This paper presents a new subband adaptive filter(SAF)algorithm for system identification scenario under impulsive interference,named generalized continuous mixed p-norm SAF(GCMPN-SAF)algorithm.The proposed algorithm ...This paper presents a new subband adaptive filter(SAF)algorithm for system identification scenario under impulsive interference,named generalized continuous mixed p-norm SAF(GCMPN-SAF)algorithm.The proposed algorithm uses a GCMPN cost function to combat the impul-sive interference.To further accelerate the convergence rate in the sparse and the block-sparse system identification processes,the proportionate versions of the proposed algorithm,the L0-norm GCMPN-SAF(L0-GCMPN-SAF)and the block-sparse GCMPN-SAF(BSGCMPN-SAF)algorithms are also developed.Moreover,the convergence analysis of the proposed algorithm is provided.Simulation results show that the proposed algorithms have a better performance than some other state-of-the-art algorithms in the literature with respect to the convergence rate and the tracking capability.展开更多
To realize effective co-phasing adjustment in large-aperture sparse-aperture telescopes,a multichannel stripe tracking approach is employed,allowing simultaneous interferometric measurements of multiple optical paths ...To realize effective co-phasing adjustment in large-aperture sparse-aperture telescopes,a multichannel stripe tracking approach is employed,allowing simultaneous interferometric measurements of multiple optical paths and circumventing the need for pairwise measurements along the mirror boundaries in traditional interferometric methods.This approach enhances detection efficiency and reduces system complexity.Here,the principles of the multibeam interference process and construction of a co-phasing detection module based on direct optical fiber connections were analyzed using wavefront optics theory.Error analysis was conducted on the system surface obtained through multipath interference.Potential applications of the interferometric method were explored.Finally,the principle was verified by experiment,an interferometric fringe contrast better than 0.4 is achieved through flat field calibration and incoherent digital synthesis.The dynamic range of the measurement exceeds 10 times of the center wavelength of the working band(1550 nm).Moreover,a resolution better than one-tenth of the working center wavelength(1550 nm)was achieved.Simultaneous three-beam interference can be achieved,leading to a 50%improvement in detection efficiency.This method can effectively enhance the efficiency of sparse aperture telescope co-phasing,meeting the requirements for observations of 8-10 m telescopes.This study provides a technological foundation for observing distant and faint celestial objects.展开更多
Convex feasibility problems are widely used in image reconstruction,sparse signal recovery,and other areas.This paper is devoted to considering a class of convex feasibility problem arising from sparse signal recovery...Convex feasibility problems are widely used in image reconstruction,sparse signal recovery,and other areas.This paper is devoted to considering a class of convex feasibility problem arising from sparse signal recovery.We rst derive the projection formulas for a vector onto the feasible sets.The centralized circumcentered-reection method is designed to solve the convex feasibility problem.Some numerical experiments demonstrate the feasibility and e ectiveness of the proposed algorithm,showing superior performance compared to conventional alternating projection methods.展开更多
Since orthogonal time-frequency space(OTFS)can effectively handle the problems caused by Doppler effect in high-mobility environment,it has gradually become a promising candidate for modulation scheme in the next gene...Since orthogonal time-frequency space(OTFS)can effectively handle the problems caused by Doppler effect in high-mobility environment,it has gradually become a promising candidate for modulation scheme in the next generation of mobile communication.However,the inter-Doppler interference(IDI)problem caused by fractional Doppler poses great challenges to channel estimation.To avoid this problem,this paper proposes a joint time and delayDoppler(DD)domain based on sparse Bayesian learning(SBL)channel estimation algorithm.Firstly,we derive the original channel response(OCR)from the time domain channel impulse response(CIR),which can reflect the channel variation during one OTFS symbol.Compare with the traditional channel model,the OCR can avoid the IDI problem.After that,the dimension of OCR is reduced by using the basis expansion model(BEM)and the relationship between the time and DD domain channel model,so that we have turned the underdetermined problem into an overdetermined problem.Finally,in terms of sparsity of channel in delay domain,SBL algorithm is used to estimate the basis coefficients in the BEM without any priori information of channel.The simulation results show the effectiveness and superiority of the proposed channel estimation algorithm.展开更多
