Patent Description:
This application claims the benefit of the <CIT>, and entitled "System and Method for Improving Performance of Learning Based Radio Communications and Sensing Systems with Radio Transformer Networks".

Machine learning models are becoming more prevalent in radio applications. Such systems couple machine learning techniques with hardware radio components to rapidly convert a sampling of a single radio signal into useful information, such as information bits, human understandable labels, or other types of information. For example, <CIT> describes a concept for mitigating impairments due to transmit noise by an apparatus for mitigating transmitter impairments of a transmit signal.

However, such legacy systems have be configured to process all types of radio signals and radio signal parameters at the time of deployment, relying on human configuration of transforms, and cannot be easily optimized for changing signal or channel parameters.

According to one innovative aspect of the present disclosure, a method that may be performed by one or more devices is disclosed for training a machine learning system to process one or more radio signals. In one aspect, the method includes actions of providing a first set of input data representing one or more radio signals to a first neural network that is configured to generate output data representing an estimation of one or more characteristics of a radio signal, obtaining first output data generated by the first neural network based on the first neural network processing the first set of input data, receiving, by a signal transformer, a second set of input data that includes (i) the first set of input data and (ii) the first output data generated by the first neural network, generating, by the signal transformer and based on the second set of input data, data representing a transformed radio signal by applying one or more transforms of a set of predetermined transforms to the first set of input data representing the one or more radio signals, providing the data representing the transformed radio signal to a second neural network that is configured to generate output data describing the one or more radio signals based on processing the data representing the transformed radio signal, obtaining second output data generated by the second neural network based on the second neural network processing the data representing the transformed radio signal, determining a target loss that is based on (i) the second output data generated by the second neural network and (ii) target information describing the one or more radio signals, and adjusting the respective parameters of the first neural network and the second neural network based on the target loss.

Other aspects include a system adapted to process one or more radio signals according to any one of claims <NUM> to <NUM> and a computer readable storage medium according to any one of claims <NUM> to <NUM>.

<FIG> is a contextual diagram of an example of a radio receiver that uses a machine learning system <NUM> with a radio signal transformer <NUM> to determine a number of wireless transmitters in a building.

The radio machine learning system <NUM> includes a radio signal parameter estimator <NUM>, a signal transformer <NUM>, and a learned model <NUM>. The learned model <NUM> may include a model for regression or classification using a neural network. The radio machine learning system <NUM> may be deployed within a radio signal receiver <NUM>. Deployment of the radio machine learning system <NUM> onto the radio signal receiver <NUM> may include, for example, providing the radio machine learning system <NUM> to the radio signal receiver <NUM> across one or more networks as a software download.

By way of example, a user <NUM> may use the radio signal receiver <NUM> that includes the radio machine learning system <NUM> with radio signal transformer <NUM> to detect radio signals <NUM>, <NUM>, <NUM> output by one or more radio signal emitters such as devices <NUM>, <NUM>, <NUM>. The radio signal receiver <NUM> can include a radio signal sensor for detecting one or more radio signals. The radio signal receiver <NUM> may also include an analog-to-digital converter for generating a digital output that represents on one or more analog radio signal inputs that have been detected by the radio signal sensor. The generated digital output may be generated by sampling one or more particular analog radio signals into a basis function such I/Q representation, OFDM subcarriers, or the like. In some implementations, the analog-to-digital convertor may only digitize a single radio signal. In other implementations, the analog-to-digital convertor may digitize an entire portion of a wireless spectrum such as the <NUM> spectrum that includes multiple different radio signals.

The output of the analog-to-digital converter can be provided to a radio signal parameter estimator <NUM>. The radio signal parameter estimator <NUM> may include a neural network that is trained to estimate parameters of the detected radio signals <NUM>, <NUM>, <NUM>. Radio signals shown are emitted from mobile computing devices (e.g. phones, laptops, tablets), but may also include embedded devices, industrial/infrastructure devices, unintended EMI, malicious attackers, vehicular radios, and numerous other electronic devices emitting radio signals. The parameters of the detected radio signals <NUM>, <NUM>, <NUM> may include any measurable characteristic of a radio signal such as timing information, frequency, center frequency, bandwidth, phase, rate of arrival, direction of arrival, offset, or the like. Alternatively, or in addition, parameters of the detected radio signals <NUM>, <NUM>, <NUM> may include any measurable characteristic of corresponding channel state information associated with the radio signal such as the channel delay response. The output of the radio signal parameter estimator <NUM> is a set of estimated radio signal parameters representing the one or more radio signals <NUM>, <NUM>, <NUM> detected by the radio signal receiver <NUM>.

