DETAILED ACTION
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (US20230015640A1) (hereinafter Kim) in the view of Cao (US20170317913A1) (hereinafter Cao)
Regarding claims 1 and 17, Cao teaches:
An apparatus for wireless communication at a user equipment (UE) / A method of wireless communication performed by a UE: Kim describes a UE transmitter system configured for digital predistortion (DPD) linearization to improve performance of a power amplifier (PA) in wireless communication systems (Kim [0030]– [0036], Fig. 2). The system includes RF transmit path components (e.g., LUT 210, DAC 212, PA 218) and a feedback loop that digitizes the PA output (ADC 222) and stores measurement data in memory (Kim [0034]– [0036]).
one or more memories and one or more processors, the one or more processors, individually or collectively and based at least in part on information stored in the one or more memories being configured to: Kim teaches that the system includes a processor executing DPD initialization and calibration logic, utilizing memory to store predistortion models and non-linearity measurements used in compensation decisions (Kim Fig. 3, [0041]; Fig. 2, [0035]– [0036]).
receive one or more non-linearity measurements, each non-linearity measurement being associated with a transmit antenna power amplifier: Kim discloses capturing non-linearity measurements via feedback from the PA 218 to the RFIC 224. These are digitized using ADC 222 and stored in capture memory 220 for later use in DPD modeling (Kim [0034]– [0036]). The measurements are directly linked to the behavior of the transmit antenna power amplifier.
receive initial antenna information: During initialization, Kim teaches that the processor configures transceiver registers (elements 202–220), including antenna-relevant parameters, to support generation of the DPD model (Kim Fig. 3, [0041]).
select a first non-linearity cancellation algorithm from among a plurality of non-linearity cancellation algorithms based, at least in part, on the one or more non-linearity measurements and the initial antenna information: Kim explains that the DPD LUT 210 selection is based on captured measurement data and antenna-specific configuration established during initialization. The LUT determines which compensation model to apply, satisfying the claim limitation (Kim [0030]– [0035], Fig. 2).
However, Kim does not explicitly teach a detailed user equipment or base station hardware arrangement comprising the integrated communication and processing architecture for implementing these operations at the system level.
Cao teaches an apparatus for wireless communication comprising arrangement 1301 with communication and processing components (1302, 1303, 1304) designed for UE or base station operations (Cao Fig. 13, [0093]– [0095]). Cao explains that the arrangement may be implemented using processors and memory configured to perform signal processing, including linearization techniques (Cao Fig. 3, [0035]– [0036]).
One of ordinary skill in the art would have found it obvious to combine Kim’s measurement-based cancellation logic with Cao’s UE hardware framework, as both are directed to transmitter linearization in wireless systems. Integrating Kim’s functional approach to non-linearity measurement and compensation into Cao’s communication device architecture would have been a predictable improvement that enhances transmitter performance through antenna-aware DPD configuration
Regarding claims 2 and 18, Kim teaches,
An apparatus for wireless communication at a user equipment (UE) / A method of wireless communication performed by a UE: Kim describes a UE transmitter architecture that includes a digital predistortion (DPD) module configured to improve the linearity of a power amplifier (PA) through measurement-based compensation (Kim Fig. 2, [0030]– [0036]).
wherein the one or more processors are further configured to transmit non-linearity feedback to a network node, the non-linearity feedback being used to determine the one or more non-linearity measurements Kim teaches that non-linearity feedback is collected via a feedback loop that routes the PA output through ADC 222 and stores it in capture memory 220 (Kim [0034]– [0036]).
However, Kim focuses on internal feedback within the UE system and does not explicitly disclose transmitting non-linearity feedback to a network node to derive the measurements.
Cao teaches transmitting non-linearity feedback to another system component or network entity. Specifically, Cao describes comparing the output of a non-linear system with a desired signal, generating an updated signal, and using this as feedback to update the input signal (Cao [0010]– [0014]). This feedback loop supports optimizing transmission and is disclosed as being communicated to a network node or external component, enabling derivation of non-linearity measurements at the network side.
One of ordinary skill in the art would have found it obvious to combine Kim’s DPD-based linearization system with Cao’s feedback-sharing mechanism. Integrating Cao’s network-directed feedback transmission into Kim’s measurement architecture would have allowed for distributed or collaborative compensation modeling, improving overall system performance and supporting adaptive feedback collection — a predictable improvement in network-based DPD systems.