L-band digital aeronautical communication system 1(L-DACS1) is a promising candidate data-link for future air-ground communication, but it is severely interfered by the pulse pairs(PPs) generated by distance measure e...L-band digital aeronautical communication system 1(L-DACS1) is a promising candidate data-link for future air-ground communication, but it is severely interfered by the pulse pairs(PPs) generated by distance measure equipment. A novel PP mitigation approach is proposed in this paper. Firstly, a deformed PP detection(DPPD) method that combines a filter bank, correlation detection, and rescanning is proposed to detect the deformed PPs(DPPs) which are caused by multiple filters in the receiver. Secondly, a finite impulse response(FIR) model is used to approximate the overall characteristic of filters, and then the waveform of DPP can be acquired by the original waveform of PP and the FIR model. Finally, sparse representation is used to estimate the position and amplitude of each DPP, and then reconstruct each DPP. The reconstructed DPPs will be subtracted from the contaminated signal to mitigate interference. Numerical experiments show that the bit error rate performance of our approach is about 5 dB better than that of recent works and is closer to interference-free environment.展开更多
The classic state methods for trajectory estimation in boost phase with multi-range-rate system include method of point-by-point manner and that of spline-model-based manner. Both are deficient in terms of model-appro...The classic state methods for trajectory estimation in boost phase with multi-range-rate system include method of point-by-point manner and that of spline-model-based manner. Both are deficient in terms of model-approximation accuracy and systematic error determination thus resulting in the estimation errors well beyond the requirements, especially, concerning the maneuvering trajectory. This article proposes a new high-precision estimation approach based on the residual error analysis. The residual error comprises three components, i. e. systematic error, model truncation error and random error. The approach realizes self-adaptive estimation of systematic errors in measurements following the theory of sparse representation of signals to minimize the low-frequency components of residual errors. By taking median- and high-frequency components as indexes, the spline model-approximation is improved by optimizing node sequence of the spline function and the weight selection for data fusion through iteration. Simulation has validated the performances of the proposed method.展开更多
Polar coded sparse code multiple access(SCMA) system is conceived in this paper. A simple but new iterative multiuser detection framework is proposed, which consists of a message passing algorithm(MPA) based multiuser...Polar coded sparse code multiple access(SCMA) system is conceived in this paper. A simple but new iterative multiuser detection framework is proposed, which consists of a message passing algorithm(MPA) based multiuser detector and a soft-input soft-output(SISO) successive cancellation(SC) polar decoder. In particular, the SISO polar decoding process is realized by a specifically designed soft re-encoder, which is concatenated to the original SC decoder. This soft re-encoder is capable of reconstructing the soft information of the entire polar codeword based on previously detected log-likelihood ratios(LLRs) of information bits. Benefiting from the soft re-encoding algorithm, the resultant iterative detection strategy is able to obtain a salient coding gain. Our simulation results demonstrate that significant improvement in error performance is achieved by the proposed polar-coded SCMA in additive white Gaussian noise(AWGN) channels, where the performance of the conventional SISO belief propagation(BP) polar decoder aided SCMA, the turbo coded SCMA and the low-density parity-check(LDPC) coded SCMA are employed as benchmarks.展开更多
