BR112022024903A2 - Aumento de rede neural para estimativa e rastreamento de canal sem fio - Google Patents

Aumento de rede neural para estimativa e rastreamento de canal sem fio

Info

Publication number
BR112022024903A2
BR112022024903A2 BR112022024903A BR112022024903A BR112022024903A2 BR 112022024903 A2 BR112022024903 A2 BR 112022024903A2 BR 112022024903 A BR112022024903 A BR 112022024903A BR 112022024903 A BR112022024903 A BR 112022024903A BR 112022024903 A2 BR112022024903 A2 BR 112022024903A2
Authority
BR
Brazil
Prior art keywords
neural network
tracking
channel estimation
wireless channel
current time
Prior art date
Application number
BR112022024903A
Other languages
English (en)
Inventor
Ali Amjad Rana
Pratik Kumar
Welling Max
Behboodi Arash
Binamira Soriaga Joseph
Original Assignee
Qualcomm Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Qualcomm Inc filed Critical Qualcomm Inc
Publication of BR112022024903A2 publication Critical patent/BR112022024903A2/pt

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/024—Channel estimation channel estimation algorithms
    • H04L25/0254—Channel estimation channel estimation algorithms using neural network algorithms
    • 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
    • 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
    • G06N3/0442—Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
    • 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
    • G06N3/047—Probabilistic or stochastic 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
    • G06N3/09—Supervised learning
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/021—Estimation of channel covariance
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/0222—Estimation of channel variability, e.g. coherence bandwidth, coherence time, fading frequency
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/0224—Channel estimation using sounding signals
    • H04L25/0228—Channel estimation using sounding signals with direct estimation from sounding signals
    • H04L25/023—Channel estimation using sounding signals with direct estimation from sounding signals with extension to other symbols
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
    • H04L25/03006—Arrangements for removing intersymbol interference
    • 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
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
    • 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
    • G06N3/048—Activation functions
    • 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
    • G06N7/00—Computing arrangements based on specific mathematical models
    • G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
    • H04L25/03006—Arrangements for removing intersymbol interference
    • H04L2025/03592—Adaptation methods
    • H04L2025/03598—Algorithms
    • H04L2025/03611—Iterative algorithms
    • H04L2025/03656—Initialisation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Computing Systems (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Biomedical Technology (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Biophysics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Health & Medical Sciences (AREA)
  • Probability & Statistics with Applications (AREA)
  • Mobile Radio Communication Systems (AREA)
  • Transceivers (AREA)

Abstract

AUMENTO DE REDE NEURAL PARA ESTIMATIVA E RASTREAMENTO DE CANAL SEM FIO. Trata-se de um método realizado por um dispositivo de comunicação que inclui gerar uma estimativa de canal inicial de um canal para uma etapa de tempo atual com um filtro de Kalman baseado em um primeiro sinal recebido no dispositivo de comunicação. O método também inclui inferir, com uma rede neural, um residual da estimativa de canal inicial da etapa de tempo atual. O método inclui adicionalmente atualizar a estimativa de canal inicial da etapa de tempo atual com base no residual.
BR112022024903A 2020-06-19 2021-06-17 Aumento de rede neural para estimativa e rastreamento de canal sem fio BR112022024903A2 (pt)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
US202063041637P 2020-06-19 2020-06-19
US17/349,744 US11616666B2 (en) 2020-06-19 2021-06-16 Neural network augmentation for wireless channel estimation and tracking
PCT/US2021/037938 WO2021257907A1 (en) 2020-06-19 2021-06-17 Neural network augmentation for wireless channel estimation and tracking

Publications (1)

Publication Number Publication Date
BR112022024903A2 true BR112022024903A2 (pt) 2022-12-27

Family

ID=79022149

Family Applications (1)

Application Number Title Priority Date Filing Date
BR112022024903A BR112022024903A2 (pt) 2020-06-19 2021-06-17 Aumento de rede neural para estimativa e rastreamento de canal sem fio

