针对时变水声信道造成的严重多途干扰问题,提出基于虚拟训练序列的双向水声信道精准估计(Virtual Training Based Bidirectional Channel Estimation, VT-BCE)算法。基于叠加训练(Superimposed Training, ST)方案,将训练序列和符号序列线性叠加,使得训练序列和符号序列的信道信息一致,提高信号的跟踪能力;基于置信传播,双向信道估计(Bidirectional Channel Estimation, BCE)算法将一个数据块分成多个短块,利用整个数据块的信息估计当前短块信道,实现对当前短块的精准信道估计。将ST方案、BCE算法和信道均衡(频域)以迭代的方式相结合,使估计的符号序列可以作为信道估计的虚拟训练(Virtual Training, VT)序列,提升信道的估计性能,进而提高系统的解码性能。最后,通过计算机仿真和水池试验,验证了所提算法的有效性。
A bidirectional accurate channel estimation algorithm based on virtual training (VT-BCE) is proposed in view of the multi-path interference problem caused by time-varying underwater acoustic channels. The superimposed training (ST) scheme is used, where the training sequence and the symbol sequence are linearly superimposed, so that the channel information of the training and symbol sequences is consistent and the tracking ability of the signal is improved. Based on confidence propagation, a bidirectional channel estimation (BCE) algorithm is developed, where a data block is divided into several segments, using the information of the whole data block to estimate the current segment channel to achieve accurate channel estimation of the current segment. The ST scheme, the BCE algorithm, and channel equalization (frequency domain) are combined in an iterative way, so that the estimated symbol sequence can be used as virtual training sequence for channel estimation to improve the estimation performance of the channel and thus the decoding performance of the system. Finally, the validity of the proposed algorithm is verified by computer simulation and pool experiment.
2024,46(7): 121-127 收稿日期:2023-4-15
DOI:10.3404/j.issn.1672-7649.2024.07.020
分类号:TN929.3
基金项目:国家自然科学基金资助项目(61771271);山东省自然科学基金面上项目(ZR2020MF010, ZR2020MF001);青岛市源头创新计划-青年专项(19-6-2-4-cg);青岛市关键技术攻关及产业化示范类项目(22-3-3-hygg-8-hy)
作者简介:梁俊燕(1999-),女,硕士研究生,研究方向为水声通信
参考文献:
[1] BERGER C R, ZHOU S, PREISIG J C, et al. Sparse channel estimation for multicarrier underwater acoustic communication: From subspace methods to compressed sensing[J]. IEEE Transactions on Signal Processing, 2010, 58(3): 1708-1721.
[2] 梁仕杰, 王彪, 张岑. 基于gOMP算法的FBMC水声通信信道估计方法[J]. 声学技术, 2021, 40(3): 329-335.
[3] SONG A, MILIOA S, MANDAR C. Editorial underwater acoustic communications: where we stand and what is next?[J]. IEEE Journal of Oceanic Engineering, 2019, 44(1): 1-6.
[4] 邵宗战. 现代水声通信技术发展探讨[J]. 科技创新与应用, 2022, 12(20): 152-155.
[5] 杨斌斌, 鄢社锋, 章绍晨, 等. 基于Kalman滤波的水声混合双向迭代信道均衡算法[J]. 电子与信息学报, 2022, 44(6): 1879-1886.
[6] YANG Z, ZHENG Y R. Iterative channel estimation and turbo equalization for multiple-input multiple-output underwater acoustic communications[J]. IEEE Journal of Oceanic Engineering, 2016, 41(1): 232-242.
[7] LI W, PREISIG J C. Estimation of rapidly time-varying sparse channels[J]. IEEE Journal of Oceanic Engineering, 2007, 32(4): 927-939.
[8] Y CHO, H KO. Channel estimation based on adaptive denoising for underwater acoustic OFDM systems[J]. IEEE Access, 2020, 8: 157197-157210.
[9] 伍飞云, 周跃海, 童峰, 等. 稀疏长时延水声信道的压缩感知估计[J]. 东南大学学报(英文版), 2014(3): 271-277.
[10] JIANG W H, TONG F, ZHU Z L. Exploiting rapidly time-varying sparsity for underwater acoustic communication[J]. IEEE Transactions on Vehicular Technology, 2022, 71(9): 9721-9734.
[11] GUO Q H, HUANG D F, NORDHOLM S, et al. Iterative frequency domain equalization with generalized approximate message passing[J]. IEEE Signal Processing Letters, 2013, 20(6): 559-562.
[12] GUO Q H, HUANG D F. A concise representation for the soft-in soft-out LMMSE detector[J]. IEEE Communications Letters, 2011, 15(5): 566-568.
[13] GUO Q H, PING L, HUANG D F. A low-complexity iterative channel estimation and detection technique for doubly selective channels[J]. IEEE Transactions on Wireless Communications, 2009, 8(8): 4340-4349.
[14] GUO Q H, HUANG D F. EM-based joint channel estimation and detection for frequency selective channels using gaussian message passing[J]. IEEE Transactions on Signal Processing, 2011, 59(8): 4030-4035.
[15] YANG G, LIU T L, DING H X, et al. Joint channel estimation and generalized approximate messaging passing-based equalization for underwater acoustic communications[J]. IEEE Access, 2021, 9: 56757-56764.
[16] YANG G, WANG L, QIAO P Y, et al. Joint multiple turbo equalization for harsh time-varying underwater acoustic channels[J]. IEEE Access, 2021, 9: 82364-82372.
[17] YANG G, GUO Q H, DING H X, et al. Joint message-passing-based bidirectional channel estimation and equalization with superimposed training for underwater acoustic communications[J]. IEEE Journal of Oceanic Engineering, 2021, 46(4): 1463-1476.
[18] 杨光, 丁寒雪, 郭庆华, 等. 基于叠加训练序列和低复杂度频域 Turbo 均衡的时变水声信道估计和均衡[J]. 电子与信息学报, 2021, 43(3): 850-856.
[19] LOELIGER HA, DAUWELS J, HU J L, et al. The factor graph approach to model-based signal processing[J]. Proceedings of the IEEE, 2007, 95(6): 1295-1322.