Deep learning aided underwater acoustic OFDM receivers: Model-driven or data-driven?^☆

Abstract

The Underwater Acoustic (UWA) channel is bandwidth-constrained and experiences doubly selective fading. It is challenging to acquire perfect channel knowledge for Orthogonal Frequency Division Multiplexing (OFDM) communications using a finite number of pilots. On the other hand, Deep Learning (DL) approaches have been very successful in wireless OFDM communications. However, whether they will work underwater is still a mystery. For the first time, this paper compares two categories of DL-based UWA OFDM receivers: the Data-Driven (DD) method, which performs as an end-to-end black box, and the Model-Driven (MD) method, also known as the model-based data-driven method, which combines DL and expert OFDM receiver knowledge. The encoder-decoder framework and Convolutional Neural Network (CNN) structure are employed to establish the DD receiver. On the other hand, an unfolding-based Minimum Mean Square Error (MMSE) structure is adopted for the MD receiver. We analyze the characteristics of different receivers by Monte Carlo simulations under diverse communications conditions and propose a strategy for selecting a proper receiver under different communication scenarios. Field trials in the pool and sea are also conducted to verify the feasibility and advantages of the DL receivers. It is observed that DL receivers perform better than conventional receivers in terms of bit error rate.

Keywords

Deep learning / Doubly-selective channels / Data-driven / Model-driven / Underwater acoustic communication / OFDM

CRediT authorship contribution statement

Hao Zhao: Writing - review & editing, Writing - original draft, Visualization, Validation, Software, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Miaowen Wen: Writing - review & editing, Visualization, Resources, Project administration, Methodology, Data curation, Conceptualization. Fei Ji: Writing - review & editing, Validation, Resources, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. Yaokun Liang: Writing - review & editing, Validation, Supervision, Software, Methodology, Formal analysis, Data curation, Conceptualization. Hua Yu: Writing - review & editing, Visualization, Validation, Resources, Project administration, Data curation. Cui Yang: Writing - review & editing, Writing - original draft, Resources, Project administration, Investigation, Data curation.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This research was funded in part by the National Natural Science Foundation of China under Grant 62401167 and 62192712, in part by the Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources, P. R. China under Grant MESTA-2023-B001, and in part by the Stable Supporting Fund of National Key Laboratory of Underwater Acoustic Technology under Grant JCKYS2022604SSJS007.

References

[1] B. Li, S. Zhou, M. Stojanovic, L. Freitag, P. Willett, Multicarrier communication over underwater acoustic channels with nonuniform Doppler shifts, IEEE J. Ocean. Eng. 33 (2) (2008) 198-209.

[2] Z. Tang, R.C. Cannizzaro, G. Leus, P. Banelli, Pilot-assisted time-varying channel estimation for ofdm systems, IEEE Trans. Signal Process. 55 (5) (2007) 2226-2238.

[3] L. Wan, Z. Wang, S. Zhou, T.C. Yang, Z. Shi, Performance comparison of Doppler scale estimation methods for underwater acoustic ofdm, J. Electr. Comput. Eng. 2012 ( 2012) 1-11.

[4] B. Li, J. Huang, S. Zhou, K. Ball, M. Stojanovic, L. Freitag, P. Willett, Mimo-ofdm for high-rate underwater acoustic communications, IEEE J. Ocean. Eng. 34 (4) (2009) 634-644.

[5] J. Tao, Y.R. Zheng, Turbo detection for mimo-ofdm underwater acoustic communi-cations, Int. J. Wirel. Inf. Netw. 20 (1) (2013) 27-38.

[6] M. Wen, X. Cheng, L. Yang, Y. Li, X. Cheng, F. Ji, Index modulated ofdm for under-water acoustic communications, IEEE Commun. Mag. 54 (5) (2016) 132-137.

[7] T. O’Shea, J. Hoydis, An introduction to deep learning for the physical layer, IEEE Trans. Cogn. Commun. Netw. 3 (4) (2017) 563-575.

[8] Z. Qin, H. Ye, G.Y. Li, B.-H.F. Juang, Deep learning in physical layer communications, IEEE Wirel. Commun. 26 (2) (2019) 93-99.

[9] E. Bjornson, P. Giselsson, Two applications of deep learning in the physical layer of communication systems, lecture notes, IEEE Signal Process. Mag. 37 (5) (2020) 134-140.

[10] N. Farsad, A. Goldsmith, Neural network detection of data sequences in communi-cation systems, IEEE Trans. Signal Process. 66 (21) (2018) 5663-5678.

[11] H. Ye, G.Y. Li, B.-H. Juang, Power of deep learning for channel estimation and signal detection in ofdm systems, IEEE Wirel. Commun. Lett. 7 (1) (2018) 114-117.

[12] T.V. Luong, Y. Ko, N.A. Vien, D.H.N. Nguyen, M. Matthaiou, Deep learning-based detector for ofdm-im, IEEE Wirel. Commun. Lett. 8 (4) (2019) 1159-1162.

[13] M. Kim, W. Lee, D.-H. Cho, A novel papr reduction scheme for ofdm system based on deep learning, IEEE Commun. Lett. 22 (3) (2018) 510-513.

[14] X. Gao, S. Jin, C.-K. Wen, G.Y. Li, Comnet: combination of deep learning and expert knowledge in ofdm receivers, IEEE Commun. Lett. 22 (12) (2018) 2627-2630.

