DETAILED ACTION
This action is responsive to communications filed on November 14, 2023. This action is made Non-Final.
Claims 1-18, 27, and 28 are pending in the case.
Claims 1, 10, 18, and 27 are independent claims.
Claims 1-18, 27, and 28 are rejected.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS(s)) submitted on 12/27/2023 is/are in compliance with the provisions of 37 C.F.R. 1.97. Accordingly, the IDS(s) is/are being considered by the examiner.
Claim Objections
Claim 12 is objected to because of the following informalities:
Claim 12 appears to recite “the digital pre-distortion function” as “the digital pre-distribution function.”
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 2, 4-12, 15-18, 27, and 28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1: Independent claims 1, 10, 18, and 27 are directed towards apparatuses and methods for uplink digital predistortion, respectively. Therefore, these claims, as well as their dependent claims, are directed towards one of the four statutory categories (process, machine (i.e. apparatus), manufacture, or composition of matter.
With respect to claim 1:
2A Prong 1:
Claim 1 recites the following judicial exceptions:
to approximate one or more power amplifier parameters based on an estimation of the power amplifier nonlinearity; to approximate the one or more power amplifier parameters (mathematical concept – mathematical relationships, mathematical formulas or equations, or mathematical calculations (e.g. approximating parameters based on an estimation of nonlinearity is merely mathematical calculations.).
2A Prong 2: The additional elements recited in the claim do not integrate the judicial exception into a practical application.
Additional elements:
an apparatus, comprising: at least one processor; and at least one memory including computer program code, wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to: receive an uplink reference signal for power amplifier measurement; measure a power amplifier nonlinearity of a user equipment based on the uplink reference signal ... transmit, to a user equipment, signaling comprising the one or more power amplifier parameters (mere instructions to apply the exception or implement the exception on a computer (e.g. receiving a signal, processing to perform a measurement, and transmitting further output; see MPEP §2106.05(f).). The additional elements do not effectively integrate the abstract idea into a practical application. 2B: revisiting the additional elements, the additional elements do not amount to significantly more than the judicial exception – recited high level of generality and corresponds to storing and retrieving information in memory and performing calculations).
train an artificial intelligence-based model ... use the trained artificial intelligence-based model (generally linking the use of a judicial exception to a particular technological environment or field of use (e.g. training and using an AI model or algorithm; see MPEP §2106.05(h).). The additional elements do not effectively integrate the abstract idea into a practical application. 2B: revisiting the additional elements, the additional elements do not amount to significantly more than the judicial exception – recited high level of generality and corresponds to storing and retrieving information and performing calculations.).
With respect to claim 2:
2A Prong 1:
Claim 2 recites the following judicial exceptions:
determine a power amplifier profile difference between the received uplink reference signal and an output power profile associated with a known reference signal (mathematical concept – mathematical relationships, mathematical formulas or equations, or mathematical calculations (e.g. determining a difference is merely a mathematical calculation.).
2A Prong 2: The additional elements recited in the claim do not integrate the judicial exception into a practical application.
Additional elements:
wherein the at least one memory and the computer program code are configured to, with the at least one processor, further cause the apparatus, when measuring the power amplifier nonlinearity, at least to: (mere instructions to apply the exception or implement the exception on a computer (e.g. receiving a signal, processing to perform a measurement, and transmitting further output; see MPEP §2106.05(f).). The additional elements do not effectively integrate the abstract idea into a practical application. 2B: revisiting the additional elements, the additional elements do not amount to significantly more than the judicial exception – recited high level of generality and corresponds to storing and retrieving information in memory and performing calculations).
With respect to claim 4:
Claim 4 recites the following judicial exceptions:
wherein the known reference signal is generated based on an analytical power amplifier model (mathematical concept – mathematical relationships, mathematical formulas or equations, or mathematical calculations (e.g. determining a difference is merely a mathematical calculation.).
With respect to claim 5:
Claim 5 recites the following judicial exceptions:
wherein the one or more power amplifier parameters comprise an alpha parameter or a beta parameter associated with a general analytical power amplifier model (mathematical concept – mathematical relationships, mathematical formulas or equations, or mathematical calculations (e.g. determining a difference is merely a mathematical calculation.).
With respect to claim 6:
2A Prong 2: The additional elements recited in the claim do not integrate the judicial exception into a practical application.
Additional elements:
wherein the at least one memory and the computer program code are configured to, with the at least one processor, further cause the apparatus at least to: transmit a configuration related to the uplink reference signal for power amplifier measurement (mere instructions to apply the exception or implement the exception on a computer (e.g. receiving a signal, processing to perform a measurement, and transmitting further output; see MPEP §2106.05(f).). The additional elements do not effectively integrate the abstract idea into a practical application. 2B: revisiting the additional elements, the additional elements do not amount to significantly more than the judicial exception – recited high level of generality and corresponds to storing and retrieving information in memory and performing calculations).
With respect to claim 7:
2A Prong 1:
Claim 7 recites the following judicial exceptions:
with a determined digital pre-distortion function adjusted based on the one or more power amplifier parameters (mathematical concept – mathematical relationships, mathematical formulas or equations, or mathematical calculations (e.g. determining a difference is merely a mathematical calculation.).
2A Prong 2: The additional elements recited in the claim do not integrate the judicial exception into a practical application.
Additional elements:
wherein the at least one memory and the computer program code are configured to, with the at least one processor, further cause the apparatus at least to: receive another uplink reference signal, wherein the uplink signal is transmitted (mere instructions to apply the exception or implement the exception on a computer (e.g. receiving a signal, processing to perform a measurement, and transmitting further output; see MPEP §2106.05(f).). The additional elements do not effectively integrate the abstract idea into a practical application. 2B: revisiting the additional elements, the additional elements do not amount to significantly more than the judicial exception – recited high level of generality and corresponds to storing and retrieving information in memory and performing calculations).
With respect to claim 8:
2A Prong 2: The additional elements recited in the claim do not integrate the judicial exception into a practical application.
Additional elements:
wherein the artificial intelligence-based model comprises a neural network-based model (generally linking the use of a judicial exception to a particular technological environment or field of use (e.g. training and using an AI model or algorithm; see MPEP §2106.05(h).). The additional elements do not effectively integrate the abstract idea into a practical application. 2B: revisiting the additional elements, the additional elements do not amount to significantly more than the judicial exception – recited high level of generality and corresponds to storing and retrieving information and performing calculations.).
With respect to claim 9:
2A Prong 2: The additional elements recited in the claim do not integrate the judicial exception into a practical application.
Additional elements:
wherein the transmitted signaling further comprises one or more digital pre-distortion function parameters (mere instructions to apply the exception or implement the exception on a computer (e.g. receiving a signal, processing to perform a measurement, and transmitting further output; see MPEP §2106.05(f).). The additional elements do not effectively integrate the abstract idea into a practical application. 2B: revisiting the additional elements, the additional elements do not amount to significantly more than the judicial exception – recited high level of generality and corresponds to storing and retrieving information in memory and performing calculations).
With respect to claim 10:
2A Prong 1:
Claim 10 recites the following judicial exceptions:
determine a digital pre-distortion function of the apparatus based on the one or more power amplifier parameters (mathematical concept – mathematical relationships, mathematical formulas or equations, or mathematical calculations (e.g. determining a function based on parameters is merely a mathematical calculation.).
2A Prong 2: The additional elements recited in the claim do not integrate the judicial exception into a practical application.
Additional elements:
an apparatus, comprising: at least one processor; and at least one memory including computer program code, wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to: transmit an uplink reference signal for power amplifier measurement; receive signaling comprising one or more power amplifiers; transmit an uplink signal with the digital pre-distortion function adjusted based on the one or more power amplifier parameters (mere instructions to apply the exception or implement the exception on a computer (e.g. receiving a signal, processing to perform a measurement, and transmitting further output; see MPEP §2106.05(f).). The additional elements do not effectively integrate the abstract idea into a practical application. 2B: revisiting the additional elements, the additional elements do not amount to significantly more than the judicial exception – recited high level of generality and corresponds to storing and retrieving information in memory and performing calculations).
With respect to claim 11:
2A Prong 1:
Claim 11 recites the following judicial exceptions:
use an analytical model to determine the digital pre-distortion function based on the one or more power amplifier parameters (mathematical concept – mathematical relationships, mathematical formulas or equations, or mathematical calculations (e.g. determining a function using a model is merely a mathematical calculation.).
2A Prong 2: The additional elements recited in the claim do not integrate the judicial exception into a practical application.
Additional elements:
wherein the at least one memory and the computer program code are configured to, with the at least one processor, further cause the apparatus, when determining the digital pre-distortion function, at least to: (mere instructions to apply the exception or implement the exception on a computer (e.g. receiving a signal, processing to perform a measurement, and transmitting further output; see MPEP §2106.05(f).). The additional elements do not effectively integrate the abstract idea into a practical application. 2B: revisiting the additional elements, the additional elements do not amount to significantly more than the judicial exception – recited high level of generality and corresponds to storing and retrieving information in memory and performing calculations).
With respect to claim 12:
2A Prong 1:
Claim 12 recites the following judicial exceptions:
to determine the digital pre-distribution function (mathematical concept – mathematical relationships, mathematical formulas or equations, or mathematical calculations (e.g. determining a function using a model is merely a mathematical calculation.).
