Signaling for additional training of neural networks for multiple channel conditions

A method of wireless communication by a user equipment (UE) includes receiving, from a base station, a configuration to train a neural network for multiple different signal to noise ratios (SNRs) of a channel estimate for a wireless communication channel. The method also includes determining a current SNR of the channel estimate is above a first threshold value. The method further includes training the neural network based on the channel estimate, to obtain a first trained neural network. The method still further includes perturbing the channel estimate to obtain a perturbed channel estimate, and training the neural network based on the perturbed channel estimate, to obtain a second trained neural network. The method includes reporting, to the base station, parameters of the first trained neural network along with the channel estimate, and parameters of the second trained neural network.

FIELD OF THE DISCLOSURE

The present disclosure relates generally to wireless communications, and more specifically to signaling for training neural networks for different types of channel conditions.

BACKGROUND

A wireless communications network may include a number of base stations (BSs) that can support communications for a number of user equipment (UEs). A user equipment (UE) may communicate with a base station (BS) via the downlink and uplink. The downlink (or forward link) refers to the communications link from the base station to the UE, and the uplink (or reverse link) refers to the communications link from the UE to the base station. As will be described in more detail, a base station may be referred to as a Node B, an evolved Node B (eNB), a gNB, an access point (AP), a radio head, a transmit and receive point (TRP), a new radio (NR) base station, a 5G Node B, and/or the like.

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.

SUMMARY

In aspects of the present disclosure, a method of wireless communication by a user equipment (UE) includes receiving, from a base station, a configuration to train a neural network for multiple different signal to noise ratios (SNRs) of a channel estimate for a wireless communication channel. The method also includes determining a current signal to noise ratio (SNR) of the channel estimate is above a first threshold value, and training the neural network based on the channel estimate, to obtain a first trained neural network. The method further includes perturbing the channel estimate to obtain a perturbed channel estimate, and training the neural network based on the perturbed channel estimate, to obtain a second trained neural network. The method still further includes reporting, to the base station, parameters of the first trained neural network along with the channel estimate, and parameters of the second trained neural network.

In other aspects of the present disclosure, a method of wireless communication by a base station includes transmitting, to a user equipment (UE), a configuration to train a neural network for multiple different signal to noise ratios (SNRs) of a channel estimate for a wireless communication channel. The method also includes receiving, from the UE, parameters of a first trained neural network along with the channel estimate, and parameters of a second trained neural network.

Other aspects of the present disclosure are directed to an apparatus for wireless communication by a user equipment (UE) having a memory and one or more processor(s) coupled to the memory. The processor(s) is configured to receive, from a base station, a configuration to train a neural network for multiple different signal to noise ratios (SNRs) of a channel estimate for a wireless communication channel. The processor(s) is also configured determine a current signal to noise ratio (SNR) of the channel estimate is above a first threshold value, and to train the neural network based on the channel estimate, to obtain a first trained neural network. The processor(s) is further configured to perturb the channel estimate to obtain a perturbed channel estimate, and to train the neural network based on the perturbed channel estimate, to obtain a second trained neural network. The processor(s) is still further configured to report, to the base station, parameters of the first trained neural network along with the channel estimate, and parameters of the second trained neural network.

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

DETAILED DESCRIPTION

Various aspects of the disclosure are described more fully below with reference to the accompanying drawings. This disclosure may, however, be embodied in many different forms and should not be construed as limited to any specific structure or function presented throughout this disclosure. Rather, these aspects are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. Based on the teachings, one skilled in the art should appreciate that the scope of the disclosure is intended to cover any aspect of the disclosure, whether implemented independently of or combined with any other aspect of the disclosure. 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.

It should be noted that while aspects may be described using terminology commonly associated with 5G and later wireless technologies, aspects of the present disclosure can be applied in other generation-based communications systems, such as and including 3G and/or 4G technologies.

User equipments (UEs) may experience differing channel conditions within a cell. Cell center UEs see a significantly higher channel estimation signal to noise ratio (SNR) than UEs at the cell edge, for instance. UEs may use neural networks to compress data prior to sending the data across a wireless channel. UEs entering a cell may download weights for a channel compression neural network model from a base station (BS or gNB), to start off their training of the neural network model. For cell edge UEs, because of the amount of noise in the channel estimate, it is not advisable to train the channel compression neural network using noisy channel estimates. At low signal to noise ratios (SNRs), channel estimation noise may change a distribution of the input to the neural network (NN). Moreover, at low SNR, the channel may appear to have greater rank than what actually exists. If the SNR drops too low, the neural network should be retrained. A single neural network may not work well for multiple channel estimation SNRs. For example, a network trained with a high channel estimation SNR may not work well for a UE experiencing a low SNR, or vice-versa.

