Method and apparatus for generating a predictive model

Abstract

A method and apparatus for generating a predictive model include operating a radio signal receiving device while collecting data corresponding to at least one frequency-related parameter associated with a frequency source associated with the receiving device, at least one operational characteristic of the receiving device, and / or at least one environmental event associated with the receiving device, where the at least one frequency-related parameter changes with changes in the at least one operational characteristic and / or the at least one environmental event, and processing the data to generate a predictive model to be used to compensate for the changes in the at least one frequency-related parameter corresponding to the at least one operational characteristic and / or the at least one environmental event.

Description

METHOD AND APPARATUS FOR GENERATING A PREDICTIVE MODELCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims benefit of and priority to U.S. Provisional Patent Application Serial No. 63 / 445,757 filed February 15, 2023, which is herein incorporated by reference in its entirety.FIELD

[0002] Embodiments of the present principles generally relate to radio signal processing and, in particular, to a method and apparatus for generating a predictive model to compensate for frequency-related parameter errors.BACKGROUND

[0003] Radio transmissions are used in various communications and positioning systems. For example, in a handset (e.g., mobile phone), a frequency source (e.g., an oscillator, a synthesizer, etc.) generates an oscillating signal that is used by various components of the handset to process data and facilitate operation of various signal receivers (e.g., a WiFi modem, a cellular modem, a Bluetooth modem, a GNSS receiver, etc.). Unfortunately, in most handsets, the stability of the oscillating signal varies significantly with environmental and operational conditions of the handset. Such frequency variability impacts a receiver’s ability to accurately process received radio signals.

[0004] A technique for performing motion compensation of received radio signals that improves the accuracy of a local frequency source is known as SUPERCORRELATION™ and is described in commonly assigned US patent 9,780,829, issued 3 October 2017; US patent 10,321 ,430, issued 11 June 2019; US patent 10,816,672, issued 27 October 2020; US patent publication 2020 / 0264317, published 20 August 2020; US patent publication 2020 / 0319347, published 8 October 2020; US patent application 63 / 394667, filed 3 August 2022; and US patent application 63 / 424185, filed 10 November 2022. SUPERCORRELATION™ techniques can be used to phase compensate received signals for motion of the handset and instability of the local frequency source. In performing the SUPERCORRELATION™ technique, aprocessor in the handset generates a number of hypotheses of the phase compensation capable of correcting the received signals for the instability of the local frequency source as well as the motion of the receiver.

[0005] Initially, the hypotheses cover a large search space and, over time, as the frequency source is stabilized, the number and density of hypotheses is reduced and the hypotheses track and correct for changes of the frequency source output. Unfortunately, the output of a frequency source, especially inexpensive oscillators, can vary greatly in view of the handset’s operational characteristics and / or environmental events. For example, external and internal temperature changes and / or physical shock to the handset can cause significant and rapid changes in the frequency of the frequency source output. Such unexpected variations require the SUPERCORRELATION™ technique to expand the hypotheses search space to find the preferred hypotheses that, for example, maximizes a receiver signal correlation output. Such search space expansion can slow signal acquisition, increase receiver power consumption, or cause position location using GNSS signals to be inaccurate for a period of time.

[0006] There is therefore a need for a method and apparatus for generating a predictive model to anticipate frequency changes that occur from environmental events and device operational characteristics.SUMMARY

[0007] Embodiments of the present principles generally relate to a method and apparatus for generating a predictive model to anticipate frequency changes that occur from environmental events and / or device operational characteristics.

[0008] Various features and advantages of the present disclosure may be appreciated from a review of the following detailed description of the present disclosure, along with the accompanying figures in which like reference numerals refer to like parts throughout.BRIEF DESCRIPTION OF THE DRAWINGS

[0009] So that the manner in which various features of the present principles can be understood in detail, a more particular description of the principles, briefly summarized above, may be had by reference to embodiments, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only typical embodiments in accordance with the present principles and are therefore not to be considered limiting of its scope, for the principles may admit to other equally effective embodiments.

[0010] Figure 1 depicts a high-level block diagram of an exemplary test platform for generating a predictive model in accordance with at least one embodiment of the present principles;

[0011] Figure 2 depicts a flow diagram of a method of generating a dataset of test data in accordance with at least one embodiment of the present principles; and

[0012] Figure 3 depicts a flow diagram of a method of using the dataset of FIG. 2 to generate a predictive model in accordance with at least one alternate embodiment of the present principles.

[0013] To facilitate understanding, identical reference numerals have been used, where possible, to designate identical elements that are common to the figures. The figures are not drawn to scale and may be simplified for clarity. It is contemplated that elements and features of one embodiment may be beneficially incorporated in other embodiments without further recitation.DETAILED DESCRIPTION

[0014] Embodiments of the present principles generally relate to methods and apparatuses for generating a predictive model to anticipate frequency changes that occur from environmental events and / or device operational characteristics. While the concepts of the present principles are susceptible to various modifications and alternative forms, specific embodiments thereof are shown by way of example in the drawings and are described in detail below. It should be understood that there is nointent to limit the concepts of the present principles to the particular forms disclosed. On the contrary, the intent is to cover all modifications, equivalents, and alternatives consistent with the present principles and the appended claims.

