Sparse Channel Estimation Research Articles

Sparse channel estimation for underwater acoustic OFDM systems with super-nested pilot design

Underwater acoustic channels are usually sparse and have large delay spread. In this paper, super-nested array structure in the field of array signal processing is borrowed to be the pilot design of underwater acoustic OFDM systems, in order to better estimate large delay spread channels with limited number of pilots. Specifically, by constructing the pilot subcarriers’ covariance matrix and the pilot position difference, the virtual pilot on the differential co-array are employed for sparse channel estimation. In order to reduce the error between the estimated pilot subcarriers’ covariance matrix and the ideal covariance matrix, the cross-correlation matrix of pilot subcarriers is estimated in advance for interference cancellation. Then the sparse iterative covariance estimation algorithm (SPICE) is adopted to further refine the covariance matrix and improve the channel estimation performance. Simulation, pool and sea experimental results show that the proposed method can effectively estimate the large delay spread sparse channels and improve the performance of underwater acoustic OFDM systems.

A switching norm based least mean Square/Fourth adaptive technique for sparse channel estimation and echo cancellation

To realize rapid data transmission, the broadband transmission technique is being extensively explored and applied in existing wireless communication systems. The multi-path channel in broadband wireless communication systems is sparse and this sparsity can be used as prior knowledge to estimate the channel. To make use of sparsity, this paper recommends a switching norm-based least mean square/fourth (SN-LMS/F) adaptive approach for sparse channel estimation and echo cancellation. The suggested SN-LMS/F is implemented by adding a soft parameter adjustment function (SPF) into the conventional LMS/F adaptive method's cost function and utilizes both the l0 and l1 norm to exploit system sparsity with reduced complexity. The simulated output indicates that the suggested SN-LMS/F adaptive technique provides a more desirable performance for sparse channel estimation and echo cancellation with reduced execution time.

Artificial neural network-based sparse channel estimation for V2V communication systems

Abstract Artificial neural networks (ANNs) have gained a lot of attention from researchers in the past few years and have been employed on a large scale. They have also been gaining momentum in wireless communication systems. For efficient vehicle-to-vehicle (V2V) channel communication, a sparse multipath channel issue must be studied. To minimize the multipath effect, a time reversal (TR) operation and time division synchronization orthogonal frequency division multiplexing (TDS-OFDM) have been appealing because of their fast synchronization and active spectral efficiency. To improve the transceiver's execution in a frequency-selective fading channel environment, an OFDM system is used to reduce inter- symbol interference (ISI). Simultaneous Orthogonal Matching Pursuit (SOMP) channel state estimator algorithm suffer from high computational cost and high computational complexity. The ANN algorithm has better performance than SOMP algorithm. The proposed neural network technologies have lower complexity than the SOMP algorithm. The application of ANN is capable of solving complex problems, such as those encountered in image, signal processing and have been implemented for channel estimation in OFDM. The proposed ANN outperformed the SOMP algorithm with regard to signal compensation. Overall, the ANN algorithm achieved the best performance. This study proposes an ANN-based sparse channel state estimator. Regarding the bit error rate (BER) metric, the proposed estimator outperforms the channel estimation approach based on the SOMP. The simulation results confirm the efficacy of the proposed approach.

Implementing intelligent‐based sparse channel estimation in Multi User‐Multiple Input Multiple Output‐Orthogonal Frequency Division Multiplexing system with hybridized optimization algorithm

SummaryThe channel plays a significant role as the multipath in Multiple Input Multiple Output‐Orthogonal Frequency Division Multiplexing (MIMO‐OFDM) systems. The precoding‐based channel estimation algorithms estimate the channel capacity at the receiver node and they provide efficient channel estimates under worst case channel scenarios. To estimate the channel capacity in the mobile node, semi‐blind scenario is a crucial task in Multi User (MU) MIMO systems. Therefore, a semi blind sparse channel estimation algorithm is demonstrated in the MU‐MIMO system to provide better performance. In the transmitter side, the signal modulation is performed with the help of Quadrature Phase Shift Keying (QPSK), and the modulated signal is given to Pulse Shaping Algorithm (PSA) to reduce the inter‐carrier interference as well as inter symbol interference. At each transmitter, the Inverse Fast Fourier Transforms (IFFT) technique is used for mapping the symbols and then the mapped signal is send to the receiver. In the receiver side, the FFT, pulse reshaping, and QPSK demodulation blocks are serially connected and perform inverse operations of the transmitter in the receiver. Then, the sparse channel is estimated at the receiver by using the semi blind sparse algorithm, where the parameters are optimized by using the Hybridized Drosophila with Virus Colony Search Optimization (HD‐VCSO). The objective function of the developed sparse channel estimation is the reduction of Minimum Mean Square Error (MMSE) in the channel. The performance of the developed sparse channel estimation algorithm is analyzed through different conventional algorithms to validate the effectiveness of the proposed algorithm.

