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Intelligent and Converged Networks 2023, 4(1): 1-14 https://doi.org/10.23919/ICN.2023.0001
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Denoising enabled channel estimation for underwater acoustic communications: A sparsity-aware model-driven learning approach

Show Author's Information Sicong Liu1( ),  Younan Mou2,  Xianyao Wang2,  Danping Su2,  Ling Cheng3
School of Informatics, Xiamen University, Xiamen 361005, China, and also with National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China
School of Informatics, Xiamen University, Xiamen 361005, China
Information Engineering, University of the Witwatersrand, Johannesburg 2050, South Africa
Keywords:
deep learning, denoising, sparse recovery, Orthogonal Frequency Division Multiplexing (OFDM), Underwater Acoustic Communications (UAC), sparse learning, approximate message passing
Cite this article:
Liu S, Mou Y, Wang X, et al. Denoising enabled channel estimation for underwater acoustic communications: A sparsity-aware model-driven learning approach. Intelligent and Converged Networks, 2023, 4(1): 1-14. https://doi.org/10.23919/ICN.2023.0001
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Abstract Full text About this article

It has always been difficult to achieve accurate information of the channel for underwater acoustic communications because of the severe underwater propagation conditions, including frequency-selective property, high relative mobility, long propagation latency, and intensive ambient noise, etc. To this end, a deep unfolding neural network based approach is proposed, in which multiple layers of the network mimic the iterations of the classical iterative sparse approximation algorithm to extract the inherent sparse features of the channel by exploiting deep learning, and a scheme based on the Sparsity-Aware DNN (SA-DNN) for UAC estimation is proposed to improve the estimation accuracy. Moreover, we propose a Denoising Sparsity-Aware DNN (DeSA-DNN) based enhanced method that integrates a denoising CNN module in the sparsity-aware deep network, so that the degradation brought by intensive ambient noise could be eliminated and the estimation accuracy can be further improved. Simulation results demonstrate that the performance of the proposed schemes is superior to the state-of-the-art compressed sensing based and iterative sparse recovery schems in the aspects of channel recovery precision, pilot overhead, and robustness, particularly under unideal circumstances of intensive ambient noise or inadequate measurement pilots.


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About this article

Denoising enabled channel estimation for underwater acoustic communications: A sparsity-aware model-driven learning approach

Show Author's information Sicong Liu1( ), Younan Mou2, Xianyao Wang2, Danping Su2, Ling Cheng3
School of Informatics, Xiamen University, Xiamen 361005, China, and also with National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China
School of Informatics, Xiamen University, Xiamen 361005, China
Information Engineering, University of the Witwatersrand, Johannesburg 2050, South Africa

Abstract

It has always been difficult to achieve accurate information of the channel for underwater acoustic communications because of the severe underwater propagation conditions, including frequency-selective property, high relative mobility, long propagation latency, and intensive ambient noise, etc. To this end, a deep unfolding neural network based approach is proposed, in which multiple layers of the network mimic the iterations of the classical iterative sparse approximation algorithm to extract the inherent sparse features of the channel by exploiting deep learning, and a scheme based on the Sparsity-Aware DNN (SA-DNN) for UAC estimation is proposed to improve the estimation accuracy. Moreover, we propose a Denoising Sparsity-Aware DNN (DeSA-DNN) based enhanced method that integrates a denoising CNN module in the sparsity-aware deep network, so that the degradation brought by intensive ambient noise could be eliminated and the estimation accuracy can be further improved. Simulation results demonstrate that the performance of the proposed schemes is superior to the state-of-the-art compressed sensing based and iterative sparse recovery schems in the aspects of channel recovery precision, pilot overhead, and robustness, particularly under unideal circumstances of intensive ambient noise or inadequate measurement pilots.

