Proposal on rain attenuation prediction method using convolutional neural network

被引：0
|
作者：
Komatsuya, Yuji [1 ]
Imai, Tetsuro [1 ]
Hirose, Miyuki [2 ]
机构：
[1] Tokyo Denki Univ, Dept Informat & Commun Engn, Adachi Ku, Tokyo 1208551, Japan
[2] Kyushu Inst Technol, Dept Elect Engn & Elect, Tobata Ku, Kitakyushu Shi, Fukuoka 8048550, Japan
来源：
IEICE COMMUNICATIONS EXPRESS | 2024年 / 13卷 / 06期
关键词：
rain attenuation; convolutional neural network; deep learning;
D O I：
10.23919/comex.2024SPL0015
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Recently, the practical application of HAPS (High Altitude Platform Station) as the next -generation communication platform is studied actively. HAPS employs adaptive rain attenuation countermeasure techniques such as site diversity methods, therefore it is ideal to predict rain attenuation on the path in real time. We proposed real-time rain attenuation prediction method by convolutional neural network that inputs image of rainfall rate and path distance. Result showed that prediction accuracy of our proposed method is better than a method using conventional formulas.
引用
收藏
页码：181 / 184
页数：4
相关论文
共 50 条
  • [1] Rain Attenuation Prediction Using Artificial Neural Network for Dynamic Rain Fade Mitigation
    Ahuna, M. N.
    Afullo, T. J.
    Alonge, A. A.
    SAIEE AFRICA RESEARCH JOURNAL, 2019, 110 (01): : 11 - 18
  • [2] Prediction of slant path rain attenuation based on artificial neural network
    Yang, HW
    He, C
    Zhu, HW
    Song, WT
    ISCAS 2000: IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS - PROCEEDINGS, VOL I: EMERGING TECHNOLOGIES FOR THE 21ST CENTURY, 2000, : 152 - 155
  • [3] Convolutional Neural Network Pruning Using Filter Attenuation
    Mousa-Pasandi, Morteza
    Hajabdollahi, Mohsen
    Karimi, Nader
    Samavi, Shadrokh
    Shirani, Shahram
    2020 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2020, : 2905 - 2909
  • [4] Stock Prediction Using Convolutional Neural Network
    Chen, Sheng
    He, Hongxiang
    2018 2ND INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE APPLICATIONS AND TECHNOLOGIES (AIAAT 2018), 2018, 435
  • [5] Bioactivity Prediction Using Convolutional Neural Network
    Hamza, Hentabli
    Nasser, Maged
    Salim, Naomie
    Saeed, Faisal
    EMERGING TRENDS IN INTELLIGENT COMPUTING AND INFORMATICS: DATA SCIENCE, INTELLIGENT INFORMATION SYSTEMS AND SMART COMPUTING, 2020, 1073 : 341 - 351
  • [6] Seismic response prediction method for building structures using convolutional neural network
    Oh, Byung Kwan
    Park, Youngjun
    Park, Hyo Seon
    STRUCTURAL CONTROL & HEALTH MONITORING, 2020, 27 (05):
  • [7] A deep learning method for prediction of cardiovascular disease using convolutional neural network
    Sajja T.K.
    Kalluri H.K.
    Revue d'Intelligence Artificielle, 2020, 34 (05) : 601 - 606
  • [8] Traffic State Prediction using Convolutional Neural Network
    Toncharoen, Ratchanon
    Piantanakulchai, Mongkut
    2018 15TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER SCIENCE AND SOFTWARE ENGINEERING (JCSSE), 2018, : 250 - 255
  • [9] Software Defect Prediction using Convolutional Neural Network
    Wongpheng, Kittisak
    Visutsak, Porawat
    35TH INTERNATIONAL TECHNICAL CONFERENCE ON CIRCUITS/SYSTEMS, COMPUTERS AND COMMUNICATIONS (ITC-CSCC 2020), 2020, : 240 - 243
  • [10] Disease Risk Prediction by Using Convolutional Neural Network
    Ambekar, Sayali
    Phalnikar, Rashmi
    2018 FOURTH INTERNATIONAL CONFERENCE ON COMPUTING COMMUNICATION CONTROL AND AUTOMATION (ICCUBEA), 2018,
12345