Frequency-Domain Turbo Equalization for MIMO Underwater Acoustic Communications

This paper investigates a low-complexity frequency-domain turbo equalization (FDTE) based on linear minimum mean square error (LMMSE) criterion for single-carrier (SC) multiple-input multiple-output (MIMO) underwater acoustic communications (UAC). The receiver incorporates both the equalizer and the decoder which exchange the extrinsic information on the coded bits for each other to implement the iterative detection. The channel impulse responses (CIRs) required in the equalization are estimated in the frequency domain (FD) by inserting the well-designed pilot blocks which are frequency-orthogonal Chu sequences. The proposed SC-MIMO-FDTE architecture is applied to the fixed-to-fixed underwater data gathered during SPACE08 ocean experiments in October 2008, where multiple transducers and hydrophones are deployed in communication ranges of 200m and 1000m, and the channel bandwidth is 9.765625 kHz. The phase shift keying (PSK) signals are transmitted from multiple transducers in various block sizes. The proposed transceiver has been demonstrated to improve the bit-error-rate (BER) performance significantly by processing the QPSK data blocks with block length of 1024 in 200m and 1000m ranges. The average BERs obtained by turbo detection with 3 iterations can achieve approximately 1.4 × 10-4 for the 200m system and 4.4 × 10-5 for the 1000m system

Soft-Decision-Driven Sparse Channel Estimation and Turbo Equalization for MIMO Underwater Acoustic Communications

Multi-input multi-output (MIMO) detection based on turbo principle has been shown to provide a great enhancement in the throughput and reliability of underwater acoustic (UWA) communication systems. Benefits of the iterative detection in MIMO systems, however, can be obtained only when a high quality channel estimation is ensured. In this paper, we develop a new soft-decision-driven sparse channel estimation and turbo equalization scheme in the triply selective MIMO UWA. First, the Homotopy recursive least square dichotomous coordinate descent (Homotopy RLS-DCD) adaptive algorithm, recently proposed for sparse single-input single-output system identification, is extended to adaptively estimate rapid time-varying MIMO sparse channels. Next, the more reliable a posteriori soft-decision symbols, instead of the hard decision symbols or the a priori soft-decision symbols, at the equalizer output, are not only feedback to the Homotopy RLS-DCD-based channel estimator but also to the minimum mean-square-error (MMSE) equalizer. As the turbo iterations progress, the accuracy of channel estimation and the quality of the MMSE equalizer are improved gradually, leading to the enhancement in the turbo equalization performance. This also allows the reduction in pilot overhead. The proposed receiver has been tested by using the data collected from the SHLake2013 experiment. The performance of the receiver is evaluated for various modulation schemes, channel estimators, and MIMO sizes. Experimental results demonstrate that the proposed a posteriori soft-decision-driven sparse channel estimation based on the Homotopy RLS-DCD algorithm and turbo equalization offer considerable improvement in system performance over other turbo equalization schemes

Low-complexity soft-decision feedback turbo equalization for multilevel modulations

This dissertation proposes two new decision feedback equalization schemes suitable for multilevel modulation systems employing turbo equalization. One is soft-decision feedback equalization (SDFE) that takes into account the reliability of both soft a priori information and soft decisions of the data symbols. The proposed SDFE exhibits lower signal to noise ratio (SNR) threshold that is required for water fall bit error rate (BER) and much faster convergence than the near-optimal exact minimum mean square error linear equalizer (Exact-MMSE-LE) for high-order constellation modulations. The proposed SDFE also offers a low computational complexity compared to the Exact-MMSE-LE. The drawback of the SDFE is that its coefficients cannot reach the matched filter bound (MFB) and therefore after a large number of iterations (e.g. 10), its performance becomes inferior to that of the Exact-MMSE-LE. Therefore, soft feedback intersymbol interference (ISI) canceller-based (SIC) structure is investigated. The SIC structure not only exhibits the same low complexity, low SNR threshold and fast convergence as the SDFE but also reaches the MFB after a large number of iterations. Both theoretical analysis and numerical simulations demonstrate why the SIC achieves MFB while the SDFE cannot. These two turbo equalization structures are also extended from single-input single-output (SISO) systems to multiple-input multiple-output (MIMO) systems and applied in high data-rate underwater acoustic (UWA) communications --Abstract, page iv