[0015] The phrase device under test (DUT) used herein is intended to describe a receiving device, such as a radio signal receiving device, which can implement embodiments of the present principles to generate a predictive model to anticipate frequency changes that occur from environmental events and / or device operational characteristics and to implement such predictive model to correct for a frequency response of a frequency source associated with the DUT to changes in environmental events and / or device operational characteristics of the DUT. The phrase device under test is not intended to limit any receiving device to the collection of data during a test mode as embodiments of the present principles described herein include a collection of data during normal operation of the receiving device.

[0016] In embodiments of the present principles, a generated predictive model can be used during signal processing within a device (i.e., user equipment) to compensate for expected frequency-related parameter changes that occur in response to device operational characteristics and / or environmental events. The frequency-related parameters can include, but are not limited to, phase, frequency, frequency rate or higher-order terms of the device’s frequency source. The frequency-related parameter variation resulting from environmental and operational conditions can be modeled through a priori empirical studies and / or through real-time updates of the model as the device is used. In some embodiments, a model of the present principles can include a lookup table or machine learning model that associates an environmental or an operational event with a change in a frequency-related parameter. In some embodiments of the present principles, the change in the frequency-related parameter can be represented by a phasor or phasors (i.e., hypotheses of signal phases) to be used in, for example, the SUPERCORRELATION™ technique to compensate for the predicted frequency-related parameter variation.

[0017] In some embodiments, the model or models can be populated using a priori empirical data generated through, for example, handset testing using a test platform. For example, embodiments of the present principles can be used to create a datasetof frequency change information as the handset (e.g., a device under test (DUT)) experiences various environment conditions (e.g., temperature change, physical shock, vibration) and / or operational events (e.g., activate and deactivate the screen, modems, GNSS receiver, and the like). The SUPERCORRELATION™ technique can be used to accurately measure the frequency-related parameter variations. The dataset can then be processed to create a predictive model that anticipates a particular frequency-related parameter change that will occur when a specific environmental or operational event occurs. A model of the present principles enables a number and density of hypotheses used in, for example, the SUPERCORRELATION™ technique when receiving signals during normal use to be constrained. In embodiments of the present principles, a dataset of information containing condition / event versus frequency-related parameter can be created. Such a dataset can include information representing changes in at least one frequency-related parameter (phase, frequency, frequency rate or higher-order terms) that occurs in view of combinations of conditions / events (e.g., frequency and frequency rate change that results when a handset is dropped on a hot day). In some embodiments, a model created from the dataset can be in the form of a lookup table or the dataset can be used to train a machine learning model. In some embodiments, a model of the present principles can be used to predict a frequency-related parameter that occurs as a result of the occurrence of a condition / event or a combination of conditions / events.

[0018] In some embodiments, a model of the present principles can be initially created using a priori empirical information that is ported to new devices as an initial predictive model. Such initial predictive model of the present principles can be updated and / or corrected as the device is used over time. Consequently, as the device is used, the predictive model becomes more accurate at predicting frequency variations as the device is used.

[0019] Alternatively, in some embodiments, a model of the present principles can be generated in real-time as a device (e.g., a handset / mobile communication device) is used, without the implementation of an a priori created dataset. For example, in such embodiments a predictive model generator of the present principles can be embedded in a handset rather than as part of a test platform.

[0020] In other embodiments, a model of the present principles and / or information about a model can be shared among devices such that the accuracy of a model and / or device functionality is improved. In such embodiments, the sharing can be accomplished on a peer-to-peer basis and / or via a server distributing available model information. For particular models of devices, the server can include a reference model developed from the shared information that can be periodically shared with currently used devices and / or that can be used as an initial model in new or reset devices.

[0021] FIG. 1 depicts a block diagram of an exemplary test platform 100 for creating a frequency source predictive model in accordance with at least one embodiment of the present principles. In the embodiment of FIG. 1 , the platform 100 comprises a device under test (DUT) 102 coupled to a predictive model generator 128 and an environmental event controller 126. The predictive model generator 128 runs test scripts to collect empirical data from the DUT 102 regarding frequency variation in response to specific environmental and operational events. In some embodiments of the present principles, the platform 100 of FIG. 1 can be implemented, for example, at the DUT manufacturing facility.

[0022] In some embodiments, the predictive model generator 128 can be embedded within the DUT 102 and can be executed as a software application of the DUT 102. In such embodiments, the generator 128 can function to update the predictive model as the DUT 102 is used in the field.

[0023] In some embodiments, the DUT 102 can include, but not be limited to, a laptop computer, a mobile phone, a tablet computer, an Internet of Things (loT) device, a purpose-built positioning device, etc. and / or a combination thereof. In general, lhe DUT 102 of FIG. 1 can include any device in which the SUPERCORRELATION™ technique can be implemented to enhance signal reception and / or integrity of the local frequency source.

[0024] In the embodiment of FIG. 1 , the DUT 102 illustratively comprises at least one processor 104, support circuits 108 and a memory 110. The at least one processor 104 can include any form of processor or combination of processors including, but not limited to, central processing units, microprocessors, microcontrollers, fieldprogrammable gate arrays, graphics processing units, digital signal processors, and the like. The support circuits 108 can include well-known circuits and devices facilitating functionality of the processor(s). That is, the support circuits 108 can comprise one or more of and / or a combination of, power supplies, clock circuits, analog to digital converters, communications circuits, cache, displays, and the like.