2A Prong 2: The additional elements recited in the claim do not integrate the judicial exception into a practical application.
Additional elements:
wherein the at least one memory and the computer program code are configured to, with the at least one processor, further cause the apparatus, when determining the digital pre-distortion function, at least to: (mere instructions to apply the exception or implement the exception on a computer (e.g. receiving a signal, processing to perform a measurement, and transmitting further output; see MPEP §2106.05(f).). The additional elements do not effectively integrate the abstract idea into a practical application. 2B: revisiting the additional elements, the additional elements do not amount to significantly more than the judicial exception – recited high level of generality and corresponds to storing and retrieving information in memory and performing calculations).
use a trained artificial intelligence-based model (generally linking the use of a judicial exception to a particular technological environment or field of use (e.g. using a trained AI model or algorithm; see MPEP §2106.05(h).). The additional elements do not effectively integrate the abstract idea into a practical application. 2B: revisiting the additional elements, the additional elements do not amount to significantly more than the judicial exception – recited high level of generality and corresponds to storing and retrieving information and performing calculations.).
With respect to claim 15:
2A Prong 2: The additional elements recited in the claim do not integrate the judicial exception into a practical application.
Additional elements:
wherein the at least one memory and the computer program code are configured to, with the at least one processor, further cause the apparatus, when transmitting the uplink signal, at least to: transmit the uplink signal with the digital pre-distortion function (mere instructions to apply the exception or implement the exception on a computer (e.g. receiving a signal, processing to perform a measurement, and transmitting further output; see MPEP §2106.05(f).). The additional elements do not effectively integrate the abstract idea into a practical application. 2B: revisiting the additional elements, the additional elements do not amount to significantly more than the judicial exception – recited high level of generality and corresponds to storing and retrieving information in memory and performing calculations).
determined using the trained artificial intelligence model (generally linking the use of a judicial exception to a particular technological environment or field of use (e.g. using a trained AI model or algorithm; see MPEP §2106.05(h).). The additional elements do not effectively integrate the abstract idea into a practical application. 2B: revisiting the additional elements, the additional elements do not amount to significantly more than the judicial exception – recited high level of generality and corresponds to storing and retrieving information and performing calculations.).
With respect to claim 16:
Claim 16 recites the following judicial exceptions:
wherein the one or more power amplifier parameters comprise an alpha parameter or a beta parameter associated with a general analytical power amplifier model (mathematical concept – mathematical relationships, mathematical formulas or equations, or mathematical calculations (e.g. determining a difference is merely a mathematical calculation.).
With respect to claim 17:
2A Prong 2: The additional elements recited in the claim do not integrate the judicial exception into a practical application.
Additional elements:
wherein the received signaling further comprises one or more digital pre-distortion function parameters (mere instructions to apply the exception or implement the exception on a computer (e.g. receiving a signal, processing to perform a measurement, and transmitting further output; see MPEP §2106.05(f).). The additional elements do not effectively integrate the abstract idea into a practical application. 2B: revisiting the additional elements, the additional elements do not amount to significantly more than the judicial exception – recited high level of generality and corresponds to storing and retrieving information in memory and performing calculations).
With respect to claim 18:
Claim 18 corresponds to claim 1 and is rejected under the same rationale.
With respect to claim 27:
Claim 27 corresponds to claim 10 and is rejected under the same rationale.
With respect to claim 28:
Claim 28 corresponds to claim 11 and is rejected under the same rationale.
2B continued: After considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-18, 27, and 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al., US Publication 2021/0175962 (“Li”), and further in view of Sagi et al., US Publication 2019/0190552 (“Sagi”).
Claim 1:
Li teaches or suggests an apparatus, comprising:
at least one processor; and at least one memory including computer program code, wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus to:
receive ... for power amplifier measurement (see Fig. 1-3, 11, and 13; para. 0006 - PD controller stores a database including a set of environmental parameters and a set of PD parameters corresponding to the set of environmental parameters. AM-AM and AM-PM characteristics of the HPA ; para. 0026 – nonlinear problems of HPAs in the presence of radio interference. any nonlinear distortion is detected at the ground hub; para. 0027 - estimate and correct the AM-AM and AM-PM nonlinear distortions. U/L jammer or other external factors such as equipment imperfections and non-ambient temperature drives the HPA input power to the saturation point. on-ground PD controller may use a machine learning model pre-trained by various transmit/receive signals and HPA characteristics to adjust the parameters of the PD physical model, and may send the correction signal to the transmitter; para. 0028 - the AM AM and AM-PM effects of the HPA characterizing HPA's amplitude (AM-AM) and phase (AMPM) distortions accurately; para. 0068 - States: a state may be an observable (at least partially) status of the PD controller's relation with the environment, and is defined as power and phase of the output power; para. 0076 - noise/interference (AWGN, constant, and partial time partial band (PTPB)) existing between the PD and the HPA; para. 0081 - data storage may be configured to store a database that includes a set of environmental parameters and a set of PD parameters corresponding to the set of environmental parameters.);
measure a power amplifier nonlinearity of a user equipment ... (see Fig. 1-3, 11, and 13; para. 0006 - PD controller stores a database including a set of environmental parameters and a set of PD parameters corresponding to the set of environmental parameters. AM-AM and AM-PM characteristics of the HPA ; para. 0026 – nonlinear problems of HPAs in the presence of radio interference. any nonlinear distortion is detected at the ground hub; para. 0027 - estimate and correct the AM-AM and AM-PM nonlinear distortions. U/L jammer or other external factors such as equipment imperfections and non-ambient temperature drives the HPA input power to the saturation point. on-ground PD controller may use a machine learning model pre-trained by various transmit/receive signals and HPA characteristics to adjust the parameters of the PD physical model, and may send the correction signal to the transmitter; para. 0028 - the AM AM and AM-PM effects of the HPA characterizing HPA's amplitude (AM-AM) and phase (AMPM) distortions accurately; para. 0068 - States: a state may be an observable (at least partially) status of the PD controller's relation with the environment, and is defined as power and phase of the output power; para. 0076 - noise/interference (AWGN, constant, and partial time partial band (PTPB)) existing between the PD and the HPA; para. 0081 - data storage may be configured to store a database that includes a set of environmental parameters and a set of PD parameters corresponding to the set of environmental parameters.);
train an artificial intelligence-based model to approximate one or more power amplifier parameters based on an estimation of the power amplifier nonlinearity (see Fig. 1-3, 11, and 13; para. 0027 - estimate and correct the AM-AM and AM-PM nonlinear distortions. U/L jammer or other external factors such as equipment imperfections and non-ambient temperature drives the HPA input power to the saturation point. on-ground PD controller may use a machine learning model pre-trained by various transmit/receive signals and HPA characteristics to adjust the parameters of the PD physical model, and may send the correction signal to the transmitter; para. 0059 - to compensate the AM-AM and AM-PM effects of the HPA, an accurate physical-model based PD may be developed based on an extended Saleh's model. The Saleh's model is a commonly used power amplifier model, and has been proposed for characterizing HPA's amplitude (AM-AM) and phase (AMPM) distortions accurately; para. 0065 - controller may behave as an agent taking actions, and may use the rewards or punishments returned from environment and target states to train and make the best decision in certain circumstances; para. 0068 - States: a state may be an observable (at least partially) status of the PD controller's relation with the environment, and is defined as power and phase of the output power; claim 2 – calculating AM-AM and AM-PM characteristics of the HPA according to the signal sent to the satellite transponder and the signal received from the satellite transponder.);
use the trained artificial intelligence-based model to approximate the one or more power amplifier parameters (see Fig. 1-3, 11, and 13; para. 0027 - estimate and correct the AM-AM and AM-PM nonlinear distortions. U/L jammer or other external factors such as equipment imperfections and non-ambient temperature drives the HPA input power to the saturation point. on-ground PD controller may use a machine learning model pre-trained by various transmit/receive signals and HPA characteristics to adjust the parameters of the PD physical model, and may send the correction signal to the transmitter; para. 076 – proposed machine learning approach can successfully update the PD coefficients to better adjust to the ambient environment efficiently; para. 0077 - proposed machine learning approach can update the PD parameters based on the reward defined above to improve the HPA linearity; para. 0079 - machine learning approaches can dynamically update the PD parameter set to adjust to the changing environment; para. 0082 - taking an action, based on the environment parameters and the actionvalue function, to adjust the plurality of PD parameters for the PD to generate an updated correction signal; sending the update correction signal to the transmitter to compensate the AM-AM and AM-PM characteristics of the HPA.); and
transmit, ... , signaling comprising the one or more power amplifier parameters (see Fig. 1-3, 11, and 13; para. 0005 – machine-learning based PD controller is deployed to deal with the impact of the external factors, such as equipment imperfections, temperature variation, interference signals, etc., and also feed an error-correction signal to the PD to adjust the parameters in a real-time fashion; para. 0006 - adjust the plurality of PD parameters for the PD to generate an updated correction signal. sending the update correction signal to the transmitter to compensate the AM-AM and AM-PM characteristics of the HPA; para. 0007 – send the initial correction signal to the transmitter to compensate AM-AM and AM-PM characteristics of the HPA; and in respond to the PD controller taking an action to adjust the plurality of PD parameters, generate an updated correction signal, and send the updated correction signal to the transmitter to compensate the AM-AM and AM-PM characteristics of the HPA; para. 0008 - an action, based on the measured environment parameters and the action-value function, to adjust a plurality of PD parameters for the PD to generate an updated correction signal; para. 0025 – proposed machine learning algorithm may also be able to adjust the parameters of the PD; para. 0027 - estimate and correct the AM-AM and AM-PM nonlinear distortions. U/L jammer or other external factors such as equipment imperfections and non-ambient temperature drives the HPA input power to the saturation point. on-ground PD controller may use a machine learning model pre-trained by various transmit/receive signals and HPA characteristics to adjust the parameters of the PD physical model, and may send the correction signal to the transmitter; para. 0058 - and in respond to the PD controller taking an action to adjust the plurality of PD parameters, generate an updated correction signal, and send the updated correction signal to the transmitter to compensate the AM-AM and AM-PM characteristics of the HPA; para. 076 – proposed machine learning approach can successfully update the PD coefficients to better adjust to the ambient environment efficiently; para. 0077 - proposed machine learning approach can update the PD parameters based on the reward defined above to improve the HPA linearity; para. 0079 - machine learning approaches can dynamically update the PD parameter set to adjust to the changing environment; para. 0082 - taking an action, based on the environment parameters and the actionvalue function, to adjust the plurality of PD parameters for the PD to generate an updated correction signal; sending the update correction signal to the transmitter to compensate the AM-AM and AM-PM characteristics of the HPA; Claim 1 - sending the update correction signal to the transmitter to compensate the AM-AM and AM-PM characteristics of the HPA.).