According to aspects of the present disclosure, a neural network provided by a base station will be used by a UE until the channel estimate SNR improves beyond a threshold. In addition, the base station may customize the neural network weights to the channel estimation SNR that the UE experiences. The special neural network weights may be provided to the base station by UEs experiencing good channel conditions, by perturbing their channel estimates appropriately.

According to aspects of the present disclosure, a base station may detect UEs that are located in very good path loss locations, using, for example, power control logic. These UEs may be configured to train multiple neural networks (NNs), while emulating multiple channel estimation SNRs. These UEs may have enough memory and processing power to train multiple neural networks. The UEs may signal these capabilities to the base station. In order to obtain different SNRs, the UEs may perturb the channel estimate and train the neural networks. The UEs then feedback each such neural network (or the neural network weights) to the base station.

For cell edge UEs, the base station may configure them to not train the neural networks. In this case, the cell edge UEs use the neural network received from the base station without any training. When a cell edge UE moves into an environment with a better signal, if an estimate of channel estimation signal to noise ratio (SNR) increases above a configurable threshold value, the base station may configure the UE to train the neural network for use by the UE. When the UE moves into an environment with an even stronger signal, for example above another configurable threshold value, the base station may configure the UE to train the neural network for multiple different SNRs. In this case, the UE may perturb the channel estimate to train the neural network at different SNRs. Similarly, when a UE moves from an environment with a very strong signal to an environment with a weaker signal, the base station may configure the UE to only train the neural network for the UE's use, instead of training multiple neural networks with different SNRs. If the conditions further deteriorate, the base station may configure the UE to use the model without any training. Aspects of the present disclosure enable recovery of some loss from channel estimation by retraining the neural network and providing SNR appropriate neural network models.

In some aspects, two or more UEs120(e.g., shown as UE120aand UE120e) may communicate directly using one or more sidelink channels (e.g., without using a base station110as an intermediary to communicate with one another). For example, the UEs120may communicate using peer-to-peer (P2P) communications, device-to-device (D2D) communications, a vehicle-to-everything (V2X) protocol (e.g., which may include a vehicle-to-vehicle (V2V) protocol, a vehicle-to-infrastructure (V2I) protocol, and/or the like), a mesh network, and/or the like. In this case, the UE120may perform scheduling operations, resource selection operations, and/or other operations described elsewhere as being performed by the base station110. For example, the base station110may configure a UE120via downlink control information (DCI), radio resource control (RRC) signaling, a media access control-control element (MAC-CE) or via system information (e.g., a system information block (SIB).

As indicated above,FIG.1is provided merely as an example. Other examples may differ from what is described with regard toFIG.1.

FIG.2shows a block diagram of a design200of the base station110and UE120, which may be one of the base stations and one of the UEs inFIG.1. The base station110may be equipped with T antennas234athrough234t, and UE120may be equipped with R antennas252athrough252r, where in general T≥1 and R≥1.

At the UE120, antennas252athrough252rmay receive the downlink signals from the base station110and/or other base stations and may provide received signals to demodulators (DEMODs)254athrough254r, respectively. Each demodulator254may condition (e.g., filter, amplify, downconvert, and digitize) a received signal to obtain input samples. Each demodulator254may further process the input samples (e.g., for OFDM and/or the like) to obtain received symbols. A MIMO detector256may obtain received symbols from all R demodulators254athrough254r, perform MIMO detection on the received symbols if applicable, and provide detected symbols. A receive processor258may process (e.g., demodulate and decode) the detected symbols, provide decoded data for the UE120to a data sink260, and provide decoded control information and system information to a controller/processor280. A channel processor may determine reference signal received power (RSRP), received signal strength indicator (RSSI), reference signal received quality (RSRQ), channel quality indicator (CQI), and/or the like. In some aspects, one or more components of the UE120may be included in a housing.