Regarding claim 3, Kim teaches the apparatus of claim 2 and further teaches wherein the one or more processors are further configured to Kim discloses a processor configured to determine a training dataset consisting of input and output data from the PA, apply frequency-domain regression to derive compensation data, store the resulting model, and then linearize the PA output using that stored compensation data (Kim [0006]).
transmit a grant request for the non-linearity feedback Kim teaches that non-linearity feedback is captured from the PA output through RFIC 224 and ADC 222, then stored in capture memory 220 (Kim [0033]– [0035]). This feedback mechanism implies that a request is issued for feedback access or resource usage to transmit this data, aligning with the concept of transmitting a grant request for the non-linearity feedback.
and receive a resource grant for the non-linearity feedback, the resource grant identifying one or more resources for transmission of the non-linearity feedback to the network node:Kim further teaches that the processor coordinates hardware resources and manages the LUT and firmware updates used for DPD operations (Kim [0027], [0032]– [0036]). This implies a form of internal resource allocation to enable feedback processing and transmission.
However, Kim does not expressly disclose a network-layer resource grant or signaling from a network node identifying resources for transmission — only internal coordination is clearly described.
Cao teaches transmitting non-linearity feedback to an external network node as part of a system that compares actual output with a desired signal and updates the input accordingly (Cao [0010]– [0014]). This feedback loop is described in the context of cross-device optimization and would inherently involve resource assignment and signaling to and from the network.
One of ordinary skill in the art would have found it obvious to combine Kim’s internal resource coordination and compensation architecture with Cao’s network-based feedback system. Doing so would result in a communication framework where the UE not only processes internal measurements but also requests and receives resource grants from the network, enabling adaptive and externally-coordinated non-linearity feedback — a predictable enhancement for improving transmitter performance and network integration.
Regarding claims 4, Kim teaches the apparatus of claim 2:
wherein the one or more processors are further configured to (Kim [0006]) Kim teaches that the processor is configured to determine a training data set comprising input/output data of the power amplifier, perform frequency-domain regression to generate compensation data, store that data, and use it to linearize the PA output.
assign a quality metric to the non-linearity feedback, the quality metric indicating an accuracy of the non-linearity feedback (Kim [0040]- [0049], Fig 3) Kim teaches assigning a quality metric to the non-linearity feedback by using EVM (Error Vector Magnitude) and ACLR (Adjacent Channel Leakage Ratio) as indicators of accuracy in the frequency-domain weighted least squares regression process (Kim [0040]– [0049], Fig. 3). These metrics are explicitly measured during validation and testing (steps 310, 316) to evaluate the accuracy of the feedback model and guide the selection of optimal compensation data
and transmit the quality metric to the network node (Kim [0030]- [0032]). Kim teaches transmitting the quality metric to a network node by implementing a feedback architecture within the radio frequency transceiver 200, which can be part of a terrestrial base transceiver station 180 or another networked electronic device. The system includes envelope tracking and digital predistortion components that adjust power supply parameters and share signal performance data, implying that quality-related metrics—such as EVM and ACLR from earlier processing—are used for real-time optimization and are available to the network node for further processing or monitoring.
Regarding claim 5, Cao teaches the apparatus of claim 4, wherein the quality metric includes a signal-to-noise ratio (SNR):While Kim teaches using quality metrics such as EVM and ACLR to evaluate the accuracy of non-linearity feedback (Kim [0040]– [0049]), it does not explicitly disclose signal-to-noise ratio (SNR) as part of the metric.
Cao teaches that linearization performance can be evaluated using metrics such as ACLR and normalized mean square error (NMSE), as shown in Table 1 and described in [0089]– [0091]. Although Cao does not explicitly mention SNR, the NMSE values shown (improving from approximately –11 dB to –44 dB) reflect enhancements in signal fidelity and suppression of noise. These metrics inherently represent SNR-like behavior and serve the same purpose in evaluating signal quality and feedback accuracy.
One of ordinary skill in the art would have found it obvious to incorporate SNR as an additional or alternative signal quality metric alongside EVM, ACLR, or NMSE in Kim’s architecture, based on Cao’s disclosure that similar metrics are used for evaluating transmitter performance. Using SNR as part of the feedback accuracy evaluation would have been a predictable and commonly accepted variation, further improving model selection and system tuning.