Sagi more specifically teaches or suggests an uplink reference signal; based on the uplink reference signal; to a user equipment (see Fig. 1, 4, 6-11; Para. 0007 - In order perform DPD and/or DPoD operations, the non-linearity characteristics of various components in the transmitter and/or the receiver (e.g., amplifiers, signal converters, etc.) may be estimated; para. 0009 - may generate a reference signal having a non-constant envelope for nonlinearity estimation by a receiver. The apparatus may transmit the reference signal, e.g., to a receiver. In an aspect, the reference signal includes a primary synchronization signal. receive, from the receiver, feedback associated with the nonlinearity estimation, and perform at least one DPD operation based on the feedback. feedback comprises adjusting one or more coefficients associated with at least one of a high-power amplifier (HPA); para. 0010 - apparatus may receive a reference signal having a non-constant envelope. The apparatus may estimate at least one nonlinearity characteristic based on the reference signal having the non-constant envelope. The apparatus may at least one of: transmit feedback based on the at least one nonlinearity characteristic, or perform at least one DPoD operation based on the at least one nonlinearity characteristic; para. 0029 - base stations 102 may wirelessly communicate with the UEs 104; para. 0037 - certain aspects, the base station 180 may be configured to generate a reference signal 198 having a non-constant envelope. The base station 180 may transmit the reference signal 198 having the non-constant envelope to the UE 104. The UE 104 may be configured to receive the reference signal 198 having the non-constant envelope from the base station 180. The UE 104 may estimate one or more nonlinearity characteristics based on the reference signal 198. Thereafter, the UE 104 may (I) transmit, to the base station 180, feedback based on the one or more nonlinearity characteristics, (2) perform at least one digital post distortion (DPoD) operation based on the one or more nonlinearity characteristics, or (3) both transmit feedback based on the one or more nonlinearity characteristics and perform the at least one DPoD operation based on the one or more nonlinearity characteristics. When the UE 104 transmits feedback based on the one or more nonlinearity characteristics to the base station 180, the base station 180 may perform at least one digital pre-distortion (DPD) operation based on the feedback. In this way, the UE 104 and/or the base station 180 may improve the response, throughput, and/or capacity of one or more channels on which the UE 104 and the base station 180 communicate; para. 0056 - may include at least one transmitter 402 and at least one receiver 404. The transmitter 402 and the receiver 404 are illustrated as a base station and a UE, respectively ( e.g., for uplink communication); however, this arrangement is to be regarded as illustrative and the transmitter 402 and/or the receiver 404 may be any apparatuses configured for wireless communication.).
Accordingly, it would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the system and method, taught in Li, to include an uplink reference signal; based on the uplink reference signal; to a user equipment for the purpose of enabling a specific signal related to base stations and user equipments to be processed and digital pre-distortion to be dertemined, improving the response, throughput, and/or capacity of one or more channels on which the UEs and base stations communicate, as taught by Sagi (0007, 0010, and 0037).
Claim(s) 18:
Claim(s) 18 correspond to Claim 1, and thus, Li and Sagi teach or suggest the limitations of claim(s) 18 as well.
Claim 2:
Li further teaches or suggests determine a power amplifier profile difference between the received uplink ... signal and an output power profile associated with a known ... signal (see Fig. 1-3, 11, and 13; para. 0006 - examining an action-value function for actions taken in a preset past period based on reward functions of the actions; para. 0027 - estimate and correct the AM-AM and AM-PM nonlinear distortions. U/L jammer or other external factors such as equipment imperfections and non-ambient temperature drives the HPA input power to the saturation point. on-ground PD controller may use a machine learning model pre-trained by various transmit/receive signals and HPA characteristics to adjust the parameters of the PD physical model, and may send the correction signal to the transmitter; para. 0062 – machine learning based PD controller for HPA linearization. The machine-learning based PD controller may be able to promptly determine the PD parameters based on past experience and correspondingly update the PD parameters. Therefore, the disclosed machine-learning based PD con troller may provide dynamic and real-time control for optimizing the performance of the PD; para. 0069 – for given "Input Power" (or P;n) and "Input Phase" ( or Phase;n), the rewards function may be the distance between estimated and measured output power and phase of the HPA; para. 0079 - machine learning approaches can dynamically update the PD parameter set to adjust to the changing environment; para. 0082 – examining an action-value function for actions taken in a preset past period based on reward functions of the actions; taking an action, based on the environment parameters and the action value function, to adjust the plurality of PD parameters for the PD to generate an updated correction signal; sending the update correction signal to the transmitter to compensate the AM-AM and AM-PM characteristics of the HPA.).
Sagi more specifically teaches or suggests reference (see Fig. 1, 4, 6-11; Para. 0007 - In order perform DPD and/or DPoD operations, the non-linearity characteristics of various components in the transmitter and/or the receiver (e.g., amplifiers, signal converters, etc.) may be estimated; para. 0009 - may generate a reference signal having a non-constant envelope for nonlinearity estimation by a receiver. The apparatus may transmit the reference signal, e.g., to a receiver. In an aspect, the reference signal includes a primary synchronization signal. receive, from the receiver, feedback associated with the nonlinearity estimation, and perform at least one DPD operation based on the feedback. feedback comprises adjusting one or more coefficients associated with at least one of a high-power amplifier (HPA); para. 0010 - apparatus may receive a reference signal having a non-constant envelope. The apparatus may estimate at least one nonlinearity characteristic based on the reference signal having the non-constant envelope. The apparatus may at least one of: transmit feedback based on the at least one nonlinearity characteristic, or perform at least one DPoD operation based on the at least one nonlinearity characteristic; para. 0029 - base stations 102 may wirelessly communicate with the UEs 104; para. 0037 - certain aspects, the base station 180 may be configured to generate a reference signal 198 having a non-constant envelope. The base station 180 may transmit the reference signal 198 having the non-constant envelope to the UE 104. The UE 104 may be configured to receive the reference signal 198 having the non-constant envelope from the base station 180. The UE 104 may estimate one or more nonlinearity characteristics based on the reference signal 198. Thereafter, the UE 104 may (I) transmit, to the base station 180, feedback based on the one or more nonlinearity characteristics, (2) perform at least one digital post distortion (DPoD) operation based on the one or more nonlinearity characteristics, or (3) both transmit feedback based on the one or more nonlinearity characteristics and perform the at least one DPoD operation based on the one or more nonlinearity characteristics. When the UE 104 transmits feedback based on the one or more nonlinearity characteristics to the base station 180, the base station 180 may perform at least one digital pre-distortion (DPD) operation based on the feedback. In this way, the UE 104 and/or the base station 180 may improve the response, throughput, and/or capacity of one or more channels on which the UE 104 and the base station 180 communicate; para. 0056 - may include at least one transmitter 402 and at least one receiver 404. The transmitter 402 and the receiver 404 are illustrated as a base station and a UE, respectively ( e.g., for uplink communication); however, this arrangement is to be regarded as illustrative and the transmitter 402 and/or the receiver 404 may be any apparatuses configured for wireless communication.).
Accordingly, it would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the system and method, taught in Li, to include reference for the purpose of enabling a specific signal related to base stations and user equipments to be processed and digital pre-distortion to be dertemined, improving the response, throughput, and/or capacity of one or more channels on which the UEs and base stations communicate, as taught by Sagi (0007, 0010, and 0037).