On the uplink, at the UE120, a transmit processor264may receive and process data from a data source262and control information (e.g., for reports comprising RSRP, RSSI, RSRQ, CQI, and/or the like) from the controller/processor280. Transmit processor264may also generate reference symbols for one or more reference signals. The symbols from the transmit processor264may be precoded by a TX MIMO processor266if applicable, further processed by modulators254athrough254r(e.g., for DFT-s-OFDM, CP-OFDM, and/or the like), and transmitted to the base station110. At the base station110, the uplink signals from the UE120and other UEs may be received by the antennas234, processed by the demodulators254, detected by a MIMO detector236if applicable, and further processed by a receive processor238to obtain decoded data and control information sent by the UE120. The receive processor238may provide the decoded data to a data sink239and the decoded control information to a controller/processor240. The base station110may include communications unit244and communicate to the network controller130via the communications unit244. The network controller130may include a communications unit294, a controller/processor290, and a memory292.

The controller/processor240of the base station110, the controller/processor280of the UE120, and/or any other component(s) ofFIG.2may perform one or more techniques associated with signaling for training neural networks for different types of channel conditions, as described in more detail elsewhere. For example, the controller/processor240of the base station110, the controller/processor280of the UE120, and/or any other component(s) ofFIG.2may perform or direct operations of, for example, the processes ofFIGS.7and8and/or other processes as described. Memories242and282may store data and program codes for the base station110and UE120, respectively. A scheduler246may schedule UEs for data transmission on the downlink and/or uplink.

In some aspects, the UE120may include means for determining, means for training, means for perturbing, means for reporting, means for computing, means for receiving, means for indicating, and/or means for transmitting. In some aspects, the base station110may include means for receiving, and/or means for transmitting. Such means may include one or more components of the UE120or base station110described in connection withFIG.2.

As indicated above,FIG.2is provided merely as an example. Other examples may differ from what is described with regard toFIG.2.

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.3illustrates an example implementation of a system-on-a-chip (SOC)300, which may include a central processing unit (CPU)302or a multi-core CPU configured for generating gradients for neural network training, in accordance with certain aspects of the present disclosure. The SOC300may be included in the base station110or UE120. 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)308, in a memory block associated with a CPU302, in a memory block associated with a graphics processing unit (GPU)304, in a memory block associated with a digital signal processor (DSP)306, in a memory block318, or may be distributed across multiple blocks. Instructions executed at the CPU302may be loaded from a program memory associated with the CPU302or may be loaded from a memory block318.

The SOC300may also include additional processing blocks tailored to specific functions, such as a GPU304, a DSP306, a connectivity block310, which may include fifth generation (5G) connectivity, fourth generation long term evolution (4G LTE) connectivity, Wi-Fi connectivity, USB connectivity, Bluetooth connectivity, and the like, and a multimedia processor312that may, for example, detect and recognize gestures. In one implementation, the NPU is implemented in the CPU, DSP, and/or GPU. The SOC300may also include a sensor processor314, image signal processors (ISPs)316, and/or navigation module320, which may include a global positioning system.

The SOC300may be based on an ARM instruction set. In aspects of the present disclosure, the instructions loaded into the general-purpose processor302may comprise code to receive, from a base station, a configuration to train a neural network for multiple different signal to noise ratios (SNRs) of a channel estimate for a wireless communication channel. The processor302is also configured determine a current signal to noise ratio (SNR) of the channel estimate is above a first threshold value, and to train the neural network based on the channel estimate, to obtain a first trained neural network. The processor302is further configured to perturb the channel estimate to obtain a perturbed channel estimate, and to train the neural network based on the perturbed channel estimate, to obtain a second trained neural network. The processor302is still further configured to report, to the base station, parameters of the first trained neural network along with the channel estimate, and parameters of the second trained neural network. The general-purpose processor302may be configured to transmit, to a user equipment (UE), a configuration to train a neural network for multiple different signal to noise ratios (SNRs) of a channel estimate for a wireless communication channel. The processor302may further be configured to receive, from the UE, parameters of a first trained neural network along with the channel estimate, and parameters of a second trained neural network.

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.

The connections between layers of a neural network may be fully connected or locally connected.FIG.4Aillustrates an example of a fully connected neural network402. In a fully connected neural network402, 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.4Billustrates an example of a locally connected neural network404. In a locally connected neural network404, 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 network404may 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.,410,412,414, and416). 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.4Cillustrates an example of a convolutional neural network406. The convolutional neural network406may be configured such that the connection strengths associated with the inputs for each neuron in the second layer are shared (e.g.,408). 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.4Dillustrates a detailed example of a DCN400designed to recognize visual features from an image426input from an image capturing device430, such as a car-mounted camera. The DCN400of the current example may be trained to identify traffic signs and a number provided on the traffic sign. Of course, the DCN400may be trained for other tasks, such as identifying lane markings or identifying traffic lights.

