Showing papers on "QR decomposition published in 2019"
TL;DR: A variable regularized version of the RLS algorithm is proposed, using the DCD method to reduce the complexity, with improved robustness to double-talk and results indicate the good performance of these algorithms.
Abstract: The recursive least-squares (RLS) adaptive filter is an appealing choice in many system identification problems. The main reason behind its popularity is its fast convergence rate. However, this algorithm is computationally very complex, which may make it useless for the identification of long length impulse responses, like in echo cancellation. Computationally efficient versions of the RLS algorithm, like those based on the dichotomous coordinate descent (DCD) iterations or QR decomposition techniques, reduce the complexity, but still have to face the challenges related to long length adaptive filters (e.g., convergence/tracking capabilities). In this paper, we focus on a different approach to improve the efficiency of the RLS algorithm. The basic idea is to exploit the impulse response decomposition based on the nearest Kronecker product and low-rank approximation. In other words, a high-dimension system identification problem is reformulated in terms of low-dimension problems, which are combined together. This approach was recently addressed in terms of the Wiener filter, showing appealing features for the identification of low-rank systems, like real-world echo paths. In this paper, besides the development of the RLS algorithm based on this approach, we also propose a variable regularized version of this algorithm (using the DCD method to reduce the complexity), with improved robustness to double-talk. Simulations are performed in the context of echo cancellation and the results indicate the good performance of these algorithms.
43 citations
TL;DR: A novel method for moving force identification called preconditioned least square QR-factorization (PLSQR) method, which is more robust towards ill-posed problem and has higher identification precision than the conventional time domain method (TDM).
35 citations
TL;DR: A narrative on how methodologies presented here could be generalised and applied to other models is provided.
Abstract: Several techniques for automatic parameterisation are explored using the software PEST. We parameterised the biophysical systems model APSIM with measurements from a maize cropping experiment with the objective of finding algorithms that resulted in the least distance between modelled and measured data (φ) in the shortest possible time. APSIM parameters were optimised using a weighted least-squares approach that minimised the value of φ. Optimisation techniques included the Gauss-Marquardt-Levenberg (GML) algorithm, singular value decomposition (SVD), least squares with QR decomposition (LSQR), Tikhonov regularisation, and covariance matrix adaptation-evolution strategy (CMAES). In general, CMAES with log transformed APSIM parameters and larger population size resulted in the lowest φ, but this approach required significantly longer to converge compared with other optimisation algorithms. Regularisation treatments with log transformed parameters also resulted in low φ values when combined with SVD or LSQR; LSQR treatments with no regularisation tended to converge earliest. In addition to an analysis of several PEST algorithms, this study provides a narrative on how methodologies presented here could be generalised and applied to other models.
33 citations
TL;DR: The SVD method is proposed to address the problem of numerical sensitivity in the filtering process to replace the calculation of the inverse of the filter gain matrix and further improve the robustness of the algorithm.
Abstract: In target tracking, the tracking process needs to constantly update the data information. However, during data acquisition and transmission of sensors, outliers may occur frequently, and the model is disturbed by non-Gaussian noise, that affects the performance of system state estimation. In this paper, a new filtering algorithm is proposed based on QR decomposition and singular value decomposition (SVD) method, namely adaptive robust unscented Kalman filter (QS-ARUKF) to suppress the interference of outliers, nonGaussian noise as well as a model error to achieve high accuracy state estimation. An adaptive filtering algorithm based on strong tracking idea is used in modifying the state equation of unscented Kalman filter (UKF), so that the algorithm can effectively improve the tracking ability of the state model. By using the robust filtering method to construct a new cost function used to modify the measurement covariance formula of the Kalman filter, the error of measurement model can be effectively suppressed. The QR decomposition is introduced to the time update and measurement update to avoid the covariance non-positive definite. We propose the SVD method to address the problem of numerical sensitivity in the filtering process. The purpose of this method is to replace the calculation of the inverse of the filter gain matrix and further improve the robustness of the algorithm. The simulation results showed that the proposed algorithm has higher accuracy and better robustness than the traditional filtering method.