Claim 3:
Li further teaches or suggests train the artificial intelligence-based model using the power profile difference and a training criterion comprising minimization of the power difference (see Fig. 1-3, 11, and 13; para. 0006 - examining an action-value function for actions taken in a preset past period based on reward functions of the actions; para. 0027 - estimate and correct the AM-AM and AM-PM nonlinear distortions. U/L jammer or other external factors such as equipment imperfections and non-ambient temperature drives the HPA input power to the saturation point. on-ground PD controller may use a machine learning model pre-trained by various transmit/receive signals and HPA characteristics to adjust the parameters of the PD physical model, and may send the correction signal to the transmitter; para. 0062 – machine learning based PD controller for HPA linearization. The machine-learning based PD controller may be able to promptly determine the PD parameters based on past experience and correspondingly update the PD parameters. Therefore, the disclosed machine-learning based PD con troller may provide dynamic and real-time control for optimizing the performance of the PD; para. 0065 - controller may behave as an agent taking actions, and may use the rewards or punishments returned from environment and target states to train and make the best decision in certain circumstances; para. 0068 - States: a state may be an observable (at least partially) status of the PD controller's relation with the environment, and is defined as power and phase of the output power; para. 0069 – for given "Input Power" (or P;n) and "Input Phase" ( or Phase;n), the rewards function may be the distance between estimated and measured output power and phase of the HPA; para. 0073 – Q network may utilize the neural network function as the value-function approximator. A Q network can be trained by minimizing a sequence of loss function; para. 0082 – examining an action-value function for actions taken in a preset past period based on reward functions of the actions; taking an action, based on the environment parameters and the action value function, to adjust the plurality of PD parameters for the PD to generate an updated correction signal; sending the update correction signal to the transmitter to compensate the AM-AM and AM-PM characteristics of the HPA.).
Claim 4:
Li further teaches or suggests wherein the known ... signal is generated based on an analytical power amplifier model (see Fig. 1-3, 11, and 13; para. 0006 - examining an action-value function for actions taken in a preset past period based on reward functions of the actions; para. 0027 - estimate and correct the AM-AM and AM-PM nonlinear distortions. U/L jammer or other external factors such as equipment imperfections and non-ambient temperature drives the HPA input power to the saturation point. on-ground PD controller may use a machine learning model pre-trained by various transmit/receive signals and HPA characteristics to adjust the parameters of the PD physical model, and may send the correction signal to the transmitter; para. 0028 - to compensate the AM-AM and AM-PM effects of the HPA, an accurate physical-model based PD may be developed based on an extended Saleh's model. The Saleh's model is a commonly used power amplifier model, and has been proposed for characterizing HPA's amplitude (AM-AM) and phase (AMPM) distortions accurately; para. 0062 – machine learning based PD controller for HPA linearization. The machine-learning based PD controller may be able to promptly determine the PD parameters based on past experience and correspondingly update the PD parameters. Therefore, the disclosed machine-learning based PD con troller may provide dynamic and real-time control for optimizing the performance of the PD; para. 0069 – for given "Input Power" (or P;n) and "Input Phase" ( or Phase;n), the rewards function may be the distance between estimated and measured output power and phase of the HPA; para. 0079 - machine learning approaches can dynamically update the PD parameter set to adjust to the changing environment; para. 0082 – examining an action-value function for actions taken in a preset past period based on reward functions of the actions; taking an action, based on the environment parameters and the action value function, to adjust the plurality of PD parameters for the PD to generate an updated correction signal; sending the update correction signal to the transmitter to compensate the AM-AM and AM-PM characteristics of the HPA.).
Sagi more specifically teaches or suggests reference (see Fig. 1, 4, 6-11; Para. 0007 - In order perform DPD and/or DPoD operations, the non-linearity characteristics of various components in the transmitter and/or the receiver (e.g., amplifiers, signal converters, etc.) may be estimated; para. 0009 - may generate a reference signal having a non-constant envelope for nonlinearity estimation by a receiver. The apparatus may transmit the reference signal, e.g., to a receiver. In an aspect, the reference signal includes a primary synchronization signal. receive, from the receiver, feedback associated with the nonlinearity estimation, and perform at least one DPD operation based on the feedback. feedback comprises adjusting one or more coefficients associated with at least one of a high-power amplifier (HPA); para. 0010 - apparatus may receive a reference signal having a non-constant envelope. The apparatus may estimate at least one nonlinearity characteristic based on the reference signal having the non-constant envelope. The apparatus may at least one of: transmit feedback based on the at least one nonlinearity characteristic, or perform at least one DPoD operation based on the at least one nonlinearity characteristic; para. 0029 - base stations 102 may wirelessly communicate with the UEs 104; para. 0037 - certain aspects, the base station 180 may be configured to generate a reference signal 198 having a non-constant envelope. The base station 180 may transmit the reference signal 198 having the non-constant envelope to the UE 104. The UE 104 may be configured to receive the reference signal 198 having the non-constant envelope from the base station 180. The UE 104 may estimate one or more nonlinearity characteristics based on the reference signal 198. Thereafter, the UE 104 may (I) transmit, to the base station 180, feedback based on the one or more nonlinearity characteristics, (2) perform at least one digital post distortion (DPoD) operation based on the one or more nonlinearity characteristics, or (3) both transmit feedback based on the one or more nonlinearity characteristics and perform the at least one DPoD operation based on the one or more nonlinearity characteristics. When the UE 104 transmits feedback based on the one or more nonlinearity characteristics to the base station 180, the base station 180 may perform at least one digital pre-distortion (DPD) operation based on the feedback. In this way, the UE 104 and/or the base station 180 may improve the response, throughput, and/or capacity of one or more channels on which the UE 104 and the base station 180 communicate; para. 0056 - may include at least one transmitter 402 and at least one receiver 404. The transmitter 402 and the receiver 404 are illustrated as a base station and a UE, respectively ( e.g., for uplink communication); however, this arrangement is to be regarded as illustrative and the transmitter 402 and/or the receiver 404 may be any apparatuses configured for wireless communication.).
Accordingly, it would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the system and method, taught in Li, to include reference for the purpose of enabling a specific signal related to base stations and user equipments to be processed and digital pre-distortion to be dertemined, improving the response, throughput, and/or capacity of one or more channels on which the UEs and base stations communicate, as taught by Sagi (0007, 0010, and 0037).
Claim 5:
Li further teaches or suggests wherein the one or more power amplifier parameters comprise an alpha parameter or a beta parameter associated with a general analytical power amplifier model (see para. 0028 – compensate the AM-AM and AM-PM effects of the HPA, an accurate physical-model based PD may be developed based on an extended Saleh's model. The Saleh's model is a commonly used power amplifier model, and has been proposed for characterizing HPA's amplitude (AM-AM) and phase (AMPM) distortions accurately; para. 0029 - the original Saleh's model may be extended for the HPA by including eight extra parameters (a0 , a1 , b0 , and b1 , together with a 0 , a 1 , B0 , and B1); para. 0059 - PD, a curve-fitting algorithm may be adopted to estimate the AM-AM and AM-PM nonlinear distortions. In one embodiment, based on an extended Saleh's model, the measured AM-AM characteristics of the HPA. a0 , b0 , a 0 , and Bo are PD parameters, and the measured AM-PM characteristics of the HPA may be fitted. where a1, b1 , a 1 , and B1 are PD parameters.).
Claim 6:
Li further teaches or suggests transmit a configuration related to the uplink ... signal for power amplifier measurement (see Fig. 1-3, 11, and 13; para. 0005 – machine-learning based PD controller is deployed to deal with the impact of the external factors, such as equipment imperfections, temperature variation, interference signals, etc., and also feed an error-correction signal to the PD to adjust the parameters in a real-time fashion; para. 0006 - adjust the plurality of PD parameters for the PD to generate an updated correction signal. sending the update correction signal to the transmitter to compensate the AM-AM and AM-PM characteristics of the HPA; para. 0007 – send the initial correction signal to the transmitter to compensate AM-AM and AM-PM characteristics of the HPA; and in respond to the PD controller taking an action to adjust the plurality of PD parameters, generate an updated correction signal, and send the updated correction signal to the transmitter to compensate the AM-AM and AM-PM characteristics of the HPA; para. 0008 - an action, based on the measured environment parameters and the action-value function, to adjust a plurality of PD parameters for the PD to generate an updated correction signal; para. 0025 – proposed machine learning algorithm may also be able to adjust the parameters of the PD; para. 0027 - estimate and correct the AM-AM and AM-PM nonlinear distortions. U/L jammer or other external factors such as equipment imperfections and non-ambient temperature drives the HPA input power to the saturation point. on-ground PD controller may use a machine learning model pre-trained by various transmit/receive signals and HPA characteristics to adjust the parameters of the PD physical model, and may send the correction signal to the transmitter; para. 0058 - and in respond to the PD controller taking an action to adjust the plurality of PD parameters, generate an updated correction signal, and send the updated correction signal to the transmitter to compensate the AM-AM and AM-PM characteristics of the HPA; para. 076 – proposed machine learning approach can successfully update the PD coefficients to better adjust to the ambient environment efficiently; para. 0077 - proposed machine learning approach can update the PD parameters based on the reward defined above to improve the HPA linearity; para. 0079 - machine learning approaches can dynamically update the PD parameter set to adjust to the changing environment; para. 0082 - taking an action, based on the environment parameters and the actionvalue function, to adjust the plurality of PD parameters for the PD to generate an updated correction signal; sending the update correction signal to the transmitter to compensate the AM-AM and AM-PM characteristics of the HPA; Claim 1 - sending the update correction signal to the transmitter to compensate the AM-AM and AM-PM characteristics of the HPA.).