Keywords: deep learning, denoising, sparse recovery, Orthogonal Frequency Division Multiplexing (OFDM), Underwater Acoustic Communications (UAC), sparse learning, approximate message passing

References(39)

[1]

A. C. Singer, J. K. Nelson, and S. S. Kozat, Signal processing for underwater acoustic communications, IEEE Commun. Mag., vol. 47, no. 1, pp. 90–96, 2009.
DOI Google Scholar
[2]
S. Banerjee and M. Agrawal, Underwater acoustic noise with generalized Gaussian statistics: Effects on error performance, in Proc. 2013 MTS/IEEE OCEANS - Bergen, Bergen, Norway, 2013, pp. 1–8.
DOI
[3]

P. Chen, Y. Rong, S. Nordholm, Z. He, and A. J. Duncan, Joint channel estimation and impulsive noise mitigation in underwater acoustic OFDM communication systems, IEEE Trans. Wireless Commun., vol. 16, no. 9, pp. 6165–6178, 2017.
DOI Google Scholar
[4]

A. Radosevic, R. Ahmed, T. M. Duman, J. G. Proakis, and M. Stojanovic, Adaptive OFDM modulation for underwater acoustic communications: Design considerations and experimental results, IEEE J. Oceanic Eng., vol. 39, no. 2, pp. 357–370, 2014.
DOI Google Scholar
[5]

E. Panayirci, H. Senol, M. Uysal, and H. V. Poor, Sparse channel estimation and equalization for OFDM-based underwater cooperative systems with amplify-and-forward relaying, IEEE Trans. Signal Process., vol. 64, no. 1, pp. 214–228, 2016.
DOI Google Scholar
[6]

Y. Zhang, R. Venkatesan, O. A. Dobre, and C. Li, Efficient estimation and prediction for sparse time-varying underwater acoustic channels, IEEE J. Oceanic Eng., vol. 45, no. 3, pp. 1112–1125, 2020.
DOI Google Scholar
[7]

C. R. Berger, S. Zhou, J. C. Preisig, and P. Willett, Sparse channel estimation for multicarrier underwater acoustic communication: From subspace methods to compressed sensing, IEEE Trans. Signal Process., vol. 58, no. 3, pp. 1708–1721, 2010.
DOI Google Scholar
[8]
K. S. Priyanjali and A. V. Babu, An improved least square channel estimation technique for OFDM systems in sparse underwater acoustic channel, in Proc. 2014 Int. Conf. on Advances in Computing, Communications and Informatics (ICACCI), Delhi, India, 2014, pp. 2521–2525.
DOI
[9]
Y. Zhang, H. Sun, F. Xu, and D. Wang, OFDM transform-domain channel estimation based on MMSE for underwater acoustic channels, in Prce. 2008 2nd Int. Conf. on Anti-Counterfeiting, Security and Identification, Guiyang, China, 2008, pp. 177–181.
[10]

W. Lu, T. Dai, and S. T. Xia, Binary matrices for compressed sensing, IEEE Trans. Signal Process., vol. 66, no. 1, pp. 77–85, 2018.
DOI Google Scholar
[11]

L. Wan, X. Qiang, L. Ma, Q. Song, and G. Qiao, Accurate and efficient path delay estimation in OMP based sparse channel estimation for OFDM with equispaced pilots, IEEE Wireless Commun. Lett., vol. 8, no. 1, pp. 117–120, 2019.
DOI Google Scholar
[12]

G. Qiao, Q. Song, L. Ma, and L. Wan, A low-complexity orthogonal matching pursuit based channel estimation method for time-varying underwater acoustic OFDM systems, Appl. Acoust., vol. 148, pp. 246–250, 2019.
DOI Google Scholar
[13]

F. Qu, X. Nie, and W. Xu, A two-stage approach for the estimation of doubly spread acoustic channels, IEEE J. Oceanic Eng., vol. 40, no. 1, pp. 131–143, 2015.
DOI Google Scholar
[14]