29 citations
27 citations
TL;DR: Theoretical analysis shows that IRLNM-QR is as accurate as an iteratively reweighted nuclear norm minimization method, which is much more accurate than the traditional QR-decomposition-based matrix completion methods.
Abstract: Low-rank matrix completion aims to recover matrices with missing entries and has attracted considerable attention from machine learning researchers. Most of the existing methods, such as weighted nuclear-norm-minimization-based methods and Qatar Riyal (QR)-decomposition-based methods, cannot provide both convergence accuracy and convergence speed. To investigate a fast and accurate completion method, an iterative QR-decomposition-based method is proposed for computing an approximate singular value decomposition. This method can compute the largest $r (r>0)$ singular values of a matrix by iterative QR decomposition. Then, under the framework of matrix trifactorization, a method for computing an approximate SVD based on QR decomposition (CSVD-QR)-based $L_{2,1}$ -norm minimization method (LNM-QR) is proposed for fast matrix completion. Theoretical analysis shows that this QR-decomposition-based method can obtain the same optimal solution as a nuclear norm minimization method, i.e., the $L_{2,1}$ -norm of a submatrix can converge to its nuclear norm. Consequently, an LNM-QR-based iteratively reweighted $L_{2,1}$ -norm minimization method (IRLNM-QR) is proposed to improve the accuracy of LNM-QR. Theoretical analysis shows that IRLNM-QR is as accurate as an iteratively reweighted nuclear norm minimization method, which is much more accurate than the traditional QR-decomposition-based matrix completion methods. Experimental results obtained on both synthetic and real-world visual data sets show that our methods are much faster and more accurate than the state-of-the-art methods.
25 citations
TL;DR: The proposed algorithm has linear time complexity with respect to the number of samples making it ideal for SVM training on decentralized environments such as smart embedded systems and edge-based internet of things, IoT.
Abstract: Support Vector Machines (SVM) are widely used as supervised learning models to solve the classification problem in machine learning. Training SVMs for large datasets is an extremely challenging task due to excessive storage and computational requirements. To tackle so-called big data problems, one needs to design scalable distributed algorithms to parallelize the model training and to develop efficient implementations of these algorithms. In this paper, we propose a distributed algorithm for SVM training that is scalable and communication-efficient. The algorithm uses a compact representation of the kernel matrix, which is based on the QR decomposition of low-rank approximations, to reduce both computation and storage requirements for the training stage. This is accompanied by considerable reduction in communication required for a distributed implementation of the algorithm. Experiments on benchmark data sets with up to five million samples demonstrate negligible communication overhead and scalability on up to 64 cores. Execution times are vast improvements over other widely used packages. Furthermore, the proposed algorithm has linear time complexity with respect to the number of samples making it ideal for SVM training on decentralized environments such as smart embedded systems and edge-based internet of things, IoT.
24 citations
TL;DR: This paper presents a fast CS reconstruction algorithm implemented on field-programmable gate array (FPGA) using OMP that adopts an incremental QR decomposition (QRD) method to efficiently solve the least square problem (LSP).
Abstract: Compressive sensing (CS) is a novel signal processing technology to reconstruct the sparse signal at sub-Nyquist rate. Orthogonal matching pursuit (OMP) is one of the most widely used signal reconstruction algorithms. However, the least square problem (LSP) in OMP algorithm limits its performance. This paper presents a fast CS reconstruction algorithm implemented on field-programmable gate array (FPGA) using OMP. The proposed algorithm adopts an incremental QR decomposition (QRD) method to efficiently solve the LSP. The incremental QRD is further optimized to eliminate the square root operation to facilitate hardware implementation. The proposed architecture avoiding the complex square root unit mainly consists of some more basic computing units, where the computing process is broken down into several simple operations to map to the corresponding hardware for pipelining. The proposed implementation based on Xilinx Kintex-7 FPGA exploits the parallelism by a well-planned workload schedule and reaches an optimal tradeoff between the latency and frequency. The experimental results demonstrate that the proposed architecture can run at a frequency of 210 MHz with a reconstruction time of 238 $\mu \text{s}$ for 36-sparse 1024-length signal, which improves the signal reconstruction speed by $1.43\times $ compared to the state-of-the-art implementations.