[0025] In the embodiment of FIG. 1 , the operation of and / or a change in operation of the GNSS receiver 112, the graphical user interface display 114, and the modems 116 (e.g., receivers and transmitters of WiFi, cellular, Bluetooth and the like signals) can cause temperature changes in the DUT 102 which can result in frequency drift of the frequency source 118.

[0026] In the embodiment of FIG. 1 , the memory 110 can include one or more forms of non-transitory computer readable media including one or more of, or any combination of, read-only memory or random-access memory. The memory 110 can store software and data including, for example, but not limited to, an operating system (OS) 120, application software 122, test software 124 and data 106, signal processing software 146, and SUPERCORRELATION™ software 148.

[0027] In the embodiment of FIG. 1 , the predictive model generator 128 includes at least one processor 130, support circuits 132 and a memory 134. The at least one processor 130 can include any form of processor or combination of processors including, but not limited to, central processing units, microprocessors, microcontrollers, field programmable gate arrays, graphics processing units, digital signal processors, and the like. The support circuits 132 of FIG. 1 can include well known circuits and devices facilitating functionality of the processor(s). In the embodiment of FIG. 1 , the support circuits 108 can include one or more of, or a combination of, power supplies, clock circuits, analog to digital converters, communications circuits, cache, displays, test sensors, DUT interface and control ports and circuits, environmental event controller interface, and / or the like.

[0028] The memory 134 of the predictive model generator 128 of FIG. 1 can include one or more forms of non-transitory computer readable media including one or more of, or any combination of, read-only memory or random-access memory. The memory134 stores software and data including, for example, but not limited to, test software 136, a dataset 138, a predictive model 140 and a model generator 142. As depicted in FIG. 1 , in some embodiments, the memory 134 of the model generator 128 can include artificial intelligence machine learning algorithms (Al 144).

[0029] In some embodiments, the generator 128 of FIG. 1 can be coupled to and control the operation of the environmental event controller 126. The environmental event controller 126 of FIG. 1 can include a number of sub-systems that produce controllable environmental events such as, but not limited to, heat application, cold application, shock application, shaking application, and the like.

[0030] As described above, in various embodiments the generator 128 operates the DUT 102 via, for example, the environmental event generator 126 to instigate operational and environmental events that are likely to cause a change in frequency. As the events are applied, the generator 128 causes the DUT to collect data 106 regarding the changes in at least one frequency-related parameter. In some embodiments, the collected data 106 is transferred to the generator 128 to create a dataset of frequency-related parameters correlated with the operational and environmental events and the parameters of those events (e.g., temperature, shock level, modem on time, positioning rate, etc.)

[0031] In the present disclosure, the operation of the test software 136 and DUT test software 124, as respectively executed by the processors 130 and 124, is described in detail below with respect to FIG. 2. The operation of the model generator software 142 as executed by the processor 130 is described in greater detail below with respect to FIG. 3.

[0032] In some embodiments of the present principles, the test signals for the modems 116 and / or GNSS receiver 112 can be simulated as real-world signals such that the signal processing software 146 utilizes SUPERCORRELATION™ software 148 to monitor the at least one frequency-related parameter in the same manner that the SUPERCORRELATION™ technique operates in a real-world environment, such as by processing signals received in a multipath environment.

[0033] In some embodiments, the predictive model generator 128 can be embedded in the DUT 102 and all the signal processing performed by both the DUT 102 and the predictive model generator 128 is performed by the DUT 102. In such embodiments, the predictive model generation process can begin without a model or, alternatively, it can begin with a model that is produced at, for example, the manufacturer or by a third-party (e.g., a reference model). The reference model can then be adapted and altered by the predictive model generator 128 as the DUT 102 is used in normal operating conditions. In such embodiments, the SUPERCORRELATION™ technique can be used to determine at least one frequency- related parameter in accordance with the present principles as the DUT 102 is used in both good and bad signal reception environments.

[0034] Alternatively or in addition, in some embodiments the predictive model generator 128 can be remotely located (i.e., in a cloud-based server) from the DUT 102, or a plurality of DUTs, that collects frequency-related parameter data and environment / operational event information. The processing of the frequency-related parameter data and environment / operational event information in accordance with the present principles can be performed to generate a reference model for many different DUTs or can be processed to generate models for the specific DUT 102 that supplied the specific frequency-related parameter data and environment / operational event information.

[0035] FIG. 2 depicts a flow diagram of a method 200 of generating a dataset of test data in accordance with at least one embodiment of the present principles. The method 200 can be effectuated by execution of the test software 136 and / or the DUT test software 124 of FIG. 1. In embodiments in which the predictive model generator 128 is a stand-alone computing device coupled to the DUT 102, the test software 136 and the DUT test software 124 can communicate with one another to generate the dataset. In some embodiments, if the predictive model generator 128 is embedded in the DUT, the test software 136 and the DUT test software 124 functions can be combined into a single process and executed on the DUT to generate the dataset.