S. Liu, F. Yang, W. Ding, X. Wang, and J. Song, Two-dimensional structured-compressed-sensing-based NBI cancelation exploiting spatial and temporal correlations in MIMO systems, IEEE Trans. Veh. Technol., vol. 65, no. 11, pp. 9020–9028, 2016.
DOI Google Scholar
[15]
Y. Chen, C. Clemente, J. Soraghan, and S. Weiss, Fractional Fourier based sparse channel estimation for multicarrier underwater acoustic communication system, in Proc. 2016 Sensor Signal Processing for Defence (SSPD), Edinburgh, UK, 2016, pp. 1–5.
DOI
[16]
T. T. Do, L. Gan, N. Nguyen, and T. D. Tran, Sparsity adaptive matching pursuit algorithm for practical compressed sensing, in Proc. 2008 42nd Asilomar Conf. on Signals, Systems and Computers, Pacific Grove, CA, USA, 2008, pp. 581–587.
[17]
Y. Zhang, R. Venkatesan, O. A. Dobre, and C. Li, An adaptive matching pursuit algorithm for sparse channel estimation, in Proc. 2015 IEEE Wireless Communications and Networking Conf. (WCNC), New Orleans, LA, USA, 2015, pp. 626–630.
DOI
[18]

J.Wang, Z. Yan, W. Shi, and X. Yang, Underwater acoustic sparse channel estimation based on DW-SACoSaMP reconstruction algorithm, IEEE Commun. Lett., vol. 23, no. 11, pp. 1985–1988, 2019.
DOI Google Scholar
[19]

S. Liu, F. Yang, W. Ding, and J. Song, Double kill: Compressive-sensing-based narrow-band interference and impulsive noise mitigation for vehicular communications, IEEE Trans. Veh. Technol., vol. 65, no. 7, pp. 5099–5109, 2016.
DOI Google Scholar
[20]

M. Borgerding, P. Schniter, and S. Rangan, AMP-inspired deep networks for sparse linear inverse problems, IEEE Trans. Signal Process., vol. 65, no. 16, pp. 4293–4308, 2017.
DOI Google Scholar
[21]
C. A. Metzler, A. Mousavi, and R. G. Baraniuk, Learned D-AMP: Principled neural network based compressive image recovery, in Proc. 31st Int. Conf. Neural Information Processing Systems (NIPS’17), Long Beach, CA, USA, 2017, pp. 1772–1783.
[22]

H. He, C. K. Wen, S. Jin, and G. Y. Li, Deep learning-based channel estimation for beamspace mmwave massive MIMO systems, IEEE Wireless Commun. Lett., vol. 7, no. 5, pp. 852–855, 2018.
DOI Google Scholar
[23]

X. Wei, C. Hu, and L. Dai, Deep learning for beamspace channel estimation in millimeter-wave massive MIMO systems, IEEE Trans. Commun., vol. 69, no. 1, pp. 182–193, 2021.
DOI Google Scholar
[24]

Y. Xiao, G. Niu, L. Xiao, Y. Ding, S. Liu, and Y. Fan, Reinforcement learning based energy-efficient Internet-of-things video transmission, Intelligent and Converged Networks, vol. 1, no. 3, pp. 258–270, 2020.
DOI Google Scholar
[25]

S. Nath and J. Wu, Deep reinforcement learning for dynamic computation offloading and resource allocation in cache-assisted mobile edge computing systems, Intelligent and Converged Networks, vol. 1, no. 2, pp. 181–198, 2020.
DOI Google Scholar
[26]
J. Huang, C. R. Berger, S. Zhou, and J. Huang, Comparison of basis pursuit algorithms for sparse channel estimation in underwater acoustic OFDM, in Proc. Oceans’10 IEEE Sydney, Sydney, Australia, 2010, pp. 1–6.
DOI
[27]
D. L. Donoho, A. Maleki, and A. Montanari, Message passing algorithms for compressed sensing: II. analysis and validation, in Proc. 2010 IEEE Information Theory Workshop on Information Theory (ITW 2010, Cairo), Cairo, Egypt, 2010, pp. 1–5.
DOI
[28]

