Konstantinos Pelekanakis

Adaptive Sparse Channel Estimation under Symmetric alpha-Stable Noise

We tackle the problem of channel estimation in environments that exhibit both sparse, time-varying impulse responses and impulsive noise with Symmetric alpha-Stable (SαS) statistics. Two novel frameworks are proposed for designing online adaptive algorithms that exploit channel sparseness and achieve robust performance against impulses. The first framework generates recursive least-squares (RLS)-type algorithms based on a differentiable cost function that combines robust nonlinear methods with sparse-promoting L0 norm regularization. The second framework employs the natural gradient (NG) and incorporates non-linear methods for the channel prediction error as well as the L0 norm of the channel taps. From these frameworks, we derive linear and quadratic complexity algorithms. The improved performance of the proposed RLS-type and NG-type algorithms relative to conventional robust algorithms, such as the recursive least M-estimate (RLM) algorithm and the recursive least p-norm (RLP) algorithm, is validated by using extensive computer simulations as well as signal analysis from an underwater acoustic communications experiment. In addition, we discovered that RLM is not robust under specific SαS noise conditions, contrary to the claim in\thinspace. Finally, our results also demonstrate the clear superiority of the NG-type algorithms over their RLS-type counterparts.

New Sparse Adaptive Algorithms Based on the Natural Gradient and the

A new algorithmic framework for sparse channel identification is proposed. Although the focus of this paper is on sparse underwater acoustic channels, this framework can be applied in any field where sequential noisy signal samples are obtained from a linear time-varying system. A suit of new algorithms is derived by minimizing a differentiable cost function that utilizes the underlying Riemannian structure of the channel as well as the L0-norm of the complex-valued channel taps. The sparseness effect of the proposed algorithms is successfully demonstrated by estimating a mobile shallow-water acoustic channel. The clear superiority of the new algorithms over state-of-the-art sparse adaptive algorithms is shown. Moreover, the proposed algorithms are employed by a channel-estimate-based decision-feedback equalizer (CEB DFE). These CEB DFE structures are compared with a direct-adaptation DFE (DA DFE), which is based on sparse and nonsparse adaptation. Our results confirm the improved error-rate performance of the new CEB DFEs when the channel is sparse.

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High-rate underwater acoustic (UWA) channels often demonstrate long, time-varying and sparse impulse responses. Classical and most used adaptive algorithms such as the recursive least-squares (RLS) algorithm and the normalized least-mean-square (NLMS) algorithm do not take sparseness into account when they try to match the channel. Thus, performance improvement of these algorithms is possible. Sparse adaptive algorithms developed for acoustic echo cancellation, such as the improved proportionate normalized least-mean-square (IPNLMS) algorithm and the improved proportionate affine projection algorithm (IPAPA), have shown better performance than the NLMS algorithm without any essential cost in computational complexity. In this work, we apply IPNLMS, IPAPA, RLS and NLMS in both channel estimation and decision feedback equalization (DFE) of a short-range, shallow water acoustic link. Our results confirm the superior performance of the sparse algorithms (IPAPA being the best) when the channel becomes sparse. In addition, it is shown that both IPAPA and IPNLMS have robust performance (similar to RLS) when the channel is non-sparse.

-Norm

Underwater acoustic (UWA) channels exhibit time-varying fading statistics, thus a coded modulation scheme optimally designed for a specific model (e.g., Rayleigh fading) will perform poorly when the channel statistics change. Exploiting diversity via coded modulation is a robust approach to improve the reliability of the acoustic link in a variety of channel conditions. Two coded modulation schemes drawn from the terrestrial radio literature are compared in terms of their bit error rate (BER). The first scheme combines trellis coded modulation (TCM) based on an 8-phase-shift keying (8-PSK) signal set and symbol interleaving. The second scheme is based on bit-interleaved coded modulation (BICM), which includes a convolutional encoder, a bit interleaver, and a 16-quadrature-amplitude-modulation (16-QAM) signal set. These schemes, which are designed to have the same bit rate and decoding complexity, are tested under two scenarios. In the first scenario, a single-input-multiple-output (SIMO) system is implemented by means of orthogonal frequency-division multiplexing (OFDM) modulation. In the second scenario, a multiple-input-multiple-output (MIMO) system is implemented and each of the coded modulation scheme is coupled with a 3/4-rate space-time block code (STBC) before applying OFDM. Analyzing both simulated and experimental data, the following results, which also hold for terrestrial radio, are confirmed: coded modulation schemes emphasizing higher Hamming distance (such as BICM) yield a lower error rate when spatial diversity is very limited (first scenario). On the other hand, coded modulation schemes emphasizing higher free Euclidean distance (such as TCM) demonstrate a lower error rate when spatial diversity is sufficiently high (second scenario).