Regarding claims 6 and 19, Kim teaches:
An apparatus for wireless communication at a user equipment (UE) / A method of wireless communication performed by a user equipment (UE): Kim describes a transmitter system for linearizing PA output using a DPD architecture with feedback capture, signal modeling, and performance evaluation metrics (Kim Fig. 2–3, [0030]– [0041]).
However, Kim does not explicitly teach that the one or more non-linearity measurements are based, at least in part, on an average non-linearity level.
Cao teaches wherein the one or more non-linearity measurements are based, at least in part, on an average non-linearity level (Cao [0088]– [0091]). Cao discloses measuring linearization performance using normalized mean square error (NMSE) and ACLR across multiple carriers (Cao [0088]– [0091], Table 1). The NMSE values are measured per carrier and reflect the non-linearity behavior before and after compensation.Cao further explains that differences in NMSE across carriers (~5 dB) are reduced to achieve homogeneous output, indicating that a performance metric like an average NMSE level is used to evaluate and optimize non-linearity across the system. This supports the use of an average non-linearity level as a basis for measurement.
One of ordinary skill in the art would have found it obvious to incorporate Cao’s average-based non-linearity evaluation into Kim’s feedback and compensation system. Doing so would have enabled more robust and generalized model training across varying carriers or antenna conditions, thereby improving linearization accuracy — a predictable and beneficial modification to enhance transmitter performance.
Regarding claims 7 and 20, Kim teaches:
An apparatus for wireless communication at a user equipment (UE) / A method of wireless communication performed by a user equipment (UE): Kim discloses a UE transmitter architecture that includes a processor (120), a cellular communication block (112), and a transceiver (113) for LTE and 5G-based communications (Kim [0026]– [0028]). The architecture supports signal processing and control procedures common in LTE/5G systems.
wherein the one or more processors, to receive the initial antenna information, are configured to receive the initial antenna information via a physical downlink control channel (PDCCH) Kim teaches that the transceiver is configured for LTE/5G operation (Kim [0026]– [0028]), where antenna-related configuration information is delivered via the PDCCH. While not explicitly named, it would be understood by one of ordinary skill in the art that receiving antenna initialization data via PDCCH is standard procedure in such systems.
However, Kim does not explicitly disclose the structural and system-level UE architecture required to support this initialization and compensation flow in a fully implemented hardware layout.
Cao teaches a user equipment (UE) and method for wireless communication using arrangement 1301 with communication and processing components (1302, 1303, 1304) (Cao Fig. 13, [0093]– [0094]) capable of executing linearization and antenna control functionality (Cao Fig. 3, [0035]– [0036]).
One of ordinary skill in the art would have found it obvious to incorporate Cao’s detailed UE hardware implementation into Kim’s LTE/5G transceiver system. Doing so would support Kim’s standard-based PDCCH control and initialization functions using a fully realized processor and memory architecture, improving device integration and supporting antenna configuration workflows in a practical, system-level implementation.
Regarding claims 8 and 21, Kim teaches:
An apparatus for wireless communication at a user equipment (UE) / A method of wireless communication performed by a user equipment (UE): Kim teaches a UE transmitter architecture including a processor configured to determine a training dataset comprising input and output data of the power amplifier, perform regression using a frequency-domain weighting function to derive compensation data, store that data, and then linearize the PA output using the stored compensation (Kim [0006]).
Wherein the one or more processors are further configured to receive updated antenna information based, at least in part, on a change in a number of transmit antennas, a change in a power amplifier power supply level, or a change in a transmission power Kim discloses a PA (218) with non-linearity compensation and feedback involving training data and PA output samples, where changes in PA behavior — such as power supply level or transmission power — are used to update the DPD LUT (Kim [0034]–[0035], Fig. 2). This supports the receipt of updated antenna-related transmission information based on hardware-level changes.
However, Kim does not teach Selecting a second non-linearity cancellation algorithm from among a plurality of non-linearity cancellation algorithms based, at least in part, on the updated antenna information.
Cao teaches selecting a second non-linearity cancellation algorithm from among a plurality of non-linearity cancellation algorithms based, at least in part, on the updated antenna information. Specifically, Cao describes selection among multiple cancellation algorithms — such as time-domain and frequency-domain Iterative Learning Control (ILC) — based on PA behavior and signal characteristics (Cao [0043]– [0055], Fig. 4). The ILC mechanism adjusts the predistortion input using different Γ selection strategies, and algorithm choice is influenced by conditions such as bandwidth, which may reflect updates in antenna configuration or transmit power.