[0036] Referring back to FIG. 2, the method 200 can begin at 202 and proceed to 204 during which test parameters are applied to the DUT. As described above, in someembodiments, the test parameters control various operations of the DUT such as, but not limited to, display activity, display brightness, modem activity, CPU activity, crystal age, battery voltage, as well as combinations of operations. The method 200 can proceed to optional 206 or 208.

[0037] Similar to 204, at 206 environmental events such as, but not limited to, heating, cooling, shaking, dropping, and the like are applied to the DUT. That is, in some embodiments, a predictive model generator of the present principles sends control parameters to the environmental event controller to produce the environmental events, including the severity and durations of the environmental events. The method 200 can proceed to 208.

[0038] At 208, frequency-related parameter data is collected as the operations and / or combinations of operations (i.e., from 204 and 206, above) are performed at the DUT. As described above, in some embodiments, the frequency-related parameter data can include, for example, frequency and frequency rate information relating to the operation of a frequency source of the UDT. In accordance with the present principles, frequency is defined as the frequency source output oscillation rate (e.g., cycles per second) and frequency rate is defined as the rate of change of frequency over time. In some embodiments, other higher order frequency-related parameters can also be used in accordance with the present principles. In addition, in some embodiments frequency-related parameter data can include temperature of the frequency source during the operation(s).

[0039] As mentioned above, in some embodiments determining the at least one frequency-related parameter of interest can be performed using the SUPERCORRELATION™ technique to enable signal processing even in poor signal reception environments such as, so-called, urban canyons in which multipath interference is high. For example, the at least one frequency-related parameter can relate to signal processing of GNSS signals from multiple satellites. These signals are highly susceptible to multipath interference but are important to producing an accurate frequency source in a DUT of the present principles. The SUPERCORRELATION™ technique uses a robust receiver motion compensation and joint correlation process to enable the reception of GNSS signals in poor reception environments and produceaccurate frequency-related parameters using those GNSS signals. Referring back to FIG. 2, the method 200 can proceed to 210.

[0040] At 210, the method 200 builds the dataset from the collected and determined data. That is, in some embodiments, the dataset includes a compilation of all the collected data including, but not limited to, the operations, the events, the duration of operations and / or events, the severity of operations and / or events, temperature information, frequency-related parameter information, time, and the like. In some embodiments, data can be collected in the DUT through execution of the DUT test software. In such embodiments, the data can be collected into a file and transferred to, for example, the predictive model generator. Alternatively or in addition, in some embodiments the data can be streamed to the predictive model generator.

[0041] The method 200 can end at 212.

[0042] In some embodiments, the method 200 can further include generating data as the DUT is used in the field. In such embodiments, a DUT can passively monitor operational activity and / or environmental events rather than actively controlling the occurrence of these events. In such embodiments, sensors, such as accelerometers, gyroscopes, temperature sensors, and the like, can be used to monitor the environment and the operating system of the DUT can provide operational information regarding the DUT.

[0043] The collected data can then be used to train a model to predict a frequency response / change in a frequency source due to a change in at least one of an operational characteristic of a DUT or an environment in which the DUT is operating. For example, FIG. 3 depicts a flow diagram of a method 300 of using the dataset to create a predictive model in accordance with at least one embodiment of the invention. The method 300 can begin at 302 and proceed to 304 during which the abovedescribed dataset of FIG. 2 is collected / accessed from, for example, a memory accessible to an associated device. The method 300 can proceed to 306.

[0044] At 306, it is determined what type of predictive model to create. For example and as depicted in the method 300 of FIG. 3, in some embodiments of the presentprinciples, a user of an associated device can be given an opportunity to input a request via, for example, an input / output device of the device, for what type of model (e.g., LUT, Al / machine learning model, etc.) a user wishes to be generated from the collected / stored data. The method 300 can proceed to 308.

[0045] At 308, the collected / accessed data including at least one of data regarding a change in at least one of an operational characteristic of an associated device or a change in a characteristic of the environment in which the device is operating and a respective frequency response to the change is correlated to generate a frequency response model. For example, in some embodiments, a look-up table (LUT) can be generated to correlate data parameters of at least one of an operational characteristic of a device and / or a characteristic of the environment in which the device is operating (e.g., event parameters) with a respective frequency-related parameter of, for example, a frequency source.

[0046] A generated model of the present principles, such as an LUT, can include, as an input, data related to an environmental and / or an operational event and the associated parameters, and can include as an output a frequency-related parameter. For example, an LUT input event can include an activation of a display associated with a related device and the output can include a resulting frequency of a frequency source (e.g., oscillator) associated with the device, such as an increase in frequency. In the example, the known increase in frequency to an associated frequency source can be used by the DUT to alter signal processing to compensate for the change in frequency. In other embodiments, the LUT input can include an increase in environmental temperature that causes an increase in frequency. In other embodiments, shock to a device can cause the frequency of an associated frequency source to “ring” with an initial increase followed by oscillatory changes in frequency. All of these frequency- related parameter changes can be compensated for by an associated device, for example, in signal processing.