20 citations
TL;DR: An improved TS algorithm based on the QR decomposition of the channel matrix (QR-TS), which allows for finding the best neighbor with a significantly lower complexity compared with the conventional TS algorithm.
Abstract: In the conventional tabu search (TS) detection algorithm for multiple-input multiple-output (MIMO) systems, the cost metrics of all neighboring vectors are computed to determine the best neighbor. This can require an excessively high computational complexity, especially in large MIMO systems because the number of neighboring vectors and the dimension per vector are large. In this study, we propose an improved TS algorithm based on the QR decomposition of the channel matrix (QR-TS), which allows for finding the best neighbor with a significantly lower complexity compared with the conventional TS algorithm. Specifically, QR-TS does not compute all metrics by early rejecting unpromising neighbors, which reduces the computational load of TS without causing any performance loss. To further optimize the QR-TS algorithm, we investigate novel ordering schemes, namely the transmit-ordering (Tx-ordering) and receive-ordering (Rx-ordering), which can considerably reduce the complexity of QR-TS. Simulation results show that QR-TS reduces the complexity approximately by a factor of two compared with the conventional TS. Furthermore, when both Tx-ordering and Rx-ordering are applied, QR-TS requires approximately $60\%\text{ -- }90\%$ less complexity compared with the conventional TS scheme. The proposed algorithms are suitable for both low-order and high-order modulation, and can achieve a significant complexity reduction compared to the Schnorr–Euchner and $K\text{-}$ best sphere decoders in large MIMO systems.
20 citations
TL;DR: A randomized greedy algorithm that leverages a probabilistic argument to only evaluate a subset of basis functions from the dictionary at each iteration of the incremental algorithm is demonstrated to recover model outputs with a similar level of sparsity and accuracy as OMP, but with a cost that is independent of the dictionary size.
18 citations
TL;DR: This paper proves a map based on the QR factorization to be a retraction on the generalized Stiefel manifold and proposes an efficient implementation of the retractionbased on the Cholesky QR factorizations.
Abstract: When optimizing on a Riemannian manifold, it is important to use an efficient retraction, which maps a point on a tangent space to a point on the manifold. In this paper, we prove a map based on the QR factorization to be a retraction on the generalized Stiefel manifold. In addition, we propose an efficient implementation of the retraction based on the Cholesky QR factorization. Numerical experiments show that the proposed retraction is more efficient than the existing one based on the polar factorization.
24 Jun 2019
TL;DR: A new candidates list re-calculating to improve performance of iterative nonlinear detection and decoding in Multi-User (MU) Massive Multiple Input, Multiple Output (MIMO) system is proposed and the convergence of combining the detection algorithms with the soft low-density parity-check (LDPC) decoder is analyzed.
Abstract: In this paper, we propose a new candidates list re-calculating to improve performance of iterative nonlinear detection and decoding in Multi-User (MU) Massive Multiple Input, Multiple Output (MIMO) system. The proposed nonlinear iterative detector includes a new algorithm of users (UEs) sorting before QR decomposition (QRD) and a new sorting-reduced (SR) K-best method. If MIMO detector is based on a candidates list updates, the performance can be improved by the candidates list re-calculating or using a priori information in the list generation. This is natural, because the quality of the candidates list is likely to be improved by using the decoder output as a priori information. We analyze the convergence of combining the detection algorithms with the soft low-density parity-check (LDPC) decoder. Simulation results are presented in 5G QuaDRiGa channel with QAM64 modulation in 48 × 64 MIMO system and compared with other state-of-art approaches.