One of ordinary skill in the art would have found it obvious to combine Kim with Cao to enhance Cao’s algorithm selection process using Kim’s hardware-level feedback on transmit conditions. Doing so would enable real-time adaptation of linearization strategies based on changes in antenna configuration or power parameters, resulting in improved system performance — a predictable improvement supported by the combined teachings.
Regarding claims 9 and 22, Kim teaches,
An apparatus for wireless communication at a user equipment (UE) / A method of wireless communication performed by a user equipment (UE): Kim describes a UE transmitter that includes components such as PA 218, ADC 222, and capture memory 220, operating within a DPD framework that includes a LUT 210 for non-linearity compensation based on PA output measurements and training data (Kim [0030]– [0036]).
Wherein the first non-linearity cancellation algorithm is selected based, at least in part, on a number of transmitting antennas, a non-linearity impairment level, a UE capability, or an operated modulation and coding scheme: Kim teaches selecting a non-linearity cancellation approach (via DPD LUT 210) based on power amplifier characteristics, signal conditions, and compensation data derived from envelope tracking and feedback processing (Kim [0032]– [0035]). The DPD selection process reflects variations in PA non-linearity levels and transmission parameters such as modulation or coding demands, aligning with the claimed selection criteria involving non-linearity impairment and transmission configuration.
However, Kim does not explicitly disclose the structural or algorithmic hardware layout implementing these cancellation processes within a complete user equipment design.
Cao teaches a UE comprising arrangement 1301 and communication/processing components (1302, 1303, 1304) configured for wireless communication and signal processing operations, including linearization techniques (Cao Fig. 13, [0093]– [0094]; Fig. 3, [0035]– [0036]). Cao supports system-level implementations of cancellation algorithms and shows signal path selection based on PA and bandwidth conditions (Cao [0043]– [0055], Fig. 4).
One of ordinary skill in the art would have found it obvious to combine the non-linearity correction apparatus of Cao with the algorithm selection techniques of Kim to improve adaptability and efficiency in signal linearization across varying transmission conditions. The combination yields a predictable improvement by enabling dynamic algorithm selection based on real-time transmitter characteristics — such as antenna count, modulation scheme, or impairment severity — resulting in more accurate compensation and enhanced signal integrity in diverse deployment scenarios.
Regarding claims 10 and 23, Kim teaches:
An apparatus for wireless communication at a user equipment (UE) / A method of wireless communication performed by a user equipment (UE): Kim teaches a system for digital predistortion (DPD) involving a processor, RFICs, PAs, and capture memory to compensate for power amplifier non-linearity (Kim [0030]– [0036], Fig. 2). The system includes adaptive signal correction using feedback from the PA output and frequency-domain modeling to linearize transmission.
However, Kim does not explicitly teach That the plurality of non-linearity cancellation algorithms includes a first digital post-distortion (DPoD) process and a second DPoD process, the first DPoD process being associated with a greater number of transmitting antennas than the second DPoD process.
Kim does not use the term “DPoD” but describes a DPD architecture with multiple compensation paths, such as LUT 210 and transceiver chains (e.g., RFIC 224, PA 218), that adjust signal correction based on antenna behavior, power level, and signal conditions (Kim [0034]– [0036]). The system is designed to adapt predistortion to specific PA configurations and transmission settings, including antenna-related variables.
Cao teaches an apparatus for wireless communication including arrangement 1301 and its signal processing components (1302, 1303, 1304) that support linearization techniques (Cao Fig. 3, Fig. 13, [0035]– [0036]; [0093]– [0094]). While not referring to “DPoD” explicitly, Cao discusses the use of alternative linearization algorithms (e.g., time-domain and frequency-domain ILC) and system-level scaling based on bandwidth or hardware conditions, which reasonably encompasses deploying different cancellation methods depending on the number of antennas or transmit chains (Cao [0043]– [0055]).
One of ordinary skill in the art would have found it obvious to combine Cao’s transmitter apparatus with Kim’s configuration-aware, antenna-sensitive DPD framework to improve scalability and performance in systems with multiple PAs or transmitting antennas. The combination enables predictable improvements in signal fidelity, EVM/ACLR performance, and power efficiency by applying tailored compensation strategies based on antenna count — an expected and routine design variation in the field of digital predistortion and PA linearization.