[0047] Referring back to the method of FIG. 3, the method 300 can proceed to 310. At 310, the generated predictive model, such as the LUT, can be output to be used to correct frequency-related parameter errors of, for example, a frequency source of an associated device. In some embodiments, a generated predictive model of the presentprinciples can be used to anticipate a behavior of frequency-related parameters of a frequency source and to compensate for that behavior during signal processing of received radio signals (e.g., GNSS signals, WiFi signals, Bluetooth signals, and the like). A predictive model of the present principles finds particular use in commonly assigned US Patent Application Serial Number 63 / 424,185, filed 10 November 2022, entitled “Method and Apparatus for Determining a Frequency Related Parameter of a Frequency Source Using Predictive Control”.

[0048] Referring back to and as depicted in FIG. 3, in some embodiments of the method 300, the collected / stored data can be implemented to train a machine learning neural network (artificial intelligence (Al)) to create a predictive model of the present principles. For example, if it is decided at 306 to generate a machine learning (artificial intelligence (Al)) model, the method 300 can proceed to 312.

[0049] At 312, a machine learning neural network is executed. The method 300 can proceed to 314.

[0050] At 314, the neural network is trained using the collected / accessed dataset of, for example, FIG. 2. In some embodiments, the trained model has as an input, data associated with at least one of an operational event and / or an environmental event, as described above, and has as an output a frequency-related parameter of, for example, a change to a frequency of a frequency source related to an associated device. For example, in some embodiments, the input event can include activation of a display associated with the device and the output can include that the frequency of the frequency source (e.g., oscillator) increases in frequency. Information regarding the resulting increase in frequency can then be used by the device associated with the frequency source to alter signal processing of received signals to compensate for the change in frequency. Alternatively or in addition, in some embodiments an input event can include an increase in environmental temperature that causes an increase in frequency of a frequency source associated with a receiving device. In some embodiments, an input event can include a shock to the device which can cause the frequency of the frequency source to “ring” with an initial increase in frequency followed by oscillatory changes in frequency. In such embodiments and other similar embodiments, such frequency-related parameter changes can be compensated for insignal processing by, for example, a receiving device associated with a frequency source of the frequency.

[0051] That is, in some embodiments of the present principles, data corresponding to at least one operational characteristic of a radio signal receiving device is collected while the device is in operation. A first training set comprising the collected data corresponding to the at least one operational characteristic of the radio signal receiving device is created. In addition, data corresponding to at least one environmental event associated with the radio signal receiving device is collected while the device is in operation. A second training set comprising the collected data corresponding to the at least one environmental event associated with the radio signal receiving device is created. Furthermore, data corresponding to at least one frequency-related parameter corresponding to at least one of the at least one operational characteristic and / or the at least one environmental event associated with the radio signal receiving device is collected, where the at least one frequency-related parameter changes with changes in the at least one operational characteristic and / or the at least one environmental event. A third training set comprising the collected data corresponding to the at least one frequency-related parameter is created. In such embodiments, the neural network is trained using the first training set, the second training set, and the third training set to generate a predictive model to be used to compensate for the changes in the at least one frequency-related parameter corresponding to the at least one operational characteristic and / or the at least one environmental event in accordance with the present principles.

[0052] Referring back to the method 300 of FIG. 3, the method 300 can proceed to 316. At 316, the method 300 outputs the machine learning (e.g., Al-based) predictive model. The model can then be used to correct frequency-related parameter errors in a predictive manner based upon the current operational or environmental events impacting the frequency source function. That is, in some embodiments, a predictive model of the present principles can be output to be used to correct frequency-related parameter errors of, for example, a frequency source of an associated device. In some embodiments, a generated predictive model of the present principles can be used to anticipate a behavior of frequency-related parameters of a frequency source and tocompensate for that behavior during signal processing of received radio signals (e.g., GNSS signals, WiFi signals, Bluetooth signals, and the like). Alternatively or in addition to altering signal processing, a predicted model of the present principles can be used to compensate for frequency-related parameter errors by altering an operation of the device, including but no limited to turning off / on a display, altering CPU activity, turning on / off modems, and the like and / or by adjusting an environmental characteristic of an environment in which the device associated with the frequency source is operating, including but not limited to changing a temperature and / or humidity of the environment.

[0053] Embodiments of a predictive model of the present principles generated in accordance with 312-316 above, find particular use in US Patent Application Serial Number 63 / 424,185, filed 10 November 2022, entitled “Method and Apparatus for Determining a Frequency Related Parameter of a Frequency Source Using Predictive Control”.

[0054] The method 300 can end at 318.

[0055] In some embodiments, organization of collected / stored datasets in accordance with the present principles can include processing the data, including but not limited to averaging, combining, filtering, and / or limiting, data in a dataset. Alternatively or in addition, data within a dataset can also be eliminated or corrected in instances in which the data is deemed irrelevant, redundant or inaccurate.

[0056] In some embodiments of the present principles a method for generating a predictive model includes operating a device under test while collecting data corresponding to at least one frequency-related parameter, at least one operational characteristic and / or at least one environmental event, wherein the at least one frequency-related parameter changes with changes in the at least one operational characteristic and / or the at least one environmental event, and processing the data to generate a predictive model to be used to compensate for the changes in the at least one frequency-related parameter corresponding to the at least one operational characteristic and / or the at least one environmental event.

[0057] In some embodiments of the method, processing the data can include correlating at least one of the at least one operational characteristic and / or the at least one environmental event with a respective at least one frequency-related parameter to determine a frequency response of a frequency source associated with the device under test to the change in the at least one of the at least one operational characteristic and / or the at least one environmental event.