The DCN400may be trained with supervised learning. During training, the DCN400may be presented with an image, such as the image426of a speed limit sign, and a forward pass may then be computed to produce an output422. The DCN400may include a feature extraction section and a classification section. Upon receiving the image426, a convolutional layer432may apply convolutional kernels (not shown) to the image426to generate a first set of feature maps418. As an example, the convolutional kernel for the convolutional layer432may be a 5×5 kernel that generates 28×28 feature maps. In the present example, because four different feature maps are generated in the first set of feature maps418, four different convolutional kernels were applied to the image426at the convolutional layer432. The convolutional kernels may also be referred to as filters or convolutional filters.

The first set of feature maps418may be subsampled by a max pooling layer (not shown) to generate a second set of feature maps420. The max pooling layer reduces the size of the first set of feature maps418. That is, a size of the second set of feature maps420, such as 14×14, is less than the size of the first set of feature maps418, such as 28×28. The reduced size provides similar information to a subsequent layer while reducing memory consumption. The second set of feature maps420may 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 ofFIG.4D, the second set of feature maps420is convolved to generate a first feature vector424. Furthermore, the first feature vector424is further convolved to generate a second feature vector428. Each feature of the second feature vector428may include a number that corresponds to a possible feature of the image426, such as “sign,” “60,” and “100.” A softmax function (not shown) may convert the numbers in the second feature vector428to a probability. As such, an output422of the DCN400is a probability of the image426including one or more features.

In the present example, the probabilities in the output422for “sign” and “60” are higher than the probabilities of the others of the output422, such as “30,” “40,” “50,” “70,” “80,” “90,” and “100”. Before training, the output422produced by the DCN400is likely to be incorrect. Thus, an error may be calculated between the output422and a target output. The target output is the ground truth of the image426(e.g., “sign” and “60”). The weights of the DCN400may then be adjusted so the output422of the DCN400is more closely aligned with the target output.

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 image426) and a forward pass through the network may yield an output422that may be considered an inference or a prediction of the DCN.

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.5is a block diagram illustrating a deep convolutional network550. The deep convolutional network550may include multiple different types of layers based on connectivity and weight sharing. As shown inFIG.5, the deep convolutional network550includes the convolution blocks554A,554B. Each of the convolution blocks554A,554B may be configured with a convolution layer (CONV)556, a normalization layer (LNorm)558, and a max pooling layer (MAX POOL)560.

The convolution layers556may 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 blocks554A,554B are shown, the present disclosure is not so limiting, and instead, any number of the convolution blocks554A,554B may be included in the deep convolutional network550according to design preference. The normalization layer558may normalize the output of the convolution filters. For example, the normalization layer558may provide whitening or lateral inhibition. The max pooling layer560may 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 CPU302or GPU304of an SOC300to achieve high performance and low power consumption. In alternative embodiments, the parallel filter banks may be loaded on the DSP306or an ISP316of an SOC300. In addition, the deep convolutional network550may access other processing blocks that may be present on the SOC300, such as sensor processor314and navigation module320, dedicated, respectively, to sensors and navigation.

The deep convolutional network550may also include one or more fully connected layers562(FC1 and FC2). The deep convolutional network550may further include a logistic regression (LR) layer564. Between each layer556,558,560,562,564of the deep convolutional network550are weights (not shown) that are to be updated. The output of each of the layers (e.g.,556,558,560,562,564) may serve as an input of a succeeding one of the layers (e.g.,556,558,560,562,564) in the deep convolutional network550to learn hierarchical feature representations from input data552(e.g., images, audio, video, sensor data and/or other input data) supplied at the first of the convolution blocks554A. The output of the deep convolutional network550is a classification score566for the input data552. The classification score566may be a set of probabilities, where each probability is the probability of the input data, including a feature from a set of features.

As indicated above,FIGS.3-5are provided as examples. Other examples may differ from what is described with respect toFIGS.3-5.

User equipments (UEs) may experience differing channel conditions within a cell. Cell center UEs see a significantly higher channel estimation signal to noise ratio (SNR) than UEs at the cell edge, for instance. UEs may use neural networks to compress data prior to sending across a wireless channel, and to decompress the data after receipt. UEs entering a cell may download weights for a channel compression neural network model from a base station (BS or gNB), to start off their training of the neural network model. For cell edge UEs, because of the amount of noise in the channel estimate, it is not advisable to train the channel compression neural network using noisy channel estimates. According to aspects of the present disclosure, the neural network provided by the base station will be used until the channel estimate SNR improves beyond a threshold. In addition, the base station may customize the neural network weights to the channel estimation SNR that the UE experiences. The special neural network weights may be provided to the base station by UEs experiencing good channel conditions, by perturbing their channel estimates appropriately.