Regarding claims 11 and 24, Kim teaches:
An apparatus for wireless communication at a network node / A method of wireless communication performed by a network node: Kim describes an RF transceiver system that includes a processor, memory, RFICs, and PAs configured to apply digital predistortion (DPD) techniques based on feedback from the transmission chain (Kim Fig. 2, [0030]– [0036]).
one or more memories and one or more processors, the one or more processors, individually or collectively and based at least in part on information stored in the one or more memories, being configured to Kim describes that the system includes a capture memory (220) that stores test set, training set, and validation set data, which is used by the processor to perform non-linearity compensation (Kim [0033]– [0035]).
output one or more non-linearity measurements, each non-linearity measurement being associated with a transmit antenna power amplifier (PA) Kim explains that digital predistortion is used to linearize an RF transmitter PA by analyzing and modeling its non-linearity, yielding output that reflects per-PA non-linearity characteristics (Kim [0033]).
output initial antenna information Kim teaches that the PA output is converted by RFIC 224 and digitized by ADC 222, with data stored in capture memory 220. This information reflects antenna-specific signal characteristics and qualifies as initial antenna information (Kim [0035]).
and output a configuration for a user equipment (UE) to select a first non-linearity cancellation algorithm from among a plurality of non-linearity cancellation algorithms based, at least in part, on the one or more non-linearity measurements and the initial antenna information Kim describes a transceiver system with configurable DPD strategies, including envelope tracking and lookup table–based compensation models, which use measurement data and antenna-specific output to drive selection among different linearization techniques (Kim [0030]–[0035], Fig. 2). This implies that the system may provide configuration information to a UE to guide non-linearity cancellation selection.
However, Kim does not explicitly disclose the broader system-level transmitter or network node implementation with modular processing components and software/hardware integration.
Cao teaches a network-side radio communication apparatus comprising arrangement 1301 and components (1302, 1303, 1304), implemented using processors, memory, and software (Cao Fig. 13, [0093]– [0095]) for signal processing and wireless transmission in a 5G or similar system.
One of ordinary skill in the art would have found it obvious to combine Kim’s adaptive linearization techniques with Cao’s system to enable per-antenna measurement and algorithm selection. The combination yields a predictable improvement in transmitter efficiency and dynamic non-linearity correction tailored to antenna behavior, while supporting deployment within a practical, network-based processing architecture.
Regarding claims 12 and 25, Kim teaches:
An apparatus for wireless communication at a network node / A method of wireless communication performed by a network node: Kim describes a network-side or transceiver-based digital predistortion (DPD) architecture involving a processor, memory, RF chains, and power amplifiers configured to measure, compensate, and optimize non-linear RF transmission (Kim Fig. 2, [0030]– [0036]).
wherein the one or more processors are further configured to determine a training dataset comprising input/output data of the power amplifier, perform frequency-domain regression to generate compensation data, and use it to linearize the PA output: Kim explicitly discloses a processor that determines a training dataset using input/output data from the PA, performs frequency-domain regression to derive compensation data, and stores that data to linearize the PA output using a DPD LUT (Kim [0006], [0033]–[0035]).
However, Kim does not explicitly teach receive from one or more UEs, non-linearity feedback (i.e., signal and output) and generate the one or more non-linearity measurements based, at least in part, on the non-linearity feedback.
Cao discloses receiving signal feedback from a UE by comparing an output signal to a desired signal to detect non-linearity (Cao [0009]– [0010]). Cao also describes generating non-linearity measurements through an iterative learning control (ILC) loop, refining the input signal based on error feedback using update functions such as uᵏ⁺¹(n) = uᵏ(n) + Γeᵏ(n) (Cao [0038]– [0044]). This enables the network to generate refined non-linearity measurements based on received feedback.
One of ordinary skill in the art would have found it obvious to incorporate Cao’s UE-driven feedback collection and measurement generation into Kim’s frequency-domain regression and DPD framework. Doing so would allow the network node to improve linearization performance using real-time, UE-originated signal characteristics, thereby enhancing the adaptability and responsiveness of Kim’s compensation process — a predictable and beneficial improvement in wireless transmission systems.
