BR112023002790A2 - Aprendizado não supervisionado para localização e mapeamento simultâneos em redes neurais profundas usando informações de estado de canal - Google Patents

Aprendizado não supervisionado para localização e mapeamento simultâneos em redes neurais profundas usando informações de estado de canal

Info

Publication number
BR112023002790A2
BR112023002790A2 BR112023002790A BR112023002790A BR112023002790A2 BR 112023002790 A2 BR112023002790 A2 BR 112023002790A2 BR 112023002790 A BR112023002790 A BR 112023002790A BR 112023002790 A BR112023002790 A BR 112023002790A BR 112023002790 A2 BR112023002790 A2 BR 112023002790A2
Authority
BR
Brazil
Prior art keywords
mapping
channel state
state information
neural networks
unsupervised learning
Prior art date
Application number
BR112023002790A
Other languages
English (en)
Inventor
Behboodi Arash
Ghazvinian Zanjani Farhad
Binamira Soriaga Joseph
Ferrari Lorenzo
Ali Amjad Rana
Welling Max
Yoo Taesang
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 BR112023002790A2 publication Critical patent/BR112023002790A2/pt

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0278—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves involving statistical or probabilistic considerations
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0273—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves using multipath or indirect path propagation signals in position determination
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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; CALCULATING OR 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; CALCULATING OR 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; CALCULATING OR 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0619—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal using feedback from receiving side
    • H04B7/0621—Feedback content
    • H04B7/0626—Channel coefficients, e.g. channel state information [CSI]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
    • H04B7/0837—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station using pre-detection combining
    • H04B7/0842—Weighted combining
    • H04B7/086—Weighted combining using weights depending on external parameters, e.g. direction of arrival [DOA], predetermined weights or beamforming
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Software Systems (AREA)
  • Computing Systems (AREA)
  • Artificial Intelligence (AREA)
  • Mathematical Physics (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • Biophysics (AREA)
  • Molecular Biology (AREA)
  • General Health & Medical Sciences (AREA)
  • Computational Linguistics (AREA)
  • Biomedical Technology (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Medical Informatics (AREA)
  • Probability & Statistics with Applications (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

APRENDIZADO NÃO SUPERVISIONADO PARA LOCALIZAÇÃO E MAPEAMENTO SIMULTÂNEOS EM REDES NEURAIS PROFUNDAS USANDO INFORMAÇÕES DE ESTADO DE CANAL. Trata-se de um método de treinamento de uma rede neural artificial (ANN) que recebe, de uma estação-base, informações de sinal para um sinal de radiofrequência entre a estação-base e um equipamento de usuário (UE). A rede neural artificial é treinada para determinar uma localização do UE e mapear o ambiente com base nas informações de sinal recebido e na ausência de dados rotulados.
BR112023002790A 2020-08-31 2021-08-31 Aprendizado não supervisionado para localização e mapeamento simultâneos em redes neurais profundas usando informações de estado de canal BR112023002790A2 (pt)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
US202063072821P 2020-08-31 2020-08-31
US17/461,927 US20220070822A1 (en) 2020-08-31 2021-08-30 Unsupervised learning for simultaneous localization and mapping in deep neural networks using channel state information
PCT/US2021/048511 WO2022047410A2 (en) 2020-08-31 2021-08-31 Unsupervised learning for simultaneous localization and mapping in deep neural networks using channel state information

Publications (1)

Publication Number Publication Date
BR112023002790A2 true BR112023002790A2 (pt) 2023-03-14

Family

ID=78087497

Family Applications (1)

Application Number Title Priority Date Filing Date
BR112023002790A BR112023002790A2 (pt) 2020-08-31 2021-08-31 Aprendizado não supervisionado para localização e mapeamento simultâneos em redes neurais profundas usando informações de estado de canal

Country Status (6)

Country Link
US (1) US20220070822A1 (pt)
EP (1) EP4204841A2 (pt)
KR (1) KR20230054834A (pt)
CN (1) CN116075833A (pt)
BR (1) BR112023002790A2 (pt)
WO (1) WO2022047410A2 (pt)

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20230318725A1 (en) * 2022-04-04 2023-10-05 Qualcomm Incorporated Predicting wireless measurements based on virtual access points
WO2023197329A1 (zh) * 2022-04-15 2023-10-19 北京小米移动软件有限公司 定位方法、装置、通信设备和存储介质
WO2023211627A1 (en) * 2022-04-29 2023-11-02 Qualcomm Incorporated Network-assisted and round-trip radio frequency fingerprint-based (rffp) position estimation
WO2023225444A1 (en) * 2022-05-17 2023-11-23 Qualcomm Incorporated Spatial area layout reconstruction based on radio frequency measurements
WO2023229716A1 (en) * 2022-05-26 2023-11-30 Qualcomm Incorporated Method of determining zone membership in zone-based federated learning
KR20230172326A (ko) * 2022-06-15 2023-12-22 삼성전자주식회사 무선 통신 시스템에서 채널 상태 정보 피드백을 위한 오토인코더를 업데이트하기 위한 방법 및 장치

Family Cites Families (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE10249866B4 (de) * 2002-10-25 2012-02-16 Continental Automotive Gmbh Vorrichtung zur Positionsbestimmung wenigstens einer zweiten Sende- und Empfangseinrichtung bezüglich einer ersten Sende- und Empfangseinrichtung in einem GHz-Bereich arbeitenden passiven Zugangskontrollsystem
CN111512323A (zh) * 2017-05-03 2020-08-07 弗吉尼亚科技知识产权有限公司 自适应无线通信的学习与部署
US10380896B2 (en) * 2017-12-27 2019-08-13 Intel Corporation Characterizing proximity risks within a radio mesh
US20210004677A1 (en) * 2018-02-09 2021-01-07 Deepmind Technologies Limited Data compression using jointly trained encoder, decoder, and prior neural networks
US11463980B2 (en) * 2019-02-22 2022-10-04 Huawei Technologies Co., Ltd. Methods and apparatuses using sensing system in cooperation with wireless communication system
US11537896B2 (en) * 2019-03-15 2022-12-27 Intel Corporation Machine learning techniques for precise position determination
US10762650B1 (en) * 2019-09-13 2020-09-01 Toyota Motor Engineering & Manufacturing North America, Inc. System and method for estimating depth using a monocular camera
CN112580795B (zh) * 2019-09-29 2024-09-06 华为技术有限公司 一种神经网络的获取方法以及相关设备
US10733325B1 (en) * 2019-11-15 2020-08-04 Capital One Services, Llc Securing user-entered text in-transit
US11586212B2 (en) * 2020-02-19 2023-02-21 Ford Global Technologies, Llc Vehicle device localization

Also Published As

Publication number Publication date
WO2022047410A2 (en) 2022-03-03
EP4204841A2 (en) 2023-07-05
US20220070822A1 (en) 2022-03-03
WO2022047410A3 (en) 2022-06-02
CN116075833A (zh) 2023-05-05
KR20230054834A (ko) 2023-04-25

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