Low-complexity iterative receiver algorithms for multiple-input multiple-output underwater wireless communications

This dissertation proposes three low-complexity iterative receiver algorithms for multiple-input multiple-output (MIMO) underwater acoustic (UWA) communications. First is a bidirectional soft-decision feedback Turbo equalizer (Bi-SDFE) which harvests the time-reverse diversity in severe multipath MIMO channels. The Bi-SDFE outperforms the original soft-decision feedback Turbo equalizer (SDFE) while keeping its total computational complexity similar to that of the SDFE. Second, this dissertation proposes an efficient direct adaptation Turbo equalizer for MIMO UWA communications. Benefiting from the usage of soft-decision reference symbols for parameter adaptation as well as the iterative processing inside the adaptive equalizer, the proposed algorithm is efficient in four aspects: robust performance in tough channels, high spectral efficiency with short training overhead, time efficient with fast convergence and low complexity in hardware implementation. Third, a frequency-domain soft-decision block iterative equalizer combined with iterative channel estimation is proposed for the uncoded single carrier MIMO systems with high data efficiency. All the three new algorithms are evaluated by data recorded in real world ocean experiment or pool experiment. Finally, this dissertation also compares several Turbo equalizers in single-input single-output (SISO) UWA channels. Experimental results show that the channel estimation based Turbo equalizers are robust in SISO underwater transmission under harsh channel conditions --Abstract, page iv

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

Robust frequency-domain turbo equalization for multiple-input multiple-output (MIMO) wireless communications

This dissertation investigates single carrier frequency-domain equalization (SC-FDE) with multiple-input multiple-output (MIMO) channels for radio frequency (RF) and underwater acoustic (UWA) wireless communications. It consists of five papers, selected from a total of 13 publications. Each paper focuses on a specific technical challenge of the SC-FDE MIMO system. The first paper proposes an improved frequency-domain channel estimation method based on interpolation to track fast time-varying fading channels using a small amount of training symbols in a large data block. The second paper addresses the carrier frequency offset (CFO) problem using a new group-wise phase estimation and compensation algorithm to combat phase distortion caused by CFOs, rather than to explicitly estimate the CFOs. The third paper incorporates layered frequency-domain equalization with the phase correction algorithm to combat the fast phase rotation in coherent communications. In the fourth paper, the frequency-domain equalization combined with the turbo principle and soft successive interference cancelation (SSIC) is proposed to further improve the bit error rate (BER) performance of UWA communications. In the fifth paper, a bandwidth-efficient SC-FDE scheme incorporating decision-directed channel estimation is proposed for UWA MIMO communication systems. The proposed algorithms are tested by extensive computer simulations and real ocean experiment data. The results demonstrate significant performance improvements in four aspects: improved channel tracking, reduced BER, reduced computational complexity, and enhanced data efficiency --Abstract, page iv

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

Doctor of Philosophy

dissertationThis dissertation addresses several key challenges in multiple-antenna communications, including information-theoretical analysis of channel capacity, capacity-achieving signaling design, and practical statistical detection algorithms. The first part of the thesis studies the capacity limits of multiple-input multiple-output (MIMO) multiple access channel (MAC) via virtual representation (VR) model. The VR model captures the physical scattering environment via channel gains in the angular domain, and hence is a realistic MIMO channel model that includes many existing channel models as special cases. This study provides analytical characterization of the optimal input distribution that achieves the sum-capacity of MAC-VR. It also investigates the optimality of beamforming, which is a simple scalar coding strategy desirable in practice. For temporally correlated channels, beamforming codebook designs are proposed that can efficiently exploit channel correlation. The second part of the thesis focuses on statistical detection for time-varying frequency-selective channels. The proposed statistical detectors are developed based on Markov Chain Monte Carlo (MCMC) techniques. The complexity of such detectors grows linearly in system dimensions, which renders them applicable to inter-symbol-interference (ISI) channels with long delay spread, for which the traditional trellis-based detectors fail due to prohibitive complexity. The proposed MCMC detectors provide substantial gain over the de facto turbo minimum-mean square-error (MMSE) detector for both synthetic channel and underwater acoustic (UWA) channels. The effectiveness of the proposed MCMC detectors is successfully validated through experimental data collected from naval at-sea experiments

Efficient low-complexity data detection for multiple-input multiple-output wireless communication systems