The output of the radio signal parameter estimator <NUM> can be provided to the signal transformer <NUM> along with the original output from the analog-to-digital converter that represents the detected radio signals <NUM>, <NUM>, <NUM>. The signal transformer <NUM> is configured to apply one or more transforms from a set of transforms to the detected radio signals <NUM>, <NUM>, <NUM>. The set of one or more transforms may include, for example, an affine transform, an oscillator and mixer/multiplier, a convolution with a parametric set of filter taps, or any other transform capable of inverting effects of the detected radio signals <NUM>, <NUM>, <NUM> such as effects of the detected radio signals <NUM>, <NUM>, <NUM> imparted by physics or other processes acting on the radio signal or other processes acting on information in the radio transmitters. The signal transformer <NUM>, via use of the transforms, functions to manipulate the detected radio signals <NUM>, <NUM>, <NUM> into a canonical form for analysis or processing by a learned model such as a classification neural network, a regression neural network, or the like. The signal transformer's manipulation of the detected radio signals <NUM>, <NUM>, <NUM> reduces the burden on the learned model <NUM> by eliminating the need for the learned model to perform complicated calculations to develop canonical forms for each of the detected radio signals <NUM>, <NUM>, <NUM>. This results in significant performance increases by the radio receiver, such as a reduction in the amount of processing power used to make inferences based on the detected radio signals <NUM>, <NUM>, <NUM>. For example, by imparting the appropriate parametric transform operations which invert physical phenomena accurately, the target manifold mapping input to estimates and transform output to learned model output than simply attempting to learn the manifold which maps the input to the learned model output directly. By reducing this complexity, less training data is needed, the model will generalize better, and size, weight and power requirements are reduced for both training and deployment.

The output of the signal transformer <NUM> is provided to the learned model <NUM>. The learned model <NUM> may include a regression neural network, a classification neural network, or another other type of learned model. The learned model <NUM> may be trained to provide an output <NUM> that represents a particular type of information that can be inferred based on the canonicalized input that the learning model <NUM> receives from the signal transformer <NUM>. In one example, the learning model <NUM> may be trained to generate output data <NUM> that indicates a number of wireless devices <NUM>, <NUM>, <NUM> that are emitting radio signals <NUM>, <NUM>, <NUM> within a predetermined geographical region. In some implementations, the geographical region may include a region associated with a building such as building <NUM>. One example of a canonicalized form might be that of a fully synchronized communications signal, where the constellation is aligned to an ideal set of constellation points such as a grid, and timing is aligned to discrete sampling times for each symbol (e.g. timing, frequency, and phase recovery).

However, the learned model <NUM> need not be so limited. For example, the learned model <NUM> can also be trained to generate different types of output data <NUM>. In some implementations, for example, the learned model can be trained to generate output data <NUM> such as a one-hot vector (or other classification label target such as a hierarchical label) that indicates whether the detected radio signals <NUM>, <NUM>, <NUM> include a particular type of signal or a zero vector if the detected radio signals <NUM>, <NUM>, <NUM> do not include the particular type of radio signal.

Yet other types of output data <NUM> can also be generated that are not limited to the examples set forth herein. For example, in some implementations, the radio machine learning system <NUM> with transformer <NUM> may be configured to share output data <NUM> by the learned model <NUM> with other devices to help improve their respective networks. For example, the receiver <NUM> may provide output <NUM> generated by the learned model <NUM> to another device that is configured to use the output <NUM> to adjust one or more communications systems associated with the other device. The data provided to the other device may also include, for example, one or more characteristics of the radio signal or its corresponding channel state information include estimates of timing information, center frequency, bandwidth, phase, frequency and rate of arrival, direction of arrival, channel delay response, offset, or bandwidth of the particular radio signal. These measurements may be produced in a number of ways including the output of the learned network, or as estimated parameters to the transformer network where the learned network is trained for a different end objective.