TL;DR: Theoretical analysis demonstrates that the proposed method can provide the optimal solution of the formulated non-convex minimization problem and its estimation mean-square-error (MSE) is able to reach the corresponding CRB under moderate noise level.
TL;DR: In this paper, the authors proposed a new technique that dynamically estimates and updates the coefficients of a digital predistorter (DPD) for power amplifier (PA) linearization.
Abstract: This paper presents a new technique that dynamically estimates and updates the coefficients of a digital predistorter (DPD) for power amplifier (PA) linearization. The proposed technique is dynamic in the sense of estimating, at every iteration of the coefficient’s update, only the minimum necessary parameters according to a criterion based on the residual estimation error. At the first step, the original basis functions defining the DPD in the forward path are orthonormalized for DPD adaptation in the feedback path by means of a precalculated principal component analysis (PCA) transformation. The robustness and reliability of the precalculated PCA transformation (i.e., PCA transformation matrix obtained off line and only once) is tested and verified. Then, at the second step, a properly modified partial least squares (PLS) method, named dynamic partial least squares (DPLS), is applied to obtain the minimum and most relevant transformed components required for updating the coefficients of the DPD linearizer. The combination of the PCA transformation with the DPLS extraction of components is equivalent to a canonical correlation analysis (CCA) updating solution, which is optimum in the sense of generating components with maximum correlation (instead of maximum covariance as in the case of the DPLS extraction alone). The proposed dynamic extraction technique is evaluated and compared in terms of computational cost and performance with the commonly used QR decomposition approach for solving the least squares (LS) problem. Experimental results show that the proposed method (i.e., combining PCA with DPLS) drastically reduces the amount of DPD coefficients to be estimated while maintaining the same linearization performance.
20 May 2019
TL;DR: In this article, the authors proposed a comprehensive SI model for single-antenna full-duplex systems based on direct-conversion transceiver structure considering nonlinearities of all the transceiver radio frequency (RF) components, in-phase and frequency-quadrature (IQ) imbalances, phase noise effect, and receiver noise figure.
Abstract: Single-antenna full-duplex communication technology has the potential to substantially increase spectral efficiency. However, limited propagation domain cancellation of single-antenna system results in a higher impact of receiver chain nonlinearities on the residual self-interference (SI) signal. In this paper, we offer a comprehensive SI model for single-antenna full-duplex systems based on direct-conversion transceiver structure considering nonlinearities of all the transceiver radio frequency (RF) components, in-phase/quadrature (IQ) imbalances, phase noise effect, and receiver noise figure. To validate our model, we also propose a more appropriate digital SI cancellation approach considering receiver chain RF and baseband nonlinearities. The proposed technique employs orthogonalization of the design matrix using QR decomposition to alleviate the estimation and cancellation error. Finally, through circuit-level waveform simulation, the performance of the digital cancellation strategy is investigated, which achieves 20 dB more cancellation compared to existing methods.
TL;DR: A novel hardware architecture of orthogonal matching pursuit (OMP) is presented here, and the test is implemented on a field-programmable gate array (FPGA) using the Gabor time-frequency dictionary.
Abstract: A novel hardware architecture of orthogonal matching pursuit (OMP) is presented here, and the test is implemented on a field-programmable gate array (FPGA). The performance is evaluated by taking RADAR pulses that are compressively sampled synthetically using the random modulation preintegrator (RMPI). Basic test signals such as Gaussian pulse and its variations are taken as input to the RMPI. The output of the OMP algorithm is multiplied by the Gabor time-frequency dictionary to obtain the reconstructed RADAR signal. A novel method to implement the Gabor time-frequency dictionary is also presented. The OMP algorithm generates an estimate of a signal in $m~(\geq M)$ iterations for an $M$ -sparse signal. The proposed design is implemented on the Artix7 FPGA device for $K=80$ , $N=1024$ , and $m=16$ , where $N$ is the number of samples and $K$ is the measurement vector length. The design is also implemented for $K=256$ , $N=1024$ , and $m=36$ using the Virtex6 FPGA device for comparison with other existing designs. The recovery signal-to-noise ratio (RSNR) of 18.336 dB is achieved with this technique. The proposed design utilizes $(3m-1)$ fewer multipliers and consumes 27% less dynamic power compared to previously published FPGA implementation of OMP. The proposed design is hardware efficient even for the higher value of $m/K$ .
