Low-complexity Iterative Sparse Channel Estimation for Underwater Acoustic OFDM Systems Based on Generalized Path Identification Algorithm

Shiduo ZHAO; Shefeng YAN

doi:10.11999/JEIT200582

Volume 43 Issue 3

Mar. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(3): 752-757

Shiduo ZHAO, Shefeng YAN. Low-complexity Iterative Sparse Channel Estimation for Underwater Acoustic OFDM Systems Based on Generalized Path Identification Algorithm[J]. Journal of Electronics & Information Technology, 2021, 43(3): 752-757. doi: 10.11999/JEIT200582

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Shiduo ZHAO, Shefeng YAN. Low-complexity Iterative Sparse Channel Estimation for Underwater Acoustic OFDM Systems Based on Generalized Path Identification Algorithm[J]. Journal of Electronics & Information Technology, 2021, 43(3): 752-757. doi: 10.11999/JEIT200582

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Low-complexity Iterative Sparse Channel Estimation for Underwater Acoustic OFDM Systems Based on Generalized Path Identification Algorithm

doi: 10.11999/JEIT200582

Shiduo ZHAO,
Shefeng YAN^,

1.
Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China

Funds: The National Natural Science Foundation of China (61725106)

Received Date: 2020-07-15
Rev Recd Date: 2020-12-04

Available Online: 2020-12-30

Publish Date: 2021-03-22

Abstract

Abstract

In mobile OFDM underwater acoustic communication systems, the compressed sensing-based sparse channel estimation methods suffer from high computational complexity, which is not suitable for real-time communication. To solve this problem, this paper proposes a Generalized Path Identification (GPI) algorithm for estimating uniform Doppler distorted channel. This scheme first constructs equivalent transmitted symbols using Doppler spread matrices, and thus the channel is converted into an equivalent linear time-invariant one. Then the GPI algorithm is utilized to estimate the channel parameters. Furthermore, the GPI algorithm is extended to Turbo receivers to iteratively improve the channel estimation accuracy. Simulation results show that the performance of the proposed method is better than that of the conventional path identification algorithm, and is close to the Orthogonal Matching Pursuit (OMP) algorithm. Its computational complexity, however, is much lower than OMP algorithm.

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References(17)

References

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