Yet other applications may include providing the output data <NUM> to an application that can analyze the output data <NUM> and infer, based on the analysis, additional information about one or more radio signal emitters. As discussed above, one example of this analysis of output data <NUM> and inference may include determining a number of emitters. In other implementations, other attributes about radio signal emitters may be determined, based on the output data <NUM>, such as a location of an emitter, movement of an emitter, behavior of an emitter, or pattern of life of an emitter, modulation and encoding methods used in the transmitter, types of information carried, effects occurring in the transmitter or channel or the like.

The radio machine learning system <NUM> with transformer <NUM> also provides other advantages over legacy systems. For example, the radio machine learning system <NUM> with signal transformer <NUM> is faster and cheaper to design, optimize, deploy, and operate than alternatives because the radio signal parameter estimator <NUM> need not be trained in a supervised fashion (although they may be trained separately in this way alternatively), or explicitly programed with a manually derived estimation expression to infer a particular set of signal parameters for each type or types of radio signals that may be provided as an input to the radio signal parameter estimator <NUM>. Instead, the radio signal parameter estimator <NUM> is trained jointly using a loss that is determined based on the difference between the output of the learning model <NUM> for a particular set of radio signals input to the radio signal parameter estimator <NUM> and for the loss associated with the end-to-end objective of the input data and the learned model's targets utilizing the parametric transformers and estimator model.

<FIG> is a block diagram of an example of a prior art radio machine learning system 210A without a radio transformer. The radio machine learning system 210A includes a hardware sensor <NUM>, an analog-to-digital converter <NUM>, a learned model <NUM> for regression, classification, or both, and extracted information <NUM>.

With reference to <FIG>, a hardware sensor <NUM> is used to detect one or more analog radio signals. The hardware sensor <NUM> may include an antenna with amplifiers, filters, oscillators, mixers, or a combination thereof.

In a radio machine learning system 210A without a radio signal transformer the digital version of the radio signal is directly provided into a learned model <NUM>. This model directly produces extracted information <NUM> which describes the incoming signal based on the digital version of the radio signal. Such a system requires the learned model <NUM> to perform complex calculations in order determine how to map the input into a canonical form of the digital version of the radio signal. The extracted information <NUM> may take the form of information bits or code-words in the case of a learned communications systems decoder (such as in <CIT>), it may take the form of label information about the signal in the case of a sensing system (such as in <CIT>), or the like.

<FIG> is a block diagram of an example of a radio machine learning system 210B with a radio transformer <NUM>.

In contrast to the radio machine learning system 210A of <FIG>, the <FIG> provides an example of a radio machine learning system 210B with a radio signal transformer <NUM> in accordance with the present disclosure.

The radio machine learning system 210B similarly is configured to detect one or more analog radio signals using hardware sensor <NUM>. The hardware sensor <NUM> may include, for example, an antenna with amplifiers, filters, oscillators, mixers, or a combination thereof. However, instead of directly providing the digital version of the one or more radio signals to the learned model <NUM>, the radio machine learning system 210B first provides the digital version of the one or more radio signals to a radio signal parameter estimation unit <NUM>.

The radio signal parameter estimator <NUM> may include a neural network that is trained to estimate parameters of a digital version of the one or more radio signals received by the radio signal parameter estimator <NUM>. The parameters of the one or more received radio signals may include any estimable characteristic of a radio signal such as timing information, frequency, center frequency, bandwidth, phase, rate of arrival, direction of arrival, offset, or the like. Alternatively, or in addition, parameters of the received radio signals may include any estimable characteristic of corresponding channel state information associated with the radio signal such as the channel delay response. The output of the radio signal parameter estimator <NUM> is a set of estimated radio signal parameters representing or describing the one or more radio signals received by the radio signal parameter estimator <NUM>. In some implementations, the first neural network may generate a first output data parameterizing the set of transformers to extract one or more isolated radio signals using one or more operations that include sub-band tuning, mixing with an oscillator, and/or filtering.

The signal transformer <NUM> is configured to receive the output of the radio signal parameter estimator <NUM> and the digital version of the one or more radio signals that were provided as an input to the radio signal parameter estimator <NUM>. The signal transformer <NUM> is configured to apply one or more transforms from a set of transforms to the digital version of the one or more radio signals received by the signal transformer <NUM> as an input to the signal transformer <NUM>. The set of one or more transforms may include, for example, an affine transform, an oscillator and mixer, a convolution with a parametric set of filter taps, sub-band tuning, spatial combining, carrier and clock correction, or any other transform capable of inverting effects of the digital version of the radio signals imparted by physics or other processes acting on the radio signal.

