IEICE TRANSACTIONS on Fundamentals
Improved Metric Function for AlphaSeq Algorithm to Design Ideal Complementary Codes for Multi-Carrier CDMA Systems
Summary :
AlphaSeq is a new paradigm to design sequencess with desired properties based on deep reinforcement learning (DRL). In this work, we propose a new metric function and a new reward function, to design an improved version of AlphaSeq. We show analytically and also through numerical simulations that the proposed algorithm can discover sequence sets with preferable properties faster than that of the previous algorithm.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E105-A No.5 pp.901-905
- Publication Date
- 2022/05/01
- Publicized
- 2021/11/15
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2021EAL2060
- Type of Manuscript
- LETTER
- Category
- Communication Theory and Signals
Authors
Shucong TIAN
Southwest Jiaotong University
Meng YANG
Southwest Jiaotong University
Jianpeng WANG
Southwest Jiaotong University
Rui WANG
Southwest Jiaotong University
Avik R. ADHIKARY
Southwest Jiaotong University
Keyword
Latest Issue
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Shucong TIAN, Meng YANG, Jianpeng WANG, Rui WANG, Avik R. ADHIKARY, "Improved Metric Function for AlphaSeq Algorithm to Design Ideal Complementary Codes for Multi-Carrier CDMA Systems" in IEICE TRANSACTIONS on Fundamentals,
vol. E105-A, no. 5, pp. 901-905, May 2022, doi: 10.1587/transfun.2021EAL2060.
Abstract: AlphaSeq is a new paradigm to design sequencess with desired properties based on deep reinforcement learning (DRL). In this work, we propose a new metric function and a new reward function, to design an improved version of AlphaSeq. We show analytically and also through numerical simulations that the proposed algorithm can discover sequence sets with preferable properties faster than that of the previous algorithm.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2021EAL2060/_p
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@ARTICLE{e105-a_5_901,
author={Shucong TIAN, Meng YANG, Jianpeng WANG, Rui WANG, Avik R. ADHIKARY, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Improved Metric Function for AlphaSeq Algorithm to Design Ideal Complementary Codes for Multi-Carrier CDMA Systems},
year={2022},
volume={E105-A},
number={5},
pages={901-905},
abstract={AlphaSeq is a new paradigm to design sequencess with desired properties based on deep reinforcement learning (DRL). In this work, we propose a new metric function and a new reward function, to design an improved version of AlphaSeq. We show analytically and also through numerical simulations that the proposed algorithm can discover sequence sets with preferable properties faster than that of the previous algorithm.},
keywords={},
doi={10.1587/transfun.2021EAL2060},
ISSN={1745-1337},
month={May},}
Copy
TY - JOUR
TI - Improved Metric Function for AlphaSeq Algorithm to Design Ideal Complementary Codes for Multi-Carrier CDMA Systems
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 901
EP - 905
AU - Shucong TIAN
AU - Meng YANG
AU - Jianpeng WANG
AU - Rui WANG
AU - Avik R. ADHIKARY
PY - 2022
DO - 10.1587/transfun.2021EAL2060
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E105-A
IS - 5
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - May 2022
AB - AlphaSeq is a new paradigm to design sequencess with desired properties based on deep reinforcement learning (DRL). In this work, we propose a new metric function and a new reward function, to design an improved version of AlphaSeq. We show analytically and also through numerical simulations that the proposed algorithm can discover sequence sets with preferable properties faster than that of the previous algorithm.
ER -
English