[15] H. He, S. Jin, C.-K. Wen, F. Gao, G.Y. Li, Z. Xu, Model-driven deep learning for physical layer communications, IEEE Wirel. Commun. 26 (5) (2019) 77-83.

[16] X. Wang, N. Jin, J. Wei, A model-driven dl algorithm for papr reduction in ofdm system, IEEE Commun. Lett. 25 (7) (2021) 2270-2274.

[17] X. Zhou, J. Zhang, C.-W. Syu, C.-K. Wen, J. Zhang, S. Jin, Model-driven deep learning-based mimo-ofdm detector: design, simulation, and experimental results, IEEE Trans. Commun. 70 (8) (2022) 5193-5207.

[18] P. Jiang, T. Wang, B. Han, X. Gao, J. Zhang, C.-K. Wen, S. Jin, G.Y. Li, Ai-aided online adaptive ofdm receiver: design and experimental results, IEEE Trans. Wirel. Commun. 20 (11) (2021) 7655-7668.

[19] K. Li, M. Chitre, Data-aided underwater acoustic ray propagation modeling, IEEE J. Ocean. Eng. (2023) 1-22.

[20] Y. Zhang, J. Li, Y.V. Zakharov, J. Li, Y. Li, C. Lin, X. Li, Deep learning based single carrier communications over time-varying underwater acoustic channel, IEEE Access 7 (2019) 38420-38430.

[21] A. Lee-Leon, C. Yuen, D. Herremans, Underwater acoustic communication receiver using deep belief network, IEEE Trans. Commun. 69 (6) (2021) 3698-3708.

[22] Y. Liu, F. Zhou, G. Qiao, Y. Zhao, G. Yang, X. Liu, Y. Lu, Deep learning-based cyclic shift keying spread spectrum underwater acoustic communication, J. Mar. Sci. Eng. 9 (11) (2021).

[23] H. Zhao, F. Ji, Q. Li, Q. Guan, S. Wang, M. Wen, Federated meta-learning enhanced acoustic radio cooperative framework for ocean of things, IEEE J. Sel. Top. Signal Process. 16 (3) (2022) 474-486.

[24] L. Huang, Y. Wang, Q. Zhang, J. Han, W. Tan, Z. Tian, Machine learning for under-water acoustic communications, IEEE Wirel. Commun. 29 (3) (2022) 102-108.

[25] Deep learning based underwater acoustic ofdm communications, Appl. Acoust. 154 (2019) 53-58.

[26] Deep learning aided ofdm receiver for underwater acoustic communications, Appl. Acoust. 187 (2022) 108515.

[27] H. Zhao, F. Ji, M. Wen, H. Yu, Q. Guan,Multi-task learning based underwater acous-tic ofdm communications, in:Proceedings of the 2021 IEEE International Conference on Signal Processing, Communications and Computing, IEEE, 2021, pp. 1-5.

[28] Y. Zhang, H. Wang, C. Li, D. Chen, F. Meriaudeau, Meta-learning-aided orthogonal frequency division multiplexing for underwater acoustic communications, J. Acoust. Soc. Am. 149 (6) (2021) 4596-4606.

[29] Y. Zhang, H. Wang, C. Li, F. Meriaudeau, Data augmentation aided complex-valued network for channel estimation in underwater acoustic orthogonal frequency divi-sion multiplexing system, J. Acoust. Soc. Am. 151 (6) (2022) 4150-4164.

[30] J. Liu, F. Ji, H. Zhao, J. Li, M. Wen,Cnn-based underwater acoustic ofdm communi-cations over doubly-selective channels, in:Proceedings of the 2021 IEEE Vehicular Technology Conference, IEEE, 2021, pp. 01-06.

[31] H. Zhao, C. Yang, Y. Xu, F. Ji, M. Wen, Y. Chen, Model-driven based deep unfolding equalizer for underwater acoustic ofdm communications, IEEE Trans. Veh. Technol. 72 (5) (2023) 6056-6067.

[32] P. Ceballos Carrascosa, M. Stojanovic, Adaptive channel estimation and data detec-tion for underwater acoustic mimo-ofdm systems, IEEE J. Ocean. Eng. 35 (3) (2010) 635-646.

[33] H. Yu, A. Song, M. Badiey, F. Chen, F. Ji, Iterative estimation of doubly selective underwater acoustic channel using basis expansion models, Ad Hoc Netw. 34 (2015) 52-61.

[34] T. Kang, R.A. Iltis, Iterative carrier frequency offset and channel estimation for underwater acoustic ofdm systems, IEEE J. Sel. Areas Commun. 26 (9) (2008) 1650-1661.

[35] Y. Zhang, R. Venkatesan, O.A. Dobre, C. Li, Efficient estimation and prediction for sparse time-varying underwater acoustic channels, IEEE J. Ocean. Eng. 45 (3) (2020) 1112-1125.

[36] Y.-L. Boureau, F. Bach, Y. LeCun, J. Ponce,Learning mid-level features for recogni-tion, in:Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, 2010, pp. 2559-2566.

[37] P.A. van Walree, F.-X. Socheleau, R. Otnes, T. Jenserud, The watermark benchmark for underwater acoustic modulation schemes, IEEE J. Ocean. Eng. 42 (4) (2017) 1007-1018.