recently the indexed modulation(IM)technique in conjunction with the multi-carrier modulation gains an increasing attention.It conveys additional information on the subcarrier indices by activating specific subcarrier...recently the indexed modulation(IM)technique in conjunction with the multi-carrier modulation gains an increasing attention.It conveys additional information on the subcarrier indices by activating specific subcarriers in the frequency domain besides the conventional amplitude-phase modulation of the activated subcarriers.Orthogonal frequency division multiplexing(OFDM)with IM(OFDM-IM)is deeply compared with the classical OFDM.It leads to an attractive trade-off between the spectral efficiency(SE)and the energy efficiency(EE).In this paper,the concept of the combinatorial modulation is introduced from a new point of view.The sparsity mapping is suggested intentionally to enable the compressive sensing(CS)concept in the data recovery process to provide further performance and EE enhancement without SE loss.Generating artificial data sparsity in the frequency domain along with naturally embedded channel sparsity in the time domain allows joint data recovery and channel estimation in a double sparsity framework.Based on simulation results,the performance of the proposed approach agrees with the predicted CS superiority even under low signal-to-noise ratio without channel coding.Moreover,the proposed sparsely indexed modulation system outperforms the conventional OFDM system and the OFDM-IM system in terms of error performance,peak-to-average power ratio(PAPR)and energy efficiency under the same spectral efficiency.展开更多
Applying the atomic sparse decomposition in the distribution network with harmonics and small current grounding to decompose the transient zero sequence current that appears after the single phase to ground fault occu...Applying the atomic sparse decomposition in the distribution network with harmonics and small current grounding to decompose the transient zero sequence current that appears after the single phase to ground fault occurred. Based on dictionary of Gabor atoms and matching pursuit algorithm, the method extracts the atomic components iteratively from the feature signals and translated them to damped sinusoidal components. Then we can obtain the parametrical and analytical representation of atomic components. The termination condition of decomposing iteration is determined by the threshold of the initial residual energy with the purpose of extract the features more effectively. Accordingly, the proposed method can extract the starting and ending moment of disturbances precisely as well as their magnitudes, frequencies and other features. The numerical examples demonstrate its effectiveness.展开更多
In this article algebraic multigrid as preconditioners are designed, with biorthogonal wavelets, as intergrid operators for the Krylov subspace iterative methods. Construction of hierarchy of matrices in algebraic mul...In this article algebraic multigrid as preconditioners are designed, with biorthogonal wavelets, as intergrid operators for the Krylov subspace iterative methods. Construction of hierarchy of matrices in algebraic multigrid context is based on lowpass filter version of Wavelet Transform. The robustness and efficiency of this new approach is tested by applying it to large sparse, unsymmetric and ill-conditioned matrices from Tim Davis collection of sparse matrices. Proposed preconditioners have potential in reducing cputime, operator complexity and storage space of algebraic multigrid V-cycle and meet the desired accuracy of solution compared with that of orthogonal wavelets.展开更多
This paper addresses the problem of complex and challenging disturbance localization in the current power system operation environment by proposing a disturbance localization method for power systems based on group sp...This paper addresses the problem of complex and challenging disturbance localization in the current power system operation environment by proposing a disturbance localization method for power systems based on group sparse representation and entropy weight method.Three different electrical quantities are selected as observations in the compressed sensing algorithm.The entropy weighting method is employed to calculate the weights of different observations based on their relative disturbance levels.Subsequently,by leveraging the topological information of the power system and pre-designing an overcomplete dictionary of disturbances based on the corresponding system parameter variations caused by disturbances,an improved Joint Generalized Orthogonal Matching Pursuit(J-GOMP)algorithm is utilized for reconstruction.The reconstructed sparse vectors are divided into three parts.If at least two parts have consistent node identifiers,the node is identified as the disturbance node.If the node identifiers in all three parts are inconsistent,further analysis is conducted considering the weights to determine the disturbance node.Simulation results based on the IEEE 39-bus system model demonstrate that the proposed method,utilizing electrical quantity information from only 8 measurement points,effectively locates disturbance positions and is applicable to various disturbance types with strong noise resistance.展开更多