Sagi more specifically teaches or suggests reference (see Fig. 1, 4, 6-11; Para. 0007 - In order perform DPD and/or DPoD operations, the non-linearity characteristics of various components in the transmitter and/or the receiver (e.g., amplifiers, signal converters, etc.) may be estimated; para. 0009 - may generate a reference signal having a non-constant envelope for nonlinearity estimation by a receiver. The apparatus may transmit the reference signal, e.g., to a receiver. In an aspect, the reference signal includes a primary synchronization signal. receive, from the receiver, feedback associated with the nonlinearity estimation, and perform at least one DPD operation based on the feedback. feedback comprises adjusting one or more coefficients associated with at least one of a high-power amplifier (HPA); para. 0010 - apparatus may receive a reference signal having a non-constant envelope. The apparatus may estimate at least one nonlinearity characteristic based on the reference signal having the non-constant envelope. The apparatus may at least one of: transmit feedback based on the at least one nonlinearity characteristic, or perform at least one DPoD operation based on the at least one nonlinearity characteristic; para. 0029 - base stations 102 may wirelessly communicate with the UEs 104; para. 0037 - certain aspects, the base station 180 may be configured to generate a reference signal 198 having a non-constant envelope. The base station 180 may transmit the reference signal 198 having the non-constant envelope to the UE 104. The UE 104 may be configured to receive the reference signal 198 having the non-constant envelope from the base station 180. The UE 104 may estimate one or more nonlinearity characteristics based on the reference signal 198. Thereafter, the UE 104 may (I) transmit, to the base station 180, feedback based on the one or more nonlinearity characteristics, (2) perform at least one digital post distortion (DPoD) operation based on the one or more nonlinearity characteristics, or (3) both transmit feedback based on the one or more nonlinearity characteristics and perform the at least one DPoD operation based on the one or more nonlinearity characteristics. When the UE 104 transmits feedback based on the one or more nonlinearity characteristics to the base station 180, the base station 180 may perform at least one digital pre-distortion (DPD) operation based on the feedback. In this way, the UE 104 and/or the base station 180 may improve the response, throughput, and/or capacity of one or more channels on which the UE 104 and the base station 180 communicate; para. 0056 - may include at least one transmitter 402 and at least one receiver 404. The transmitter 402 and the receiver 404 are illustrated as a base station and a UE, respectively ( e.g., for uplink communication); however, this arrangement is to be regarded as illustrative and the transmitter 402 and/or the receiver 404 may be any apparatuses configured for wireless communication.).
Accordingly, it would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the system and method, taught in Li, to include reference for the purpose of enabling a specific signal related to base stations and user equipments to be processed and digital pre-distortion to be dertemined, improving the response, throughput, and/or capacity of one or more channels on which the UEs and base stations communicate, as taught by Sagi (0007, 0010, and 0037).
Claim 7:
Li further teaches or suggests receive another uplink ... signal, wherein the uplink ... signal is transmitted with a determined digital pre-distortion function adjusted based on the one or more power amplifier parameters (see Fig. 1-3, 11, and 13; para. 0005 – machine-learning based PD controller is deployed to deal with the impact of the external factors, such as equipment imperfections, temperature variation, interference signals, etc., and also feed an error-correction signal to the PD to adjust the parameters in a real-time fashion; para. 0006 - adjust the plurality of PD parameters for the PD to generate an updated correction signal. sending the update correction signal to the transmitter to compensate the AM-AM and AM-PM characteristics of the HPA; para. 0007 – send the initial correction signal to the transmitter to compensate AM-AM and AM-PM characteristics of the HPA; and in respond to the PD controller taking an action to adjust the plurality of PD parameters, generate an updated correction signal, and send the updated correction signal to the transmitter to compensate the AM-AM and AM-PM characteristics of the HPA; para. 0008 - an action, based on the measured environment parameters and the action-value function, to adjust a plurality of PD parameters for the PD to generate an updated correction signal; para. 0025 – proposed machine learning algorithm may also be able to adjust the parameters of the PD; para. 0027 - estimate and correct the AM-AM and AM-PM nonlinear distortions. U/L jammer or other external factors such as equipment imperfections and non-ambient temperature drives the HPA input power to the saturation point. on-ground PD controller may use a machine learning model pre-trained by various transmit/receive signals and HPA characteristics to adjust the parameters of the PD physical model, and may send the correction signal to the transmitter; para. 0058 - and in respond to the PD controller taking an action to adjust the plurality of PD parameters, generate an updated correction signal, and send the updated correction signal to the transmitter to compensate the AM-AM and AM-PM characteristics of the HPA; para. 076 – proposed machine learning approach can successfully update the PD coefficients to better adjust to the ambient environment efficiently; para. 0077 - proposed machine learning approach can update the PD parameters based on the reward defined above to improve the HPA linearity; para. 0079 - machine learning approaches can dynamically update the PD parameter set to adjust to the changing environment; para. 0082 - taking an action, based on the environment parameters and the actionvalue function, to adjust the plurality of PD parameters for the PD to generate an updated correction signal; sending the update correction signal to the transmitter to compensate the AM-AM and AM-PM characteristics of the HPA; Claim 1 - sending the update correction signal to the transmitter to compensate the AM-AM and AM-PM characteristics of the HPA.).
Sagi more specifically teaches or suggests reference (see Fig. 1, 4, 6-11; Para. 0007 - In order perform DPD and/or DPoD operations, the non-linearity characteristics of various components in the transmitter and/or the receiver (e.g., amplifiers, signal converters, etc.) may be estimated; para. 0009 - may generate a reference signal having a non-constant envelope for nonlinearity estimation by a receiver. The apparatus may transmit the reference signal, e.g., to a receiver. In an aspect, the reference signal includes a primary synchronization signal. receive, from the receiver, feedback associated with the nonlinearity estimation, and perform at least one DPD operation based on the feedback. feedback comprises adjusting one or more coefficients associated with at least one of a high-power amplifier (HPA); para. 0010 - apparatus may receive a reference signal having a non-constant envelope. The apparatus may estimate at least one nonlinearity characteristic based on the reference signal having the non-constant envelope. The apparatus may at least one of: transmit feedback based on the at least one nonlinearity characteristic, or perform at least one DPoD operation based on the at least one nonlinearity characteristic; para. 0029 - base stations 102 may wirelessly communicate with the UEs 104; para. 0037 - certain aspects, the base station 180 may be configured to generate a reference signal 198 having a non-constant envelope. The base station 180 may transmit the reference signal 198 having the non-constant envelope to the UE 104. The UE 104 may be configured to receive the reference signal 198 having the non-constant envelope from the base station 180. The UE 104 may estimate one or more nonlinearity characteristics based on the reference signal 198. Thereafter, the UE 104 may (I) transmit, to the base station 180, feedback based on the one or more nonlinearity characteristics, (2) perform at least one digital post distortion (DPoD) operation based on the one or more nonlinearity characteristics, or (3) both transmit feedback based on the one or more nonlinearity characteristics and perform the at least one DPoD operation based on the one or more nonlinearity characteristics. When the UE 104 transmits feedback based on the one or more nonlinearity characteristics to the base station 180, the base station 180 may perform at least one digital pre-distortion (DPD) operation based on the feedback. In this way, the UE 104 and/or the base station 180 may improve the response, throughput, and/or capacity of one or more channels on which the UE 104 and the base station 180 communicate; para. 0056 - may include at least one transmitter 402 and at least one receiver 404. The transmitter 402 and the receiver 404 are illustrated as a base station and a UE, respectively ( e.g., for uplink communication); however, this arrangement is to be regarded as illustrative and the transmitter 402 and/or the receiver 404 may be any apparatuses configured for wireless communication.).
Accordingly, it would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the system and method, taught in Li, to include reference for the purpose of enabling a specific signal related to base stations and user equipments to be processed and digital pre-distortion to be dertemined, improving the response, throughput, and/or capacity of one or more channels on which the UEs and base stations communicate, as taught by Sagi (0007, 0010, and 0037).
Claim 8:
Li further teaches or suggests wherein the artificial intelligence-based model comprises a neural network-based model (see Fig. 1-3, 11, and 13; Fig. 1-3, 11, and 13; para. 0027 - estimate and correct the AM-AM and AM-PM nonlinear distortions. U/L jammer or other external factors such as equipment imperfections and non-ambient temperature drives the HPA input power to the saturation point. on-ground PD controller may use a machine learning model pre-trained by various transmit/receive signals and HPA characteristics to adjust the parameters of the PD physical model, and may send the correction signal to the transmitter; para. 0059 - to compensate the AM-AM and AM-PM effects of the HPA, an accurate physical-model based PD may be developed based on an extended Saleh's model. The Saleh's model is a commonly used power amplifier model, and has been proposed for characterizing HPA's amplitude (AM-AM) and phase (AMPM) distortions accurately; para. 0065 - controller may behave as an agent taking actions, and may use the rewards or punishments returned from environment and target states to train and make the best decision in certain circumstances; para. 0068 - States: a state may be an observable (at least partially) status of the PD controller's relation with the environment, and is defined as power and phase of the output power; para. 0073 - non-linear function approximator, such as a neural network, may be adopted instead. Q-network may utilize the neural network function as the value-function approximator. A Q network can be trained by minimizing a sequence of loss function; claim 2 – calculating AM-AM and AM-PM characteristics of the HPA according to the signal sent to the satellite transponder and the signal received from the satellite transponder.).
