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

Joint Channel Estimation Algorithm via Weighted Homotopy for Massive MIMO OFDM System

Massive (or large-scale) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system is widely acknowledged as a key technology for future communication. One main challenge to implement this system in practice is the high dimensional channel estimation, where the large number of channel matrix entries requires prohibitively high computational complexity. To solve this problem efficiently, a channel estimation approach using few number of pilots is necessary. In this paper, we propose a weighted Homotopy based channel estimation approach which utilizes the sparse nature in MIMO channels to achieve a decent channel estimation performance with much less pilot overhead. Moreover, inspired by the fact that MIMO channels are observed to have approximately common support in a neighborhood, an information exchange strategy based on the proposed approach is developed to further improve the estimation accuracy and reduce the required number of pilots through joint channel estimation. Compared with the traditional sparse channel estimation methods, the proposed approach can achieve more than 2dB gain in terms of mean square error (MSE) with the same number of pilots, or achieve the same performance with much less pilots

Advanced OFDM Receivers for Underwater Acoustic Communications

In underwater acoustic (UWA) communications, an emerging research area is the high data rate and robust transmission using multi-carrier modulation, such as orthogonal frequency-division multiplexing (OFDM). However, difficulties in the OFDM communications include Doppler estimation/compensation, beamforming, and channel estimation/equalization. In this thesis, to overcome these difficulties, advanced low complexity OFDM receivers of high performance are developed. A novel low complexity Doppler estimation method based on computing multi-channel autocorrelation is proposed, which provides accurate Doppler estimates. In simulations and sea trials with guard-free OFDM signal transmission, this method outperforms conventional single-channel autocorrelation method, and shows a less complexity than the method based on computing the cross-ambiguity function between the received and pilot signals with a comparable performance. Space-time clustering in UWA channels is investigated, and a low complexity multi-antenna receiver including a beamformer that exploits this channel property is proposed. Various space-time processing techniques are investigated and compared, and the results show that the space-time clustering demonstrates the best performance. Direction of arrival (DOA) fluctuations in time-varying UWA channels are investigated, and a further developed beamforming technique with DOA tracking is proposed. In simulation and sea trials, this beamforming is compared with the beamforming without DOA tracking. The results show that the tracking beamforming demonstrates a better performance. For the channel estimation, two low complexity sparse recursive least squares adaptive filters, based on diagonal loading and homotopy, are presented. In two different UWA communication systems, the two filters are investigated and compared with various existing adaptive filters, and demonstrate better performance. For the simulations, the Waymark baseband UWA channel model is used, to simulate the virtual signal transmission in time-varying UWA channels. This model is modified from the previous computationally efficient Waymark passband model, improving the computational efficiency further

Low-complexity DCD-based sparse recovery algorithms

Sparse recovery techniques find applications in many areas. Real-time implementation of such techniques has been recently an important area for research. In this paper, we propose computationally efficient techniques based on dichotomous coordinate descent (DCD) iterations for recovery of sparse complex-valued signals. We first consider $\ell_2 \ell_1$ optimization that can incorporate \emph{a priori} information on the solution in the form of a weight vector. We propose a DCD-based algorithm for $\ell_2 \ell_1$ optimization with a fixed $\ell_1$-regularization, and then efficiently incorporate it in reweighting iterations using a \emph{warm start} at each iteration. We then exploit homotopy by sampling the regularization parameter and arrive at an algorithm that, in each homotopy iteration, performs the $\ell_2 \ell_1$ optimization on the current support with a fixed regularization parameter and then updates the support by adding/removing elements. We propose efficient rules for adding and removing the elements. The performance of the homotopy algorithm is further improved with the reweighting. We then propose an algorithm for $\ell_2 \ell_0$ optimization that exploits homotopy for the $\ell_0$ regularization; it alternates between the least-squares (LS) optimization on the support and the support update, for which we also propose an efficient rule. The algorithm complexity is reduced when DCD iterations with a \emph{warm start} are used for the LS optimization, and, as most of the DCD operations are additions and bit-shifts, it is especially suited to real time implementation. The proposed algorithms are investigated in channel estimation scenarios and compared with known sparse recovery techniques such as the matching pursuit (MP) and YALL1 algorithms. The numerical examples show that the proposed techniques achieve a mean-squared error smaller than that of the YALL1 algorithm and complexity comparable to that of the MP algorithm

Self-Interference Cancellation for Full-Duplex Underwater Acoustic Systems

This work develops and investigates self-interference (SI) cancellation (SIC) techniques for full-duplex (FD) underwater acoustic (UWA) systems. To enable the FD operation in UWA systems, a high level of SIC is required. The main approach used in this work is the digital cancellation based on adaptive filtering. A general structure of the digital canceller is proposed which addresses key factors affecting the SIC performance, including the power amplifier and pre-amplifier nonlinearities, up- and down-sampling effects. With the proposed structure, the SI can be effectively cancelled in time-invariant channels by classical recursive least-square (RLS) adaptive filters, e.g., the sliding-window RLS (SRLS), but the SIC performance degrades in time-varying channels. A new SRLS adaptive filter based on parabolic interpolation of the channel time variations is proposed, which improves the SIC performance at the expense of the high complexity. To reduce the complexity, while providing the high SIC, a new family of interpolating adaptive filters which combine the SRLS adaptive algorithm with Legendre polynomials (SRLS-L) is proposed. A sparse adaptive filter is further proposed to exploit the sparsity in the expansion coefficients of the Legendre polynomials. For interpolating adaptive filtering algorithms, the mean squared error is unsuitable for measuring the SIC performance due to the overfitting. Therefore, a new evaluation metric, SIC factor, is proposed. The SIC performance of the proposed adaptive filters is investigated and compared with that of the classical SRLS algorithm by simulation, water tank and lake experiments. Results indicate that the proposed adaptive filters significantly improve the SIC performance in time-varying scenarios, especially with high-order sparse SRLS-L adaptive filter. Furthermore, SIC schemes with multiple antennas are investigated to explore the possibility of achieving extra amount of SIC in acoustic domain and cancelling the fast-varying surface reflections by adaptive beamforming

Image registration for sonar applications

This work develops techniques to estimate the motion of an underwater platform by processing data from an on-board sonar, such as a Forward Looking Sonar (FLS). Based on image registration, a universal algorithm has been developed and validated with in field datasets. The proposed algorithm gives a high quality registration to a fine (sub-pixel) precision using an adaptive filter and is suitable for both optical and acoustic images. The efficiency and quality of the result can be improved if an initial estimate of the motion is made. Therefore, a coarse (pixel-wide) registration algorithm is proposed, this is based on the assumption of local sparsity in the pixel motion between two images. Using a coarse and then fine registration, large displacements can be accommodated with a result that is to a sub-pixel precision. The registration process produces a displacement map (DM) between two images. From a sequence of DMs, an estimation of the sensor's motion is made. This is performed by a proposed fast searching and matching technique applied to a library of modelled DMs. Further, this technique exploits regularised splines to estimate the attitude and trajectory of the platform. To validate the results, a mosaic has been produced from three sets of in field data. Using a more detailed model of the acoustic propagation has the potential to improve the results further. As a step towards this a baseband underwater channel model has been developed. A physics simulator is used to characterise the channel at waymark points in a changing environment. A baseband equivalent representation of the time varying channel is then interpolated from these points. Processing in the baseband reduces the sample rate and hence reduces the run time for the model. A comparison to a more established channel model has been made to validate the results