TL;DR: This paper investigates the utilization of QR factorization for performing efficient minimum distance calculation between capsules and concludes with numerical tests, showing that the proposed method compares favorably with the most efficient method reported in the literature.
Abstract: The problem of minimum distance calculation between line-segments/capsules, in 3D space, is an important subject in many engineering applications, spanning CAD design, computer graphics, simulation, and robotics. In the latter, the human–robot minimum distance is the main input for collision avoidance/detection algorithms to measure collision imminence. Capsules can be used to represent humans and objects, including robots, in a given dynamic environment. In this scenario, it is important to calculate the minimum distance between capsules efficiently, especially for scenes (situations) that include a high number of capsules. This paper investigates the utilization of QR factorization for performing efficient minimum distance calculation between capsules. The problem is reformulated as a bounded variable optimization in which an affine transformation, deduced from QR factorization, is applied on the region of feasible solutions. A geometrical approach is proposed to calculate the solution, which is achieved by computing the point closest to the origin from the transferred region of feasible solutions. This paper is concluded with numerical tests, showing that the proposed method compares favorably with the most efficient method reported in the literature.
TL;DR: Better imperceptibility, large capacity, and poor detection accuracy compared to existing work validate the efficacy of the proposed image steganography algorithm.
Abstract: This paper presents the transform domain image steganography schemes using three popular matrix factorization techniques and contourlet transform. It is known that security of image steganography is mainly evaluated using undetectability of stego image when steganalyzer examines it in order to detect the presence of hidden secret information. Good imperceptibility only suggests eavesdropper’s inability to suspect about the hidden information; however stego image may be analyzed by applying certain statistical checks when it is being transmit- ted through the channel. This work focusses on improving undetectability by employing ma- trix decomposition techniques along with transform domain image steganography. Singular value decomposition (SVD), QR factorization, Nonnegative matrix factorization (NMF) are employed to decompose contourlet coefficients of cover image and secret is embedded into its matrix factorized coefficients. The variety of investigations include the effect of matrix decomposition techniques on major attributes of image steganography like imperceptibility, robustness to a variety of image processing operations, and universal steganalysis perfor- mance. Better imperceptibility, large capacity, and poor detection accuracy compared to existing work validate the efficacy of the proposed image steganography algorithm. Compa- rative analysis amongst three matrix factorization methods is also presented and analyzed.
TL;DR: This paper proposes to estimate time-varying pieces of channel taps for pilot symbols based on basis expansion model (BEM), and subsequently to reconstruct time-domain (TD) channel response for data symbols by utilizing the Slepian sequences-based piece-wise interpolation, and designs an iterative least-squares QR decomposition algorithm to equalize the SC-FDM symbols.