According to aspects of the present disclosure, a base station may detect UEs that are located in very good path loss locations, using, for example, power control logic. These UEs may be configured to train multiple neural networks (NNs), while emulating multiple channel estimation SNRs. These UEs may have enough memory and processing power to train multiple neural networks. The UEs may signal these capabilities to the base station. In order to obtain different SNRs, the UEs may perturb the channel estimate and train the neural networks. The UEs then feedback each such neural network (or the neural network weights) to the base station.

For cell edge UEs, the base station configures them to not train the neural networks. In this case, the cell edge UEs use the neural network received from the base station without any training. When a cell edge UE moves into an environment with a better signal, if an estimate of channel estimation signal to noise ratio (SNR) increases above a configurable threshold value, the base station may configure the UE to train the neural network for use by the UE. When the UE moves into an environment with an even stronger signal, for example above another configurable threshold value, the base station may configure the UE to train the neural network for multiple different SNRs. In this case, the UE may perturb the channel estimate to train the neural network at different SNRs. Similarly, when a UE moves from an environment with a very strong signal to an environment with a weaker signal, the base station may configure the UE to only train the neural network for the UE's use, instead of training multiple neural networks with different SNRs. If the conditions further deteriorate, the base station may configure the UE to use the model without any training.

At low signal to noise ratios (SNRs), channel estimation noise may change a distribution of the input to the neural network (NN). It has been shown that rank per user equipment (UE) increases under the influence of channel estimation error. Moreover, at low SNR, the channel may appear whiter than it actually is. If the SNR drops too low, the neural network should be retrained. However, a single neural network may not work well for multiple channel estimation SNRs. For example, a network trained with a high channel estimation SNR may not work well for a UE experiencing a low SNR. It should also be considered as to whether a high payload is specified for transmission of compressed channel at low SNRs. That is, if the SNR is too low, the base station may ask the UE to compress the channel to a lower payload size than if the UE is experiencing a good pathloss.

According to aspects of the present disclosure, a neural network is trained on an ideal channel. To address the issues resulting from low channel estimation SNRs, the performance using a neural network trained at high SNR, on low channel estimation realizations, may be quantified. An input vector may be represented as x_ideal, which may be normalized to unit power. A noisy input vector may be represented as x, which equals x_ideal plus noise, n. The noisy input vector x may also be referred to as a sum vector and may be normalized to unit power. A reconstructed channel output is defined as y, which is a function of x, in other words, y=f(x). An ideal channel estimate (ch-est) is represented as y_ideal and is equal to f(x_ideal).

A first mean square error (MSE1) may have a value equal to MSE(y, x). This value indicates how a probability density function (PDF) of the noisy input vector, x, deviates from the reconstructed channel “y”. A second mean square error (MSE2) may have a value equal to MSE(y, x_ideal). This value is of particular interest, as it measures the deviation of the reconstructed channel from the ideal input vector. In an ideal no-noise situation, an ideal mean square error (MSE ideal) value, e.g., MSE(y_ideal, x_ideal) would be the same as MSE2 and MSE1.

At high signal to noise ratios (SNRs), the ideal channel estimate (x_ideal) is similar to the noisy channel “x”, and the first mean square error (MSE1) is similar to both the second mean square error (MSE2) and the ideal mean square error (MSE ideal). At low SNR, the noisy-estimate of the channel, “x”, is very different from the ideal channel estimate (x_ideal). As such, the ideal MSE (MSE ideal) is less than both MSE2 and MSE1.

If an SNR becomes too low, retraining of the neural network may be beneficial. According to aspects of the present disclosure, a neural network may be retrained by computing a loss metric between an output of the neural network and an ideal or noiseless input. The loss metric may be incorporated during back propagation.

FIG.6is a block diagram illustrating an example model600for retraining a neural network with multiple signal to noise ratios (SNRs) of a channel estimate, in accordance with various aspects of the present disclosure. The retraining incorporates a loss metric. As seen inFIG.6, an adder602receives as input an ideal channel estimate (x_ideal) and also receives noise. In the example ofFIG.6, the noise (N(0, 1/SNR)) has a Gaussian distribution with a mean of zero and a variance of 1/SNR. The adder602adds the noise to the ideal channel estimate to obtain a noisy input vector x. Block604rescales the noisy input vector, x, to unit power. A neural network encoder606receives the sum vector normalized to unit power, performs an operation on the received data, and transmits the processed data to a neural network decoder608. The neural network decoder608processes the received data to obtain and output a reconstructed channel, y.

