JP7665642B2 - ピーク低減トーンを使用する機械学習ベースのレシーバ性能改善 - Google Patents

ピーク低減トーンを使用する機械学習ベースのレシーバ性能改善 Download PDF

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JP7665642B2
JP7665642B2 JP2022549736A JP2022549736A JP7665642B2 JP 7665642 B2 JP7665642 B2 JP 7665642B2 JP 2022549736 A JP2022549736 A JP 2022549736A JP 2022549736 A JP2022549736 A JP 2022549736A JP 7665642 B2 JP7665642 B2 JP 7665642B2
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prt
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transmitter
wireless communication
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ジュネ・ナムグン
クリシュナ・キラン・ムッカヴィリ
テサン・ユー
ナガ・ブーシャン
ティンファン・ジ
パヴァン・クマール・ヴィッタラデヴニ
ジェイ・クマール・スンダララジャン
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クアルコム，インコーポレイテッド
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/26—Systems using multi-frequency codes
    • H04L27/2601—Multicarrier modulation systems
    • H04L27/2614—Peak power aspects
    • H04L27/2618—Reduction thereof using auxiliary subcarriers
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/26—Systems using multi-frequency codes
    • H04L27/2601—Multicarrier modulation systems
    • H04L27/2614—Peak power aspects
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G06F18/2148—Generating training patterns; Bootstrap methods, e.g. bagging or boosting characterised by the process organisation or structure, e.g. boosting cascade
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G06N3/0455—Auto-encoder networks; Encoder-decoder networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0499—Feedforward networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/088—Non-supervised learning, e.g. competitive learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/09—Supervised learning
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B3/00—Line transmission systems
    • H04B3/02—Details
    • H04B3/04—Control of transmission; Equalising
    • H04B3/06—Control of transmission; Equalising by the transmitted signal
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B3/00—Line transmission systems
    • H04B3/02—Details
    • H04B3/20—Reducing echo effects or singing; Opening or closing transmitting path; Conditioning for transmission in one direction or the other
    • H04B3/23—Reducing echo effects or singing; Opening or closing transmitting path; Conditioning for transmission in one direction or the other using a replica of transmitted signal in the time domain, e.g. echo cancellers
    • H04B3/238—Reducing echo effects or singing; Opening or closing transmitting path; Conditioning for transmission in one direction or the other using a replica of transmitted signal in the time domain, e.g. echo cancellers using initial training sequence
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B3/00—Line transmission systems
    • H04B3/02—Details
    • H04B3/46—Monitoring; Testing
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/26—Systems using multi-frequency codes
    • H04L27/2601—Multicarrier modulation systems
    • H04L27/2614—Peak power aspects
    • H04L27/2615—Reduction thereof using coding
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path
    • H04L5/003—Arrangements for allocating sub-channels of the transmission path
    • H04L5/0048—Allocation of pilot signals, i.e. of signals known to the receiver
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Signal Processing (AREA)
  • Data Mining & Analysis (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Evolutionary Computation (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Artificial Intelligence (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Health & Medical Sciences (AREA)
  • Software Systems (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • Biophysics (AREA)
  • Biomedical Technology (AREA)
  • Mathematical Physics (AREA)
  • Computational Linguistics (AREA)
  • Health & Medical Sciences (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Biology (AREA)
  • Mobile Radio Communication Systems (AREA)
  • Transceivers (AREA)
  • Dc Digital Transmission (AREA)
JP2022549736A 2020-02-24 2021-01-22 ピーク低減トーンを使用する機械学習ベースのレシーバ性能改善 Active JP7665642B2 (ja)

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Application Number Priority Date Filing Date Title
US202062980776P 2020-02-24 2020-02-24
US62/980,776 2020-02-24
US202063009369P 2020-04-13 2020-04-13
US63/009,369 2020-04-13
US17/154,215 US11804998B2 (en) 2020-02-24 2021-01-21 Machine learning based receiver performance improvement using peak reduction tones
US17/154,215 2021-01-21
PCT/US2021/014503 WO2021173267A1 (en) 2020-02-24 2021-01-22 Machine learning based receiver performance improvement using peak reduction tones

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JP2023515453A5 JP2023515453A5 (https=) 2024-01-10
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US (2) US11677605B2 (https=)
EP (2) EP4111655A1 (https=)
JP (1) JP7665642B2 (https=)
KR (1) KR20220139893A (https=)
CN (2) CN115104292B (https=)
BR (1) BR112022015851A2 (https=)
PH (1) PH12022551801A1 (https=)
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US20210314270A1 (en) 2020-04-01 2021-10-07 Qualcomm Incorporated Dynamic packet buffering duration
US11743086B2 (en) * 2020-05-04 2023-08-29 Qualcomm Incorporated Tone reservation for peak to average power ratio reduction
US11817986B2 (en) * 2020-07-09 2023-11-14 Qualcomm Incorporated Carrier aggregation peak to average power ratio reduction using peak reduction tones
US11996968B2 (en) 2020-08-25 2024-05-28 Qualcomm Incorporated Neural augmentation for device nonlinearity mitigation in x-node machine learning
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US11658708B2 (en) * 2021-07-02 2023-05-23 Qualcomm Incorporated Codebook embedding generation and processing
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US11595237B1 (en) * 2022-05-03 2023-02-28 Qualcomm Incorporated Peak reduction tone allocation
CN114884793A (zh) * 2022-07-08 2022-08-09 清华四川能源互联网研究院 基于凸优化的ofdm信号峰均比抑制算法、系统、设备及介质
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KR102879796B1 (ko) * 2022-12-08 2025-11-03 한국전자기술연구원 강화학습 기반 sc-fdma 시스템의 papr 감소 장치
CN116189427B (zh) * 2023-01-12 2024-05-28 东风悦享科技有限公司 一种基于prt的可移动式站点换乘方法及系统
CN116975032B (zh) * 2023-07-14 2024-04-12 南京领行科技股份有限公司 数据对齐方法、系统和电子设备及存储介质
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CN115152190B (zh) 2024-03-22
TW202135510A (zh) 2021-09-16
JP2023515453A (ja) 2023-04-13
EP4111656A1 (en) 2023-01-04
WO2021173267A1 (en) 2021-09-02
CN115152190A (zh) 2022-10-04
WO2021173272A1 (en) 2021-09-02
KR20220139893A (ko) 2022-10-17
US11804998B2 (en) 2023-10-31
CN115104292A (zh) 2022-09-23
EP4111655A1 (en) 2023-01-04
US20210266210A1 (en) 2021-08-26
US11677605B2 (en) 2023-06-13
PH12022551801A1 (en) 2024-02-12
CN115104292B (zh) 2024-06-25
US20210266036A1 (en) 2021-08-26
BR112022015851A2 (pt) 2022-10-04
TWI891712B (zh) 2025-08-01

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