Normalised Multi-Stage Clustering Equaliser For Underwater Acoustic Channels

In this letter, a novel robust two-dimensional nearfield millimeter-wave (MMW) image reconstruction algorithm is developed based on compressed sensing (CS) in the presence of impulsive measurement noise. To enable the sparse MMW image recovery, the CS algorithm usually employs the popular l 2 -norm as the data fidelity term and a combination of multiple sparsityinduced functions as the penalty term. The l 2 -norm is non-robust against impulsive noise since the Gaussian noise distribution assumption is not valid, and the presence of impulsive noise will severely degrade the robustness of the compressive MMW image recovery. To gain more robustness performance, a complex correntropy based data-fitting term, namely complex correntropic loss (CC-loss), is used to replace the l2-norm for the near-field MMW measurement contaminated by impulsive noise. In order to solve the corresponding minimization problem, an additive half quadratic method is used to transform the CC-loss term to its convex form, and then a parallel primal-dual process is used to guarantee the convergence of the proposed algorithm. In total, 150-GHz MMW experimental results show that the proposed algorithm can achieve accurate image reconstruction from compressive measurements under different impulsive noise levels.

The paper presents a brief survey on the underwater acoustic communication channel estimation of orthogonal frequency division multiplexing using deep learning techniques. In recent times, deep learning has shown great potential in the field of communication. The underwater acoustic channel is a highly challenging system as it has a restricted bandwidth, enlarged multi-path, medium refractive characteristics, acute fading, rapid time variation, large Doppler shifts, etc. For the UWA-OFDM communication model, DL-based receivers can be elucidated as a deep neural network for a better estimation than the conventional techniques. Sufficient training is required for the recovery of the transmitted symbols. It does not require any explicit CE or equalization and balancing. The estimation is accomplished in two stages. The first one is training stage. For training, this deep neural network-transmitted data, the signal received in the unknown channel, etc., are used as labeled data. The second one is test stage, where the deep neural network receiver recovers transmitted symbols, given the received signal. The accuracy and the computational complexity, these two have been a great challenge for the underwater acoustic channel estimation with the existing traditional channel estimation methods. The deep learning method is useful to solve these problems. This survey is to find out a few optimum methods of deep learning to estimate the UWA channel in an efficient way.

This paper analyzes the effect of bubble noise and Doppler shift in an underwater acoustic communication in addition to the influences by ambient noise and severe multipath propagation. Here Bubbles are assumed to be formed due to breaking of waves under strong winds. The bubble layer thus formed alters the speed of the sound in the field. The changed speed of the sound in the field, results in transmission loss during propagation and scattering of the signals. Existing Underwater Acoustic channel (UWA) models do not account for the combined effects. In this paper, first UWA multipath channel is modeled in the presence of ambient ocean noise and bubble noise. Subsequently a new method is proposed for Doppler shift compensation that avoids resampling as well as the need for preloading the whole data packet prior to demodulation and facilitates real time operation at receiver for an Orthogonal Frequency Division Multiplexing (OFDM) based UWA communication system. The proposed algorithm is simulated and verification is carried out by studying the Quality of Service parameter Bit Error Ratio (BER) for different Doppler shifts. The proposed method can be implemented with significantly reduced computational complexity.

One of the challenges of modulation techniques used in Fifth-Generation (5G) is their robustness in noisy environment. Conventional Orthogonal Frequency Division Multiplexing (OFDM) cannot be considered as a 5G waveform in its original form because of its certain limitations, such as performance degradation by impulsive noise (IN) and high peak to average power ratio (PAPR). Numerous modulation schemes proposed for 5G communications are able to overcome these drawbacks. Generalised Frequency Division Multiplexing (GFDM) is one of them. This paper analyses the performance of GFDM in presence of Additive White Gaussian Noise (AWGN), IN and Narrow Band Interference (NBI). It is found that GFDM is able to perform better than OFDM and Vector Orthogonal Frequency Division Multiplexing (VOFDM) in presence of noises, which can potentially be present in 5G applications. Simulation results show that GFDM achieve lower PAPR and Symbol Error Rate (SER) and an average of 10.73 dB and 4.73 dB gain in Signal to Noise Ratio (SNR) in presence of IN and combined IN and NBI respectively, as compared to OFDM and VOFDM.