Claim 9:
Li further teaches or suggests wherein the transmitted signaling further comprises one or more digital pre-distortion function parameters (see Fig. 1-3, 11, and 13; para. 0005 – machine-learning based PD controller is deployed to deal with the impact of the external factors, such as equipment imperfections, temperature variation, interference signals, etc., and also feed an error-correction signal to the PD to adjust the parameters in a real-time fashion; para. 0006 - adjust the plurality of PD parameters for the PD to generate an updated correction signal. sending the update correction signal to the transmitter to compensate the AM-AM and AM-PM characteristics of the HPA; para. 0007 – send the initial correction signal to the transmitter to compensate AM-AM and AM-PM characteristics of the HPA; and in respond to the PD controller taking an action to adjust the plurality of PD parameters, generate an updated correction signal, and send the updated correction signal to the transmitter to compensate the AM-AM and AM-PM characteristics of the HPA; para. 0008 - an action, based on the measured environment parameters and the action-value function, to adjust a plurality of PD parameters for the PD to generate an updated correction signal; para. 0025 – proposed machine learning algorithm may also be able to adjust the parameters of the PD; para. 0027 - estimate and correct the AM-AM and AM-PM nonlinear distortions. U/L jammer or other external factors such as equipment imperfections and non-ambient temperature drives the HPA input power to the saturation point. on-ground PD controller may use a machine learning model pre-trained by various transmit/receive signals and HPA characteristics to adjust the parameters of the PD physical model, and may send the correction signal to the transmitter; para. 0058 - and in respond to the PD controller taking an action to adjust the plurality of PD parameters, generate an updated correction signal, and send the updated correction signal to the transmitter to compensate the AM-AM and AM-PM characteristics of the HPA; para. 076 – proposed machine learning approach can successfully update the PD coefficients to better adjust to the ambient environment efficiently; para. 0077 - proposed machine learning approach can update the PD parameters based on the reward defined above to improve the HPA linearity; para. 0079 - machine learning approaches can dynamically update the PD parameter set to adjust to the changing environment; para. 0082 - taking an action, based on the environment parameters and the actionvalue function, to adjust the plurality of PD parameters for the PD to generate an updated correction signal; sending the update correction signal to the transmitter to compensate the AM-AM and AM-PM characteristics of the HPA; Claim 1 - sending the update correction signal to the transmitter to compensate the AM-AM and AM-PM characteristics of the HPA.).
Claim 10:
Li teaches or suggests an apparatus, comprising: a least one processor; and at least one memory including computer program code, wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to:
transmit an uplink ... signal for power amplifier measurement (see Fig. 1-3, 11, and 13; para. 0029 - AM-AM and AM-PM responses of the HPA due to the input signal X(t); para. 0032 – desired PD output for a given input X(t); para. 0057- when the PD is deployed in the system, because the developed PD is able to estimate the power and phase shift of the input signal and provide corresponding power and shift compensations; para. 0058 - determine an initial correction signal, including an AM-AM correction signal and an AM-PM correction signal, based on a physical model with a plurality of PD parameters; para. 0059 - PD, a curve-fitting algorithm may be adopted to estimate the AM-AM and AM-PM nonlinear distortions. In one embodiment, based on an extended Saleh's model, the measured AM-AM characteristics of the HPA may be fitted.);
receive signaling comprising one or more power amplifier parameters (see Fig. 1-3, 11, and 13; para. 0029 - AM-AM and AM-PM responses of the HPA due to the input signal X(t); para. 0032 – desired PD output for a given input X(t); para. 0057- when the PD is deployed in the system, because the developed PD is able to estimate the power and phase shift of the input signal and provide corresponding power and shift compensations; para. 0058 - determine an initial correction signal, including an AM-AM correction signal and an AM-PM correction signal, based on a physical model with a plurality of PD parameters; para. 0059 - PD, a curve-fitting algorithm may be adopted to estimate the AM-AM and AM-PM nonlinear distortions. In one embodiment, based on an extended Saleh's model, the measured AM-AM characteristics of the HPA may be fitted.);
determine a digital pre-distortion function of the apparatus based on the one or more power amplifier parameters (see Fig. 1-3, 11, and 13; para. 0028 - to compensate the AM-AM and AM-PM effects of the HPA, an accurate physical-model based PD may be developed based on an extended Saleh's model. The Saleh's model is a commonly used power amplifier model, and has been proposed for characterizing HPA's amplitude (AM-AM) and phase (AMPM) distortions accurately; para. 0056 - Digital predistortion may be a baseband signal processing approach that compensates the power and phase shift caused by the power amplifiers; para. 0058 - to determine an initial correction signal, including an AM-AM correction signal and an AM-PM correction signal, based on a physical model with a plurality of PD parameters, and send the initial correction signal to the transmitter to compensate AM-AM and AM-PM characteristics of the HPA; and in respond to the PD controller taking an action to adjust the plurality of PD parameters, generate an updated correction signal, and send the updated correction signal to the transmitter to compensate the AM-AM and AM-PM characteristics of the HPA; para. 0059 - PD, a curve-fitting algorithm may be adopted to estimate the AM-AM and AM-PM nonlinear distortions. In one embodiment, based on an extended Saleh's model, the measured AM-AM characteristics of the HPA may be fitted; para. 0061 - normalized correction signal may be sent to the HPA through a transmitter that connects to the output terminal of the on-ground physical-model based PD via a control channel; para. 0082 - determining an initial correction signal including an AM-AM correction signal and an AM-PM correction signal based on a physical model with a plurality of PD parameters, and sending the initial correction signal to the transmitter to compensate AM-AM and AM-PM characteristics of the HPA.); and
transmit an uplink signal with the digital pre-distortion function adjusted based on the one or more power amplifier parameters (see Fig. 1-3, 11, and 13; para. 0028 - to compensate the AM-AM and AM-PM effects of the HPA, an accurate physical-model based PD may be developed based on an extended Saleh's model. The Saleh's model is a commonly used power amplifier model, and has been proposed for characterizing HPA's amplitude (AM-AM) and phase (AMPM) distortions accurately; para. 0056 - Digital predistortion may be a baseband signal processing approach that compensates the power and phase shift caused by the power amplifiers; para. 0058 - to determine an initial correction signal, including an AM-AM correction signal and an AM-PM correction signal, based on a physical model with a plurality of PD parameters, and send the initial correction signal to the transmitter to compensate AM-AM and AM-PM characteristics of the HPA; and in respond to the PD controller taking an action to adjust the plurality of PD parameters, generate an updated correction signal, and send the updated correction signal to the transmitter to compensate the AM-AM and AM-PM characteristics of the HPA; para. 0059 - PD, a curve-fitting algorithm may be adopted to estimate the AM-AM and AM-PM nonlinear distortions. In one embodiment, based on an extended Saleh's model, the measured AM-AM characteristics of the HPA may be fitted; para. 0061 - normalized correction signal may be sent to the HPA through a transmitter that connects to the output terminal of the on-ground physical-model based PD via a control channel; para. 0082 - determining an initial correction signal including an AM-AM correction signal and an AM-PM correction signal based on a physical model with a plurality of PD parameters, and sending the initial correction signal to the transmitter to compensate AM-AM and AM-PM characteristics of the HPA.).
Sagi more specifically teaches or suggests reference (see Fig. 1, 4, 6-11; Para. 0007 - In order perform DPD and/or DPoD operations, the non-linearity characteristics of various components in the transmitter and/or the receiver (e.g., amplifiers, signal converters, etc.) may be estimated; para. 0009 - may generate a reference signal having a non-constant envelope for nonlinearity estimation by a receiver. The apparatus may transmit the reference signal, e.g., to a receiver. In an aspect, the reference signal includes a primary synchronization signal. receive, from the receiver, feedback associated with the nonlinearity estimation, and perform at least one DPD operation based on the feedback. feedback comprises adjusting one or more coefficients associated with at least one of a high-power amplifier (HPA); para. 0010 - apparatus may receive a reference signal having a non-constant envelope. The apparatus may estimate at least one nonlinearity characteristic based on the reference signal having the non-constant envelope. The apparatus may at least one of: transmit feedback based on the at least one nonlinearity characteristic, or perform at least one DPoD operation based on the at least one nonlinearity characteristic; para. 0029 - base stations 102 may wirelessly communicate with the UEs 104; para. 0037 - certain aspects, the base station 180 may be configured to generate a reference signal 198 having a non-constant envelope. The base station 180 may transmit the reference signal 198 having the non-constant envelope to the UE 104. The UE 104 may be configured to receive the reference signal 198 having the non-constant envelope from the base station 180. The UE 104 may estimate one or more nonlinearity characteristics based on the reference signal 198. Thereafter, the UE 104 may (I) transmit, to the base station 180, feedback based on the one or more nonlinearity characteristics, (2) perform at least one digital post distortion (DPoD) operation based on the one or more nonlinearity characteristics, or (3) both transmit feedback based on the one or more nonlinearity characteristics and perform the at least one DPoD operation based on the one or more nonlinearity characteristics. When the UE 104 transmits feedback based on the one or more nonlinearity characteristics to the base station 180, the base station 180 may perform at least one digital pre-distortion (DPD) operation based on the feedback. In this way, the UE 104 and/or the base station 180 may improve the response, throughput, and/or capacity of one or more channels on which the UE 104 and the base station 180 communicate; para. 0056 - may include at least one transmitter 402 and at least one receiver 404. The transmitter 402 and the receiver 404 are illustrated as a base station and a UE, respectively ( e.g., for uplink communication); however, this arrangement is to be regarded as illustrative and the transmitter 402 and/or the receiver 404 may be any apparatuses configured for wireless communication.).