Regarding claim 26, Kim teaches the method of claim 25 and further teaches:
wherein the non-linearity measurements are based, at least in part, on a weighted average of non-linearity feedback received from the one or more UEs. Kim teaches that non-linearity measurements are derived from feedback components in the transceiver chain, including a shaping function lookup table (LUT), envelope tracking DAC, and power supply modulator, which dynamically adjusts PA power based on envelope feedback signals (Kim [0031]– [0032]). The shaping function LUT uses time-varying envelope information from the baseband modem to optimize power supply behavior in real time.This implies a system where feedback is integrated from multiple signal sources over time and modulated to improve efficiency — a process consistent with applying a weighted averaging strategy across non-linearity feedback received from one or more UEs.
Regarding claims 13 and 27, Kim teaches,
An apparatus for wireless communication at a network node / A method of wireless communication performed by a network node: Kim describes a wireless communication system in which a processor controls communication across internal components such as a transceiver, power amplifier, RFIC, and lookup tables used in LTE/5G environments (Kim [0026]– [0028], Fig. 2).
wherein the one or more processors, to output the initial antenna information, are configured to transmit the initial antenna information via a physical downlink control channel (PDCCH) Kim teaches that the processor in the electronic device coordinates transceiver components within an LTE/5G system. The transceiver includes control logic for sending antenna-related configuration and signal data (Kim [0026]– [0027]). Although PDCCH is not explicitly named, it would be understood by one of ordinary skill in the art that control signaling for antenna initialization in LTE/5G systems is transmitted via the PDCCH, consistent with Kim’s disclosed architecture.
However, Kim does not explicitly disclose a system-level network node architecture with modular linearization components and iterative compensation loops.
Cao teaches a network-side apparatus comprising arrangement 1301 and modular components (1302, 1303, 1304) used for signal processing and transmission in a 5G wireless system (Cao Fig. 13, [0093]– [0094]). Cao further supports linearization and non-linearity measurement systems at the base station (Cao [0035]– [0036]).
One of ordinary skill in the art would have found it obvious to combine Cao’s non-linearity compensation apparatus with Kim’s control channel transmission method because integrating the transmission of antenna configuration information over the PDCCH would provide a predictable and standardized mechanism for communicating control information in LTE/5G systems. This combination would enhance coordination between network nodes and user equipment, resulting in improved performance and system compatibility without requiring undue experimentation.
Regarding claims 14 and 28, Kim teaches,
An apparatus for wireless communication at a network node / A method of wireless communication performed by a network node: Kim discloses a network-side transceiver system that includes a processor, memory, and power amplifier components, coordinated through DPD techniques to perform dynamic non-linearity compensation in LTE/5G communication systems (Kim Fig. 2, [0030]– [0036]).
wherein the one or more processors are further configured to: Kim teaches that the processor is configured to determine a training dataset comprising input and output data of the power amplifier, perform regression using a frequency-domain weighting function to derive compensation data, store that data, and then linearize the PA output using the stored compensation (Kim [0006]).
output updated antenna information based, at least in part, on a change in a number of transmit antennas, a change in a power amplifier power supply level, or a change in a transmission power: Kim describes a transceiver architecture including a shaping function LUT, envelope tracking DAC, power supply modulator, and power amplifier, where the voltage applied to the PA is dynamically adjusted in response to changing conditions to maintain peak efficiency (Kim [0031]–[0032], Fig. 2). These adjustments inherently respond to variations in transmission power and PA power supply, and influence antenna-related parameters. As such, the system supports the output of updated antenna information based on power dynamics and transmission characteristics.
configure the UE to select a second non-linearity cancellation algorithm from among the plurality of non-linearity cancellation algorithms based, at least in part, on the updated antenna information Kim teaches that the transceiver includes DPD logic (e.g., LUT 210) and processor control that allows updates to lookup tables and firmware (Kim [0027]– [0028]). This setup supports reconfiguring or selecting among multiple compensation algorithms in response to hardware or transmission state changes, such as antenna adjustments or power supply variation. Therefore, the system supports adaptive algorithm selection based on updated antenna information.
However, Kim does not explicitly disclose the full system-level layout of a network node comprising the physical modular communication hardware and antenna architecture.
Cao teaches a base station or network node apparatus for wireless communication that includes arrangement 1301 with processor and communication components (1302, 1303, 1304) designed to perform radio functions and signal processing (Cao Fig. 13, [0093]– [0094]; Fig. 3, [0035]– [0036]).
