Adaptive Linear Turbo Equalization Over Doubly Selective Channels

Over the last decade, tremendous gains, leading to near-capacity achieving performance, have been shown for a variety of communication systems through the application of the turbo principle, i.e., the exchange of extrinsic information between constituent algorithms for tasks such as channel decoding, equalization, and multiple-input-multiple-output (MIMO) detection. In this paper, we study the practical application of such an iterative detection and decoding (IDD) framework to underwater acoustic communications. We explore complexity and performance tradeoffs of a variety of turbo equalization (TEQ)-based receiver architectures. First, we elaborate on two popular but suboptimal turbo equalization techniques: a channel-estimate-based minimum mean-square error TEQ (CE-based MMSE-TEQ) and a direct-adaptive TEQ (DA-TEQ). We study the behavior of both TEQ approaches in the presence of channel estimation errors and adaptive filter adjustment errors. We confirm that after a sufficient number of iterations, the performance gap between these two TEQ algorithms becomes small. Next, we demonstrate that an underwater receiver architecture built upon the least mean squares (LMS) DA-TEQ technique can leverage and dramatically improve the performance of the conventional implementation based on the decision-feedback equalizer at a feasible complexity. To maintain performance gains over time-varying channels, the slow convergence speed of the LMS algorithm has been improved via two methods: 1) repeating the weight update for the same set of data with decreasing step size and 2) reducing the dimensionality of the equalizer by capturing sparse channel structure. This receiver architecture was used to process collected data from the SPACE 08 experiment (Martha's Vineyard, MA). Receiver performance for different modulation orders, channel codes, and hydrophone configurations is examined at a variety of distance, up to 1 km from the transmitters. Experimental results show great promise for this approach, as data rates in excess of 15 kb/s could readily be achieved without error.

[1]  J. Preisig,et al.  Estimation of Rapidly Time-Varying Sparse Channels , 2007, IEEE Journal of Oceanic Engineering.

[2]  J. Shynk,et al.  Analysis of the data-reusing LMS algorithm , 1989, Proceedings of the 32nd Midwest Symposium on Circuits and Systems,.

[3]  R. Otnes,et al.  Underwater Acoustic Communications: Long-Term Test of Turbo Equalization in Shallow Water , 2008, IEEE Journal of Oceanic Engineering.

[4]  Jun Won Choi,et al.  Iterative multi-channel equalization and decoding for high frequency underwater acoustic communications , 2008, 2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop.

[5]  Simon Haykin,et al.  Adaptive Filter Theory 4th Edition , 2002 .

[6]  Achilleas Anastasopoulos,et al.  Adaptive soft-input soft-output algorithms for iterative detection with parametric uncertainty , 2000, IEEE Trans. Commun..

[7]  Mathini Sellathurai,et al.  Turbo-BLAST for wireless communications: theory and experiments , 2002, IEEE Trans. Signal Process..

[8]  Andrew C. Singer,et al.  Turbo equalization: principles and new results , 2002, IEEE Trans. Commun..

[9]  L. Freitag,et al.  This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE JOURNAL OF OCEANIC ENGINEERING 1 Peer-Reviewed Technical Communication Multicarrier Communication Over Un , 2022 .

[10]  Ali H. Sayed,et al.  Fundamentals Of Adaptive Filtering , 2003 .

[11]  Andrea M. Tonello,et al.  Space-time bit-interleaved coded modulation with an iterative decoding strategy , 2000, Vehicular Technology Conference Fall 2000. IEEE VTS Fall VTC2000. 52nd Vehicular Technology Conference (Cat. No.00CH37152).

[12]  Bernard Widrow,et al.  Adaptive Signal Processing , 1985 .

[13]  Milica Stojanovic,et al.  Sparse equalization for real-time digital underwater acoustic communications , 1995, 'Challenges of Our Changing Global Environment'. Conference Proceedings. OCEANS '95 MTS/IEEE.

[14]  Andrew C. Singer,et al.  Minimum mean squared error equalization using a priori information , 2002, IEEE Trans. Signal Process..

[15]  W. Y. Chen,et al.  A variable step size LMS algorithm , 1990, Proceedings of the 33rd Midwest Symposium on Circuits and Systems.

[16]  J. A. Catipovic,et al.  Phase-coherent digital communications for underwater acoustic channels , 1994 .

[17]  G. Leus,et al.  Robust Underwater Telemetry With Adaptive Turbo Multiband Equalization , 2009, IEEE Journal of Oceanic Engineering.

[18]  H. Vincent Poor,et al.  Iterative (turbo) soft interference cancellation and decoding for coded CDMA , 1999, IEEE Trans. Commun..

[19]  S. Qureshi,et al.  Adaptive equalization , 1982, Proceedings of the IEEE.

[20]  Alain Glavieux,et al.  Reflections on the Prize Paper : "Near optimum error-correcting coding and decoding: turbo codes" , 1998 .

[21]  Alain Glavieux,et al.  Turbo equalization: adaptive equalization and channel decoding jointly optimized , 2001, IEEE J. Sel. Areas Commun..

[22]  Stephan ten Brink,et al.  Achieving near-capacity on a multiple-antenna channel , 2003, IEEE Trans. Commun..

[23]  Alain Glavieux,et al.  Iterative correction of intersymbol interference: Turbo-equalization , 1995, Eur. Trans. Telecommun..

[24]  Andrew C. Singer,et al.  Markov Chain Monte Carlo detection for underwater acoustic channels , 2010, 2010 Information Theory and Applications Workshop (ITA).

[25]  Michael Tüchler,et al.  Iterative channel estimation for turbo equalization of time-varying frequency-selective channels , 2004, IEEE Transactions on Wireless Communications.

[26]  Geert Leus,et al.  Low-Complexity Block Turbo Equalization for OFDM Systems in Time-Varying Channels , 2007, IEEE Transactions on Signal Processing.

[27]  Subbarayan Pasupathy,et al.  Soft-Input Soft-Output Equalizers for Turbo Receivers: A Statistical Physics Perspective , 2007, IEEE Trans. Commun..

[28]  John G. Proakis,et al.  Digital Communications , 1983 .

[29]  T.M. Duman,et al.  High-Rate Communication for Underwater Acoustic Channels Using Multiple Transmitters and Space–Time Coding: Receiver Structures and Experimental Results , 2007, IEEE Journal of Oceanic Engineering.

[30]  B. Nilsson,et al.  Underwater communication link with iterative equalization , 2006, OCEANS 2006.

[31]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[32]  S. Brink Convergence of iterative decoding , 1999 .

[33]  Tolga M. Duman,et al.  Error Rate Improvement in Underwater MIMO Communications Using Sparse Partial Response Equalization , 2006 .

[34]  Sirikiat Lek Ariyavisitakul,et al.  Turbo space-time processing to improve wireless channel capacity , 2000, IEEE Trans. Commun..