We consider the problem of estimating channel in reconfigurable intelligent surface (RIS) assisted millimeter wave (mmWave) systems. We propose two variational expectation maximization (VEM) based channel estimation a...
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We consider the problem of estimating channel in reconfigurable intelligent surface (RIS) assisted millimeter wave (mmWave) systems. We propose two variational expectation maximization (VEM) based channel estimation algorithms, which exploit the angular domain sparsity of RIS-assisted mmWave channel. To fully capture this sparsity, both within and across UEs, we construct a novel column-wise coupled Gaussian prior. The first proposed structured-mean-field-based VEM (SMF-VEM) algorithm uses the proposed prior, and calculates the posterior distribution of the unknown channel by assuming that it belongs to a set of multivariate distributions. This algorithm inverts a high-dimensional matrix in its posterior update, and consequently does not scale well for a large number of RIS elements and base station antennas, which are commonly used in practical systems. The second proposed fast mean field-based VEM (FMF-VEM) algorithm reduces complexity by assuming a fully-factorized posterior. It also bounds the variational objective to remove the residue coupling between the channel and phase matrices. Using extensive numerical investigations for a practical RIS mmWave system, and by using multiple metrics, we show that the proposed i) SMF- and FMF-VEM algorithms outperform several of their state-of-the-art counterparts;and ii) FMF-VEM has a much lower time complexity than SMF-VEM.
Robust nonlinear regression frequently arises in data analysis that is affected by outliers in various application fields such as system identification, signalprocessing, and machine learning. However, it is still qu...
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Robust nonlinear regression frequently arises in data analysis that is affected by outliers in various application fields such as system identification, signalprocessing, and machine learning. However, it is still quite challenge to design an efficient algorithm for such problems due to the nonlinearity and nonsmoothness. Previous researches usually ignore the underlying structure presenting in the such nonlinear regression models, where the variables can be partitioned into a linear part and a nonlinear part. Inspired by the high efficiency of variable projection algorithm for solving separable nonlinear least squares problems, in this article, we develop a robust variable projection (RoVP) method for the parameter estimation of separable nonlinear regression problem with L-1 norm loss. The proposed algorithm eliminates the linear parameters by solving a linear programming subproblem, resulting in a reduced problem that only involves nonlinear parameters. More importantly, we derive the Jacobian matrix of the reduced objective function, which tackles the coupling between the linear parameters and nonlinear parameters. Furthermore, we observed an intriguing phenomenon in the landscape of the original separable nonlinear objective function, where some narrow valleys frequently exist. The RoVP strategy can effectively diminish the likelihood of the algorithm getting stuck in these valleys and accelerate its convergence. Numerical experiments confirm the effectiveness and robustness of the proposed algorithm.
This letter presents an RS-SPC concatenated code blindrecognition algorithm based on the single-error correction. The algorithm corrects the least reliable bit of the single parity check (SPC) codewords based on the p...
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This letter presents an RS-SPC concatenated code blindrecognition algorithm based on the single-error correction. The algorithm corrects the least reliable bit of the single parity check (SPC) codewords based on the parity check characteristics, thereby increasing correct Reed-Solomon (RS) codewords and laying the foundation for improving the recognition probability. In addition, this algorithm combines threshold judgement with the matrix recording method, thereby eliminating unnecessary iterative operations under the condition that accurate recognition is possible. At the same time, it employs probability theory as a theoretical basis to quantify the degree of dispersion of the data through sample variance. The experimental results demonstrate that the recognition probability of this algorithm is superior to that of all other algorithms. When the codeword error rate (CER) is 0.5, RS(15,9)-SPC(4,3) still has a recognition probability of 20%. For RS(255,239)-SPC(8,7), the gain of the proposed algorithm exceeds 1.3dB compared to the upper bound of the recognition probability.
We consider the problem of uniformity testing of Lipschitz continuous distributions with bounded support. The alternative hypothesis is a composite set of Lipschitz continuous distributions whose l(1) distances from t...