Abstract: Similar to orthogonal frequency division multiplexing receiver, the frequency-domain (FD) channel estimation (CE) and equalization are indispensable parts in the coherent single carrier frequency division multiplexing (SC-FDM) receiver. When the channel varies slowly, the FD processing is cost effective. For the applications with high mobility, the traditional implementation of the SC-FDM receiver causes significant performance degradation. In this paper, we propose to estimate time-varying pieces of channel taps for pilot symbols based on basis expansion model (BEM), and subsequently to reconstruct time-domain (TD) channel response for data symbols by utilizing the Slepian sequences-based piece-wise interpolation. Furthermore, two simplified schemes, i.e., the Slepian sequences-based multiple-point interpolation and the segmented BEM, are developed to reduce the computational complexity of the TD-CE. The Cramer-Rao lower bound (CRLB) of channel impulse response (CIR) estimation is also analyzed. In the light of the sparsity of TD channel gain matrix, we design an iterative least-squares QR decomposition algorithm to equalize the SC-FDM symbols. In the simulated SC-FDM system, when considering the carrier frequency of 5.9 GHz and the velocity of about 510 km/h, we observe that the traditional FD methods cause the demodulation failure, while the proposed TD processing schemes achieve ideal error-probability performance and preserve a relatively low complexity.
TL;DR: The ease, accuracy, and efficiency of this synthesis method for the design of BFN make it very useful in modern applications of multi-beam antenna arrays.
Abstract: A synthesis method for orthogonal beam-forming networks (BFNs) with arbitrary N inputs and N outputs is presented. Compared to those formerly developed, the new method allows the design of a BFN in order to not only generate arbitrary N orthogonal beams and N inputs but also to make the 180° hybrids less. This skill is obtained by means of a new approach to decompose the matrices $Q_{1}$ and $Q_{2}$ which are mentioned by Sodin. The solution of such a design problem can be carried out by applying $QR$ decomposition based on Givens transformations. Such a design method also takes into account the computer programming realization. Numerical results are obtained through the commercial simulator to prove the correctness of the method. The ease, accuracy, and efficiency of this synthesis method for the design of BFN make it very useful in modern applications of multi-beam antenna arrays.
TL;DR: The stacked Alamouti code is used as the transmitted signal matrix and the SA-SM scheme carries more symbols than the spatially modulated orthogonal space-time block codes (SM-OSTBC) scheme, thus it can achieve higher spectral efficiency.
Abstract: In this paper, a high-rate and diversity-achieving spatial modulation transmission scheme is proposed, which uses the stacked Alamouti code as the transmitted signal matrix and then is named as stacked Alamouti based spatial modulation (SA-SM) scheme. In this scheme, the spatial constellation (SC) is composed of all the possible activated antennas combinations. According to a specific SC codeword, the corresponding symbols in the stacked Alamouti code are simultaneously activated. Owing to the structure of stacked Alamouti code, the SA-SM scheme can work with arbitrary even number of transmit antennas $n_{T}$ , and any number of activated antennas from 1 to $n_{T}$ . With the same number of activated antennas $n_{A}$ , the SA-SM scheme carries more symbols than the spatially modulated orthogonal space-time block codes (SM-OSTBC) scheme, thus SA-SM can achieve higher spectral efficiency. Without the need of any parameter or matrix optimization, the SA-SM scheme enjoys the non-vanishing determinants property. Moreover, owing to possessing the block-orthogonal structure, SA-SM enables faster metric computations in the QR decomposition-M detection, and then it achieves significant decoding complexity reduction without performance loss. Under various system configurations, the performance advantages of SA-SM over several existing diversity-achieving SM schemes such as GSTSK and SM-OSTBC, are shown by simulation results for various spectral efficiencies.
TL;DR: The proposed method is applied to several different layered or multipanel structures, and the predicted acoustical properties are compared to results obtained by using previously-existing methods in order to validate the modified transfer matrix method.