Accordingly, it would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the system and method, taught in Li, to include reference for the purpose of enabling a specific signal related to base stations and user equipments to be processed and digital pre-distortion to be dertemined, improving the response, throughput, and/or capacity of one or more channels on which the UEs and base stations communicate, as taught by Sagi (0007, 0010, and 0037).
Claim(s) 27:
Claim(s) 27 correspond to Claim 10, and thus, Li and Sagi teach or suggest the limitations of claim(s) 27 as well.
Claim 11:
Li further teaches or suggests when determining the digital pre-distortion function, at least to: use an analytical model to determine the digital pre-distortion function based on the one or more power amplifier parameters (see Fig. 1-3, 11, and 13; para. 0028 - to compensate the AM-AM and AM-PM effects of the HPA, an accurate physical-model based PD may be developed based on an extended Saleh's model. The Saleh's model is a commonly used power amplifier model, and has been proposed for characterizing HPA's amplitude (AM-AM) and phase (AMPM) distortions accurately; para. 0056 - Digital predistortion may be a baseband signal processing approach that compensates the power and phase shift caused by the power amplifiers; para. 0058 - to determine an initial correction signal, including an AM-AM correction signal and an AM-PM correction signal, based on a physical model with a plurality of PD parameters, and send the initial correction signal to the transmitter to compensate AM-AM and AM-PM characteristics of the HPA; and in respond to the PD controller taking an action to adjust the plurality of PD parameters, generate an updated correction signal, and send the updated correction signal to the transmitter to compensate the AM-AM and AM-PM characteristics of the HPA; para. 0059 - PD, a curve-fitting algorithm may be adopted to estimate the AM-AM and AM-PM nonlinear distortions. In one embodiment, based on an extended Saleh's model, the measured AM-AM characteristics of the HPA may be fitted; para. 0061 - normalized correction signal may be sent to the HPA through a transmitter that connects to the output terminal of the on-ground physical-model based PD via a control channel; para. 0082 - determining an initial correction signal including an AM-AM correction signal and an AM-PM correction signal based on a physical model with a plurality of PD parameters, and sending the initial correction signal to the transmitter to compensate AM-AM and AM-PM characteristics of the HPA.).
Claim(s) 28:
Claim(s) 28 correspond to Claim 11, and thus, Li and Sagi teach or suggest the limitations of claim(s) 28 as well.
Claim 12:
Li further teaches or suggests when determining the digital pre-distortion function, at least to: us a trained artificial intelligence-based model to determine the pre-distribution function (see Fig. 1-3, 11, and 13; Fig. 1-3, 11, and 13; para. 0027 - estimate and correct the AM-AM and AM-PM nonlinear distortions. U/L jammer or other external factors such as equipment imperfections and non-ambient temperature drives the HPA input power to the saturation point. on-ground PD controller may use a machine learning model pre-trained by various transmit/receive signals and HPA characteristics to adjust the parameters of the PD physical model, and may send the correction signal to the transmitter; para. 0059 - to compensate the AM-AM and AM-PM effects of the HPA, an accurate physical-model based PD may be developed based on an extended Saleh's model. The Saleh's model is a commonly used power amplifier model, and has been proposed for characterizing HPA's amplitude (AM-AM) and phase (AMPM) distortions accurately; para. 0065 - controller may behave as an agent taking actions, and may use the rewards or punishments returned from environment and target states to train and make the best decision in certain circumstances; para. 0068 - States: a state may be an observable (at least partially) status of the PD controller's relation with the environment, and is defined as power and phase of the output power; para. 0073 - non-linear function approximator, such as a neural network, may be adopted instead. Q-network may utilize the neural network function as the value-function approximator. A Q network can be trained by minimizing a sequence of loss function; claim 2 – calculating AM-AM and AM-PM characteristics of the HPA according to the signal sent to the satellite transponder and the signal received from the satellite transponder.).
Claim 13:
Li further teaches or suggests train an artificial intelligence-based model to fit a power amplifier model controlled by one or more power amplifier parameters, wherein the training forms the trained artificial intelligence-based model, wherein the training is based on an uplink ... signal transmitted to a network node and a non-linear profile difference between the uplink ... signal and a known ... signal associated with the power amplifier model (see Fig. 1-3, 11, and 13; para. 0006 - examining an action-value function for actions taken in a preset past period based on reward functions of the actions; para. 0027 - estimate and correct the AM-AM and AM-PM nonlinear distortions. U/L jammer or other external factors such as equipment imperfections and non-ambient temperature drives the HPA input power to the saturation point. on-ground PD controller may use a machine learning model pre-trained by various transmit/receive signals and HPA characteristics to adjust the parameters of the PD physical model, and may send the correction signal to the transmitter; para. 0062 – machine learning based PD controller for HPA linearization. The machine-learning based PD controller may be able to promptly determine the PD parameters based on past experience and correspondingly update the PD parameters. Therefore, the disclosed machine-learning based PD con troller may provide dynamic and real-time control for optimizing the performance of the PD; para. 0065 - controller may behave as an agent taking actions, and may use the rewards or punishments returned from environment and target states to train and make the best decision in certain circumstances; para. 0068 - States: a state may be an observable (at least partially) status of the PD controller's relation with the environment, and is defined as power and phase of the output power; para. 0069 – for given "Input Power" (or P;n) and "Input Phase" ( or Phase;n), the rewards function may be the distance between estimated and measured output power and phase of the HPA; para. 0073 – Q network may utilize the neural network function as the value-function approximator. A Q network can be trained by minimizing a sequence of loss function; para. 0079 - machine learning approaches can dynamically update the PD parameter set to adjust to the changing environment; para. 0082 – examining an action-value function for actions taken in a preset past period based on reward functions of the actions; taking an action, based on the environment parameters and the action value function, to adjust the plurality of PD parameters for the PD to generate an updated correction signal; sending the update correction signal to the transmitter to compensate the AM-AM and AM-PM characteristics of the HPA.).
Sagi more specifically teaches or suggests reference (see Fig. 1, 4, 6-11; Para. 0007 - In order perform DPD and/or DPoD operations, the non-linearity characteristics of various components in the transmitter and/or the receiver (e.g., amplifiers, signal converters, etc.) may be estimated; para. 0009 - may generate a reference signal having a non-constant envelope for nonlinearity estimation by a receiver. The apparatus may transmit the reference signal, e.g., to a receiver. In an aspect, the reference signal includes a primary synchronization signal. receive, from the receiver, feedback associated with the nonlinearity estimation, and perform at least one DPD operation based on the feedback. feedback comprises adjusting one or more coefficients associated with at least one of a high-power amplifier (HPA); para. 0010 - apparatus may receive a reference signal having a non-constant envelope. The apparatus may estimate at least one nonlinearity characteristic based on the reference signal having the non-constant envelope. The apparatus may at least one of: transmit feedback based on the at least one nonlinearity characteristic, or perform at least one DPoD operation based on the at least one nonlinearity characteristic; para. 0029 - base stations 102 may wirelessly communicate with the UEs 104; para. 0037 - certain aspects, the base station 180 may be configured to generate a reference signal 198 having a non-constant envelope. The base station 180 may transmit the reference signal 198 having the non-constant envelope to the UE 104. The UE 104 may be configured to receive the reference signal 198 having the non-constant envelope from the base station 180. The UE 104 may estimate one or more nonlinearity characteristics based on the reference signal 198. Thereafter, the UE 104 may (I) transmit, to the base station 180, feedback based on the one or more nonlinearity characteristics, (2) perform at least one digital post distortion (DPoD) operation based on the one or more nonlinearity characteristics, or (3) both transmit feedback based on the one or more nonlinearity characteristics and perform the at least one DPoD operation based on the one or more nonlinearity characteristics. When the UE 104 transmits feedback based on the one or more nonlinearity characteristics to the base station 180, the base station 180 may perform at least one digital pre-distortion (DPD) operation based on the feedback. In this way, the UE 104 and/or the base station 180 may improve the response, throughput, and/or capacity of one or more channels on which the UE 104 and the base station 180 communicate; para. 0056 - may include at least one transmitter 402 and at least one receiver 404. The transmitter 402 and the receiver 404 are illustrated as a base station and a UE, respectively ( e.g., for uplink communication); however, this arrangement is to be regarded as illustrative and the transmitter 402 and/or the receiver 404 may be any apparatuses configured for wireless communication.).