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We consider the problem of uniformity testing of Lipschitz continuous distributions with bounded support. The alternative hypothesis is a composite set of Lipschitz continuous distributions whose l(1) distances from the uniform distribution are bounded by epsilon from below. We propose a sequential test that adapts to the unknown distribution under the alternative hypothesis. Referred to as the Adaptive Binning Coincidence (ABC) test, the proposed strategy adapts in two ways. First, it partitions the set of alternative distributions into layers based on their distances to the uniform distribution. It then sequentially eliminates the alternative distributions layer by layer in decreasing distance to the uniform, allowing it to take advantage of favorable situations of a distant alternative by terminating early. Second, it adapts, across layers of the alternative distributions, the resolution level of the discretization for computing the coincidence statistic. The farther away the layer is from the uniform, the coarser the discretization necessary for eliminating this layer or terminating altogether. It thus terminates the test both early (via the layered partition of the alternative set) and quickly (via adaptive discretization) to take advantage of favorable alternative distributions. The ABC test builds on an adaptive sequential test for discrete distributions, which is of independent interest.
Recent results in one-bit sampling provide a framework for a relatively low-cost, low-power sampling, at a high rate by employing time-varying sampling threshold sequences. Another recent development in sampling theor...
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Recent results in one-bit sampling provide a framework for a relatively low-cost, low-power sampling, at a high rate by employing time-varying sampling threshold sequences. Another recent development in sampling theory is unlimited sampling, which is a high-resolution technique that relies on modulo ADCs to yield an unlimited dynamic range. In this paper, we leverage the appealing attributes of the two afore mentioned techniques to propose a novel unlimited one-bit(UNO) sampling approach. In this framework, the information on the distance between the input signal value and the threshold is stored and utilized to accurately reconstruct the one-bit sampled signal. We then utilize this information to accurately reconstruct the signal from its one-bit samples via the randomized Kaczmarz algorithm (RKA). In the presence of noise, we employ the recent plug-and-play (PnP) priors technique with alternating direction method of multipliers (ADMM) to exploit integration of state-of-the-art regularizers in the reconstruction process. Numerical experiments with RKA and PnP-ADMM-based reconstruction illustrate the effectiveness of our proposed UNO, including its superior performance compared to the one-bit Sigma Delta sampling
The realization of anti-interference technologies via beamforming for applications in frequency diverse arrays and multiple-input and multiple-output (FDA-MIMO) radar is a field that is undergoing intensive research d...
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The realization of anti-interference technologies via beamforming for applications in frequency diverse arrays and multiple-input and multiple-output (FDA-MIMO) radar is a field that is undergoing intensive research due to its two-dimensional range-angle-dependent beampattern characteristics. To solve the missing covariance matrix problem and improve the anti-interference capability of FDA-MIMO radar, we present a two-stage based intelligent anti-interference scheme for FDA-MIMO radar under the nonideal condition. The scheme consists of two parts: signal covariance matrix missing data recovery and intelligent beamforming vector estimation. A dual-channel generation adversarial network (DC-GAN) structure is proposed to effectively recover both real and imaginary parts of data from a covariance matrix. Based on the recovered covariance matrix, the beamforming vectors are accurately estimated by constructing a one-dimensional convolution neural network (1D-CNN). Meanwhile, a multiple-target process scheme combined with the 1D-CNN is introduced to deal with multitarget situation. In the numerical simulation part, the simulation results reveal that the DC-GAN network can effectively recover the missing data from the covariance matrix, and the lower the missing data rate, the better the data recovery performance. In addition, in the simulation of beamforming vector estimation, the effects of two different input modes on network training performance are evaluated, and the performance differences between a fully connected neural network and 1D-CNN are analyzed and compared. The numerical simulation results verify the effectiveness of the proposed FDA-MIMO radar anti-interference scheme under different number of interference signal scenarios and improve the interference suppression capability of FDA-MIMO radar.
This paper addresses the localization of a target in 3D scenarios using bistatic range measurements with a single passive receiver and multiple transmitters. The main-lobe beamwidth of the receiving antenna contains v...
