Patent application EP21177348A (EP), filed 2021-06-02
Publication number: EP3923471A1
Title: MACHINE LEARNING BASED DIGITAL PRE-DISTORTION FOR POWER AMPLIFIERS
Abstract: Example embodiments relate to machine learning based digital pre-distortion for power amplifiers. A device may amplify a signal with a power amplifier and transmit the signal. The signal may be received by an internal feedback receiver of the device. The device may further comprise a first machine learning model configured to emulate an external feedback receiver and to generate an emulated feedback signal based on the internal feedback signal. The device may further comprise a second machine learning model configured to determine digital pre-distortion parameters for the power amplifier based on the emulated feedback signal. Apparatuses, methods, and computer programs are disclosed.
IPC codes: H03F   1/    32            A I
H03F   3/    19            A I
H03F   3/    24            A I
Claims: number: 1
text: An apparatus, comprising:
number: 2
text: The apparatus according to claim 1, wherein a bandwidth of the internal feedback receiver is lower than a bandwidth of the external feedback receiver, and/or wherein a dynamic range of the internal feedback receiver is lower than a dynamic range of the external feedback receiver.
number: 3
text: The apparatus according to claim 1 or claim 2, further comprising:
means for linearizing the power amplifier based on the digital pre-distortion parameters, wherein the digital pre-distortion parameters comprise a subset of the second machine learning model.
number: 4
text: The apparatus according to any of claims 1 to 3, further comprising:
means for re-training the second machine learning model based on the emulated feedback signal and the signal in response to one of:
number: 5
text: The apparatus according to any preceding claim, further comprising:
number: 6
text: The apparatus according to claim 5, further comprising:
number: 7
text: The apparatus according to claim 5 or claim 6, wherein the first machine learning model comprises a generator network of a generative adversarial network (GAN).
number: 8
text: A method, comprising:
number: 9
text: A system, comprising:
number: 10
text: A method, comprising:
number: 11
text: The method according to claim 10, further comprising:
training a plurality of devices with a subset of test signals, wherein training of each device of the plurality of devices comprises:
number: 12
text: The method according to claim 11, further comprising:
re-training the first baseline machine learning model and the second baseline machine learning model with the set of test signals after training a predetermined number of devices.
number: 13
text: The method according to claim 11 or claim 12, further comprising:
re-training the first baseline machine learning model and the second baseline machine learning model with the set of test signals, in response to detecting a shift in power amplifier linearization performance among the plurality of devices.
number: 14
text: The method according to any of claims 11 to 13, wherein the set of test signals is characterized by a set of parameters comprising at least one of: transmit power, signal bandwidth, beam configuration, modulation and/or error correction coding scheme, carrier frequency, temperature, battery level, or antenna load.
number: 15
text: The method according to any of claims 11 to 14, wherein the subset of test signals corresponds to a subset of the parameters, and/or wherein the subset of test signals corresponds to a subset of parameter values.
