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
Skim anggaran saluran kerumitan rendah menggunakan latihan bertindih bergantung data (DDST) dicadangkan dalam kertas ini, di mana juruterbang dimasukkan dalam lebih daripada satu blok, bukannya satu blok DDST asal. Berbanding dengan DDST asal (yang meningkatkan prestasi anggaran saluran pada kos overhed pengiraan yang besar), skim DDST yang dicadangkan meningkatkan prestasi anggaran saluran dengan hanya peningkatan sedikit dalam penggunaan sumber pengiraan. Prapengode optimum direka untuk meminimumkan herotan data yang disebabkan oleh prapengekodan kekurangan pangkat. Perintis dan penempatan yang optimum juga disediakan untuk meningkatkan prestasi anggaran saluran. Di samping itu, kesan peruntukan kuasa antara data dan juruterbang pada pengesanan simbol dianalisis, skema peruntukan kuasa optimum diperoleh untuk memaksimumkan nisbah isyarat-ke-bunyi yang berkesan pada penerima. Keputusan simulasi dibentangkan untuk menunjukkan kelebihan pengiraan skim yang dicadangkan, dan kelebihan skim perintis optimum dan peruntukan kuasa.
Qingbo WANG
Naval University of Engineering
Gaoqi DOU
Naval University of Engineering
Jun GAO
Naval University of Engineering
Xianwen HE
Naval University of Engineering
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Salinan
Qingbo WANG, Gaoqi DOU, Jun GAO, Xianwen HE, "Optimal Power Allocation for Low Complexity Channel Estimation and Symbol Detection Using Superimposed Training" in IEICE TRANSACTIONS on Communications,
vol. E102-B, no. 5, pp. 1027-1036, May 2019, doi: 10.1587/transcom.2017EBP3408.
Abstract: A low complexity channel estimation scheme using data-dependent superimposed training (DDST) is proposed in this paper, where the pilots are inserted in more than one block, rather than the single block of the original DDST. Comparing with the original DDST (which improves the performance of channel estimation at the cost of huge computational overheads), the proposed DDST scheme improves the performance of channel estimation with only a slight increase in the consumption of computation resources. The optimal precoder is designed to minimize the data distortion caused by the rank-deficient precoding. The optimal pilots and placement are also provided to improve the performance of channel estimation. In addition, the impact of power allocation between the data and pilots on symbol detection is analyzed, the optimal power allocation scheme is derived to maximize the effective signal-to-noise ratio at the receiver. Simulation results are presented to show the computational advantage of the proposed scheme, and the advantages of the optimal pilots and power allocation scheme.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2017EBP3408/_p
Salinan
@ARTICLE{e102-b_5_1027,
author={Qingbo WANG, Gaoqi DOU, Jun GAO, Xianwen HE, },
journal={IEICE TRANSACTIONS on Communications},
title={Optimal Power Allocation for Low Complexity Channel Estimation and Symbol Detection Using Superimposed Training},
year={2019},
volume={E102-B},
number={5},
pages={1027-1036},
abstract={A low complexity channel estimation scheme using data-dependent superimposed training (DDST) is proposed in this paper, where the pilots are inserted in more than one block, rather than the single block of the original DDST. Comparing with the original DDST (which improves the performance of channel estimation at the cost of huge computational overheads), the proposed DDST scheme improves the performance of channel estimation with only a slight increase in the consumption of computation resources. The optimal precoder is designed to minimize the data distortion caused by the rank-deficient precoding. The optimal pilots and placement are also provided to improve the performance of channel estimation. In addition, the impact of power allocation between the data and pilots on symbol detection is analyzed, the optimal power allocation scheme is derived to maximize the effective signal-to-noise ratio at the receiver. Simulation results are presented to show the computational advantage of the proposed scheme, and the advantages of the optimal pilots and power allocation scheme.},
keywords={},
doi={10.1587/transcom.2017EBP3408},
ISSN={1745-1345},
month={May},}
Salinan
TY - JOUR
TI - Optimal Power Allocation for Low Complexity Channel Estimation and Symbol Detection Using Superimposed Training
T2 - IEICE TRANSACTIONS on Communications
SP - 1027
EP - 1036
AU - Qingbo WANG
AU - Gaoqi DOU
AU - Jun GAO
AU - Xianwen HE
PY - 2019
DO - 10.1587/transcom.2017EBP3408
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E102-B
IS - 5
JA - IEICE TRANSACTIONS on Communications
Y1 - May 2019
AB - A low complexity channel estimation scheme using data-dependent superimposed training (DDST) is proposed in this paper, where the pilots are inserted in more than one block, rather than the single block of the original DDST. Comparing with the original DDST (which improves the performance of channel estimation at the cost of huge computational overheads), the proposed DDST scheme improves the performance of channel estimation with only a slight increase in the consumption of computation resources. The optimal precoder is designed to minimize the data distortion caused by the rank-deficient precoding. The optimal pilots and placement are also provided to improve the performance of channel estimation. In addition, the impact of power allocation between the data and pilots on symbol detection is analyzed, the optimal power allocation scheme is derived to maximize the effective signal-to-noise ratio at the receiver. Simulation results are presented to show the computational advantage of the proposed scheme, and the advantages of the optimal pilots and power allocation scheme.
ER -