. After receiving updated decoder/encoder coefficients from multiple UEs, the base station can learn common features from the feedback, and then make or propose to the UEs updates to the network coefficients. The coefficient updates can be for the decoder and/or the encoder.

Some users can have common properties. For example, five users sitting at the same coffee shop will have some similarities for channel state because they are in the same environment. Aspects of the present disclosure leverage these similarities to improve efficiencies for the base station and also for the UEs.

To leverage the similarities, the base station neural network extracts common parts and unique parts from the UE neural networks based on the received decoder and encoder coefficients. "Common parts" refers to layers and/or weights of layers of the neural network. Each layer of the neural network extracts a specific feature of the channel. For example, neighboring UEs may have similar weights in layer one and layer two, but different weights in layer three of their encoders. In this example, the common parts would be the weights of layers one and two.

The common parts can be transmitted to existing UEs and/or new UEs joining this base station as a serving cell. For example, the base station can determine common layers for a subset of UEs. The weights for those common layers can then be updated and transmitted to the UEs. That is, after the base station transmits the initial neural network structure, the base station can later identify common layers and transmit common layer weights to multiple UEs, such as a UE subset. Subsets may be defined as neighbors having common weights, for example, because of a common environment.

Subsets of the UEs may receive common layer weights that are associated with the other UEs of the subset. Referring to the previous example, new users entering the coffee shop where five other users are sitting may receive the common layer weights. By receiving the common layer weights, the subset of UEs can more efficiently learn decoder and encoder coefficients. That is, the base station can push the coefficients to the new user. The new user can start with those coefficients when training its neural network, to reduce the training process for the new UE. Another example of a new user in the coffee shop is a UE waking up from deep sleep or a UE receiving a new data burst.

<FIG> illustrates a process for learning, in accordance with the claimed invention. A group of UEs 720a, 720b, 720c transmits compressed channel state feedback to a base station <NUM>. The base station <NUM> has a decoder 730a-730n corresponding to each of the UEs 720a-c. The base station <NUM> also stores an encoder (not shown) for each UE 720a-c. Each decoder 730a-n decompresses the received channel state feedback to recover an approximated channel state <NUM> - channel state n. The UEs 720a-c also periodically send encoder and decoder weights to the base station <NUM>, as seen in block <NUM>. The decoder weights can update the decoders 730a-n.

The base station <NUM> analyzes the received decoder weights and the encoder weights to extract common parts. That is, the base station <NUM> aggregates the information received from the UEs 720a-c and derives a new model, as seen in block <NUM>. For example, the weights of common layers received from the various UEs 720a-c can be averaged. In one configuration, all weights from the common layers are averaged. In other configurations, only a portion of the weights from the common layer are averaged. The base station <NUM> pushes the updates (e.g., new model) to the UEs 720a-c to improve their learning of the encoder and decoder coefficients. The base station can also push the updates to new UEs (e.g., the UE 720d) to speed up training of the encoder and decoder coefficients at the new UE 720d.

Signaling changes can implement the federated channel state learning. For periodic transmissions, the base station can inform the UE of the frequency of the channel state feedback and also of the coefficient updates. For aperiodic transmissions, the base station can provide an uplink grant for uplink transmission of the coefficients. In case of scheduling conflicts, priority rules for physical uplink shared channel (PUSCH) feedback can be provided to the UE via RRC signaling. For example, the channel state feedback can be assigned a lowest priority.

A new UE capability is introduced for receiving neural network coefficients on a media access control-control element (MAC-CE) or physical downlink shared channel (PDSCH). The base station can transmit neural network (NN) coefficients to the UE on the PDSCH. Alternatively, a new MAC-CE can be provided for transmission of neural network coefficients. Not all UEs may have the capability to receive the updated network coefficients. Thus, a new mode can be introduced to enable UEs to receive the updated coefficients by decoding the new control element.

<FIG> is a diagram illustrating an example process <NUM> performed, for example, by a UE, in accordance with the claimed invention. The example process <NUM> is an example of federated channel state information (CSI) learning.

As shown in <FIG>, in some aspects, the process <NUM> may include receiving a channel state information (CSI) decoder and CSI encoder from a base station on a physical downlink shared channel (PDSCH) or a media access control-control element (MAC-CE) (block <NUM>). For example, the UE (e.g., using the antenna <NUM>, DEMOD <NUM>, MIMO detector <NUM>, receive processor <NUM>, controller/processor <NUM>, memory <NUM>, and/or the like) can receive a channel state information (CSI) decoder and CSI encoder from a base station.

As shown in <FIG>, in some aspects, the process <NUM> may include training the CSI decoder and CSI encoder based on observed channel and interference conditions to obtain updated decoder coefficients and updated encoder coefficients (block <NUM>). For example, the UE (e.g., using the controller/processor <NUM>, memory <NUM>, and/or the like) can train the CSI decoder and CSI encoder based on observed channel and interference conditions to obtain updated decoder coefficients and updated encoder coefficients.

