The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Kertas kerja ini membincangkan kod superposisi jarang (SSC) pendek ke atas saluran hingar Gaussian putih tambahan. Lembap anggaran penghantaran mesej (AMP) digunakan untuk menyahkod SSC pendek dengan kamus Gaussian bebas min sifar dan diedarkan secara sama. Untuk mereka bentuk faktor redaman dalam AMP melalui pembelajaran mendalam, kertas kerja ini membina rangkaian penyahkodan AMP terlembap yang terbongkar secara mendalam. Kaedah penyepuhlindapan untuk pembelajaran mendalam dicadangkan untuk mereka bentuk faktor redaman yang hampir optimum dengan kebarangkalian yang tinggi. Dalam penyepuhlindapan, faktor redaman pertama kali dioptimumkan melalui pembelajaran mendalam dalam rejim nisbah isyarat-ke-bunyi (SNR) rendah. Kemudian, faktor redaman yang diperoleh ditetapkan kepada nilai awal dalam keturunan kecerunan stokastik, yang mengoptimumkan faktor redaman untuk SNR yang lebih besar sedikit. Mengulangi proses penyepuhlindapan ini mereka bentuk faktor redaman dalam rejim SNR tinggi. Simulasi berangka menunjukkan bahawa penyepuhlindapan mengurangkan turun naik dalam faktor redaman yang dipelajari dan mengatasi prestasi carian menyeluruh berdasarkan faktor redaman bebas lelaran.
Toshihiro YOSHIDA
Toyohashi University of Technology
Keigo TAKEUCHI
Toyohashi University of Technology
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Salinan
Toshihiro YOSHIDA, Keigo TAKEUCHI, "Deep Learning of Damped AMP Decoding Networks for Sparse Superposition Codes via Annealing" in IEICE TRANSACTIONS on Fundamentals,
vol. E106-A, no. 3, pp. 414-421, March 2023, doi: 10.1587/transfun.2022TAP0009.
Abstract: This paper addresses short-length sparse superposition codes (SSCs) over the additive white Gaussian noise channel. Damped approximate message-passing (AMP) is used to decode short SSCs with zero-mean independent and identically distributed Gaussian dictionaries. To design damping factors in AMP via deep learning, this paper constructs deep-unfolded damped AMP decoding networks. An annealing method for deep learning is proposed for designing nearly optimal damping factors with high probability. In annealing, damping factors are first optimized via deep learning in the low signal-to-noise ratio (SNR) regime. Then, the obtained damping factors are set to the initial values in stochastic gradient descent, which optimizes damping factors for slightly larger SNR. Repeating this annealing process designs damping factors in the high SNR regime. Numerical simulations show that annealing mitigates fluctuation in learned damping factors and outperforms exhaustive search based on an iteration-independent damping factor.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2022TAP0009/_p
Salinan
@ARTICLE{e106-a_3_414,
author={Toshihiro YOSHIDA, Keigo TAKEUCHI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Deep Learning of Damped AMP Decoding Networks for Sparse Superposition Codes via Annealing},
year={2023},
volume={E106-A},
number={3},
pages={414-421},
abstract={This paper addresses short-length sparse superposition codes (SSCs) over the additive white Gaussian noise channel. Damped approximate message-passing (AMP) is used to decode short SSCs with zero-mean independent and identically distributed Gaussian dictionaries. To design damping factors in AMP via deep learning, this paper constructs deep-unfolded damped AMP decoding networks. An annealing method for deep learning is proposed for designing nearly optimal damping factors with high probability. In annealing, damping factors are first optimized via deep learning in the low signal-to-noise ratio (SNR) regime. Then, the obtained damping factors are set to the initial values in stochastic gradient descent, which optimizes damping factors for slightly larger SNR. Repeating this annealing process designs damping factors in the high SNR regime. Numerical simulations show that annealing mitigates fluctuation in learned damping factors and outperforms exhaustive search based on an iteration-independent damping factor.},
keywords={},
doi={10.1587/transfun.2022TAP0009},
ISSN={1745-1337},
month={March},}
Salinan
TY - JOUR
TI - Deep Learning of Damped AMP Decoding Networks for Sparse Superposition Codes via Annealing
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 414
EP - 421
AU - Toshihiro YOSHIDA
AU - Keigo TAKEUCHI
PY - 2023
DO - 10.1587/transfun.2022TAP0009
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E106-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 2023
AB - This paper addresses short-length sparse superposition codes (SSCs) over the additive white Gaussian noise channel. Damped approximate message-passing (AMP) is used to decode short SSCs with zero-mean independent and identically distributed Gaussian dictionaries. To design damping factors in AMP via deep learning, this paper constructs deep-unfolded damped AMP decoding networks. An annealing method for deep learning is proposed for designing nearly optimal damping factors with high probability. In annealing, damping factors are first optimized via deep learning in the low signal-to-noise ratio (SNR) regime. Then, the obtained damping factors are set to the initial values in stochastic gradient descent, which optimizes damping factors for slightly larger SNR. Repeating this annealing process designs damping factors in the high SNR regime. Numerical simulations show that annealing mitigates fluctuation in learned damping factors and outperforms exhaustive search based on an iteration-independent damping factor.
ER -