Sparse underwater acoustic channel estimation in colored gaussian noise

Various channel estimation methods have been proposed for shallow underwater communication channel with sparse channel type. A matching pursuit (MP), which is one of typical sparse channel estimation methods, can detect dominant components of sparse channel sequentially as a portion of matching. Mean squared error of MP, however, converges to fixed value even though signal-to-noise ratio (SNR) increases. This paper proposes a minimum mean squared error (MMSE) estimation method with constant false alarm (CFAR) detection for sparse channel estimation. Unlike MP, the mean squared error of the proposed method decreases as SNR increases and is lower than that of conventional MMSE estimators on the sparse channel. Experimental results show that the proposed method estimates sparse underwater communication channel efficiently.

In this paper, a fast orthogonal matching pursuit (OMP) algorithm based on optimized iterative process is proposed for sparse time-varying underwater acoustic (UWA) channel estimation. The channel estimation consists of calculating amplitude, delay and Doppler scaling factor of each path using the received multi-path signal. This algorithm, called as OIP-FOMP, can reduce the computationally complexity of the traditional OMP algorithm and maintain accuracy in the presence of severe inter-carrier interference that exists in the time-varying UWA channels. In this algorithm, repeated inner product operations used in the OMP algorithm are removed by calculating the candidate path signature Hermitian inner product matrix in advance. Efficient QR decomposition is used to estimate the path amplitude, and the problem of reconstruction failure caused by inaccurate delay selection is avoided by optimizing the Hermitian inner product matrix. Theoretical analysis and simulation results show that the computational complexity of the OIP-FOMP algorithm is reduced by about 1/4 compared with the OMP algorithm, without any loss of accuracy.

This paper is concerned with a challenging problem of channel estimation for amplify-and-forward cooperative relay based orthogonal frequency division multiplexing (OFDM) systems in the presence of sparse underwater acoustic channels and of the correlative non-Gaussian noise. We exploit the sparse structure of the channel impulse response to improve the performance of the channel estimation algorithm, due to the reduced number of taps to be estimated. The resulting novel algorithm initially estimates the overall sparse channel taps from the source to the destination as well as their locations using the matching pursuit (MP) approach. The correlated non-Gaussian effective noise is modeled as a Gaussian mixture. Based on the Gaussian mixture model, an efficient and low complexity algorithm is developed based on the combinations of the MP and the space-alternating generalized expectation-maximization (SAGE) technique, to improve the estimates of the channel taps and their location as well as the noise distribution parameters in an iterative way. The proposed SAGE algorithm is designed in such a way that, by choosing the admissible hidden data properly on which the SAGE algorithm relies, a subset of parameters is updated for analytical tractability and the remaining parameters for faster convergence Computer simulations show that underwater acoustic (UWA) channel is estimated very effectively and the proposed algorithm has excellent symbol error rate and channel estimation performance.

We propose a simple sparse channel estimation and tracking method for orthogonal frequency-division multiplexing (OFDM) systems based on a dynamic parametric channel model, where the channel is parameterized by a small number of distinct paths, each characterized by path delay and path gain, and all parameters are time varying. In the proposed method, we adaptively choose the delay grid and estimate each channel path delay iteratively. To further reduce the complexity, we also propose a tracking algorithm based on the fact that the changes in the channel path number and path delays are small over a few adjacent OFDM symbols. After the physical path delays are estimated, we then estimate the channel path gains by using the polynomial basis expansion model (P-BEM). Simulation results demonstrate the effectiveness of the proposed channel estimation and tracking method in dynamic environments. Compared with the compressive-sensing-based channel estimator using the orthogonal matching pursuit (OMP) algorithm, the new technique proposed here has much lower computational complexity while offering comparable performance.

In this paper, equalization of channels with large delay spread but with few nonzero taps, such as those encountered in HDTV, are considered. Exploiting the sparse nature of the channel impulse response, we propose a decision feedback equalizer (DFE) which is based on a channel estimate obtained via a matching pursuit (MP) algorithm rather than the standard least squares (LS) channel estimate. It is shown that equalizers so designed result in a large performance gain. It is also shown that while truncation of the LS channel estimate to give a sparse channel estimate leads to some performance improvement, it falls well short of the MP-based DFE. In addition, an adaptive scheme which is intermediate in complexity between the LMS and RLS is presented for updating the sparse channel estimate in a time-varying environment. This adaptive algorithm outperforms the LMS.

In this paper, an efficient channel estimation algorithm is proposed for amplify-and-forward (AF) cooperative relay based orthogonal frequency division multiplexing (OFDM) system in the presence of sparse underwater acoustic channels and of the correlative non-Gaussian noise. The algorithm is based on the combinations of the matching pursuit (MP) and the space-alternating generalized expectation-maximization (SAGE) technique, to improve the estimates of the channel taps and their location as well as the Gaussian mixture noise distribution parameters in an iterative way. Computer simulations show that underwater acoustic channel is estimated very effectively and the proposed algorithm has excellent symbol error rate (SER) and channel estimation performance, as compared to the existing ones.

This paper addresses the effective sparse channel estimation based on the most significant taps (MST) selection with high spectral efficiency and moderate computational complexity for orthogonal frequency division multiplexing (OFDM) systems. The main goal of this paper is to obtain an efficient time domain threshold and achieve the channel estimation performance approaching the least squares (LS) method with exact sparsity (exact number of non-zero channel taps) without using prior knowledge of both channel statistics and noise standard deviation. Firstly, classical LS method is used to get an initial channel impulse response (CIR) estimate. Then a novel effective threshold, estimated from the noise coefficients of the initial estimated CIR, is proposed. Finally, the obtained threshold is used to select the most significant taps. Simulations show that the proposed method is able to achieve good channel estimation performances for sparse channel within a comparatively wide range of sparsity with small number of pilots and moderate computational complexity.