As shown in <FIG>, in some aspects, the process <NUM> may include receiving an indication of resources for transmission of the updated encoder coefficients and updated decoder coefficients (block <NUM>). For example, the UE (e.g., using the antenna <NUM>, DEMOD <NUM>, MIMO detector <NUM>, receive processor <NUM>, controller/processor <NUM>, memory <NUM>, and/or the like) can receive an indication of resources for transmission of the updated encoder coefficients and updated decoder coefficients.

As shown in <FIG>, in some aspects, the process <NUM> may include transmitting the updated decoder coefficients and updated encoder coefficients to the base station in accordance with the indication of resources (block <NUM>). For example, the UE (e.g., using the antenna <NUM>, DEMOD <NUM>, TX MIMO processor <NUM>, transmit processor <NUM>, controller/processor <NUM>, memory <NUM>, and/or the like) can transmit the updated decoder coefficients and updated encoder coefficients to the base station in accordance with the indication of resources.

As shown in <FIG>, in some aspects, the process <NUM> may include receiving an updated CSI decoder and updated CSI encoder from the base station, for further training (block <NUM>). For example, the UE (e.g., using the antenna <NUM>, DEMOD <NUM>, MIMO detector <NUM>, receive processor <NUM>, controller/processor <NUM>, memory <NUM>, and/or the like) can receive an updated CSI decoder and updated CSI encoder.

<FIG> is a diagram illustrating an example process <NUM> performed, for example, by a base station, in accordance with the claimed invention. The example process <NUM> is an example of federated channel state information (CSI) learning.

As shown in <FIG>, in some aspects, the process <NUM> may include transmitting a channel state information (CSI) decoder and a CSI encoder to multiple user equipments (UEs) via a physical downlink shared channel (PSDCH) or a media access control-control element (MAC-CE) (block <NUM>). For example, a base station (e.g., using the antenna <NUM>, MOD <NUM>, TX MIMO processor <NUM>, transmit processor <NUM>, controller/processor <NUM>, memory <NUM>, and/or the like) can transmit a channel state information (CSI) decoder and a CSI encoder to user equipments (UEs).

As shown in <FIG>, in some aspects, the process <NUM> may include assigning resources for receiving updated CSI coefficients for the CSI decoder and CSI encoder (block <NUM>). For example, a base station (e.g., using the controller/processor <NUM>, memory <NUM>, and/or the like) can assign resources for receiving updated CSI coefficients for the CSI decoder and CSI encoder.

As shown in <FIG>, in some aspects, the process <NUM> may include receiving updated CSI coefficients from the UEs, in accordance with the assigned resources (block <NUM>). For example, a base station (e.g., using the antenna <NUM>, MOD <NUM>, MIMO detector <NUM>, receive processor <NUM>, controller/processor <NUM>, memory <NUM>, and/or the like) can receive updated CSI coefficients from the UEs, in accordance with the assigned resources.

As shown in <FIG>, in some aspects, the process <NUM> may include extracting weights of common layers associated with the updated coefficients for a subset of UEs (block <NUM>). For example, a base station (e.g., using the controller/processor <NUM>, memory <NUM>, and/or the like) can extract weights of common layers. The base station may even employ separate neural networks for these purposes.

As shown in <FIG>, in some aspects, the process <NUM> may include generating updated common weights for the CSI encoder and CSI decoder based on the extracted weights (block <NUM>). For example, a base station (e.g., using the controller/processor <NUM>, memory <NUM>, and/or the like) can generate updated common weights based on the extracted weights.

As shown in <FIG>, in some aspects, the process <NUM> may include distributing the updated common weights to the subset of UEs (block <NUM>). For example, a base station (e.g., using the antenna <NUM>, MOD <NUM>, TX MIMO processor <NUM>, transmit processor <NUM>, controller/processor <NUM>, memory <NUM>, and/or the like) can distribute the updated common weights.

Claim 1:
A method of wireless communications by a user equipment, UE, in a subset of UEs, comprising:
receiving (<NUM>) a channel state information, CSI, decoder model and CSI encoder model from a base station on physical downlink control channel, PDSCH, or a media access control-control element, MAC-CE;
training (<NUM>) the CSI decoder model and CSI encoder model based on observed channel and interference conditions to obtain updated decoder coefficients and updated encoder coefficients;
receiving (<NUM>) an indication of resources for transmission of the updated encoder coefficients and the updated decoder coefficients;
transmitting (<NUM>) the updated decoder coefficients and the updated encoder coefficients to the base station in accordance with the indication of resources; and
receiving (<NUM>) updated common weights for an updated CSI decoder model and updated CSI encoder model from the base station, the updated common weights being for the subset of UEs for further training.