Accordingly, it would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the system and method, taught in Li, to include reference for the purpose of enabling a specific signal related to base stations and user equipments to be processed and digital pre-distortion to be dertemined, improving the response, throughput, and/or capacity of one or more channels on which the UEs and base stations communicate, as taught by Sagi (0007, 0010, and 0037).
Claim 14:
Li further teaches or suggests train an artificial intelligence-based model to fit a power amplifier model controlled by the one or more power amplifier parameters, wherein the training forms the trained artificial intelligence-based model, wherein the training is based on an uplink ... signal transmitted to a network node, a non-linear power profile difference between an uplink ... signal and a known ... signal associated with the power amplifier model, and the one or more power amplifier parameters (see Fig. 1-3, 11, and 13; para. 0006 - examining an action-value function for actions taken in a preset past period based on reward functions of the actions; para. 0027 - estimate and correct the AM-AM and AM-PM nonlinear distortions. U/L jammer or other external factors such as equipment imperfections and non-ambient temperature drives the HPA input power to the saturation point. on-ground PD controller may use a machine learning model pre-trained by various transmit/receive signals and HPA characteristics to adjust the parameters of the PD physical model, and may send the correction signal to the transmitter; para. 0062 – machine learning based PD controller for HPA linearization. The machine-learning based PD controller may be able to promptly determine the PD parameters based on past experience and correspondingly update the PD parameters. Therefore, the disclosed machine-learning based PD con troller may provide dynamic and real-time control for optimizing the performance of the PD; para. 0065 - controller may behave as an agent taking actions, and may use the rewards or punishments returned from environment and target states to train and make the best decision in certain circumstances; para. 0068 - States: a state may be an observable (at least partially) status of the PD controller's relation with the environment, and is defined as power and phase of the output power; para. 0069 – for given "Input Power" (or P;n) and "Input Phase" ( or Phase;n), the rewards function may be the distance between estimated and measured output power and phase of the HPA; para. 0073 – Q network may utilize the neural network function as the value-function approximator. A Q network can be trained by minimizing a sequence of loss function; ; para. 0079 - machine learning approaches can dynamically update the PD parameter set to adjust to the changing environment; para. 0082 – examining an action-value function for actions taken in a preset past period based on reward functions of the actions; taking an action, based on the environment parameters and the action value function, to adjust the plurality of PD parameters for the PD to generate an updated correction signal; sending the update correction signal to the transmitter to compensate the AM-AM and AM-PM characteristics of the HPA.).
Sagi more specifically teaches or suggests reference (see Fig. 1, 4, 6-11; Para. 0007 - In order perform DPD and/or DPoD operations, the non-linearity characteristics of various components in the transmitter and/or the receiver (e.g., amplifiers, signal converters, etc.) may be estimated; para. 0009 - may generate a reference signal having a non-constant envelope for nonlinearity estimation by a receiver. The apparatus may transmit the reference signal, e.g., to a receiver. In an aspect, the reference signal includes a primary synchronization signal. receive, from the receiver, feedback associated with the nonlinearity estimation, and perform at least one DPD operation based on the feedback. feedback comprises adjusting one or more coefficients associated with at least one of a high-power amplifier (HPA); para. 0010 - apparatus may receive a reference signal having a non-constant envelope. The apparatus may estimate at least one nonlinearity characteristic based on the reference signal having the non-constant envelope. The apparatus may at least one of: transmit feedback based on the at least one nonlinearity characteristic, or perform at least one DPoD operation based on the at least one nonlinearity characteristic; para. 0029 - base stations 102 may wirelessly communicate with the UEs 104; para. 0037 - certain aspects, the base station 180 may be configured to generate a reference signal 198 having a non-constant envelope. The base station 180 may transmit the reference signal 198 having the non-constant envelope to the UE 104. The UE 104 may be configured to receive the reference signal 198 having the non-constant envelope from the base station 180. The UE 104 may estimate one or more nonlinearity characteristics based on the reference signal 198. Thereafter, the UE 104 may (I) transmit, to the base station 180, feedback based on the one or more nonlinearity characteristics, (2) perform at least one digital post distortion (DPoD) operation based on the one or more nonlinearity characteristics, or (3) both transmit feedback based on the one or more nonlinearity characteristics and perform the at least one DPoD operation based on the one or more nonlinearity characteristics. When the UE 104 transmits feedback based on the one or more nonlinearity characteristics to the base station 180, the base station 180 may perform at least one digital pre-distortion (DPD) operation based on the feedback. In this way, the UE 104 and/or the base station 180 may improve the response, throughput, and/or capacity of one or more channels on which the UE 104 and the base station 180 communicate; para. 0056 - may include at least one transmitter 402 and at least one receiver 404. The transmitter 402 and the receiver 404 are illustrated as a base station and a UE, respectively ( e.g., for uplink communication); however, this arrangement is to be regarded as illustrative and the transmitter 402 and/or the receiver 404 may be any apparatuses configured for wireless communication.).
Accordingly, it would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the system and method, taught in Li, to include reference for the purpose of enabling a specific signal related to base stations and user equipments to be processed and digital pre-distortion to be dertemined, improving the response, throughput, and/or capacity of one or more channels on which the UEs and base stations communicate, as taught by Sagi (0007, 0010, and 0037).
Claim 15:
Li further teaches or suggests when transmitting the uplink signal, at least to: transmit the uplink signal with the digital pre-distortion function determined using the trained artificial intelligence model (see Fig. 1-3, 11, and 13; para. 0027 - on-ground PD controller may use a machine learning model pre-trained by various transmit/receive signals and HPA characteristics to adjust the parameters of the PD physical model, and may send the correction signal to the transmitter; para. 0028 - to compensate the AM-AM and AM-PM effects of the HPA, an accurate physical-model based PD may be developed based on an extended Saleh's model. The Saleh's model is a commonly used power amplifier model, and has been proposed for characterizing HPA's amplitude (AM-AM) and phase (AMPM) distortions accurately; para. 0056 - Digital predistortion may be a baseband signal processing approach that compensates the power and phase shift caused by the power amplifiers; para. 0058 - to determine an initial correction signal, including an AM-AM correction signal and an AM-PM correction signal, based on a physical model with a plurality of PD parameters, and send the initial correction signal to the transmitter to compensate AM-AM and AM-PM characteristics of the HPA; and in respond to the PD controller taking an action to adjust the plurality of PD parameters, generate an updated correction signal, and send the updated correction signal to the transmitter to compensate the AM-AM and AM-PM characteristics of the HPA; para. 0059 - PD, a curve-fitting algorithm may be adopted to estimate the AM-AM and AM-PM nonlinear distortions. In one embodiment, based on an extended Saleh's model, the measured AM-AM characteristics of the HPA may be fitted; para. 0061 - normalized correction signal may be sent to the HPA through a transmitter that connects to the output terminal of the on-ground physical-model based PD via a control channel; para. 0082 - determining an initial correction signal including an AM-AM correction signal and an AM-PM correction signal based on a physical model with a plurality of PD parameters, and sending the initial correction signal to the transmitter to compensate AM-AM and AM-PM characteristics of the HPA.).
Claim 16:
Li further teaches or suggests wherein the power amplifier parameters comprise an alpha parameter or a beta parameter associated with a general analytical power amplifier model (see para. 0028 – compensate the AM-AM and AM-PM effects of the HPA, an accurate physical-model based PD may be developed based on an extended Saleh's model. The Saleh's model is a commonly used power amplifier model, and has been proposed for characterizing HPA's amplitude (AM-AM) and phase (AMPM) distortions accurately; para. 0029 - the original Saleh's model may be extended for the HPA by including eight extra parameters (a0 , a1 , b0 , and b1 , together with a 0 , a 1 , B0 , and B1); para. 0059 - PD, a curve-fitting algorithm may be adopted to estimate the AM-AM and AM-PM nonlinear distortions. In one embodiment, based on an extended Saleh's model, the measured AM-AM characteristics of the HPA. a0 , b0 , a 0 , and Bo are PD parameters, and the measured AM-PM characteristics of the HPA may be fitted. where a1, b1 , a 1 , and B1 are PD parameters.).
Claim 17:
Li further teaches or suggests wherein the received signaling further comprises one or more digital pre-distortion function parameters (see Fig. 1-3, 11, and 13; para. 0029 - AM-AM and AM-PM responses of the HPA due to the input signal X(t); para. 0032 – desired PD output for a given input X(t); para. 0057- when the PD is deployed in the system, because the developed PD is able to estimate the power and phase shift of the input signal and provide corresponding power and shift compensations; para. 0058 - determine an initial correction signal, including an AM-AM correction signal and an AM-PM correction signal, based on a physical model with a plurality of PD parameters; para. 0059 - PD, a curve-fitting algorithm may be adopted to estimate the AM-AM and AM-PM nonlinear distortions. In one embodiment, based on an extended Saleh's model, the measured AM-AM characteristics of the HPA may be fitted.).
Conclusion
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/ANDREW T MCINTOSH/Primary Examiner, Art Unit 2144
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