Comparison of sparse adaptive filters for underwater acoustic channel equalization/Estimation

Several underwater acoustic channels exhibit impulsive ambient noise. As a consequence, communication receivers implemented on the basis of the Gaussian noise assumption may yield poor performance even at moderate signal-to-noise ratios (SNRs). This paper presents a new channel-estimate-based decision feedback equalizer (CEB-DFE) that deals with high platform mobility, exploits any sparse multipath structure, and maintains robustness under impulsive noise. The key component of this DFE is a linear-complexity sparse channel estimator, which has the ability to detect and reject impulses based on two noise models: contaminated Gaussian and symmetric alpha stable (SαS). By processing phase-shift keying (PSK) signals from three mobile shallow-water acoustic links, the gain of the proposed receiver over existing equalizers is demonstrated.

Exploiting Space–Time–Frequency Diversity With MIMO–OFDM for Underwater Acoustic Communications

Underwater network simulation and performance analysis require accurate packet error models. The packet error probability depends on the packet length and the temporal distribution of bit errors. We analyze error traces from decision-feedback-equalized single-carrier acoustic communication links from several shallow-water experiments and show that clustering of bit errors occurs at several timescales. We propose a two-part statistical error model consisting of a generalized Pareto fractal renewal parent process that drives Bernoulli daughter processes with generalized extreme value distributed lifetimes. We present an algorithm to simulate communication errors using this error process model and show that the simulated packet loss probability accurately matches experimental observations.

Robust Equalization of Mobile Underwater Acoustic Channels

Single Input Single Output (SISO) Orthogonal Frequency Division-Multiplexing (OFDM) over short to medium range shallow water channels suffers from low bandwidth efficiency. This happens due to large Cyclic Prefix (CP) and relative small number of sub-carriers. To increase the bandwidth efficiency, a time domain channel shortening equalizer (CSE) can be inserted before the Fast Fourier Transform (FFT). The CSE shortens the channel impulse response (CIR) so that a smaller CP is needed. This paper analyses the performance of four time domain CSEs: 1) Minimum Mean Square Error (MMSE) Unit Tap Constraint (UTC) 2) MMSE Unit Energy Constraint (UEC) 3) Maximum Shortening Signal to Noise Ratio (MSSNR) 4) Minimum Inter Symbol Interference (Min ISI) and Frequency Domain Decision Feedback Equalizer (FD-DFE). Analysing simulated and real data, the MMSE UTC equalizer shows the best performance in terms of Bit Error Rate (BER). When bit-loading is applied, the BER of Min ISI approach has comparable performance to UTC.

Statistical Bit Error Trace Modeling of Acoustic Communication Links Using Decision Feedback Equalization

Real-time telemetry during drilling thousands of meters below the surface plays a key role in efficient utilization of oilfields. Current mud-pulse commercial systems are limited to only a few bits per second (bps). In this paper, we are interested in acoustic data transmission through the steel body of the drill string. We propose trellis coded modulation (TCM) coupled with multicarrier modulation. To cope with the intersymbol interference (ISI) and any synchronization issues at the receiver, joint carrier-phase synchronization and decision feedback equalization is used. In addition, weights based on the knowledge of the signal-to-noise-ratio (SNR) at different passbands are incorporated into the modified Viterbi decoder to improve the bit-error rate (BER). By using a realistic model of a 1-km-drill string channel, the BER performance of the acoustic system is demonstrated while achieving a signaling rate of 400 bps, about two orders of magnitude higher than current state-of-art mud-pulse telemetry systems. In addition, our results demonstrate that the decision feedback equalizer (DFE) is vastly less power-hungry than orthogonal frequency division multiplexing (OFDM).