Improved Metric Function for AlphaSeq Algorithm to Design Ideal Complementary Codes for Multi-Carrier CDMA Systems

Shucong TIAN; Meng YANG; Jianpeng WANG; Rui WANG; Avik R. ADHIKARY

doi:10.1587/transfun.2021EAL2060

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Improved Metric Function for AlphaSeq Algorithm to Design Ideal Complementary Codes for Multi-Carrier CDMA Systems

Shucong TIAN, Meng YANG, Jianpeng WANG, Rui WANG, Avik R. ADHIKARY

著者情報

キーワード: Sequence design, AlphaSeq, deep reinforcement learning (DRL), Markov decission process (MDP), Monte Carlo tree search (MCTS), reward function, multi-carrier code-division multiple access (MC-CDMA)

ジャーナル認証あり早期公開

論文ID: 2021EAL2060

DOI https://doi.org/10.1587/transfun.2021EAL2060

この記事には本公開記事があります。

The final version of this article is now available: Vol. E105.A (2022), No. 5 pp. 901-905

詳細

抄録

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.

責任著者(Corresponding author)

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