[0058] In some embodiments, the method can further include using the predictive model to correct for a frequency response of a frequency source associated with the device under test to the change in the at least one of the at least one operational characteristic and / or the at least one environmental event.

[0059] In some embodiments, in the method correcting for the frequency response includes at least one of adjusting for a change in frequency of the frequency source during signal processing of signals received by the associated device under test, adjusting an operational characteristic of the device under test, and / or adjusting an environmental characteristic of an environment in which the device under test is operating.

[0060] In some embodiments, in the method the predictive model includes at least one of a look-up table and / or a machine learning model.

[0061] In some embodiments, in the method the machine learning model includes a neural network which is trained using the collected data corresponding to the least one frequency-related parameter, the at least one operational characteristic and / or the at least one environmental event.

[0062] In some embodiments, in the method the at least one operational characteristic includes at least one of display activity of a display associated with the device under test, display brightness of a display associated with the device under test, modem activity of a modem associated with the device under test, CPU activity of a CPU associated with the device under test, crystal age of a crystal associated with the device under test, and / or battery voltage of a battery associated with the device under test.

[0063] In some embodiments, in the method the at least one environmental event includes at least one of heating, cooling, shaking, and / or dropping of the device under test.

[0064] In some embodiments of the present principles, an apparatus for generating a predictive model includes at least one processor and at least one memory including at least one of programs and / or instructions that when executed by the processor cause the apparatus to perform operations including operating a device under test while collecting data corresponding to at least one frequency-related parameter, at least one operational characteristic and / or at least one environmental event, wherein the at least one frequency-related parameter changes with changes in the at least one operational characteristic and / or the at least one environmental event, and processing the data to generate a predictive model to be used to compensate for the changes in the at least one frequency-related parameter corresponding to the at least one operational characteristic and / or the at least one environmental event.

[0065] In some embodiments, in the apparatus processing the data includes correlating at least one of the at least one operational characteristic and / or the at least one environmental event with a respective at least one frequency-related parameter to determine a frequency response of a frequency source associated with the device under test to the change in the at least one of the at least one operational characteristic and / or the at least one environmental event.

[0066] In the apparatus, the apparatus is configured to further perform using the predictive model to correct for a frequency response of a frequency source associated with the device under test to the change in the at least one of the at least one operational characteristic and / or the at least one environmental event

[0067] In some embodiments, in the apparatus correcting for the frequency response includes at least one of adjusting for a change in frequency of the frequency source during signal processing of signals received by the associated device under test, adjusting an operational characteristic of the device under test, and / or adjustingan environmental characteristic of an environment in which the device under test is operating.

[0068] In some embodiments, in the apparatus the predictive model includes at least one of a look-up table and / or a machine learning model.

[0069] In some embodiments, in the apparatus the machine learning model includes a neural network which is trained using the collected data corresponding to the least one frequency-related parameter, the at least one operational characteristic and / or the at least one environmental event.

[0070] In some embodiments, in the apparatus the at least one operational characteristic includes at least one of display activity of a display associated with the device under test, display brightness of a display associated with the device under test, modem activity of a modem associated with the device under test, CPU activity of a CPU associated with the device under test, crystal age of a crystal associated with the device under test, and / or battery voltage of a battery associated with the device under test.

[0071] In some embodiments, in the apparatus the at least one environmental event includes at least one of heating, cooling, shaking, and / or dropping of the device under test.

[0072] In some embodiments, a computer-implemented method of training a neural network for generating a predictive model to compensate for frequency-related errors includes collecting data corresponding to at least one operational characteristic of a radio signal receiving device while in operation, creating a first training set comprising the collected data corresponding to the at least one operational characteristic of the radio signal receiving device, collecting data corresponding to at least one environmental event associated with the radio signal receiving device while in operation, creating a second training set comprising the collected data corresponding to the at least one environmental event associated with the radio signal receiving device, collecting data corresponding to at least one frequency-related parameter corresponding to at least one of the at least one operational characteristicand / or the at least one environmental event associated with the radio signal receiving device, , where the at least one frequency-related parameter changes with changes in the at least one operational characteristic and / or the at least one environmental event, creating a third training set comprising the collected data corresponding to the at least one frequency-related parameter, and training the neural network using the first training set, the second training set, and the third training set to generate a predictive model to be used to compensate for the changes in the at least one frequency-related parameter corresponding to the at least one operational characteristic and / or the at least one environmental event.

[0073] In such embodiments, the generated predictive model can be used to correct for a frequency response of a frequency source associated with the radio signal receiving device to the change in the at least one of the at least one operational characteristic and / or the at least one environmental event. In such embodiments, correcting for the frequency response can include at least one of adjusting for a change in frequency of the frequency source during signal processing of signals received by the radio signal receiving device, adjusting an operational characteristic of the radio signal receiving device, and / or adjusting an environmental characteristic of an environment in which the radio signal receiving device is operating.

