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
Dengan mengeksploitasi keterlaluan saluran wayarles yang wujud, anggaran saluran dalam sistem pemultipleksan pembahagian frekuensi ortogon (OFDM) boleh dijadikan sebagai masalah penderiaan termampat (CS) untuk menganggarkan saluran dengan lebih tepat. Secara praktikal, algoritma pengejaran padanan seperti pengejaran padanan ortogonal (OMP) digunakan, di mana kelewatan laluan saluran diteka berdasarkan nilai korelasi untuk setiap kelewatan terkuantasi dengan baki. Pendekatan carian penuh ini memerlukan grid kelewatan yang dipratentukan dengan resolusi tinggi, yang mendorong kerumitan pengiraan yang tinggi kerana nilai korelasi dengan baki pada sejumlah besar titik grid harus dikira. Sementara itu, nilai korelasi dengan resolusi tinggi boleh diperoleh melalui interpolasi antara nilai korelasi pada grid resolusi rendah. Selain itu, interpolasi boleh dilaksanakan dengan penapis lulus rendah (LPF). Dengan menggunakan fakta ini, dalam kertas ini kami mengurangkan kerumitan pengiraan dengan ketara untuk mengira nilai korelasi dalam anggaran saluran menggunakan CS.
Kee-Hoon KIM
Soonchunhyang University
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Salinan
Kee-Hoon KIM, "A Low-Complexity Path Delay Searching Method in Sparse Channel Estimation for OFDM Systems" in IEICE TRANSACTIONS on Communications,
vol. E101-B, no. 11, pp. 2297-2303, November 2018, doi: 10.1587/transcom.2018EBP3026.
Abstract: By exploiting the inherent sparsity of wireless channels, the channel estimation in an orthogonal frequency division multiplexing (OFDM) system can be cast as a compressed sensing (CS) problem to estimate the channel more accurately. Practically, matching pursuit algorithms such as orthogonal matching pursuit (OMP) are used, where path delays of the channel is guessed based on correlation values for every quantized delay with residual. This full search approach requires a predefined grid of delays with high resolution, which induces the high computational complexity because correlation values with residual at a huge number of grid points should be calculated. Meanwhile, the correlation values with high resolution can be obtained by interpolation between the correlation values at a low resolution grid. Also, the interpolation can be implemented with a low pass filter (LPF). By using this fact, in this paper we substantially reduce the computational complexity to calculate the correlation values in channel estimation using CS.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2018EBP3026/_p
Salinan
@ARTICLE{e101-b_11_2297,
author={Kee-Hoon KIM, },
journal={IEICE TRANSACTIONS on Communications},
title={A Low-Complexity Path Delay Searching Method in Sparse Channel Estimation for OFDM Systems},
year={2018},
volume={E101-B},
number={11},
pages={2297-2303},
abstract={By exploiting the inherent sparsity of wireless channels, the channel estimation in an orthogonal frequency division multiplexing (OFDM) system can be cast as a compressed sensing (CS) problem to estimate the channel more accurately. Practically, matching pursuit algorithms such as orthogonal matching pursuit (OMP) are used, where path delays of the channel is guessed based on correlation values for every quantized delay with residual. This full search approach requires a predefined grid of delays with high resolution, which induces the high computational complexity because correlation values with residual at a huge number of grid points should be calculated. Meanwhile, the correlation values with high resolution can be obtained by interpolation between the correlation values at a low resolution grid. Also, the interpolation can be implemented with a low pass filter (LPF). By using this fact, in this paper we substantially reduce the computational complexity to calculate the correlation values in channel estimation using CS.},
keywords={},
doi={10.1587/transcom.2018EBP3026},
ISSN={1745-1345},
month={November},}
Salinan
TY - JOUR
TI - A Low-Complexity Path Delay Searching Method in Sparse Channel Estimation for OFDM Systems
T2 - IEICE TRANSACTIONS on Communications
SP - 2297
EP - 2303
AU - Kee-Hoon KIM
PY - 2018
DO - 10.1587/transcom.2018EBP3026
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E101-B
IS - 11
JA - IEICE TRANSACTIONS on Communications
Y1 - November 2018
AB - By exploiting the inherent sparsity of wireless channels, the channel estimation in an orthogonal frequency division multiplexing (OFDM) system can be cast as a compressed sensing (CS) problem to estimate the channel more accurately. Practically, matching pursuit algorithms such as orthogonal matching pursuit (OMP) are used, where path delays of the channel is guessed based on correlation values for every quantized delay with residual. This full search approach requires a predefined grid of delays with high resolution, which induces the high computational complexity because correlation values with residual at a huge number of grid points should be calculated. Meanwhile, the correlation values with high resolution can be obtained by interpolation between the correlation values at a low resolution grid. Also, the interpolation can be implemented with a low pass filter (LPF). By using this fact, in this paper we substantially reduce the computational complexity to calculate the correlation values in channel estimation using CS.
ER -