Robust Equalization of Mobile Underwater Acoustic Channels

Pelekanakis, Konstantinos

doi:10.1109/joe.2015.2469895

Cited by 42 publications

(26 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Under CSI uncertainty, the Viterbi algorithm implements Algorithm 1 with the loglikelihoods computed using the noisy channel estimate, while ViterbiNet is trained using samples taken from different realizations of the noisy h(γ). [41], underwater acoustic channels [42], and impulsive noise channels [43]. In particular, we simulate an alphastable noise with stability parameter α = 0.5, skewness parameter β = 0.75, scale parameter c = 1, and location parameter µ = 0, following […”

Section: A Time-invariant Channelsmentioning

confidence: 99%

ViterbiNet: A Deep Learning Based Viterbi Algorithm for Symbol Detection

Shlezinger

Farsad

Eldar

et al. 2020

IEEE Trans. Wireless Commun.

148

130

View full text Add to dashboard Cite

Symbol detection plays an important role in the implementation of digital receivers. In this work, we propose ViterbiNet, which is a data-driven symbol detector that does not require channel state information (CSI). ViterbiNet is obtained by integrating deep neural networks (DNNs) into the Viterbi algorithm. We identify the specific parts of the Viterbi algorithm that are channel-model-based, and design a DNN to implement only those computations, leaving the rest of the algorithm structure intact.We then propose a meta-learning based approach to train ViterbiNet online based on recent decisions, allowing the receiver to track dynamic channel conditions without requiring new training samples for every coherence block. Our numerical evaluations demonstrate that the performance of ViterbiNet, which is ignorant of the CSI, approaches that of the CSI-based Viterbi algorithm, and is capable of tracking time-varying channels without needing instantaneous CSI or additional training data. Moreover, unlike conventional Viterbi detection, ViterbiNet is robust to CSI uncertainty, and it can be reliably implemented in complex channel models with constrained computational burden. More broadly, our results demonstrate the conceptual benefit of designing communication systems to that integrate DNNs into established algorithms.Parts of this work were accepted for presentation in the

show abstract

Section: A Time-invariant Channelsmentioning

confidence: 99%

ViterbiNet: A Deep Learning Based Viterbi Algorithm for Symbol Detection

Shlezinger

Farsad

Eldar

et al. 2020

IEEE Trans. Wireless Commun.

148

130

View full text Add to dashboard Cite

show abstract

“…In these experiments, we compare the BER and MSE of our methods with those of the conventional DFE and the state-of-the-art algorithms in the literature including: reweighted zero-attracting least mean p-power (RZALMP) [35], improved least sum of exponentials (ILSE) [34], l 0 -RLS [32] (indicated as LZRLS in the figures), as well as the conventional SA, RLS and LMS algorithms. We emphasize that all of these algorithms are designed to combat the impulsive noise through minimization of different cost functions summarized in Table 2.…”

Section: Resultsmentioning

confidence: 99%

“…Recently, in [32] and [33], the authors proposed recursive least squares (RLS)-type robust adaptive estimation and equalization methods, which leverage the sparsity of the underwater channels by adding an l 0 -norm to the cost function. However, the methods in [32] and [33] are not completely adaptive in the sense that they need a few threshold values to be determined in advance, and the values of these thresholds are highly dependent on the transmitted data statistics. In [34], a hyperbolic function, e.g., C (e m ) = cosh(e m ), is used as the cost function to inherently combine different powers of the error.…”

Section: Introductionmentioning

confidence: 99%

“…However, the methods in [32] and [33] are not completely adaptive in the sense that they need a few threshold values to be determined in advance, and the values of these thresholds are highly dependent on the transmitted data statistics. In [34], a hyperbolic function, e.g., C (e m ) = cosh(e m ), is used as the cost function to inherently combine different powers of the error. Moreover, [35] uses a sparse least mean p-power (LMP) algorithm to adaptively provide a robust estimate of the underwater channel.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust adaptive algorithms for underwater acoustic channel estimation and their performance analysis

Kari

Marivani

Khan

et al. 2017

Digital Signal Processing

View full text Add to dashboard Cite

We introduce a novel family of adaptive robust channel estimators for highly challenging underwater acoustic (UWA) channels. Since the underwater environment is highly non-stationary and subjected to impulsive noise, we use adaptive filtering techniques based on minimization of a logarithmic cost function, which results in a better trade-off between the convergence rate and the steady state performance of the algorithm. To improve the convergence performance of the conventional first and second order linear estimation methods while mitigating the stability issues related to impulsive noise, we intrinsically combine different norms of the error in the cost function using a logarithmic term. Hence, we achieve a comparable convergence rate to the faster algorithms, while significantly enhancing the stability against impulsive noise in such an adverse communication medium. Furthermore, we provide a thorough analysis for the tracking and steady-state performances of our proposed methods in the presence of impulsive noise. In our analysis, we not only consider the impulsive noise, but also take into account the frequency and phase offsets commonly experienced in real life experiments. We demonstrate the performance of our algorithms through highly realistic experiments performed on accurately simulated underwater acoustic channels.

show abstract

“…But it would occupy more bandwidth and reduce communication effectiveness, and this would be a deadly drawback for the fact that underwater acoustic channel is badly band-limited due to low-frequency ship noise and absorption of high-frequency energy [2]. It can be concluded that enhancing convergence rate of equalizer is a better option than enlarging the training sequences in underwater acoustic communication.…”

Section: Introductionmentioning

confidence: 99%

A new joint channel equalization and estimation algorithm for underwater acoustic channels

Yang

Liu

et al. 2017

J Wireless Com Network

View full text Add to dashboard Cite

Underwater acoustic channel (UAC) is one of the most challenging communication channels in the world, owing to its complex multi-path and absorption as well as variable ambient noise. Although adaptive equalization could effectively eliminate the inter-symbol interference (ISI) with the help of training sequences, the convergence rate of equalization in sparse UAC decreased remarkably. Besides, channel estimation algorithms could roughly figure out channel impulse response and other channel parameters through several specific mathematical criterions. In this paper, a typical channel estimation method, least square (LS) algorithm, is applied in adaptive equalization to obtain the initial tap weights of least mean square (LMS) algorithm. Simulation results show that the proposed method significantly enhances the convergence rate of the LMS algorithm.

show abstract

Robust Equalization of Mobile Underwater Acoustic Channels

Cited by 42 publications

References 26 publications

ViterbiNet: A Deep Learning Based Viterbi Algorithm for Symbol Detection

ViterbiNet: A Deep Learning Based Viterbi Algorithm for Symbol Detection

Robust adaptive algorithms for underwater acoustic channel estimation and their performance analysis

A new joint channel equalization and estimation algorithm for underwater acoustic channels

Contact Info

Product

Resources

About