This paper proposes Adaptive Step-Size Least Mean Modulus (ASS-LMM) algorithm for fast convergent and robust adaptive filtering in the presence of impulse noise. The ASS-LMM algorithm for use in adaptive filters defined in the complex-number domain is basically the LMM algorithm combined with a new adaptive step-size control algorithm to improve the convergence speed of the LMM algorithm while preserving its robustness against additive impulsive observation noise. Through analysis and experiment, we demonstrate effectiveness of the proposed ASS-LMM algorithm in making adaptive filters fast convergent and highly robust in the presence of impulse noise. Good agreement between simulated and theoretical convergence behavior in the transient phase and in the steady state proves the validity of the analysis.

The direct sequence-code division multiple accesses (DS-CDMA) signals are transmitted over multipath channels that introduce fading and shadowing. Combined effect of multipath fading and shadowing along with multiple access interference (MAI) and inter-symbol interference (ISI) worsens the system performance. Further, experimental results have confirmed the presence of impulsive noise in wireless mobile communication channels. Hence, this paper presents a comprehensive learning particle swarm optimization (CLPSO) based multiuser detection technique for DS-CDMA systems over generalized-K (GK) fading channels in presence of impulsive noise. Maximal ratio combining (MRC) receive diversity is also incorporated to mitigate the effects of fading and shadowing. Performance of proposed M-decorrelator is studied by evaluating average error rate. Simulation results show that the proposed M-decorrelator performs better in the presence of fading, shadowing and heavy-tailed impulsive noise when compared to least squares, Huber and Hampel M-estimator based detectors.

We address the distributed estimation of a scalar Gaussian source in wireless sensor networks. The sensor nodes transmit their noisy observations, using the amplify-and-forward relaying strategy through coherent multiple access channel to the fusion center (FC) that reconstructs the source parameter. In this letter, we assume that the received signal at the FC is corrupted by impulsive noise and channel fading, as encountered for instance within power substations. Over Rayleigh fading channel and in presence of Middleton class-A impulsive noise, we derive the minimum mean square error (MMSE) optimal Bayesian estimator along with its mean square error performance bounds. From the obtained results, we conclude that the proposed optimal MMSE estimator outperforms the linear MMSE estimator developed for Gaussian noise scenario.

Under water acoustic (UAC) channel is subjected to large delay spread compared to radio frequency (RF) channels. In order to capture this large delay spread and avoid ISI, the UAC symbol duration needs to be considerably large reducing the transmission data rate. Unlike other RF systems, in Ultra-wideband (UWB) RF systems, small pulse width in the order of nano seconds is used to transmit information. This in turn, gives high resolution multipaths. In a time hopping UWB system, to avoid multipath overlap, the chip duration and the frame duration are kept equal to or larger than the maximum delay spread of the channel. This results in large UWB symbol duration and hence reduces the data rate. In order to increase the data rate, the chip duration, and frame duration can be reduced to less than the maximum delay spread. In addition, to farther increase the data rate, guard interval between consecutive symbols can be reduced/removed allowing consecutive symbols to overlap. This results in intersymbol interference (ISI). Surprisingly, this type of overlap doesn't degrade UWB system performance significantly. In this paper, the same concept is applied to various UAC channels. In order to increase the data rate, consecutive UAC symbols are allowd to overlap. Performance under ISI is then evaluated.

Rolling element bearings are widely used in various industrial machines. Fault diagnosis of rolling element bearings is a necessary tool to prevent any unexpected accidents and improve industrial efficiency. Although proved to be a powerful method in detecting the resonance band excited by faults, the spectral kurtosis (SK) exposes an obvious weakness in the case of impulsive background noise. To well process the bearing fault signal in the presence of impulsive noise, this paper proposes a fault diagnosis method based on the cyclic correntropy (CCE) function and its spectrum. Furthermore, an important parameter of CCE function, namely kernel size, is analyzed to emphasize its critical influence on the fault diagnosis performance. Finally, comparisons with the SK-based Fast Kurtogram are conducted to highlight the superiority of the proposed method. The experimental results show that the proposed method not only largely suppresses the impulsive noise, but also has a robust self-adaptation ability. The application of the proposed method is validated on a simulated signal and real data, including rolling element bearing data of a train axle.

