Journal of South China University of Technology(Natural Science) >

2022 , Vol. 50 >Issue 12: 89 - 100

DOI: https://doi.org/10.12141/j.issn.1000-565X.220040

Electronics, Communication & Automation Technology

Optimization of Sparse Channel Equalization Algorithm in Underwater Acoustic Communication

Expand
  • 1.School of Electronic and Information Engineering，South China University of Technology，Guangzhou 510640，Guangdong，China
    2.Science and Technology on Communication Networks Laboratory，Shijiazhuang 050081，Hebei，China
    3.Key Laboratory of Marine Environmental Survey Technology and Application，Ministry of Natural Resources，Guangzhou 510300，Guangdong，China
    4.Guangdong Institute of Land and Resources Surveying and Mapping，Guangzhou 510500，Guangdong，China
陈芳炯（1975-），男，博士，教授，主要从事无线通信及组网技术研究。

Received date: 2022-01-25

  Online published: 2022-05-30

Supported by

the National Natural Science Foundation of China(62271208);the NSFC-Zhejiang Joint Fund for the Industrialization and Informatization(U1809211)

Fold

Abstract

In order to cope with the complex underwater acoustic channel environment and improve the convergence speed and symbol error rate performance of the channel equalization algorithm, this paper proposed a zero attraction sparse control proportional minimum symbol error rate decision feedback equalization algorithm. On the basis of the proposed sparse control proportional minimum symbol error rate decision feedback equalization algorithm, this algorithm added a sparse constraint of approximate l0 norm to the objective function, which pulls small amplitude equalizer taps toward zero. At the same time, phase-locked loop technology was introduced in the channel equalization process to eliminate the influence of jitter phase noise. The traditional phase-locked loop technology is based on the minimum mean square error criterion. However, existing literature and related experimental simulations have demonstrated that, when the mean square error of the system is the smallest, the symbol error rate is not necessarily the smallest. Aiming at this problem, a phase-locked loop phase tracking algorithm based on the minimum symbol error rate criterion was proposed and embedded in the sparse equalization algorithm. On the Matlab platform, experiments were carried out on the static underwater acoustic channel and the real time-varying underwater acoustic channel, respectively. The results show that the sparse control proportional minimum bit error rate decision feedback equalization algorithm with approximate l0 norm constraint converges faster without the influence of time-varying phase noise; under the channel condition affected by time-varying phase noise, the phase tracking algorithm based on the minimum bit error rate criterion has faster convergence speed and better bit error rate performance than the fragrance tracking algorithm based on the minimum mean square error criterion.

Key words： channel equalization; sparsity; zero attraction; phase-locked loop; minimum symbol error rate

Cite this article

CHEN Fangjiong, LIU Mingxing, FU Zhenhua, et al . Optimization of Sparse Channel Equalization Algorithm in Underwater Acoustic Communication[J]. Journal of South China University of Technology(Natural Science), 2022 , 50(12) : 89 -100 . DOI: 10.12141/j.issn.1000-565X.220040