Orthogonal frequency division multiplexing (OFDM) is adopted in most wireless communication systems, and the performance of OFDM is affected by impulse noise (IN). Better channel estimation (CE) performance is required to detect the channel information on the receiving side. The OFDM system is considered with the number of subcarriers carrying the data and pilot symbols. In the OFDM modulator, the symbols of the frequency domain are transformed into the signals of the time domain using inverse discrete Fourier transformation (IDFT). Effective impulsive noise mitigation is crucial for improving the effectiveness of OFDM communication systems, and it improves the signal-to-noise ratio (SNR) at the receiver. A cyclic prefix is then appended before transmission through the channel. In the receiver noise, the effect of IN is mitigated jointly with CE using the Bayesian matching pursuit (BMP) and Moth-flame algorithm (MFA). In this approach, the IN and channel information is considered as a sparse vector. Here, the data symbols used are considered an unknown parameter. The approach incorporating all subcarriers has a lower mean square error (MSE) of IN estimate for impulsive noise reduction. The MFA algorithm is used to optimize the sensor matrix in BMP. The sparsity of the channel and the impulse response are observed in the time domain to represent the channel and IN together. It exactly recovers the sparse signal for finding the convex objectives of the sparse minimizer, and the sparse solution is obtained with fixed point updates. The proposed BMP algorithm improves the CE by explicitly considering the existence of IN. The BMP is a greedy algorithm that selects the most correlated residuals at each column. Under mutual incoherence, it recovers the signal with a higher probability. The simulated outcomes proved that the proposed CE and noise mitigation model achieved better performance based on the bit error rate (BER), throughput and MSE.

Traditional orthogonal matching pursuit (OMP) algorithms for direction of arrival (DOA) estimation suffer from poor angular resolution and noise suppression. In this paper, we analyze the reason why the OMP algorithm has difficulties in resolving closely separated DOAs and conclude that it lies in the rules of support detection. Moreover, we propose a solution to this problem via developing the connection between the sparse reconstruction class algorithm and the subspace algorithm from the structure of the redundant dictionary. Based on the framework of the matching pursuit (MP) algorithm, the effective information of the signal and noise subspaces is integrated, and a noise subspace reprojection orthogonal matching pursuit (NSRomp) algorithm for DOA estimation is proposed. By adopting signal subspaces to reconstruct the original signal, the proposed NSRomp can reduce both the influence of noise on the selection of the support set and the computing time. By implementing the minimum norm method to optimize the noise subspace into a vector, which corrects the selection rules of the support set during each iteration, the angular resolution of the proposed algorithm is improved. From the simulation results, when the signal to noise ratio (SNR) is lower than or near 0, the angular resolution can be improved by > 15° using OMP algorithms to by > 5° using the proposed NSRomp algorithm.

Underwater channels being highly unpredictable and rapidly time varying, are recognized as one of the most difficult communication media to deal with. Compared to mobile radio channels, underwater channels suffer severe multipath propagation and significant Doppler shift. In order to improve communication quality, the time varying channels should be estimated at regular intervals. The sparsity of multipath channel motivates to exploit compressive sensing methods for channel estimation. The proposed work considers the simulation of OFDM communication system with QAM modulation and channel estimation is performed using both conventional and compressive sensing methods. The work explores the possibilities of using VirTEX software package for a realistic channel modelling.

This paper reports improvements in compression of both inter- and intra-frame images by the matching pursuits (MP) algorithm. For both types of image, applying a 2D wavelet decomposition prior to MP coding is beneficial. The MP algorithm is then applied using various separable ID codebooks. MERGE coding with precision limited quantization (PLQ) is used to yield a highly embedded data stream. For inter-frames (residuals) a codebook of only 8 bases with compact footprint is found to give improved fidelity at lower complexity than previous MP methods. Great improvement is also achieved in MP coding of still images (intra-frames). Compared to JPEG 2000, lower distortion is achieved on the residual images tested, and also on intra-frames at low bit rates.

MMP-DCD-CV based sparse channel estimation algorithm for underwater acoustic transform domain communication system

An improved channel estimation method using doubly reduced matching pursuit over fractional delay multipath channel

This paper investigates sparse channel estimations to improve the performance of passive-phase conjugation (PPC) communications. PPC processing achieves pulse compression for time delayed arrivals at the receiver. This property is used for underwater communications with a reduced computational load. The channel estimation is required by PPC. In multipath channels, the sparse channel estimations can be used to improve the communication performance. The dominant arrivals are estimated by matching pursuit (MP) processing for PPC processing and the remained arrivals are treated as zeros. Using experimental data collected in a range dependent acoustic channel, MP processing is assessed in improving the performance of PPC communications.

A key research area in wireless transmission is underwater communication. It has a vital role in applications like underwater sensor networks (UWSNs), disaster detection and surveillance. The underwater channel is considered as one of the most challenging communication channels due to the inherent unique characteristics of the medium. Acoustic signals are the current best candidates for underwater communication. Orthogonal frequency division multiplexing (OFDM) facilitates high data rate transmission over long underwater acoustic (UWA) channels. The main aim of this paper is to evaluate the BER performance of fractional Fourier transform (FRFT) based OFDM in a frequency selective Rician fading UWA channel in the presence of CFO. We also propose a novel 16 QAM FRFT-based OFDM to be used in the same channel conditions. The simulation results show that the BER performance of FRFT-OFDM system outperforms than that of the FFT-OFDM system at different values of FRFT angle parameter ‘α’. The plots also indicate that our proposed 16 QAM scheme can support high data rates and still can provide good BER performance.