[0074] Those skilled in the art will appreciate that, while various items are illustrated as being stored in memory or on storage while being used, these items or portions of them can be transferred between memory and other storage devices for purposes of memory management and data integrity. Alternatively, in other embodiments some or all of the software components can execute in memory on another device and communicate with the illustrated computer system via intercomputer communication. Some or all of the system components or data structures can also be stored (e.g., as instructions or structured data) on a computer-accessible medium or a portable article to be read by an appropriate drive, various examples of which are described above. In some embodiments, instructions stored on a computer- accessible medium separate can be transmitted to a computing device via transmission media or signals such as electrical, electromagnetic, or digital signals, conveyed via a communication medium such as a network and / or a wireless link.Various embodiments can further include receiving, sending or storing instructions and / or data implemented in accordance with the foregoing description upon a computer-accessible medium or via a communication medium. In general, a computer-accessible medium can include a storage medium or memory medium such as magnetic or optical media, e.g., disk or DVD / CD-ROM, volatile or non-volatile media such as RAM (e.g., SDRAM, DDR, RDRAM, SRAM, and the like), ROM, and the like.

[0075] The methods and processes described herein may be implemented in software, hardware, or a combination thereof, in different embodiments. In addition, the order of methods can be changed, and various elements can be added, reordered, combined, omitted or otherwise modified. All examples described herein are presented in a non-limiting manner. Various modifications and changes can be made as would be obvious to a person skilled in the art having benefit of this disclosure. Realizations in accordance with embodiments have been described in the context of particular embodiments. These embodiments are meant to be illustrative and not limiting. Many variations, modifications, additions, and improvements are possible. Accordingly, plural instances can be provided for components described herein as a single instance. Boundaries between various components, operations and data stores are somewhat arbitrary, and particular operations are illustrated in the context of specific illustrative configurations. Other allocations of functionality are envisioned and can fall within the scope of claims that follow. Structures and functionality presented as discrete components in the example configurations can be implemented as a combined structure or component. These and other variations, modifications, additions, and improvements can fall within the scope of embodiments as defined in the claims that follow.

[0076] In the foregoing description, numerous specific details, examples, and scenarios are set forth in order to provide a more thorough understanding of the present disclosure. It will be appreciated, however, that embodiments of the disclosure can be practiced without such specific details. Further, such examples and scenarios are provided for illustration, and are not intended to limit the disclosure in any way. Those of ordinary skill in the art, with the included descriptions, should be able to implement appropriate functionality without undue experimentation.

[0077] References in the specification to “an embodiment,” etc., indicate that the embodiment described can include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is believed to be within the knowledge of one skilled in the art to affect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly indicated.

[0078] Embodiments in accordance with the disclosure can be implemented in hardware, firmware, software, or any combination thereof. Embodiments can also be implemented as instructions stored using one or more machine-readable media, which may be read and executed by one or more processors. A machine-readable medium can include any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computing device or a “virtual machine” running on one or more computing devices). For example, a machine-readable medium can include any suitable form of volatile or non-volatile memory.

[0079] In addition, the various operations, processes, and methods disclosed herein can be embodied in a machine-readable medium and / or a machine accessible medium / storage device compatible with a data processing system (e.g., a computer system), and can be performed in any order (e.g., including using means for achieving the various operations). Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense. In some embodiments, the machine-readable medium can be a non-transitory form of machine-readable medium / storage device.

[0080] Modules, data structures, and the like defined herein are defined as such for ease of discussion and are not intended to imply that any specific implementation details are required. For example, any of the described modules and / or data structures can be combined or divided into sub-modules, sub-processes or other units of computer code or data as can be required by a particular design or implementation.

[0081] In the drawings, specific arrangements or orderings of schematic elements can be shown for ease of description. However, the specific ordering or arrangement of such elements is not meant to imply that a particular order or sequence of processing, or separation of processes, is required in all embodiments. In general,schematic elements used to represent instruction blocks or modules can be implemented using any suitable form of machine-readable instruction, and each such instruction can be implemented using any suitable programming language, library, application-programming interface (API), and / or other software development tools or frameworks. Similarly, schematic elements used to represent data or information can be implemented using any suitable electronic arrangement or data structure. Further, some connections, relationships or associations between elements can be simplified or not shown in the drawings so as not to obscure the disclosure.

[0082] This disclosure is to be considered as exemplary and not restrictive in character, and all changes and modifications that come within the guidelines of the disclosure are desired to be protected.

Claims

CLAIMS1 . A method for generating a predictive model, comprising: operating a device under test while collecting data corresponding to at least one frequency-related parameter, at least one operational characteristic and / or at least one environmental event, wherein the at least one frequency-related parameter changes with changes in the at least one operational characteristic and / or the at least one environmental event; and processing the data to generate a predictive model to be used to compensate for the changes in the at least one frequency-related parameter corresponding to the at least one operational characteristic and / or the at least one environmental event.

2. The method of claim 1 , wherein processing the data comprises correlating at least one frequency related parameter with at least one respective operational characteristic and / or at least one respective environmental event to determine a frequency response of a frequency source associated with the device under test to the change in the at least one of the at least one operational characteristic and / or the at least one environmental event.

3. The method according to any of claims 1 and 2, further comprising: using the predictive model to correct for a frequency response of a frequency source associated with the device under test to the change in the at least one of the at least one operational characteristic and / or the at least one environmental event.