A novel direction finding algorithm for distributed sources under impulsive noise environments

Techniques for achieving reliable communications over underwater acoustic channels are investigated in this work. We consider implementation of an underwater spread-spectrum communication system. To compensate the multipath fading and Doppler effects in the underwater acoustic channel, a transform-domain approximate RLS (TARLS) blind equalizer is incorporated in the receiver. The computational complexity of the TARLS blind equalizer is in the same order as that of conventional LMS blind equalizers. Nevertheless, the TARLS blind equalizer significantly outperforms conventional LMS blind equalizers in the convergence and tracking ability. The TARLS blind equalizer is also shown to have good numerical stability in a finite-precision environment and thus is ready for digital implementation. The goodness of the proposed equalizer is demonstrated by applying it to a simulated multipath fading channel often encountered in underwater acoustic communications.

An improved method for direction-of-arrival (DOA) estimation algorithm based on Multistage Wiener Filters (MSWF) in the presence of impulsive noise is proposed in this paper. There are many DOA estimation algorithms based on Multistage Wiener Filters, but all of them must satisfy the requirement of the noise is Gaussian white noise, which can not the accurately in the presence of impulsive noise. The improved method has good performance at DOA estimate when the noise is impulsive noise, and some computer simulations show it.

Predictions of bit error rate caused by a combination of impulsive and Gaussian noise are made for data communication systems using PSK, DPSK and FSK, with direct carrier modulation. The technique uses the amplitude probability distribution (APD) to characterise the noise at the input of the receiver demodulator. A direct numerical approach (with numerical integration, where appropriate) is used to produce predictions for the case where decisions are made by sampling the demodulated signal at times nominally at the centre of the received data symbols, there being a minimum amount of postdetector filtering. Error rates in mobile data communication systems have been measured at roadside locations and in the laboratory. In the latter case, an impulsive noise simulator capable of producing noise with specified statistical characteristics was used. Laboratory measurements have also been made with fading signals, and the prediction techniques have been extended to cover this case. Generally, agreement between theory and experiment is good, and it is concluded that the techniques ar applicable in any system where channel separations are well defined and, consequently, where IF filters are adequately standardised.

Additive asynchronous and cyclostationary impulsive noise limits\ncommunication performance in OFDM powerline communication (PLC) systems.\nConventional OFDM receivers assume additive white Gaussian noise and hence\nexperience degradation in communication performance in impulsive noise.\nAlternate designs assume a parametric statistical model of impulsive noise and\nuse the model parameters in mitigating impulsive noise. These receivers require\noverhead in training and parameter estimation, and degrade due to model and\nparameter mismatch, especially in highly dynamic environments. In this paper,\nwe model impulsive noise as a sparse vector in the time domain without any\nother assumptions, and apply sparse Bayesian learning methods for estimation\nand mitigation without training. We propose three iterative algorithms with\ndifferent complexity vs. performance trade-offs: (1) we utilize the noise\nprojection onto null and pilot tones to estimate and subtract the noise\nimpulses; (2) we add the information in the data tones to perform joint noise\nestimation and OFDM detection; (3) we embed our algorithm into a decision\nfeedback structure to further enhance the performance of coded systems. When\ncompared to conventional OFDM PLC receivers, the proposed receivers achieve SNR\ngains of up to 9 dB in coded and 10 dB in uncoded systems in the presence of\nimpulsive noise.\n

Impulse noise is a potential impairment of very-high speed digital subscriber line (VDSL) services. VDSL systems are protected from impulse noise using a combination of Reed-Solomon coding and interleaving. Since there are two competing proposals for VDSL, the first one based on the use of QAM line code and the second one using DMT modulation, it is interesting to compare their performance in terms of immunity to impulse noise. The paper presents a comparison of the behavior of QAM-VDSL and DMT-VDSL in the presence of impulse noise in real configurations. The required interleaving delay to correct all the generated errors for both systems is investigated. It is shown that when no notches are used in the radio-amateur bands, QAM and DMT systems require similar delays in order to achieve the same level of protection against impulse noise. However, when notches are implemented, DMT-VDSL achieves higher rates than QAM-VDSL. Therefore, larger error-correcting code redundancy can be used, which results in lower end-to-end delays for DMT-VDSL systems.