Abstract: The transfer matrix method that is often used to model layered or lumped acoustical systems was inspired by a classical methodology commonly used in electrical engineering. To take advantage of that procedure’s accuracy and modeling efficiency, the transfer matrix method has been further adapted here to allow coupling of layered acoustic media having different matrix dimensions. For example, in the case of fluid, or effective fluid, media, the acoustic transfer matrix elements are conventionally modeled using two-by-two matrices. In contrast, a four-by-four matrix is required to model an elastic solid layer, and a six-by-six matrix is required to model a poroelastic layer, since multiple wave types propagate within the latter elements. Here, we introduce a modified transfer matrix calculation process that draws on various matrix operations to couple four-by-four and/or six-by-six matrices with the two-by-two matrices of other acoustical elements. The matrix operations include singular value decomposition and QR decomposition. These tools are used to reduce the order of elastic solid or poroelastic layer matrices from four-by-four or six-by-six to two-by-two, respectively, so that a layered system can be modeled simply by multiplying together a sequence of two-by-two matrices representing all the layered acoustic elements regardless of their complexity, thus finally creating an overall two-by-two matrix. In this article, the proposed method is applied to several different layered or multipanel structures, and the predicted acoustical properties are compared to results obtained by using previously-existing methods in order to validate the modified transfer matrix method.
TL;DR: This paper proposes a novel two-stage metric learning via QR-Decomposition and KISS, named QRKISS, which is better than other KISS-based metric learning methods and achieves state-of-the-art performance.
Abstract: Person re-identification is a challenging task in the field of intelligent video surveillance because there are wide variations between pedestrian images. As a classical metric learning method, Keep It Simple and Straightforward (KISS) has shown good performance for person re-identification. However, when the dimension of data is high, the KISS method may perform poorly because of small sample size problem. A common solution to this problem is to apply dimensionality reduction technologies to original data before the KISS metric learning, such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). In this paper, to learn a discriminant and robust metric, we propose a novel two-stage metric learning via QR-Decomposition and KISS, named QRKISS. The first stage of QRKISS is to project original data into a lower dimensional space by QR decomposition. In this lower dimensional space, the trace of the covariance matrix of interpersonal differences can reach maximum. Based on KISS method, the second stage of QRKISS obtains a Mahalanobis matrix in the low-dimension space. We conduct thorough validation experiments on the VIPeR, PRID 450S and CUHK01 datasets, which demonstrate that QRKISS method is better than other KISS-based metric learning methods and achieves state-of-the-art performance.
TL;DR: An adaptive NF sampling method based on the skeletonization scheme is presented, which significantly reduces the expenditure of NF sampling.
Abstract: Radio frequency (RF) source reconstruction is useful for RF radiation analysis and interference diagnosis in Internet of Things. Near field (NF) sampling is a critical step of RF source reconstruction. Accurate RF source reconstruction usually requires a large number of NF samples, which results in tremendous effort of NF sampling. This article presents an adaptive NF sampling method based on the skeletonization scheme. First, sources to be reconstructed are related to NF samples through integral equation (IE). Second, the IE is discretized with the method of moments, and thus the interaction matrix between source and field points is found. Third, strong rank-revealing QR factorization is applied to the interaction matrix, which results in a permutation matrix, a row skeleton matrix, and a transformation matrix. Finally, a small number of skeleton sampling points are selected by analyzing the permutation matrix. The fields at skeleton sampling points can be used to calculate the fields at other sampling points through the transformation matrix. Hence, one only needs to perform NF sampling at a small number of skeleton sampling points, which significantly reduces the expenditure of NF sampling. Simulations using synthetic and measurement data are presented to show the effectiveness and advantages of the proposed sampling method.
23 May 2019
TL;DR: The proposed method is applied to the secure state estimation of a cyber physical system (CPS) and two different methods for solving such problems are also discussed.
Abstract: A method to recover the unknown sparse error $e$ in a corrupted linear system $b=Ax+e$ is proposed. The original problem is first transformed into a convex optimization problem with equality constraints using the QR decomposition of $A$ . The transformed problem is then solved using 1-norm minimization. The proposed method is applied to the secure state estimation of a cyber physical system (CPS). Two different methods for solving such problems are also discussed.
TL;DR: A new identification method based on QR decomposition for nonlinear time-varying systems is proposed in this paper, which is applied in a time-Varying multiple-degree-of-freedom (MDOF) dyna...