Broadband wireless channels are often time dispersive and become strongly frequency selective in delay spread domain. Commonly, these channels are composed of a few dominant coefficients and a large part of coefficien...Broadband wireless channels are often time dispersive and become strongly frequency selective in delay spread domain. Commonly, these channels are composed of a few dominant coefficients and a large part of coefficients are approximately zero or under noise floor. To exploit sparsity of multi-path channels (MPCs), there are various methods have been proposed. They are, namely, greedy algorithms, iterative algorithms, and convex program. The former two algorithms are easy to be implemented but not stable;on the other hand, the last method is stable but difficult to be implemented as practical channel estimation problems be-cause of computational complexity. In this paper, we introduce a novel channel estimation strategy using smooth L0 (SL0) algorithm which combines stable and low complexity. Computer simulations confirm the effectiveness of the introduced algorithm. We also give various simulations to verify the sensing training signal method.展开更多
We consider the sparse identification of multivariate ARX systems, i.e., to recover the zero elements of the unknown parameter matrix. We propose a two-step algorithm, where in the first step the stochastic gradient (...We consider the sparse identification of multivariate ARX systems, i.e., to recover the zero elements of the unknown parameter matrix. We propose a two-step algorithm, where in the first step the stochastic gradient (SG) algorithm is applied to obtain initial estimates of the unknown parameter matrix and in the second step an optimization criterion is introduced for the sparse identification of multivariate ARX systems. Under mild conditions, we prove that by minimizing the criterion function, the zero elements of the unknown parameter matrix can be recovered with a finite number of observations. The performance of the algorithm is testified through a simulation example.展开更多
Cyber-Physical Systems are very vulnerable to sparse sensor attacks.But current protection mechanisms employ linear and deterministic models which cannot detect attacks precisely.Therefore,in this paper,we propose a n...Cyber-Physical Systems are very vulnerable to sparse sensor attacks.But current protection mechanisms employ linear and deterministic models which cannot detect attacks precisely.Therefore,in this paper,we propose a new non-linear generalized model to describe Cyber-Physical Systems.This model includes unknown multivariable discrete and continuous-time functions and different multiplicative noises to represent the evolution of physical processes and randomeffects in the physical and computationalworlds.Besides,the digitalization stage in hardware devices is represented too.Attackers and most critical sparse sensor attacks are described through a stochastic process.The reconstruction and protectionmechanisms are based on aweighted stochasticmodel.Error probability in data samples is estimated through different indicators commonly employed in non-linear dynamics(such as the Fourier transform,first-return maps,or the probability density function).A decision algorithm calculates the final reconstructed value considering the previous error probability.An experimental validation based on simulation tools and real deployments is also carried out.Both,the new technology performance and scalability are studied.Results prove that the proposed solution protects Cyber-Physical Systems against up to 92%of attacks and perturbations,with a computational delay below 2.5 s.The proposed model shows a linear complexity,as recursive or iterative structures are not employed,just algebraic and probabilistic functions.In conclusion,the new model and reconstructionmechanism can protect successfully Cyber-Physical Systems against sparse sensor attacks,even in dense or pervasive deployments and scenarios.展开更多
LDL-factorization is an efficient way of solving Ax = b for a large symmetric positive definite sparse matrix A. This paper presents a new method that further improves the efficiency of LDL-factorization. It is based ...LDL-factorization is an efficient way of solving Ax = b for a large symmetric positive definite sparse matrix A. This paper presents a new method that further improves the efficiency of LDL-factorization. It is based on the theory of elimination trees for the factorization factor. It breaks the computations involved in LDL-factorization down into two stages： 1） the pattern of nonzero entries of the factor is predicted, and 2） the numerical values of the nonzero entries of the factor are computed. The factor is stored using the form of an elimination tree so as to reduce memory usage and avoid unnecessary numerical operations. The calculation results for some typical numerical examples demonstrate that this method provides a significantly higher calculation efficiency for the one-to-one marketing optimization algorithm.展开更多