4. The method according to claim 3, further comprising: adjusting a frequency of the frequency source using a motion compensated correlation technique.

5. The method according to claim 4, wherein the motion compensated correlation technique comprises a supercorrelation technique.

6. The method according to claim 3, wherein correcting for the frequency response comprises at least one of adjusting for a change in frequency of the frequency source during signal processing of signals received by the associated device under test, adjusting an operational characteristic of the device under test, and / or adjusting an environmental characteristic of an environment in which the device under test is operating.

7. The method according to any one of claims 1 , 2, 4, 5, and 6, wherein the predictive model comprises at least one of a look-up table and / or a machine learning model.

8. The method according to claim 5, wherein the machine learning model comprises a neural network which is trained using the collected data corresponding to the least one frequency-related parameter, the at least one operational characteristic and / or the at least one environmental event.

9. The method according to any one of claims 1 , 2, 4, 5, 6 and 8, wherein the at least one operational characteristic comprises at least one of display activity of a display associated with the device under test, display brightness of a display associated with the device under test, modem activity of a modem associated with the device under test, CPU activity of a CPU associated with the device under test, crystal age of a crystal associated with the device under test, and / or battery voltage of a battery associated with the device under test, and wherein the at least one environmental event comprises at least one of heating, cooling, shaking, and / or dropping of the device under test.

10. An apparatus for generating a predictive model, comprising: at least one processor; at least one memory including at least one of programs and / or instructions that when executed by the processor cause the apparatus to perform operations including: operating a device under test while collecting data corresponding to at least one frequency-related parameter, at least one operationalcharacteristic and / or at least one environmental event, wherein the at least one frequency-related parameter changes with changes in the at least one operational characteristic and / or the at least one environmental event; and processing the data to generate a predictive model to be used to compensate for the changes in the at least one frequency-related parameter corresponding to the at least one operational characteristic and / or the at least one environmental event.

11. The apparatus of claim 10, wherein processing the data comprises correlating at least one frequency related parameter with at least one respective operational characteristic and / or at least one respective environmental event to determine a frequency response of a frequency source associated with the device under test to the change in the at least one of the at least one operational characteristic and / or the at least one environmental event.

12. The apparatus according to any of claims 10 and 11 , wherein the apparatus further performs: using the predictive model to correct for a frequency response of a frequency source associated with the device under test to the change in the at least one of the at least one operational characteristic and / or the at least one environmental event.

13. The apparatus according to claim 12, wherein the apparatus further performs: adjusting a frequency of the frequency source using a motion compensated correlation technique.

14. The apparatus according to claim 13, wherein the motion compensated correlation technique comprises a supercorrelation technique.

15. The apparatus according to claim 12, wherein correcting for the frequency response comprises at least one of adjusting for a change in frequency ofthe frequency source during signal processing of signals received by the associated device under test, adjusting an operational characteristic of the device under test, and / or adjusting an environmental characteristic of an environment in which the device under test is operating.

16. The apparatus according to any one of claims 10, 11 , 13, 14, and 15, wherein the predictive model comprises at least one of a look-up table and / or a machine learning model.

17. The apparatus according to claim 16, wherein the machine learning model comprises a neural network which is trained using the collected data corresponding to the least one frequency-related parameter, the at least one operational characteristic and / or the at least one environmental event.

18. The apparatus according to any one of claims 10, 11 , 13, 14, 15, and 17, wherein the at least one operational characteristic comprises at least one of display activity of a display associated with the device under test, display brightness of a display associated with the device under test, modem activity of a modem associated with the device under test, CPU activity of a CPU associated with the device under test, crystal age of a crystal associated with the device under test, and / or battery voltage of a battery associated with the device under test, and wherein the at least one environmental event comprises at least one of heating, cooling, shaking, and / or dropping of the device under test.

19. A computer-implemented method of training a neural network for generating a predictive model to compensate for frequency-related errors, comprising: collecting data corresponding to at least one operational characteristic of a radio signal receiving device while in operation; creating a first training set comprising the collected data corresponding to the at least one operational characteristic of the radio signal receiving device; collecting data corresponding to at least one environmental event associated with the radio signal receiving device while in operation;creating a second training set comprising the collected data corresponding to the at least one environmental event associated with the radio signal receiving device; collecting data corresponding to at least one frequency-related parameter corresponding to at least one of the at least one operational characteristic and / or the at least one environmental event associated with the radio signal receiving device, where the at least one frequency-related parameter changes with changes in the at least one operational characteristic and / or the at least one environmental event; creating a third training set comprising the collected data corresponding to the at least one frequency-related parameter; and training the neural network using the first training set, the second training set, and the third training set to generate a predictive model to be used to compensate for the changes in the at least one frequency-related parameter corresponding to the at least one operational characteristic and / or the at least one environmental event.

20. The method of claim 19, further comprising: using the predictive model to correct for a frequency response of a frequency source associated with the radio signal receiving device to the change in the at least one of the at least one operational characteristic and / or the at least one environmental event, wherein correcting for the frequency response comprises at least one of adjusting for a change in frequency of the frequency source during signal processing of signals received by the radio signal receiving device, adjusting an operational characteristic of the radio signal receiving device, and / or adjusting an environmental characteristic of an environment in which the radio signal receiving device is operating.

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