Abstract: A new identification method based on QR decomposition for nonlinear time-varying systems is proposed in this paper, which is applied in a time-varying multiple-degree-of-freedom (MDOF) dyna...
TL;DR: A new watermarking method using ternary coding and QR decomposition for colour image, which can improve the imperceptibility, embedded watermark capacity and real-time feature of the water marking scheme.
Abstract: At present, the binary images are often used as the original watermark images of many watermarking methods, but partial methods cannot be easily extended to colour image watermarking methods. For resolving this problem, we propose a new watermarking method using ternary coding and QR decomposition for colour image. In the procedure of embedding watermark, the colour image watermark is coded to ternary information; the colour host image is also separated into image blocks of sized 3 × 3, and these image blocks are further decomposed via QR decomposition; then, one ternary watermark is embedded into one orthogonal matrix Q of QR decomposition by the proposed rules. In the procedure of extracting watermark, the proposed method uses the blind-manner to extract the embedded ternary information. The novelty of this scheme lies in the proposed ternary coding for watermark image, which can improve the imperceptibility, embedded watermark capacity and real-time feature of the watermarking scheme. The results of simulation show the presented technique is better than other compared schemes with respect to imperceptibility, embedded watermark capacity and real-time feature under the similar robustness.
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TL;DR: A comprehensive SI model for single-antenna full-duplex systems based on direct-conversion transceiver structure considering nonlinearities of all the transceiver radio frequency (RF) components, in-phase/quadrature (IQ) imbalances, phase noise effect, and receiver noise figure is offered.
Abstract: Single-antenna full-duplex communication technology has the potential to substantially increase spectral efficiency. However, limited propagation domain cancellation of single-antenna system results in a higher impact of receiver chain nonlinearities on the residual self-interference (SI) signal. In this paper, we offer a comprehensive SI model for single-antenna full-duplex systems based on direct-conversion transceiver structure considering nonlinearities of all the transceiver radio frequency (RF) components, in-phase/quadrature (IQ) imbalances, phase noise effect, and receiver noise figure. To validate our model, we also propose a more appropriate digital SI cancellation approach considering receiver chain RF and baseband nonlinearities. The proposed technique employs orthogonalization of the design matrix using QR decomposition to alleviate the estimation and cancellation error. Finally, through circuit-level waveform simulation, the performance of the digital cancellation strategy is investigated, which achieves 20 dB more cancellation compared to existing methods.
TL;DR: A new computing algorithm to achieve the matrix decomposition efficiently without compromising the performance is presented and the percentage of choosing correct steering vectors is 90%, which is as good as the OMP scheme can achieve.
Abstract: Hybrid precoding, a combination of radio frequency (RF) beamforming and digital precoding, has been investigated intensively these days for millimeter wave (mmWave) communication systems employing large antenna arrays. The key problem is constructing beamforming and precoding matrices for the RF beamformer and the digital baseband, respectively, based on the channel matrix decomposition result. This paper presents a new computing algorithm to achieve the matrix decomposition efficiently without compromising the performance. The algorithm computes beamforming (steering) and precoding matrices in separate phases to alleviate the computing overheads of iterative matrix updates. This measure also creates the computing parallelism to facilitate efficient hardware implementation. A novel computing scheme based on QR decomposition and blockwise inversion techniques is also developed to tackle the most critical least square solution module. This leads to a computing complexity reduction by a factor of $0.3~{N}$ when compared with the popular orthogonal matching pursuit (OMP) scheme, where ${N}$ is the antenna array size. The simulation results indicate the percentage of choosing correct steering vectors is 90%, which is as good as the OMP scheme can achieve. A hardware accelerator design of the proposed scheme is developed by using a TSMC 40 nm CLN40G technology. The design, with a gate count of 419.3 k, can operate up to 333 MHz with a power consumption of 267.1 mW. This suggests a throughput rate of processing 10.4 M channel matrices per second. The core size is merely 0.58 mm2 while the entire die size including I/O pads is 2.26 mm2 .