Office action: search_report, dated 2021-12-15
Rejection reasons: novelty_epc_54
Prior art cited: ref_id: EP2538553A1
ref_type: patent
severity: background
ref_id: US2018167091A1
ref_type: patent
severity: background
ref_id: - HUA QIAN ET AL: "A neural network predistorter for nonlinear power amplifiers with memory", DIGITAL SIGNAL PROCESSING WORKSHOP, 2002 AND THE 2ND SIGNAL PROCESSING EDUCATION WORKSHOP. PROCEEDINGS OF 2002 IEEE 10TH 13-16 OCT. 2002, PISCATAWAY, NJ, USA,IEEE, 13 October 2002 (2002-10-13), pages 312 - 316, XP010657818, ISBN: 978-0-7803-8116-2
ref_type: npl
severity: background
ref_id: - WANG DONGMING ET AL: "Augmented Real-Valued Time-Delay Neural Network for Compensation of Distortions and Impairments in Wireless Transmitters", IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, IEEE, PISCATAWAY, NJ, USA, vol. 30, no. 1, 1 January 2019 (2019-01-01), pages 242 - 254, XP011694482, ISSN: 2162-237X, [retrieved on 20181220], DOI: 10.1109/TNNLS.2018.2838039
ref_type: npl
severity: background
ref_id: - JARAUT PRAVEEN ET AL: "Composite Neural Network Digital Predistortion Model for Joint Mitigation of Crosstalk, $I/Q$ Imbalance, Nonlinearity in MIMO Transmitters", IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES, PLENUM, USA, vol. 66, no. 11, 1 November 2018 (2018-11-01), pages 5011 - 5020, XP011698744, ISSN: 0018-9480, [retrieved on 20181102], DOI: 10.1109/TMTT.2018.2869602
ref_type: npl
severity: background
ref_id: - BRIHUEGA ALBERTO ET AL: "Linearizing Active Antenna Arrays: Digital Predistortion Method and Measurements", 2019 IEEE MTT-S INTERNATIONAL MICROWAVE CONFERENCE ON HARDWARE AND SYSTEMS FOR 5G AND BEYOND (IMC-5G), IEEE, 15 August 2019 (2019-08-15), pages 1 - 3, XP033802358, DOI: 10.1109/IMC-5G47857.2019.9160379
ref_type: npl
severity: background
ref_id: EP2538553
ref_type: patent
severity: novelty_destroying
ref_id: US2018167091
ref_type: patent
severity: background
ref_id: - HUA QIAN ET AL, "A neural network predistorter for nonlinear power amplifiers with memory", DIGITAL SIGNAL PROCESSING WORKSHOP, 2002 AND THE 2ND SIGNAL PROCESSING EDUCATION WORKSHOP. PROCEEDINGS OF 2002 IEEE 10TH 13-16 OCT. 2002, PISCATAWAY, NJ, USA,IEEE, (20021013), ISBN 978-0-7803-8116-2, pages 312 - 316, XP010657818 [A] 1-15 * paragraph  [0001]  - paragraph  [0004]; figures 1-7 *
ref_type: npl
severity: background
ref_id: - WANG DONGMING ET AL, "Augmented Real-Valued Time-Delay Neural Network for Compensation of Distortions and Impairments in Wireless Transmitters", IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, IEEE, PISCATAWAY, NJ, USA, vol. 30, no. 1, doi:10.1109/TNNLS.2018.2838039, ISSN 2162-237X, (20190101), pages 242 - 254, (20181220), XP011694482 [A] 1-15 * paragraph  [000I]  - paragraph  [00VI]; figures 1-11 *
ref_type: npl
severity: background
ref_id: - JARAUT PRAVEEN ET AL, "Composite Neural Network Digital Predistortion Model for Joint Mitigation of Crosstalk, $I/Q$ Imbalance, Nonlinearity in MIMO Transmitters", IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES, PLENUM, USA, vol. 66, no. 11, doi:10.1109/TMTT.2018.2869602, ISSN 0018-9480, (20181101), pages 5011 - 5020, (20181102), XP011698744 [A] 1-15 * paragraph  [000I]  - paragraph  [00VI]; figures 1-8 *
ref_type: npl
severity: background
ref_id: - BRIHUEGA ALBERTO ET AL, "Linearizing Active Antenna Arrays: Digital Predistortion Method and Measurements", 2019 IEEE MTT-S INTERNATIONAL MICROWAVE CONFERENCE ON HARDWARE AND SYSTEMS FOR 5G AND BEYOND (IMC-5G), IEEE, (20190815), doi:10.1109/IMC-5G47857.2019.9160379, pages 1 - 3, XP033802358 [A] 1-15 * paragraph  [000I]  - paragraph  [00IV]; figures 1-4 *
ref_type: npl
severity: background
Final disposition: pending (2021-12-15)