IEICE TRANSACTIONS on Communications
Optimal Power Allocation for Low Complexity Channel Estimation and Symbol Detection Using Superimposed Training
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E102-B No.5 pp.1027-1036
- Publication Date
- 2019/05/01
- Publicized
- 2018/10/26
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2017EBP3408
- Type of Manuscript
- PAPER
- Category
- Wireless Communication Technologies
Authors
Qingbo WANG
Naval University of Engineering
Gaoqi DOU
Naval University of Engineering
Jun GAO
Naval University of Engineering
Xianwen HE
Naval University of Engineering
Keyword
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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
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@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},}
Copy
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 -
English