In some configurations, the neural network encoder606operates within a transmitting device to perform compression of data for transmitting across a wireless channel to the neural network decoder608, which operates within a receiver. The neural network decoder608performs the decompression. The neural network encoder606and decoder608are, however, not limited to performing compression and decompression. Other functions are also contemplated.

The neural network (including the neural network encoder606and neural network decoder608) may be retrained using back propagation techniques, as seen at block612. According to aspects of the present disclosure, the backpropagation may incorporate a normalized mean square error (NMSE) computed between the reconstructed channel, y, and the ideal channel estimate, x_ideal, as seen at block610. By retraining the network with the NMSE, some amount of loss from channel estimates is recovered, especially at low signal to noise ratios (SNRs). For example, with an encoder compressing output into thirty-two dimensions in an environment with very low SNR (e.g., −5 dB), the loss may be recovered to within 1 dB of a channel estimate obtained during ideal conditions.

Aspects of the present disclosure enable recovery of some loss from channel estimation by retraining the neural network (NN). A base station may configure high geometry UEs (e.g., UEs close to a base station) to train the neural network (NN) for multiple configurable channel estimation signal to noise ratios (SNRs). These UEs may then feedback the calculated weights to the base station. Higher layer signaling, downlink control (DCI) messages, or physical downlink shared channel (PDSCH) signaling may be used for the configuring. The UEs may transmit the feedback through upper layer signaling, uplink DCI messages, or physical uplink shared channel (PUSCH) signaling. The base station may then transmit the weights to cell edge UEs using higher layer signaling, DCI messages, or PDSCH signaling.

The base station may configure cell edge UEs to not train the neural networks unless the estimate of channel estimation signal to noise ratio (SNR) increases beyond a configured threshold. The threshold configuration may be set by the network using higher layer signaling, DCI messages, or PDSCH signaling.

FIG.7is a flow diagram illustrating an example process700performed, for example, by a UE, in accordance with various aspects of the present disclosure. The example process700is an example of signaling for training neural networks for different types of channel conditions. The operations of the process700may be implemented by a UE120.

At block702, the user equipment (UE) receives, from a base station, a configuration to train a neural network for multiple different signal to noise ratios (SNRs) of a channel estimate for a wireless communication channel. For example, the UE (e.g., using the antenna252, demodulator (DEMOD)254, MIMO detector256, receive processor258, controller/processor280, and/or memory282) may receive the configuration. The neural network may be for channel compression. The UE may receiving the configuration to train the neural network via higher layer signaling, a downlink control information (DCI) message, or physical downlink shared channel (PDSCH) signaling. The receiving may occurring in response to a current SNR exceeding a first threshold value

At block704, the user equipment (UE) determines a current signal to noise ratio (SNR) of the channel estimate is above a first threshold value. For example, the UE (e.g., using the controller/processor280, and/or memory282) may determine the signal. The UE may receive, from the base station, a configuration of the first threshold value via higher layer signaling, a downlink control information (DCI) message, or physical downlink shared channel (PDSCH) signaling. The UE may indicate, to the base station, that the current SNR exceeds the first threshold value

At block706, the user equipment (UE) trains the neural network based on the channel estimate, to obtain a first trained neural network. For example, the UE (e.g., using the controller/processor280, and/or memory282) may train the neural network. The UE may receive an instruction to train the neural network based only on a current channel estimate and then use the neural network trained based on only the current channel estimate. The instruction may be received via higher layer signaling, a downlink control information (DCI) message, or physical downlink shared channel (PDSCH) signaling, in response to the current SNR having a value between the first threshold value and a second threshold value.

At block708, the user equipment (UE) perturbs the channel estimate to obtain a perturbed channel estimate. For example, the UE (e.g., using the controller/processor280, and/or memory282) may perturb the channel estimate. At block710, the user equipment (UE) trains the neural network based on the perturbed channel estimate, to obtain a second trained neural network. For example, the UE (e.g., using the controller/processor280, and/or memory282) may train the neural network.