In order to improve the performance of linear time-varying（LTV）channel estimation,based on the sparsity of channel taps in time domain,a sparse recovery method of LTV channel in orthogonal frequency division multipl...In order to improve the performance of linear time-varying（LTV）channel estimation,based on the sparsity of channel taps in time domain,a sparse recovery method of LTV channel in orthogonal frequency division multiplexing（OFDM）system is proposed.Firstly,based on the compressive sensing theory,the average of the channel taps over one symbol duration in the LTV channel model is estimated.Secondly,in order to deal with the inter-carrier interference（ICI）,the group-pilot design criterion is used based on the minimization of mutual coherence of the measurement.Finally,an efficient pilot pattern optimization algorithm is proposed by a dual layer loops iteration.The simulation results show that the new method uses less pilots,has a smaller bit error ratio（BER）,and greater ability to deal with Doppler frequency shift than the traditional method does.展开更多
Recently,Reconfigurable Intelligent Surfaces(RISs)have drawn intensive attention in the realization of the smart radio environment.However,existing works mainly consider the RIS as a whole uniform plane,which may be u...Recently,Reconfigurable Intelligent Surfaces(RISs)have drawn intensive attention in the realization of the smart radio environment.However,existing works mainly consider the RIS as a whole uniform plane,which may be unrealistic to be installed on the facade of buildings when the RIS is extremely large.In contrast,this paper investigates a practical Sparse Array of Sub-surface(SAoS)deployment of the RIS for uplink multi-user millimeter Wave(mmWave)communication systems,in which the Mobile Stations(MSs)are distributed in the blind coverage area due to the blockage.In order to exploit the benefits of the sparse deployment,the correlation of the effective channel is firstly investigated.Then the approximation and lower bounds of the ergodic spectral efficiency are derived under frequency and spatial multiplexing scenarios,respectively.Based on the autocorrelation of the effective channel,we obtain an optimal reflect coefficient design as well as the deployment guidelines of RIS tiles.Moreover,the RIS tile scheduling algorithms are also proposed.Numerical results show that the ergodic spectral efficiency approximation matches well with the Monte Carlo result under frequency multiplexing scenarios,and the lower bound is tight under spatial multiplexing scenarios only when the effective channel is strongly correlated.On the basis of the RIS tile scheduling algorithm and the reflect coefficient design,the system performance can be significantly improved under frequency multiplexing scenarios.On the other hand,by deploying more sparse RIS tiles,we can increase the multiplexing gain under spatial multiplexing scenarios.展开更多
A channel estimator used in sparse muhipath fading channel for orthogonal frequency division multiplexing （OFDM） system is proposed. The dimension of signal subspace can be reduced to improve the performance of chan...A channel estimator used in sparse muhipath fading channel for orthogonal frequency division multiplexing （OFDM） system is proposed. The dimension of signal subspace can be reduced to improve the performance of channel estimation. The simplified version of original subspace fitting algorithm is employed to derive the sparse multipaths. In order to overcome the difficulty of termination condition, we consider it as a model identification problem and the set of nonzero paths is found under the generalized Akaike information criterion （GAIC）. The computational complexity can be kept very low under proper training design. Our proposed method is superior to other related schemes due to combining the procedure of selecting the most probable taps with GAIC model selection. Simulation in hilly terrain （HT） channel shows that the proposed method has an outstanding performance.展开更多
Millimeter-wave(mmWave)Non-Orthogonal Multiple Access(NOMA)with random beamforming is a promising technology to guarantee massive connectivity and low latency transmissions of future generations of mobile networks.In ...Millimeter-wave(mmWave)Non-Orthogonal Multiple Access(NOMA)with random beamforming is a promising technology to guarantee massive connectivity and low latency transmissions of future generations of mobile networks.In this paper,we introduce a cost-effective and energy-efficient mmWave-NOMA system that exploits sparse antenna arrays in the transmitter.Our analysis shows that utilizing low-weight and small-sized sparse antennas in the Base Station(BS)leads to better outage probability performance.We also introduce an optimum low complexity Equilibrium Optimization(EO)-based algorithm to further improve the outage probability.The simulation and analysis results show that the systems equipped with sparse antenna arrays making use of optimum beamforming vectors outperform the conventional systems with uniform linear arrays in terms of outage probability and sum rates.展开更多