The tradeoff between the computational complexity and system performance in multipleinput multiple-output (MIMO) wireless communication systems is critical to practical applications. In this dissertation, we investigate efficient low-complexity data detection schemes from conventional small-scale to recent large-scale MIMO systems, with the targeted applications in terrestrial wireless communication systems, vehicular networks, and underwater acoustic communication systems. In the small-scale MIMO scenario, we study turbo equalization schemes for multipleinput multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) and multipleinput multiple-output single-carrier frequency division multiple access (MIMO SC-FDMA) systems. For the MIMO-OFDM system, we propose a soft-input soft-output sorted QR decomposition (SQRD) based turbo equalization scheme under imperfect channel estimation. We demonstrate the performance enhancement of the proposed scheme over the conventional minimum mean-square error (MMSE) based turbo equalization scheme in terms of system bit error rate (BER) and convergence performance. Furthermore, by jointly considering channel estimation error and the a priori information from the channel decoder, we develop low-complexity turbo equalization schemes conditioned on channel estimate for MIMO systems. Our proposed methods generalize the expressions used for MMSE and MMSE-SQRD based turbo equalizers, where the existing methods can be viewed as special cases. In addition, we extend the SQRD-based soft interference cancelation scheme to MIMO SC-FDMA systems where a multi-user MIMO scenario is considered. We show an improved system BER performance of the proposed turbo detection scheme over the conventional MMSE-based detection scheme. In the large-scale MIMO scenario, we focus on low-complexity detection schemes because computational complexity becomes critical issue for massive MIMO applications. We first propose an innovative approach of using the stair matrix in the development of massive MIMO detection schemes. We demonstrate the applicability of the stair matrix through the study of the convergence conditions. We then investigate the system performance and demonstrate that the convergence rate and the system BER are much improved over the diagonal matrix based approach with the same system configuration. We further investigate low-complexity and fast processing detection schemes for massive MIMO systems where a block diagonal matrix is utilized in the development. Using a parallel processing structure, the processing time can be much reduced. We investigate the convergence performance through both the probability that the convergence conditions are satisfied and the convergence rate, and evaluate the system performance in terms of computational complexity, system BER, and the overall processing time. Using our proposed approach, we extend the block Gauss-Seidel method to large-scale array signal detection in underwater acoustic (UWA) communications. By utilizing a recently proposed computational efficient statistic UWA channel model, we show that the proposed scheme can effectively approach the system performance of the original Gauss-Seidel method, but with much reduced processing delay

Doctor of Philosophy

dissertationMultiple-input and multiple-output (MIMO) technique has emerged as a key feature for future generations of wireless communication systems. It increases the channel capacity proportionate to the minimum number of transmit and receive antennas. This dissertation addresses the receiver design for high-rate MIMO communications in at fading environments. The emphasis of the thesis is on the cases where channel state information (CSI) is not available and thus, clever channel estimation algorithms have to be developed to bene t from the maximum available channel capacity. The thesis makes four distinct novel contributions. First, we note that the conventional MCMC-MIMO detector presented in the prior work may deteriorate as SNR increases. We suggest and show through computer simulations that this problem to a great extent can be solved by initializing the MCMC detector with regulated states which are found through linear detectors. We also introduce the novel concept of staged-MCMC in a turbo receiver, where we start the detection process at a lower complexity and increase complexity only if the data could not be correctly detected in the present stage of data detection. Second, we note that in high-rate MIMO communications, joint data detection and channel estimation poses new challenges when a turbo loop is used to improve the quality of the estimated channel and the detected data. Erroneous detected data may propagate in the turbo loop and, thus, degrade the performance of the receiver signi cantly. This is referred to as error propagation. We propose a novel receiver that decorrelates channel estimation and the detected data to avoid the detrimental e ect of error propagation. Third, the dissertation studies joint channel estimation and MIMO detection over a continuously time-varying channel and proposes a new dual-layer channel estimator to overcome the complexity of optimal channel estimators. The proposed dual-layer channel estimator reduces the complexity of the MIMO detector with optimal channel estimator by an order of magnitude at a cost of a negligible performance degradation, on the order of 0.1 to 0.2 dB. The fourth contribution of this dissertation is to note that the Wiener ltering techniques that are discussed in this dissertation and elsewhere in the literature assume that channel (time-varying) statistics are available. We propose a new method that estimates such statistics using the coarse channel estimates obtained through pilot symbols. The dissertation also makes an additional contribution revealing di erences between the MCMC-MIMO and LMMSE-MIMO detectors. We nd that under the realistic condition where CSI has to be estimated, hence the available channel estimate will be noisy, the MCMC-MIMO detector outperforms the LMMSE-MIMO detector with a signi cant margin