The signal transformer <NUM>, via the application of one or more transforms based on the received parameters from the radio signal parameter estimator, functions to manipulate the received digital version of the one or more radio signals into a canonical form for analysis by a learned model <NUM> such as a classification neural network, a regression neural network, or the like. The signal transformer's <NUM> manipulation of the digital version of the one or more radio signals reduces the burden on the learned model <NUM> by eliminating the need for the learned model to perform complicated calculations to develop canonical forms for each digital version of the one or more radio signals received as an input to the signal transformer <NUM>.

Accordingly, the combined functionality of the radio signal parameter estimator <NUM> and the signal transformer <NUM> function to synchronize the radio signal into a set of canonicalized encoded information representing the radio signal such as a time, frequency, and phase aligned stream of modulated radio constellation points. The canonicalized form of the analog signal results in an elimination of more than a threshold amount of uncertainty surrounding one or more of the signal or channel charactersitics such as frequency offset, the time offset, or other the channel effects. This canonicalized form results in multiple performance increases by the radio receiver such as a reduction in processing power used to make inferences based on the digital version of the one or more radio signals received as an input to the signal transformer.

The output of the signal transformer <NUM> can be provided to the learned model <NUM>. In addition, for some implementations, the output of the signal transformer <NUM> may optionally be used to update parameters of the radio signal parameter estimator <NUM>. The learned model <NUM> may include a regression neural network, a classification neural network, or another other type of learned model. The learned model <NUM> may be trained to generate extracted information <NUM> as an output that represents a particular type of information can be inferred based on the output data of the signal transformer <NUM> that is provided to the learning model <NUM> as an input. In one example, the learning model <NUM> may be trained to generate output data <NUM> that indicates a number of wireless devices that are producing radio signals within a predetermined geographical region.

However, other types of extracted data may be generated by the learning model <NUM> as an output. For example, the extracted information generated by the learned model <NUM> may one or more of data describing signal labels, modulation type, protocol, wireless standards, equipment type, symbol values, data bits, data code-words, or any other classification of data associated with a radio signal.

<FIG> is a block diagram of an example of a radio machine learning system <NUM> with a radio transformer <NUM> for training a neural network based radio signal parameter estimator <NUM> and a learning model <NUM>.

In the example of <FIG>, the radio machine learning system <NUM> may employ a radio signal parameter estimator <NUM> and a learned model <NUM> that each include one or more neural networks. Each neural network may include, for example, one or more collections of sequential multiplies, additions, and optional non-linearities that have each been respectively trained to realize the functionality of the radio signal parameter estimator <NUM> and the learned model <NUM>. The learned model <NUM> may include a learned model for regression, classification, or both. In some implementations, a neural network is used for parameter regression <NUM> and a neural network <NUM> is used for classification, regression, or both, of output information.

The radio signal parameter estimator <NUM> and the learned model <NUM> are bridged by the signal transformer <NUM>. The signal transformer <NUM> is configured to receive, as inputs, the output of the radio signal parameter estimator and the digital version of the one or more radio signals that were input into the signal parameter estimator <NUM>. The signal transformer <NUM> is configured to generate, as an output, a transformation of or set of estimates describing aspects of the digital version of the one or more radio signals that the signal transformer <NUM> received as an input. The signal transformer <NUM> generates transformation of the digital version of the one or more radio signals by applying one or more transforms to the digital version of the one or more radio signals that are received by the signal transformer <NUM> as an input. The set of one or more transforms may include, for example, an affine transform, an oscillator and mixer, a convolution with a parametric set of filter taps, or any other transform capable of inverting effects of the digital versions of the one or more received signals such as effects of the received one or more signals imparted by physics or other processes acting on the one or more received signals. In some implementations, only a single transform may be applied to the received signals. In other implementations, multiple transforms of the set of potential transforms identified above may be applied in combination. In some instances non-parametric transforms such as Fourier transforms may additionally be applied to the data before or after the application of the parametric transformer and/or estimator model. In some instances, the estimates from the first model comprising a description of the channel state information (CSI), and the canonicalized form of the signal or a transform or representation thereof, may comprise a compact representation of the signal which can be used for transmission, analysis, reception, or otherwise. In this case, it can be very advantageous to separate the CSI from the content in this way so as to allow for better analysis, removal of random information, and improved compression in some cases by helping removing or separately maintaining unnecessary random variables from the CSI.