At block712, the user equipment (UE) reports, to the base station, parameters of the first trained neural network along with the channel estimate, and parameters of the second trained neural network. For example, the UE (e.g., using the antenna252, modulator (MOD)254, transmit MIMO processor266, transmit processor264, controller/processor280, memory282, and/or the like) may report the parameters. The UE may also transmit, to the base station, UE capability information for training the neural network for the plurality of different SNRs, the UE capability information indicating an amount of available UE memory and an amount of available UE processing power.

FIG.8is a flow diagram illustrating an example process800performed, for example, by a base station, in accordance with various aspects of the present disclosure. The example process800is an example of signaling for training neural networks for different types of channel conditions. The operations of the process800may be implemented by a base station110.

At block802, the base station transmits, to a user equipment (UE), a configuration to train a neural network for multiple different signal to noise ratios (SNRs) of a channel estimate for a wireless communication channel. For example, the base station (e.g., using the antenna234, modulator (MOD)232, TX MIMO processor230, transmit processor220, controller/processor240, and/or memory242) may transmit the configuration. The base station may transmit a first threshold value, above which the UE trains the neural network for the different SNRs. The first threshold value may be transmitted via higher layer signaling, a downlink control information (DCI) message, or physical downlink shared channel (PDSCH) signaling. The base station may also transmit an instruction to stop training the neural network, in response to a current SNR of the UE falling below a second threshold value that is lower than the first threshold value. The base station may transmit an instruction to train the neural network based only on a current channel estimate and then use the neural network trained based on only the current channel estimate, in response to a current SNR having a value between the first threshold value and a second threshold value.

At block804, the base station receives, from the UE, parameters of a first trained neural network along with the channel estimate, and parameters of a second trained neural network. For example, the base station (e.g., using the antenna234, demodulator (DEMOD)232, MIMO detector236, receive processor238, controller/processor240, and/or memory242) may receive the parameters. The base station may receive, from the UE, UE capability information for training the neural network for the different SNRs. The UE capability information indicates an amount of available UE memory and an amount of available UE processing power.