One of ordinary skill in the art would have found it obvious to combine Cao’s base transmitter architecture with Kim’s adaptive PA control and DPD algorithm selection to improve linearization accuracy under dynamic hardware conditions — resulting in a predictable performance enhancement for 5G network nodes and improved adaptation to real-time transmission states.
Regarding claims 15 and 29, Kim teaches:
An apparatus for wireless communication at a network node / A method of wireless communication performed by a network node:Kim describes a digital predistortion (DPD)-enabled transceiver system including processors, memory, shaping function LUTs, and envelope tracking components used to adaptively linearize power amplifier (PA) output in real-time under LTE/5G operating conditions (Kim Fig. 2, [0030]– [0036]).
wherein the one or more processors, to configure the UE to select the first non-linearity cancellation algorithm, are configured to configure the UE to select the first non-linearity cancellation algorithm based, at least in part, on a number of transmitting antennas, a non-linearity impairment level, a UE capability, or an operated modulation and coding scheme:Kim teaches that the DPD algorithm selection and shaping function LUT are dynamically adapted based on envelope signals provided by the baseband modem (Kim [0032]–[0035]). These signals reflect modulation and coding scheme (MCS), transmit power, and PA characteristics. Kim further describes that capture memory stores PA output samples, test/training data, and that the feedback from these elements guides algorithm selection depending on signal impairment, antenna count, and system capabilities, including support for higher-order modulation schemes (Kim [0034]– [0035]). This supports selection of a non-linearity cancellation algorithm based on all the claimed conditions.
However, Kim does not explicitly disclose the full modular system-level implementation of a network node with integrated antenna and signal routing structures.
Cao teaches a network node apparatus comprising arrangement 1301 and processing components (1302, 1303, 1304), which support RF signal processing and communication functions at a base station or network node (Cao Fig. 13, [0093]– [0094]; Fig. 3, [0035]– [0036]).
One of ordinary skill in the art would have found it obvious to incorporate Kim’s adaptive algorithm selection based on antenna number, modulation, and signal impairment into Cao’s transmitter design to improve PA linearization under varying UE capabilities and transmission conditions — yielding a predictable and optimized communication performance in real-world deployments.
Regarding claims 16 and 30, Kim teaches
An apparatus for wireless communication at a network node / A method of wireless communication performed by a network node Kim discloses a digital predistortion (DPD) system designed to linearize RF transmitter power amplifiers (PAs) using components such as DPD lookup tables (LUTs), shaping function LUTs (SF LUTs), capture memory, and regression-based modeling strategies (Kim Fig. 2, [0030]– [0036]).
wherein the plurality of non-linearity cancellation algorithms includes a first digital post-distortion (DPoD) process or a second DPoD process, the first DPoD process being associated with a greater number of transmitting antennas than the second DPoD process:Kim teaches that different DPD strategies — such as least squares regression and memory polynomial modeling — are deployed based on system conditions, waveform complexity, and performance requirements (Kim [0032]– [0036]). These strategies rely on envelope information from the baseband modem and may be adapted dynamically based on signal modulation, antenna configuration, or PA behavior. The selection of algorithms and compensation models inherently aligns with different levels of transmission complexity, including the number of transmitting antennas. Thus, Kim supports using differentiated DPoD processes depending on antenna count or hardware scale.
However, Kim does not explicitly disclose a full network node hardware platform comprising modular RF signal components, antennas, and routing structures for implementing these algorithmic choices at scale.
Cao teaches a radio communication apparatus for a network node, including arrangement 1301 and its components (1302, 1303, 1304), used in wireless transmitters or base stations to execute signal processing functions (Cao Fig. 13, [0093]– [0094]; Fig. 3, [0035]– [0036]).
One of ordinary skill in the art would have found it obvious to incorporate Kim’s differentiated DPoD selection based on system complexity (e.g., number of transmitting antennas) into Cao’s general transmitter architecture to enable scalable non-linearity compensation across devices with varying transmission configurations — resulting in predictable improvements in PA efficiency, spectral performance, and system flexibility for modern LTE/5G deployments.
Conclusion
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/ANNABELLA CHRISTOPHE/Examiner, Art Unit 2415
/JEFFREY M RUTKOWSKI/Supervisory Patent Examiner, Art Unit 2415