Non-orthogonal pilot pattern design and sparse channel estimation for underwater acoustic MIMO-OFDM systems

This paper proposes a novel non-orthogonal pilot pattern design method for underwater acoustic (UWA) multiple-input-multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems, leveraging compressive sensing (CS) channel estimation. This approach adopts the mutual incoherence property (MIP) and enhances pilot density without increasing pilot overhead. We conducted rigorous mathematical derivation to generate the explicit formula for the non-orthogonal pilot pattern with the mutual coherence of zero. In addition, we estimate multipath delay and amplitude by employing parallel orthogonal matching pursuit (OMP) channel estimation. The co-channel interference (CCI) is eliminated from the systems with the proposed non-orthogonal pilot pattern to estimate the channel coefficients from two transmitters without interference in a single iteration. Simulations and lake trial results demonstrate the superior performance of the proposed non-orthogonal pilot pattern over the orthogonal and existing non-orthogonal pilot patterns in terms of normalized mean square error (NMSE) of channel estimation and bit error rate (BER). The 2×8 UWA MIMO-OFDM system achieves a data rate of 78.11 kbps with a BER of 1.42×10−4 in the lake trial over the communication distance of 773 m.

An Optimized Sequence for Sparse Channel Estimation in a 5G MIMO System

ABSTRACT Recently, the massive Multiple-Input Multiple-Output (MIMO) system has been integrated with machine learning approaches to realise automatic channel state information detection. These methods need high computational complexity due to the lack of optimal training sequences and the failure to control the sparsity assembly based on massive MIMO. Moreover, it does not consider optimised training sequences based on a compressive sensing approach in the presence of contamination. Thus, this paper proposed an enhanced deep learning model with an optimised pilot training sequence for sparse channel estimation in a 5 G massive MIMO system. Initially, the system and sparse block channel impulse response model will be constructed by considering the time-domain synchronous Generalized Frequency Division Multiplexing (GFDM) system and additive white Gaussian noise (AWGN). In the proposed work, a seagull optimisation algorithm is developed to design the training pilot sequence depending on the coherence properties of the sensing matrix, which is used to recover the channel impulse response. Then, the sparse massive MIMO channel is evaluated by proposing a new deep channel estimator with an enhanced stacked auto encoder (D-ESAE). The proposed channel MSE is 10−6, the bit error rate is 0.04, and NMSE is 10−9 at 40 dB SNR through Matlab simulation.

ZA-LMS-based sparse channel estimator in multi-carrier VLC system

ZA-LMS-based sparse channel estimator in multi-carrier VLC system

Sparse Channel Estimation With Global Optimum Solution for Orthogonal Signal Division Multiplexing in Underwater Acoustic Communication

Sparse Channel Estimation With Global Optimum Solution for Orthogonal Signal Division Multiplexing in Underwater Acoustic Communication

Near-Field Sparse Channel Representation and Estimation in 6G Wireless Communications

Near-Field Sparse Channel Representation and Estimation in 6G Wireless Communications

Compressed Sensing Channel Estimation Algorithm Combined with Wavelet Denoising

In wireless multipath channels, there is sparsity, in which traditional channel estimation algorithms do not take advantage of and ignore the impact of noise. An effective estimation algorithm combining compressed sensing and wavelet de-noising is proposed. Before channel estimation, the pilot signal received by the receiver is de-noised by the wavelet de-noising method to obtain the de-noised measurement vector, and then the original signal is recovered by the compressed sensing technology for sparse channel estimation. This algorithm breaks away from the limitation of traditional algorithms that require predictive sparsity to achieve sparsity adaptive signal reconstruction. Through simulation analysis, it has been proven that the fusion wavelet denoising algorithm can improve the estimation performance, reduce estimation errors, and be more suitable for sparse channel estimation by denoising the signal during channel estimation.

An Adaptive Sparse Channel Estimation Algorithm for OFDM Systems Based on Distributed Compressive Sensing

The current greedy iterative pursuit algorithms for sparse channel estimation in an orthogonal frequency division multiplexing (OFDM) system based on compressive sensing (CS) or distributed CS (DCS) have the disadvantages of relying on channel priori information as halting condition and having a low support searching efficiency. Under DCS framework, this letter proposes a unique halting condition for greedy algorithms by exploiting the delay correlation between adjacent symbol channels. Additionally, we present a segmented pruning strategy that supports to select multiple atoms in a single iteration to improve the support searching efficiency. Simulation results show that our algorithm can achieve more robust sparsity-adaptive channel estimation with reduced computational complexity compared to the traditional methods.