In some implementations, the signal transformer may be implemented using a neural network that is configured to perform the transforms. In such implementations, a third neural network <NUM> is configured so that these transformations are performed within the third neural network <NUM> that bridges the first neural network <NUM> used to implemented the radio signal parameter estimator <NUM> and a second neural network <NUM> that is configured to implement the learned model <NUM> using classification, regression, or both. The third neural network <NUM> bridges the first neural network <NUM> and the second neural network <NUM> using the output generated by the third neural network that is generated based on the inputs to the third neural network that include digital version of the one or more received radio signals produced by the analog-to-digital converter <NUM> and the output of the first neural network <NUM>. The output of the first neural network <NUM> includes estimated radio signal parameters for the digital version of the one or more radio signals. In some instances the weights or network parameters of this signal transformer neural network may be frozen or used un-changed from training on a prior task during the training of the estimator model and the learned model, or they may be jointly learned, optionally leveraging prior known weights to assist in optimization.

The estimated radio signal parameters may include any measurable or estimable characteristic of a radio signal such as timing information, frequency, center frequency, bandwidth, phase, rate of arrival, direction of arrival, offset, or the like. Alternatively, or in addition, the estimated radio signal parameters may include any measurable or estimable characteristic of corresponding channel state information associated with the radio signal such as the channel impulse response or power-delay response. The digital version of the one or more radio signals are transformed using these estimated parameters by transforms for each parameter to produce a transformed signal which is fed to the learned model <NUM> to produce useful extracted information (<NUM>). The parametric transformations are crafted in order to allow for differentiation and backwards propagation of error gradients from outputs to weights and inputs.

Accordingly, the combined functionality of the neural network <NUM> of the radio signal parameter estimator <NUM> and neural network <NUM> of the signal transformer <NUM> functions to synchronize the radio signal into a set of canonicalized encoded information representing or describing aspects of the radio signal such as a time and frequency aligned stream of modulated radio constellation points. The canonicalized form of the analog signal results in an elimination of more than a threshold amount of uncertainty surrounding one or more of the signal or channel charactersitics such as frequency offset, the time offset, or other the channel effects. This cationicalized form results in multiple performance increases by the radio receiver such as a reduction in processing power used to make inferences based on the digital version of the one or more radio signals received as an input to the signal transformer.

The transformed signal that is generated by the signal transformer as an output is provided to the learned model <NUM>. In some implementations, the learned model <NUM> may include a neural network <NUM> that is trained for classification or regression and configured to take the transformed signal as an input. In such implementations, the transformed signal that is received as an input to the neural network <NUM> may be mapped to output information <NUM> through the neural network <NUM>. A target loss <NUM> may be computed using a variety of loss determination methods such as mean squared error, cross-entropy or other distance metric and be formed based on the output information <NUM> and the target output information <NUM> for a known analog radio input <NUM> upon which the digital version of the one or more radio signals was generated by the analog-to-digital converter <NUM>.

The target loss may be used to compute gradients which are propagated backwards <NUM> through the neural network <NUM> of the learned model, the neural network <NUM> of the signal transformer, and the neural network <NUM> of the radio signal parameter estimator, shown via the arrows in <FIG> extending from the target loss <NUM> to the neural network <NUM> for the learned model, the neural network for the signal transformer, and the neural network <NUM> for the radio signal parameter estimator to obtain parameter updates and dynamically optimize each of these models to minimize the target loss <NUM>. In some implementations, the analog radio input <NUM> may be generated through the use of one or more learned neural networks or channel simulations prior and the target loss function may additionally adjust the parameters of the one or more prior neural networks or simulations of one or more of the prior neural networks in addition to the backwards propagation of the target loss through the neural network <NUM> of the learned model, the neural network <NUM> of the signal transformer, and the neural network <NUM> of the radio signal parameter estimator, shown via the arrows in <FIG>. The back-propagation of the computed variants thus results in improving the performance of the entire system, improving signal labeling accuracy and/or improving bit error rate or symbol error rate within a learned communications system receiver.