Country Status (9)

Country Link
US (1) US11616666B2 (pt)
EP (1) EP4169217A1 (pt)
JP (1) JP7752140B2 (pt)
KR (1) KR20230026319A (pt)
CN (1) CN115702561B (pt)
BR (1) BR112022024903A2 (pt)
PH (1) PH12022552952A1 (pt)
TW (1) TWI886292B (pt)
WO (1) WO2021257907A1 (pt)

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116057898B (zh) * 2020-08-18 2025-09-23 高通股份有限公司 用于信道状态信息方案的神经网络或层配置指示符
US12101206B2 (en) * 2021-07-26 2024-09-24 Qualcomm Incorporated Signaling for additional training of neural networks for multiple channel conditions
CN114819407B (zh) * 2022-06-06 2025-12-02 杭州市北京航空航天大学国际创新研究院(北京航空航天大学国际创新学院) 湖泊蓝藻水华动态预测方法及装置
WO2024181777A1 (ko) 2023-02-27 2024-09-06 주식회사 엘지에너지솔루션 리튬이차전지용 분리막 및 그의 제조방법
US12244448B2 (en) * 2023-07-31 2025-03-04 James Kain Enhanced communication system
FR3152348B1 (fr) 2023-08-25 2025-07-18 Thales Sa Méthode d'estimation d'une réponse impulsionnelle d'un canal de réception, dispositif et programme correspondant.

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US7042928B2 (en) * 2002-10-23 2006-05-09 Qualcomm Incorporated Method and apparatus for pilot estimation using prediction error method
CN104067582A (zh) * 2011-10-19 2014-09-24 奥普蒂斯蜂窝技术有限责任公司 信道预测方法和设备
EP3170014B1 (en) 2014-07-16 2021-10-13 Clairvoyant Technology LLC Rfid tag tracking using phase likelihood
CN104283825B (zh) * 2014-09-24 2017-11-03 北京邮电大学 一种基于动态压缩感知的信道估计方法
CN104378787B (zh) * 2014-11-26 2017-09-29 南京航空航天大学 基于扩展Prony算法的平坦快衰落长距离信道预测方法
US20180302213A1 (en) 2017-04-17 2018-10-18 Collision Communications, Inc. Methods, systems, and computer program products for communication channel prediction from received multipath communications in a wireless communications system
EP3676966A4 (en) 2017-08-30 2021-04-21 Telefonaktiebolaget LM Ericsson (Publ) WIRELESS DEVICE FOR DETERMINING THE BEST ANTENNA BEAM AND ASSOCIATED PROCESS
WO2019138156A1 (en) 2018-01-12 2019-07-18 Nokia Technologies Oy Profiled channel impulse response for accurate multipath parameter estimation
DE112019007125T5 (de) * 2019-05-08 2022-01-27 Lg Electronics Inc. Verfahren zur kanalverfolgung in einem drahtlosen av-system und drahtloses gerät benutzend desselben
CN110855585B (zh) * 2019-09-30 2020-11-10 北京交通大学 一种毫米波大规模mimo系统的信道估计方法
EP4115571A1 (en) * 2020-03-06 2023-01-11 Nokia Technologies Oy Classes of nn parameters for channel estimation

Also Published As

Publication number Publication date
TW202201935A (zh) 2022-01-01
CN115702561A (zh) 2023-02-14
PH12022552952A1 (en) 2024-02-26
EP4169217A1 (en) 2023-04-26
JP2023531397A (ja) 2023-07-24
US11616666B2 (en) 2023-03-28
JP7752140B2 (ja) 2025-10-09
KR20230026319A (ko) 2023-02-24
CN115702561B (zh) 2025-05-06
TWI886292B (zh) 2025-06-11
US20210399924A1 (en) 2021-12-23
WO2021257907A1 (en) 2021-12-23

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