Implementation examples are described in the following numbered clauses.1. A method of wireless communication by a user equipment (UE), comprising:receiving, from a base station, a configuration to train a neural network for a plurality of different signal to noise ratios (SNRs) of a channel estimate for a wireless communication channel;determining a current signal to noise ratio (SNR) of the channel estimate is above a first threshold value;training the neural network based on the channel estimate, to obtain a first trained neural network;perturbing the channel estimate to obtain a perturbed channel estimate;training the neural network based on the perturbed channel estimate, to obtain a second trained neural network; andreporting, to the base station, parameters of the first trained neural network along with the channel estimate, and parameters of the second trained neural network.2. The method of clause 1, in which the training occurs in response to the current SNR of the channel estimate exceeding the first threshold value.3. The method of clause 1 or 2, further comprising:computing a loss metric between a reconstructed channel estimate and the channel estimate having the current SNR above the first threshold value; andtraining the neural network with a back propagation method modified by the loss metric.4. The method of any of the preceding clauses, in which the neural network is for channel compression.5. The method of any of the preceding clauses, further comprising receiving, from the base station, a configuration of the first threshold value via higher layer signaling, a downlink control information (DCI) message, or physical downlink shared channel (PDSCH) signaling.6. The method of any of the preceding clauses, further comprising receiving the configuration to train the neural network via higher layer signaling, a downlink control information (DCI) message, or physical downlink shared channel (PDSCH) signaling, the receiving occurring in response to the current SNR exceeding the first threshold value.7. The method of any of the preceding clauses, further comprising receiving an instruction to stop training the neural network, the instruction received via higher layer signaling, a downlink control information (DCI) message, or physical downlink shared channel (PDSCH) signaling, the receiving the instruction occurring in response to the current SNR falling below a second threshold value that is lower than the first threshold value.8. The method of any of the preceding clauses, further comprising receiving an instruction to train the neural network based only on a current channel estimate and then use the neural network trained based on only the current channel estimate, the instruction received via higher layer signaling, a downlink control information (DCI) message, or physical downlink shared channel (PDSCH) signaling, the receiving the instruction occurring in response to the current SNR having a value between the first threshold value and a second threshold value.9. The method of any of the preceding clauses, further comprising indicating, to the base station, that the current SNR exceeds the first threshold value.10. The method of any of the preceding clauses, further comprising transmitting, to the base station, UE capability information for training the neural network for the plurality of different SNRs, the UE capability information indicating an amount of available UE memory and an amount of available UE processing power.11. A method of wireless communication by a base station, comprising:transmitting, to a user equipment (UE), a configuration to train a neural network for a plurality of different signal to noise ratios (SNRs) of a channel estimate for a wireless communication channel; andreceiving, from the UE, parameters of a first trained neural network along with the channel estimate, and parameters of a second trained neural network.12. The method of clause 11, further comprising transmitting a first threshold value, above which the UE trains the neural network for the plurality of different SNRs.13. The method of clause 11 or 12, in which the first threshold value is transmitted via higher layer signaling, a downlink control information (DCI) message, or physical downlink shared channel (PDSCH) signaling.14. The method of any of the clauses 11-13, further comprising transmitting an instruction to stop training the neural network, the instruction transmitted via higher layer signaling, a downlink control information (DCI) message, or physical downlink shared channel (PDSCH) signaling, the instruction transmitting in response to a current SNR of the UE falling below a second threshold value that is lower than the first threshold value.15. The method of any of the clauses 11-14, further comprising transmitting an instruction to train the neural network based only on a current channel estimate and then use the neural network trained based on only the current channel estimate, the instruction transmitted via higher layer signaling, a downlink control information (DCI) message, or physical downlink shared channel (PDSCH) signaling, the instruction transmitting in response to a current SNR having a value between the first threshold value and a second threshold value.16. The method of any of the clauses 11-15, further comprising receiving, from the UE, UE capability information for training the neural network for the plurality of different SNRs, the UE capability information indicating an amount of available UE memory and an amount of available UE processing power.17. The method of any of the clauses 11-16, in which the neural network is for channel compression.18. An apparatus for wireless communication by a user equipment (UE), comprising:a memory; andat least one processor coupled to the memory, the at least one processor configured:to receive, from a base station, a configuration to train a neural network for a plurality of different signal to noise ratios (SNRs) of a channel estimate for a wireless communication channel;to determine a current signal to noise ratio (SNR) of the channel estimate is above a first threshold value;to train the neural network based on the channel estimate, to obtain a first trained neural network;to perturb the channel estimate to obtain a perturbed channel estimate;to train the neural network based on the perturbed channel estimate, to obtain a second trained neural network; andto report, to the base station, parameters of the first trained neural network along with the channel estimate, and parameters of the second trained neural network.19. The apparatus of clause 18, in which the at least one processor is configured to train in response to the current SNR of the channel estimate exceeding the first threshold value.20. The apparatus of clause 18 or 19, in which the at least one processor is further configured:to compute a loss metric between a reconstructed channel estimate and the channel estimate having the current SNR above the first threshold value; andto train the neural network with a back propagation method modified by the loss metric.21. The apparatus of any of the clauses 18-20, in which the neural network is for channel compression.22. The apparatus of any of the clauses 18-21, in which the at least one processor is further configured to receive, from the base station, a configuration of the first threshold value via higher layer signaling, a downlink control information (DCI) message, or physical downlink shared channel (PDSCH) signaling.23. The apparatus of any of the clauses 18-22, in which the at least one processor is further configured to receive the configuration to train the neural network via higher layer signaling, a downlink control information (DCI) message, or physical downlink shared channel (PDSCH) signaling, the receiving occurring in response to the current SNR exceeding the first threshold value.24. The apparatus of any of the clauses 18-23, in which the at least one processor is further configured to receive an instruction to stop training the neural network, the instruction received via higher layer signaling, a downlink control information (DCI) message, or physical downlink shared channel (PDSCH) signaling, the receiving the instruction occurring in response to the current SNR falling below a second threshold value that is lower than the first threshold value.25. The apparatus of any of the clauses 18-24, in which the at least one processor is further configured to receive an instruction to train the neural network based only on a current channel estimate and then use the neural network trained based on only the current channel estimate, the instruction received via higher layer signaling, a downlink control information (DCI) message, or physical downlink shared channel (PDSCH) signaling, the receiving the instruction occurring in response to the current SNR having a value between the first threshold value and a second threshold value.26. The apparatus of any of the clauses 18-25, in which the at least one processor is further configured to indicate, to the base station, that the current SNR exceeds the first threshold value.27. The apparatus of any of the clauses 18-26, in which the at least one processor is further configured to transmit, to the base station, UE capability information for training the neural network for the plurality of different SNRs, the UE capability information indicating an amount of available UE memory and an amount of available UE processing power.

As used, the term “component” is intended to be broadly construed as hardware, firmware, and/or a combination of hardware and software. As used, a processor is implemented in hardware, firmware, and/or a combination of hardware and software.