<FIG> is a flowchart of an example of a process <NUM> for training a radio machine learning system with radio transformer. In general, the process <NUM> may include providing a first set of input data representing a particular radio signal to a first neural network trained to estimate characteristics of one or more radio signals (<NUM>), obtaining first output data generated by the first neural network (<NUM>), receiving, by a signal transformer, a second set of input data that includes (i) the first set of input data and (ii) the first output data generated by the first neural network (<NUM>), generating, by the signal transformer and based on the second set of input data, data representing a transformed radio signal (<NUM>), providing the data representing the transformed radio signal to a second neural network that has been trained to generate output data describing the radio signal (<NUM>), obtaining second output data generated by the second neural network (<NUM>), determining a target loss that is based on (i) the second output data and (ii) target information describing the particular radio signal (<NUM>), and adjusting the respective parameters of the first neural network and the second neural network based on the target loss (<NUM>). The process <NUM> is described below as being performed by a receiver on which computer program code describing the functionality of the aforementioned neural networks is stored and executed to realize the functionality described by process <NUM>.

In more detail, a receiver may begin executing process <NUM> for training a radio machine learning system with radio transformer by providing <NUM> a first set of input data representing a particular radio signal to a first neural network trained to estimate characteristics of a radio signal (<NUM>). The first set of input data representing the particular radio signal may include a digital representation of an analog signal detected by the antenna of a receiver. The digital representation of the analog signal may be generated by sampling one or more particular analog radio signals into a basis function such I/Q representation, OFDM subcarriers, or the like. In some implementations, the analog radio signal upon which the first set of input data is based may include a plurality of radio signal communications, radar, or other signals that are received.

The first neural network may include a neural network that has been trained to estimate characteristics of a digital representation of an analog radio signal. Alternatively, or in addition, the first neural network may also be trained to estimate characteristics of corresponding channel state information associated with the radio signal such as the channel delay response. In some instances, this estimation network may be trained independently to map input data to a set of known measurements describing various aspects of the signal such as frequency, phase, timing, delay profile, or rate information.

The receiver can obtain <NUM> first output data generated by the first neural network. The first output data may include data representing any measurable or estimable characteristic of a radio signal such as timing information, frequency, center frequency, bandwidth, phase, rate of arrival, direction of arrival, offset, or the like. Alternatively, or in addition, the first output data may also include any measurable or estimable characteristic of corresponding channel state information associated with the radio signal such as the channel delay response.

The receiver can receive <NUM>, using a signal transformer, a second set of input data that includes (i) the first set of input data and (ii) the first output data generated by the first neural network. The first set of input data may include data representing the particular radio signal may include a digital representation of an analog signal detected by the antenna of a receiver. The first output data generated by the first neural network may include data representing any measurable or estimable characteristic of a radio signal such as timing information, frequency, center frequency, bandwidth, phase, rate of arrival, direction of arrival, offset, or the like. Alternatively, or in addition, the first output data may also include any measurable or estimable characteristic of corresponding channel state information associated with the radio signal such as the channel delay response.

The receiver can generate <NUM>, using the signal transformer and based on the second set of input data, data representing a transformed radio signal. The signal transformer can generate the transformed radio signal by applying one or more transforms of a set of multiple transforms. The set of multiple transforms may include, for example, an affine transform, an oscillator and mixer, a convolution with a parametric set of filter taps, or any other transform capable of inverting effects of the one or more radio signals such as effects of the one or more radio signals imparted by physics or other processes acting on the one or more radio signals.

The receiver can provide <NUM> the data representing the transformed radio signal to a second neural network that has been trained to generate output data describing the radio signal. The second neural network may include a classification neural network or a regression neural network.

The receiver can obtain <NUM> second output data generated by the second neural network (<NUM>). The second output data may include one or more of signal labels, modulation type, protocol, symbol values, data bits, data code-words, or the like.

The receiver can determine <NUM> a target loss that is based on (i) the second output data and (ii) target information describing the particular radio signal. The target loss may be determined using methods such as mean squared error, cross-entropy or other distance metric. This target loss may be used to compute gradients for backwards propagation through the neural networks that the receivers uses to perform the process <NUM>. The receiver can adjust <NUM> the respective parameters of the first neural network and the second neural network based on the target loss (<NUM>).