Joint Channel Estimation and Data Detection for IRS-Assisted OFDM Systems

Intelligent reconfigurable surface (IRS) becomes a promising technology for improving spectrum efficiency and reducing the energy consumption in the next-generation networks. To reap the passive beamforming gain of IRS, accurate channel estimation is essentially required. However, the existing channel estimation methods for the IRS-assisted orthogonal frequency division multiplexing (OFDM) systems suffer from serious performance loss, due to using the least-squares (LS) estimator and ignoring the channel sparsity in the time domain. Another drawback of the existing methods is that the channel estimation and data detection are separated from each other so that any inaccurate channel estimation would cause an error accumulation problem for the data detection. To overcome these drawbacks, we propose a novel joint sparse channel estimation and data detection method for the IRS-assisted OFDM systems. The novelties of the proposed method are twofold: (i) present a new common sparse representation model for the joint channel estimation and data detection, which can utilize the common channel sparsity between the pilots and data streams to significantly improve the channel estimation performance and reduce the training overhead; and (ii) propose an efficient variational Bayesian inference (VBI) framework to simultaneously perform the Bayesian inference for the sparse channel recovery and data detection, where the coupling effect caused by the reflection pattern is trickly evaded by adopting an additional column-independent factorization. Besides exploiting the common sparsity for the channel estimation and data detection, the proposed method also avoids using the LS estimator, and thus its performance can be greatly enhanced. Simulation results verify the superiority of the proposed method.

Energy leakage in OFDM sparse channel estimation: The drawback of OMP and the application of image deblurring

In this paper, in order to reduce the energy leakage caused by the discretized representation in sparse channel estimation for Orthogonal Frequency Division Multiplexing (OFDM) systems, we systematically have analyzed the optimal locations of atoms with discrete delays for each path reconstruction from the perspective of linear fitting theory. Then, we have investigated the adverse effects of the non-ideal inner product function on the iteration in one of the most widely used channel estimation method, Orthogonal Matching Pursuit (OMP). The study shows that the distance between the selected atoms for each path in OMP can be larger than the sampling interval, which prevents OMP-based methods from achieving better performance. To overcome this drawback, the image deblurring-based channel estimation method, in which the channel estimation problem is analogized to one-dimensional image deblurring, was proposed to improve the large compensation distance of traditional OMP. The advantage of the proposed method was validated by the results of numerical simulation and sea trial data decoding.

An Improved SAMP Algorithm for Sparse Channel Estimation in OFDM System.

Channel estimation of an orthogonal frequency division multiplexing (OFDM) system based on compressed sensing can effectively reduce the pilot overhead and improve the utilization rate of spectrum resources. The traditional SAMP algorithm with a fixed step size for sparse channel estimation has the disadvantages of a low estimation efficiency and limited estimation accuracy. An Improved SAMP (ImpSAMP) algorithm is proposed to estimate the channel state information of the OFDM system. In the proposed ImpSAMP algorithm, the received signal is firstly denoised based on the energy-detection method, which can reduce the interferences on channel estimation. Furthermore, the step size is adjusted dynamically according to the l2 norm of difference between two estimated sparse channel coefficients of adjacent phases to estimate the sparse channel coefficients quickly and accurately. In addition, the double threshold judgment is adopted to enhance the estimation efficiency. The simulation results show that the ImpSAMP algorithm outperforms the traditional SAMP algorithm in estimation efficiency and accuracy.

Sparse Channel Estimation With Distorted Signals for UWB OFDM Systems

The power amplifier (PA) is an indispensable part of ultra-wideband (UWB) orthogonal frequency division multiplexing (OFDM) systems. Nevertheless, it will produce nonlinear distortions, degrading the accuracy of channel estimation and thus the performance of the system. To improve the system performance, it is essential to investigate channel estimation using distorted signals. Taking advantage of the sparsity of the UWB channel, a sparse channel estimation method using distorted signals is proposed in this letter, referred to as sparse channel estimation combined with power amplifier nonlinear cancellation (SCE-PANC). Within SCE-PANC, the measurement matrix is constructed only with pilot subcarriers and nonlinear distortions will be eliminated iteratively. The numerical results show that the proposed method has a better performance than the state-of-the-art methods.

Hybrid Transceiver Design for Tera-Hertz MIMO Systems Relying on Bayesian Learning Aided Sparse Channel Estimation

Hybrid transceiver design in multiple-input multiple-output (MIMO) Tera-Hertz (THz) systems relying on sparse channel state information (CSI) estimation techniques is conceived. To begin with, a practical MIMO channel model is developed for the THz band that incorporates its molecular absorption and reflection losses, as well as its non-line-of-sight (NLoS) rays associated with its diffused components. Subsequently, a novel CSI estimation model is derived by exploiting the angular-sparsity of the THz MIMO channel. This is followed by designing a sophisticated Bayesian learning (BL)-based approach for efficient estimation of the sparse THz MIMO channel. The Bayesian Cramer-Rao Lower Bound (BCRLB) is also determined for benchmarking the performance of the CSI estimation techniques developed. Finally, an optimal hybrid transmit precoder and receiver combiner pair is designed, which directly relies on the beamspace domain CSI estimates and only requires limited feedback. Finally, simulation results are provided for quantifying the improved mean square error (MSE), spectral-efficiency (SE) and bit-error rate (BER) performance for transmission on practical THz MIMO channel obtained from the HIgh resolution TRANsmission (HITRAN)-database.

