Iterative Channel Estimation and Turbo Equalization for Multiple-Input Multiple-Output Underwater Acoustic Communications
This paper investigates a multiple-input multiple-output (MIMO) detector for underwater acoustic (UWA) communications, that uses the improved proportionate normalized least mean squares (IPNLMS) algorithm for iterative channel estimation in turbo equalization. By exploiting the sparse nature of UWA channels, the iterative IPNLMS channel estimator achieves fast convergence and fast tracking by data reuse of training sequence and decision-directed channel reestimation over each turbo iterations. The proposed channel estimation scheme is combined with the low-complexity minimum mean-square-error (LC-MMSE) equalization to detect single carrier MIMO signals in overlapped subblocks without guard intervals. The proposed MIMO detection scheme achieves high performance, high transmission efficiency, and low computational complexity, especially for large MIMO and high-constellation modulation schemes. The experimental results of the undersea 2008 Surface Processes and Acoustic Communications Experiment (SPACE08) demonstrate an order of magnitude improvement of bit error rate (BER) performance over those using block-wise MMSE channel estimation. We have also verified that the channel-estimation based turbo equalizer (CE-TEQ) outperforms the direct-adaptation based turbo equalizer (DA-TEQ) in terms of BER performance and robustness against error propagation.
Z. Yang and Y. R. Zheng, "Iterative Channel Estimation and Turbo Equalization for Multiple-Input Multiple-Output Underwater Acoustic Communications," IEEE Journal of Oceanic Engineering, no. 1, pp. 232-242, Institute of Electrical and Electronics Engineers (IEEE), Jan 2016.
The definitive version is available at https://doi.org/10.1109/JOE.2015.2398731
Electrical and Computer Engineering
National Science Foundation (U.S.)
Keywords and Phrases
Bit error rate; Channel estimation; Computational efficiency; Equalizers; Error analysis; Error statistics; Iterative methods; Mean square error; MIMO systems; Acoustic communications; Bit error rate (BER) performance; Iterative channel estimation; Low computational complexity; Minimum mean square errors (MMSE); Normalized least mean square; Turbo equalizations; Underwater acoustic (UWA) communications; Underwater acoustics; Adaptive equalization; Improved proportionate normalized least mean squares (IPNLMS); Multiple-input multiple-output (MIMO)
International Standard Serial Number (ISSN)
Article - Journal
© 2016 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Jan 2016