<FIG> is a flowchart of an example of a run-time process <NUM> for using a radio machine learning system with a radio transformer extract information from a plurality of radio signals. The process <NUM> is similar to the process <NUM> without determining the loss and adjusting the parameters of each respective neural network using the adjusted loss.

Accordingly the process <NUM> generally includes providing a first set of input data representing a particular radio signal to a first neural network trained to estimate characteristics of one or more radio signals (<NUM>), obtaining first output data generated by the first neural network (<NUM>), receiving, by a signal transformer, a second set of input data that includes (i) the first set of input data and (ii) the first output data generated by the first neural network (<NUM>), generating, by the signal transformer and based on the second set of input data, data representing a transformed radio signal (<NUM>), providing the data representing the transformed radio signal to a second neural network that has been trained to generate output data describing the radio signal (<NUM>), and obtaining second output data generated by the second neural network (<NUM>).

A server may also configured to deploy the radio machine learning system with a radio transformer described herein. In such embodiments, the server may be configured to provide, across one or more networks, a radio machine learning system such as the radio machine learning system such as radio machine learning system 210B or radio machine learning system <NUM> for storage and executed on a device such as a receiver. In such instances, the server may be configured to deploy a radio machine learning system that is configured to perform each and every operation of processes <NUM> and <NUM> described above. Similarly, the server may also be configured to deploy, across one or more networks such as the Internet, a radio machine learning system with radio transformer that includes any of the features described with reference to <FIG>, <FIG>, <FIG>, <FIG>, and <FIG> described in this specification.

Embodiments of the subject matter, the functional operations and the processes described in this specification can be implemented in digital electronic circuitry, in tangibly-embodied computer software or firmware, in computer hardware, including the structures disclosed in this specification or in combinations of one or more of them. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible nonvolatile program carrier for execution by, or to control the operation of, data processing apparatus. Alternatively, or in addition, the program instructions can be encoded on an artificially generated propagated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to suitable receiver apparatus for execution by a data processing apparatus.

The terms "data processing apparatus" and "processor" encompass all kinds of apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers. The apparatus or processor can include special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit). The apparatus or processor can also include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them.

A computer program (which may also be referred to or described as a program, software, a software application, a module, a software module, a script, or code) can be written in any form of programming language, including compiled or interpreted languages, or declarative or procedural languages, and it can be deployed in any form, including as a standalone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

Computer readable media suitable for storing computer program instructions and data include all forms of nonvolatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by special purpose logic circuitry.

To provide for interaction with a user, embodiments of the subject matter described in this specification can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube), LCD (liquid crystal display), LED (light-emitting diode), or OLED (organic light-emitting diode) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse, a trackball, or a touchscreen, by which the user can provide input to the computer. In addition, a computer can interact with a user by sending documents to and receiving documents from a device that is used by the user; for example, by sending web pages to a web browser on a user's user device in response to requests received from the web browser.

Claim 1:
A method (<NUM>) for training a machine learning system to process one or more radio signals, the method comprising:
providing (<NUM>) a first set of input data representing one or more radio signals to a first neural network that is configured to generate output data representing an estimation of one or more characteristics of a radio signal;
obtaining (<NUM>) first output data generated by the first neural network based on the first neural network processing the first set of input data;
receiving (<NUM>), by a signal transformer, a second set of input data that includes (i) the first set of input data and (ii) the first output data generated by the first neural network;
generating (<NUM>), by the signal transformer and based on the second set of input data, data representing a transformed radio signal by applying one or more transforms of a set of predetermined transforms to the first set of input data representing the one or more radio signals;
providing (<NUM>) the data representing the transformed radio signal to a second neural network that is configured to generate output data describing the one or more radio signals based on processing the data representing the transformed radio signal;
obtaining (<NUM>) second output data generated by the second neural network based on the second neural network processing the data representing the transformed radio signal;
determining (<NUM>) a target loss that is based on (i) the second output data generated by the second neural network and (ii) target information describing the one or more radio signals; and
adjusting (<NUM>) the respective parameters of the first neural network and the second neural network based on the target loss.