A new optimized least‐square sparse channel estimation scheme for underwater acoustic communication

SummaryIn underwater acoustic (UWA) communication, orthogonal frequency division multiplexing (OFDM) is a promising technology that is highly essential to get channel state information meant for channel estimation (CE). Nevertheless, higher complexity, slower convergence, and poor performance, which degrade the performance estimation, are the limitations of the traditional CE methodologies. Thus, by amalgamating the least square (LS)‐CE algorithm along with polynomial interpolated black widow optimization (PI‐BWO) model, an optimized least square sparse (OLSS) CE algorithm has been proposed to intend for a UWA‐OFDM communication system. Formerly, by utilizing the 2's complement shift left turbo encoding (2CSL‐TE) methodology, the input signal is encoded. After that, the modulated encoded signal is provided for inverse fast Fourier transform (IFFT) operations; subsequently, they are transferred over the UWA channel toward the receiver OFDM. By employing the OLSS methodology, the received OFDM signal's interference‐free region is utilized for sparse CE at the receiver. Regarding symbol error rate (SER), bit error rate (BER), mean square error (MSE), and peak signal‐to‐noise ratio (PSNR), the proposed model's experiential outcome is evaluated and analogized with the other prevailing methodologies. When analogized with the conventional models, the proposed estimation methodologies achieved better performance.

Channel Estimation for mmWave Massive MIMO Systems With Mixed-ADC Architecture

Millimeter wave (mmWave) communications are widely preferred due to the rich bandwidth and potentially huge spectrum resources. Nowadays, mixed-ADC architecture combined with mmWave massive MIMO has become a communication mainstream, which can effectively solve the issue of high total power consumption and cost of base station (BS) circuits. However, the channel estimation problem for mmWave massive MIMO systems with mixed-ADC architecture has not been studied yet. In this paper, we develop the sparse channel estimation method on this framework. Specifically, by exploiting the sparsity of mmWave channels, the beamspace channel estimation problem can be transformed into a sparse matrix recovery problem, the channel parameters are recovered using compressive sensing (CS) techniques. Simulation results show that the algorithms quantized by the mixed-ADC outperforms the low-resolution ADC, and the best performance can be achieved when the low-resolution ADC in the mixed-ADC architecture reaches five-bit.

A New Adaptive OMP-MAP Algorithm-based Iterative Sparse Channel Estimation for OFDM Underwater Communication

A sparse channel estimation approach based on doubly spread underwater acoustic (UWA) channels is widely used todetect coherent acoustic orthogonal frequency division multiplexing (OFDM) signals. A new time-domain channelestimation (CE) technique for OFDM based UWA communication with Rician fading is used to exploit the channel sparsity.First, to improve the estimation accuracy in high noise conditions, we have exploited the channel sparsity to generate aclosed-form equation for the termination condition. Then, in low-level noise instances, the additional criterion to balanceestimation accuracy and computing costs has been established. By incorporating these two requirements within theorthogonal-matching-pursuit (OMP) structure, an adaptive-OMP (AOMP) algorithm has been proposed. The AOMP andmaximum a posteriori probability (MAP) techniques are combined to provide a computationally efficient, and a newAOMP-MAP scheme for estimating the sparse complex channel path gain has been proposed. Further, The minimumvariance unbiased estimator is used to improve the proposed CE technique. Exploiting the experimental channel data,computer simulations reveal that the proposed CE technique obtains the outstanding outcomes.

Sparsity-Constrained Kernel Recursive Generalized Maximum Versoria Criterion Algorithm

This letter proposes a novel random Fourier features (RFF) based sparsity-aware Kernel recursive adaptive filtering algorithm which employs generalized maximum Versoria criterion (GMVC) as an adaptation cost and generalized Versoria zero attraction (GVZA) as a sparsifying-norm. The proposed RFF-based GVZA kernel recursive GMVC (RFF-GVZA-KRGMVC) algorithm is robust for nonlinear sparse channel estimation under non-Gaussian noise over both stationary and non-stationary environments. Simulations indicate that the proposed RFF-GVZA-KRGMVC approach delivers better convergence and bit error rate performance over the existing stochastic gradient based RFF-ZA-kernel-MVC (RFF-ZA-KMVC) algorithm at the cost of increased computational complexity. Furthermore, detailed convergence analysis is also performed for the proposed algorithm and corroborated by Monte-Carlo simulations.