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".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Neste artigo, consideramos o projeto de pré-codificadores para agregação de dados sem fio em redes de sensores. O problema de otimização do pré-codificador pode ser formulado como minimização do erro quadrático médio sob restrições de potência de transmissão e diagonal de bloco. Incluímos correlação estatística de dados no problema de otimização, que aparece em aplicações típicas, mas é ignorado em métodos de projeto convencionais. Propomos algoritmos de otimização de pré-codificadores baseados na descida gradiente projetada com projeção nos conjuntos de restrições. O método proposto pode alcançar melhor desempenho do que os métodos convencionais que não incorporam correlação de dados, especialmente quando os dados são altamente correlacionados. Também estendemos a abordagem proposta ao contexto da computação over-the-air.
Ayano NAKAI-KASAI
https://orcid.org/0000-0003-0832-0423
Nagoya Institute of Technology
Naoyuki HAYASHI
https://orcid.org/0000-0003-4391-4294
Nagoya Institute of Technology
Tadashi WADAYAMA
Nagoya Institute of Technology
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Ayano NAKAI-KASAI, Naoyuki HAYASHI, Tadashi WADAYAMA, "Precoder Optimization Using Data Correlation for Wireless Data Aggregation" in IEICE TRANSACTIONS on Communications,
vol. E107-B, no. 3, pp. 330-338, March 2024, doi: 10.23919/transcom.2023EBT0007.
Abstract: In this paper, we consider precoder design for wireless data aggregation in sensor networks. The precoder optimization problem can be formulated as minimization of mean squared error under transmit power and block diagonal constraints. We include statistical correlation of data into the optimization problem, which is appeared in typical applications but is ignored in conventional designing methods. We propose precoder optimization algorithms based on projected gradient descent with projection onto the constraint sets. The proposed method can achieve better performance than the conventional methods that do not incorporate data correlation, especially when data are highly correlated. We also extend the proposed approach to the context of over-the-air computation.
URL: https://global.ieice.org/en_transactions/communications/10.23919/transcom.2023EBT0007/_p
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@ARTICLE{e107-b_3_330,
author={Ayano NAKAI-KASAI, Naoyuki HAYASHI, Tadashi WADAYAMA, },
journal={IEICE TRANSACTIONS on Communications},
title={Precoder Optimization Using Data Correlation for Wireless Data Aggregation},
year={2024},
volume={E107-B},
number={3},
pages={330-338},
abstract={In this paper, we consider precoder design for wireless data aggregation in sensor networks. The precoder optimization problem can be formulated as minimization of mean squared error under transmit power and block diagonal constraints. We include statistical correlation of data into the optimization problem, which is appeared in typical applications but is ignored in conventional designing methods. We propose precoder optimization algorithms based on projected gradient descent with projection onto the constraint sets. The proposed method can achieve better performance than the conventional methods that do not incorporate data correlation, especially when data are highly correlated. We also extend the proposed approach to the context of over-the-air computation.},
keywords={},
doi={10.23919/transcom.2023EBT0007},
ISSN={1745-1345},
month={March},}
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TY - JOUR
TI - Precoder Optimization Using Data Correlation for Wireless Data Aggregation
T2 - IEICE TRANSACTIONS on Communications
SP - 330
EP - 338
AU - Ayano NAKAI-KASAI
AU - Naoyuki HAYASHI
AU - Tadashi WADAYAMA
PY - 2024
DO - 10.23919/transcom.2023EBT0007
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E107-B
IS - 3
JA - IEICE TRANSACTIONS on Communications
Y1 - March 2024
AB - In this paper, we consider precoder design for wireless data aggregation in sensor networks. The precoder optimization problem can be formulated as minimization of mean squared error under transmit power and block diagonal constraints. We include statistical correlation of data into the optimization problem, which is appeared in typical applications but is ignored in conventional designing methods. We propose precoder optimization algorithms based on projected gradient descent with projection onto the constraint sets. The proposed method can achieve better performance than the conventional methods that do not incorporate data correlation, especially when data are highly correlated. We also extend the proposed approach to the context of over-the-air computation.
ER -