Low-Cost AiP Array Design Using Machine Learning for mmWave Mobile Systems

被引:0
|
作者
Nakmouche, Mohammed Farouk [1 ]
Magray, M. Idrees [2 ]
Allam, A. M. M. A. [3 ]
Fawzy, Diaa E. [1 ]
Lin, Ding Bing [4 ]
Tarng, Jenn-Hwan [2 ]
机构
[1] Izmir Univ Econ, Fac Engn, Izmir, Turkey
[2] Natl Chiao Tung Univ, Ctr MmWave Smart Radar Syst & Technol, Hsinchu, Taiwan
[3] German Univ Cairo, Dept Commun Engn, Cairo, Egypt
[4] Natl Taiwan Univ Sci & Technol, Dept Elect & Comp Engn, Taipei, Taiwan
关键词
mmWave; Antenna in Packaging (AiP); ANN; machine learning; PACKAGE; ANTENNA;
D O I
暂无
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
Based on low-cost PCB solution, an array antenna in packaging (AiP) dedicated for mmWave mobile systems is designed using machine learning. The proposed antenna operates at 28 GHz (26.5 - 29.5 GHz) with a gain ranging from 8 dB to 15 dB in the operating bandwidth. The development process of the proposed AiP is assisted by machine learning for prediction of the optimal radiating patch's length and width in terms of resonance frequency.
引用
收藏
页数:2
相关论文
共 50 条
  • [11] Low-Cost "Ball and Plate" Design and Implementation for Learning Control Systems
    Stander, Deejay
    Jimenez-Leudo, Santiago
    Quijano, Nicanor
    2017 IEEE 3RD COLOMBIAN CONFERENCE ON AUTOMATIC CONTROL (CCAC), 2017,
  • [12] Utilization of a Low-Cost Sensor Array for Mobile Methane Monitoring
    Silberstein, Jonathan
    Wellbrook, Matthew
    Hannigan, Michael
    SENSORS, 2024, 24 (02)
  • [13] Design and Fabrication of a Low-Cost Mobile Antenna for Low VHF
    Rafaei-Booket, Mahmood
    Hasibi-Taheri, Sina
    IETE JOURNAL OF RESEARCH, 2023, 69 (08) : 5701 - 5707
  • [14] Using A Low-Cost Sensor Array and Machine Learning Techniques to Detect Complex Pollutant Mixtures and Identify Likely Sources
    Thorson, Jacob
    Collier-Oxandale, Ashley
    Hannigan, Michael
    SENSORS, 2019, 19 (17)
  • [15] Design of a low-cost mobile application for library networks
    Morato, Jorge
    Perez Velazquez, Patricia
    Sanchez-Cuadrado, Sonia
    IBERSID-REVISTA DE SISTEMAS DE INFORMACION Y DOCUMENTACION, 2020, 14 (02): : 49 - 55
  • [16] Mapping urban air quality using mobile sampling with low-cost sensors and machine learning in Seoul, South Korea
    Lim, Chris C.
    Kim, Ho
    Vilcassim, M. J. Ruzmyn
    Thurston, George D.
    Gordon, Terry
    Chen, Lung-Chi
    Lee, Kiyoung
    Heimbinder, Michael
    Kim, Sun-Young
    ENVIRONMENT INTERNATIONAL, 2019, 131
  • [17] A low-cost checkpointing scheme for mobile computing systems
    Li, GH
    Wang, HY
    Chen, JX
    ADVANCES IN WEB-AGE INFORMATION MANAGEMENT: PROCEEDINGS, 2004, 3129 : 97 - 106
  • [18] Providing Informative Feedback in a Low-Cost Rehabilitation System Using Machine Learning
    Rodrigues, Paul
    Amorim, Ivone
    Cunha, Bruno
    INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2024, PT II, 2025, 15347 : 83 - 95
  • [19] Calibration of Low-Cost Particle Sensors by Using Machine-Learning Method
    Chen, Chen-Chia
    Kuo, Chih-Ting
    Chen, Ssu-Ying
    Lin, Chih-Hsing
    Chue, Jin-Ju
    Hsieh, Yi-Jie
    Cheng, Chun-Wen
    Wu, Chieh-Ming
    Huang, Chun-Ming
    2018 IEEE ASIA PACIFIC CONFERENCE ON CIRCUITS AND SYSTEMS (APCCAS 2018), 2018, : 111 - 114
  • [20] Low-Cost Approaches in Neuroscience to Teach Machine Learning Using a Cockroach Model
    Truong, Vincent
    Moore, Johnathan E.
    Ricoy, Ulises M.
    Verpeut, Jessica L.
    ENEURO, 2024, 11 (12)