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This paper addresses the localization of a target in 3D scenarios using bistatic range measurements with a single passive receiver and multiple transmitters. The main-lobe beamwidth of the receiving antenna contains valuable a-priori information about the target position but is commonly overlooked. This study introduces an angular constraint to incorporate the beamwidth information into the elliptic localization, resulting in a Quadratically Constrained Quadratic Programming (QCQP) problem. A novel Two-Stage Main-lobe beamwidth Constrained Estimation (TSMCE) algorithm is proposed to solve this challenging localization problem. Theoretical analysis demonstrates that the algorithm can converge to the candidate solutions that satisfy the Karush-Kuhn-Tucker (KKT) optimality conditions. The corresponding covariance can achieve the Cramer-Rao Lower Bound (CRLB) under a small noise assumption. Numerical simulations confirm that the proposed algorithm achieves superior localization accuracy compared to other methods, particularly in the low signal to Noise Ratio (SNR) regime. Additionally, the algorithm demonstrates fast convergence and robustness to variations in localization geometry.
The iterative hard thresholding (IHT) algorithm is widely used for recovering sparse signals in compressed sensing. Despite the development of numerous variants of this effective algorithm, its convergence rate and ac...
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The iterative hard thresholding (IHT) algorithm is widely used for recovering sparse signals in compressed sensing. Despite the development of numerous variants of this effective algorithm, its convergence rate and accuracy in finding the optimal solution still have room for enhancement. Aiming at this issue, we propose a momentum-based iterative hard thresholding (MIHT) algorithm by introducing a new iterative search direction derived from the momentum method, which uses historical iteration information to refine the search direction and thereby accelerate convergence. We establish a sufficient condition, in terms of (3s)-order restricted isometry constant, to guarantee the convergence of MIHT. Excitingly, numerical experiments demonstrate that MIHT possesses an excellent recovery success rate and outperforms a wide range of existing IHT variants.
Array antenna is an effective way to improve the interference suppression capability of global navigation satellite System (GNSS) receivers. However, more antennas mean higher costs, which may lead to unacceptable cos...
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Array antenna is an effective way to improve the interference suppression capability of global navigation satellite System (GNSS) receivers. However, more antennas mean higher costs, which may lead to unacceptable costs in the civil fields. Dual-antenna provides a better compromise between cost and interference suppression capability for those applications in the civil fields. This article proposes a joint-pulsed and continuous wave (CW) interference suppression algorithm for dual-antenna GNSS receivers. To avoid the lack of freedom of the dual antenna, pulsed and CW interference will be suppressed separately. First, in order to avoid the impact of pulses on CW interference suppression capability, a robust covariance matrix estimation method and space-time adaptive processing power inversion algorithm are combined to suppress CW interference. In this stage, the pulses will be deliberately preserved as much as possible. Second, the residual pulses will be suppressed by utilizing a robust pulsed interference suppression method. Simulations and experiments demonstrate that the proposed algorithm can simultaneously suppress joint pulsed and CW interference for a dual-antenna GNSS receiver.
When synthetic aperture radar (SAR) works in high-squint (HS) mode, the interpolation and scaling operation of traditional frequency domain imaging algorithms will change the original structure of motion error, and le...
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When synthetic aperture radar (SAR) works in high-squint (HS) mode, the interpolation and scaling operation of traditional frequency domain imaging algorithms will change the original structure of motion error, and lead to imaging difficulties. As a classical time-domain imaging algorithm, the back-projection (BP) algorithm is linear processing with a high tolerance for motion error. Therefore, the BP algorithm is very suitable for HS SAR imaging. To further improve the estimation accuracy of motion error, an innovative affine coordinate (AC) system is introduced into the BP algorithm. Based on this AC system, a novel 2-D autofocus algorithm is proposed, which can more accurately estimate and correct the 2-D phase error of the HS SAR BP image. The proposed algorithm has the following advantages: 1) the AC imaging grid is established according to the proposed resolution calculation method based on the BP image spectrum. Under this imaging grid, the nonsystem range cell migration (NsRCM) and the range defocus term of the BP image are significantly reduced, making the phase error estimation more accurate;2) a spectrum alignment processing for BP image in the AC system is proposed to remove the spectrum aliasing so that the azimuth phase error (APE) can be accurately estimated;and 3) the spectrum of the BP image is orthogonal in the AC system, which makes the 2-D phase error compensation based on the established prior phase error structure more accurate. Simulation and real data experiments validate the performance of the proposed algorithm.
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