References

1 SINGER A C， NELSON J K， KOZAT S S ．Signal processing for underwater acoustic communications ［J］．IEEE Communications Magazine，2009，47（1）：90-96．
2 KOCIC M， BRADY D， STOJANOVIC M ．Sparse equa-lization for real-time digital underwater acoustic commu-nications ［C］∥ Proceedings of 1995 MTS/IEEE Confe-rence “Challenges of Our Changing Global Environment”．San Diego：IEEE，1995：1417-1422．
3 RONTOGIANNIS A A， BERBERIDIS K ．Efficient decision feedback equalization for sparse wireless channels ［J］．IEEE Transactions on Wireless Communications，2003，2（3）：570-581．
4 CHNG E S， CHEN S， MULGREW B ．Efficient computational schemes for the orthogonal least squares algorithm ［J］．IEEE Transactions on Signal Processing，1995，43（1）：373-376．
5 魏昕，赵力，邹采荣 ．浅海水声通信中的间接自适应均衡算法 ［J］．电子与信息学报，2009，31（4）：916-919．
5 WEI Xin， ZHAO Li， ZOU Cai-rong ．Indirect adaptive equalization algorithm for shallow water acoustic communication ［J］．Journal of Electronics and Information Te-chnology，2009，31（4）：916-919．
6 冉茂华，黄建国，韩晶 ．水声通信中基于信道估计的稀疏均衡算法研究 ［J］．系统仿真学报，2009，21（14）：4479-4482．
6 RAN Mao-hua， HUANG Jian-guo， HAN Jing ．Simulation of multichannel sparse equalization algorithm based on channel estimation for underwater acoustic communications ［J］．Journal of System Simulation，2009，21（14）：4479-4482．
7 CHEN Y， GU Y， HERO A O ．Sparse LMS for system identification ［C］∥ Proceedings of 2009 IEEE International Conference on Acoustics，Speech and Signal Processing．Taipei：IEEE，2009：3125-3128．
8 TAO J， AN L， ZHENG Y R ．Enhanced adaptive equa-lization for MIMO underwater acoustic communications ［C］∥ Proceedings of OCEANS 2017．Anchorage：IEEE，2017：1-5．
9 DUTTWEILER D L ．Proportionate normalized least-mean-squares adaptation in echo cancelers ［J］．IEEE Tran-sactions on Speech and Audio Processing，2000，8（5）：508-518．
10 BENESTY J， GAY S L ．An improved PNLMS algorithm ［C］∥ Proceedings of 2002 IEEE International Confe-rence on Acoustics，Speech，and Signal Processing．Orlando：IEEE，2002：1881-1884．
11 KHONG A W H， NAYLOR P A ．Efficient use of sparse adaptive filters ［C］∥ Proceedings of 2006 the Fortieth Asilomar Conference on Signals，Systems and Compu-ters．Pacific Grove：IEEE，2006：1375-1379．
12 YEH C C， BARRY J R ．Adaptive minimum bit-error rate equalization for binary signaling ［J］．IEEE Tran-sactions on Communications，2000，48（7）：1226-1235．
13 YEH C C， BARRY J R ．Adaptive minimum symbol-error rate equalization for quadrature-amplitude modulation ［J］．IEEE Transactions on Signal Processing，2003，51（12）：3263-3269．
14 MULGREW B， CHEN S ．Adaptive minimum-BER decision feedback equalisers for binary signalling ［J］．Signal Processing，2001，81（7）：1479-1489．
15 CHEN S， HANZO L， MULGREW B ．Adaptive minimum symbol-error-rate decision feedback equalization for multilevel pulse-amplitude modulation ［J］．IEEE Transactions on Signal Processing，2004，52（7）：2092-2101．
16 CHEN S， HANZO L， LIVINGSTONE A ．MBER space-time decision feedback equalization assisted multiuser detection for multiple antenna aided SDMA systems ［J］．IEEE Transactions on Signal Processing，2006，54（8）：3090-3098．
17 GONG M， CHEN F， YU H，et al ．Normalized adaptive channel equalizer based on minimal symbol-error-rate ［J］．IEEE Transactions on Communications，2013，61（4）：1374-1383．
18 WANG Z， CHEN F， YU H，et al ．Sparse decision feedback equalization for underwater acoustic channel based on minimum symbol error rate ［J］．International Journal of Naval Architecture and Ocean Engineering，2021，13：617-627．
19 STOJANOVIC M， CATIPOVIC J A， PROAKIS J G ．Phase-coherent digital communications for underwater acoustic channels ［J］．IEEE Journal of Oceanic Engineering，1994，19（1）：100-111．
20 李记龙，冯海泓，黄敏燕 ．强多径干扰下的水声通信均衡算法研究 ［J］．声学技术，2016，35（1）：73-77．
20 LI Ji-long， FENG Hai-hong， HUANG Min-yan ．Study of channel equalization of underwater acoustic communication in multipath horizontal channel ［J］．Technical Acoustics，2016，35（1）：73-77．
21 CANDèS E J， WAKIN M B， BOYD S P ．Enhancing sparsity by reweighted l1 minimization ［J］．Journal of Fourier Analysis and Applications，2008，14（5）：877-905．
22 HOYER P O ．Non-negative matrix factorization with sparseness constraints ［J］．Journal of Machine Lear-ning Research，2004，5（9）：1457-1469．
23 王振忠 ．水声通信中基于最小误码率的稀疏均衡 ［D］．广州：华南理工大学，2019．
24 ARIYAVISITAKUL S ．A decision-feedback equalizer with selective time-reversal operation for high-rate indoor radio communication ［C］∥ Proceedings of 1990 IEEE Global Telecommunications Conference and Exhibition．San Diego：IEEE，1990：2035-2039．
25 ZHONG X， CHEN F， ZHENG B，et al ．Minimum symbol-error rate equalization based Turbo receiver for underwater acoustic communications ［C］∥ Proceedings of the 10th International Conference on Underwater Networks & Systems．New York：Association for Compu-ting Machinery，2015：1-5．
Options
Outlines

/

〈 〉