IEICE TRANSACTIONS on Fundamentals
A Unified Software and Hardware Platform for Machine Learning Aided Wireless Systems
Summary :
This paper presents a unified software and hardware wireless AI platform (USHWAP) for developing and evaluating machine learning in wireless systems. The platform integrates multi-software development such as MATLAB and Python with hardware platforms like FPGA and SDR, allowing for flexible and scalable device and edge computing application development. The USHWAP is implemented and validated using FPGAs and SDRs. Wireless signal classification, wireless LAN sensing, and rate adaptation are used as examples to showcase the platform's capabilities. The platform enables versatile development, including software simulation and real-time hardware implementation, offering flexibility and scalability for multiple applications. It is intended to be used by wireless-AI researchers to develop and evaluate intelligent algorithms in a laboratory environment.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E106-A No.12 pp.1493-1503
- Publication Date
- 2023/12/01
- Publicized
- 2023/08/22
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2023SDP0006
- Type of Manuscript
- Special Section PAPER (Special Section on Signal Design and Its Applications in Communications)
- Category
- Digital Signal Processing
Authors
Dody ICHWANA PUTRA
Kyushu Institute of Technology
Muhammad HARRY BINTANG PRATAMA
Kyushu Institute of Technology
Ryotaro ISSHIKI
Kyushu Institute of Technology
Yuhei NAGAO
Offino LLC
Leonardo LANANTE JR
Offino LLC
Hiroshi OCHI
Kyushu Institute of Technology
Keyword
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Dody ICHWANA PUTRA, Muhammad HARRY BINTANG PRATAMA, Ryotaro ISSHIKI, Yuhei NAGAO, Leonardo LANANTE JR, Hiroshi OCHI, "A Unified Software and Hardware Platform for Machine Learning Aided Wireless Systems" in IEICE TRANSACTIONS on Fundamentals,
vol. E106-A, no. 12, pp. 1493-1503, December 2023, doi: 10.1587/transfun.2023SDP0006.
Abstract: This paper presents a unified software and hardware wireless AI platform (USHWAP) for developing and evaluating machine learning in wireless systems. The platform integrates multi-software development such as MATLAB and Python with hardware platforms like FPGA and SDR, allowing for flexible and scalable device and edge computing application development. The USHWAP is implemented and validated using FPGAs and SDRs. Wireless signal classification, wireless LAN sensing, and rate adaptation are used as examples to showcase the platform's capabilities. The platform enables versatile development, including software simulation and real-time hardware implementation, offering flexibility and scalability for multiple applications. It is intended to be used by wireless-AI researchers to develop and evaluate intelligent algorithms in a laboratory environment.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2023SDP0006/_p
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@ARTICLE{e106-a_12_1493,
author={Dody ICHWANA PUTRA, Muhammad HARRY BINTANG PRATAMA, Ryotaro ISSHIKI, Yuhei NAGAO, Leonardo LANANTE JR, Hiroshi OCHI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Unified Software and Hardware Platform for Machine Learning Aided Wireless Systems},
year={2023},
volume={E106-A},
number={12},
pages={1493-1503},
abstract={This paper presents a unified software and hardware wireless AI platform (USHWAP) for developing and evaluating machine learning in wireless systems. The platform integrates multi-software development such as MATLAB and Python with hardware platforms like FPGA and SDR, allowing for flexible and scalable device and edge computing application development. The USHWAP is implemented and validated using FPGAs and SDRs. Wireless signal classification, wireless LAN sensing, and rate adaptation are used as examples to showcase the platform's capabilities. The platform enables versatile development, including software simulation and real-time hardware implementation, offering flexibility and scalability for multiple applications. It is intended to be used by wireless-AI researchers to develop and evaluate intelligent algorithms in a laboratory environment.},
keywords={},
doi={10.1587/transfun.2023SDP0006},
ISSN={1745-1337},
month={December},}
Copy
TY - JOUR
TI - A Unified Software and Hardware Platform for Machine Learning Aided Wireless Systems
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1493
EP - 1503
AU - Dody ICHWANA PUTRA
AU - Muhammad HARRY BINTANG PRATAMA
AU - Ryotaro ISSHIKI
AU - Yuhei NAGAO
AU - Leonardo LANANTE JR
AU - Hiroshi OCHI
PY - 2023
DO - 10.1587/transfun.2023SDP0006
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E106-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 2023
AB - This paper presents a unified software and hardware wireless AI platform (USHWAP) for developing and evaluating machine learning in wireless systems. The platform integrates multi-software development such as MATLAB and Python with hardware platforms like FPGA and SDR, allowing for flexible and scalable device and edge computing application development. The USHWAP is implemented and validated using FPGAs and SDRs. Wireless signal classification, wireless LAN sensing, and rate adaptation are used as examples to showcase the platform's capabilities. The platform enables versatile development, including software simulation and real-time hardware implementation, offering flexibility and scalability for multiple applications. It is intended to be used by wireless-AI researchers to develop and evaluate intelligent algorithms in a laboratory environment.
ER -
English