Y. Yin, S. Liu, G. Qiao, Y. Yang, and Y. Yang, OFDM demodulation using virtual time reversal processing in underwater acoustic communications, J. Comput. Acoust., vol. 23, no. 4, p. 1540011, 2015.
DOI Google Scholar
[29]

M. Nassar, P. Schniter, and B. L. Evans, A factor graph approach to joint OFDM channel estimation and decoding in impulsive noise environments, IEEE Trans. Signal Process., vol. 62, no. 6, pp. 1576–1589, 2014.
DOI Google Scholar
[30]

J. Dong, W. Wu, Y. Gao, X. Wang, and P. Si, Deep reinforcement learning based worker selection for distributed machine learning enhanced edge intelligence in internet of vehicles, Intelligent and Converged Networks, vol. 1, no. 3, pp. 234–242, 2020.
DOI Google Scholar
[31]

B. Hartpence and A. Kwasinski, CNN and MLP neural network ensembles for packet classification and adversary defense, Intelligent and Converged Networks, vol. 2, no. 1, pp. 66–82, 2021.
DOI Google Scholar
[32]
M. Zhou, J. Wang, H. Sun, J. Qi, X. Feng, and H. Esmaiel, A novel DNN based channel estimator for underwater acoustic communications with IM-OFDM, in Proc. 2020 IEEE Int. Conf. Signal Processing, Communications and Computing (ICSPCC), Macau, China, 2020, pp. 1–6.
DOI
[33]

B. Li, S. Zhou, M. Stojanovic, L. Freitag, and P. Willett, Multicarrier communication over underwater acoustic channels with nonuniform Doppler shifts, IEEE J. Oceanic Eng., vol. 33, no. 2, pp. 198–209, 2008.
DOI Google Scholar
[34]

P. Qarabaqi and M. Stojanovic, Statistical characterization and computationally efficient modeling of a class of underwater acoustic communication channels, IEEE J. Oceanic Eng., vol. 38, no. 4, pp. 701–717, 2013.
DOI Google Scholar
[35]
M. Borgerding and P. Schniter, Onsager-corrected deep learning for sparse linear inverse problems, in Proc. 2016 IEEE Global Conf. Signal and Information Processing (GlobalSIP), Washington, DC, USA, 2016, pp. 227–231.
DOI
[36]

K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising, IEEE Trans. Image Process., vol. 26, no. 7, pp. 3142–3155, 2017.
DOI Google Scholar
[37]

C. A. Metzler, A. Maleki, and R. G. Baraniuk, From denoising to compressed sensing, IEEE Trans. Inform. Theory, vol. 62, no. 9, pp. 5117–5144, 2016.
DOI Google Scholar
[38]

S. Ramani, T. Blu, and M. Unser, Monte-Carlo sure: A black-box optimization of regularization parameters for general denoising algorithms, IEEE Trans. Image Process., vol. 17, no. 9, pp. 1540–1554, 2008.
DOI Google Scholar
[39]
S. Zhou and Z. Wang, OFDM for Underwater Acoustic Communications. Hoboken, NJ, USA: John Wiley & Sons, Ltd, 2014.
DOI
Publication history
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Publication history

Received: 22 November 2022
Revised: 25 December 2022
Accepted: 07 January 2023
Published: 20 March 2023
Issue date: March 2023

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© All articles included in the journal are copyrighted to the ITU and TUP.

Acknowledgements

Acknowledgment

This work was supported by the National Natural Science Foundation of China (No. 61901403), the Science and Technology Key Project of Fujian Province, China (Nos. 2021HZ021004 and 2019HZ020009), the Open Research Fund of National Mobile Communications Research Laboratory, Southeast University (No. 2023D10), the Youth Innovation Fund of Natural Science Foundation of Xiamen (No. 3502Z20206039), the Science and Technology Key Project of Xiamen (No. 3502Z20221027), and the Xiamen Special Fund for Marine and Fishery Development (No. 21CZB011HJ02).

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This work is available under the CC BY-NC-ND 3.0 IGO license:https://creativecommons.org/licenses/by-nc-nd/3.0/igo/

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