LetΩbe homogeneous of degree zero,integrable on S^(d−1) and have vanishing moment of order one,a be a function on R^(d) such that ∇a∈L^(∞)(R^(d)).Let T*_(Ω,a) be the maximaloperator associated with the d-dimensional...LetΩbe homogeneous of degree zero,integrable on S^(d−1) and have vanishing moment of order one,a be a function on R^(d) such that ∇a∈L^(∞)(R^(d)).Let T*_(Ω,a) be the maximaloperator associated with the d-dimensional Calder´on commutator defined by T*_(Ωa)f(x):=sup_(ε>0)|∫_(|x-y|>ε)^Ω(x-y)/|x-y|^(d+1)(a(x)-a(y))f(y)dy.In this paper,the authors establish bilinear sparse domination for T*_(Ω,a) under the assumption Ω∈L∞(Sd−1).As applications,some quantitative weighted bounds for T*_(Ω,a) are obtained.展开更多
文摘This paper presents a new subband adaptive filter(SAF)algorithm for system identification scenario under impulsive interference,named generalized continuous mixed p-norm SAF(GCMPN-SAF)algorithm.The proposed algorithm uses a GCMPN cost function to combat the impul-sive interference.To further accelerate the convergence rate in the sparse and the block-sparse system identification processes,the proportionate versions of the proposed algorithm,the L0-norm GCMPN-SAF(L0-GCMPN-SAF)and the block-sparse GCMPN-SAF(BSGCMPN-SAF)algorithms are also developed.Moreover,the convergence analysis of the proposed algorithm is provided.Simulation results show that the proposed algorithms have a better performance than some other state-of-the-art algorithms in the literature with respect to the convergence rate and the tracking capability.
文摘To realize effective co-phasing adjustment in large-aperture sparse-aperture telescopes,a multichannel stripe tracking approach is employed,allowing simultaneous interferometric measurements of multiple optical paths and circumventing the need for pairwise measurements along the mirror boundaries in traditional interferometric methods.This approach enhances detection efficiency and reduces system complexity.Here,the principles of the multibeam interference process and construction of a co-phasing detection module based on direct optical fiber connections were analyzed using wavefront optics theory.Error analysis was conducted on the system surface obtained through multipath interference.Potential applications of the interferometric method were explored.Finally,the principle was verified by experiment,an interferometric fringe contrast better than 0.4 is achieved through flat field calibration and incoherent digital synthesis.The dynamic range of the measurement exceeds 10 times of the center wavelength of the working band(1550 nm).Moreover,a resolution better than one-tenth of the working center wavelength(1550 nm)was achieved.Simultaneous three-beam interference can be achieved,leading to a 50%improvement in detection efficiency.This method can effectively enhance the efficiency of sparse aperture telescope co-phasing,meeting the requirements for observations of 8-10 m telescopes.This study provides a technological foundation for observing distant and faint celestial objects.
基金Supported by the Natural Science Foundation of Guangxi Province(Grant Nos.2023GXNSFAA026067,2024GXN SFAA010521)the National Natural Science Foundation of China(Nos.12361079,12201149,12261026).
文摘Convex feasibility problems are widely used in image reconstruction,sparse signal recovery,and other areas.This paper is devoted to considering a class of convex feasibility problem arising from sparse signal recovery.We rst derive the projection formulas for a vector onto the feasible sets.The centralized circumcentered-reection method is designed to solve the convex feasibility problem.Some numerical experiments demonstrate the feasibility and e ectiveness of the proposed algorithm,showing superior performance compared to conventional alternating projection methods.
基金supported by the Natural Science Foundation of Chongqing(No.cstc2019jcyj-msxmX0017)。
文摘Since orthogonal time-frequency space(OTFS)can effectively handle the problems caused by Doppler effect in high-mobility environment,it has gradually become a promising candidate for modulation scheme in the next generation of mobile communication.However,the inter-Doppler interference(IDI)problem caused by fractional Doppler poses great challenges to channel estimation.To avoid this problem,this paper proposes a joint time and delayDoppler(DD)domain based on sparse Bayesian learning(SBL)channel estimation algorithm.Firstly,we derive the original channel response(OCR)from the time domain channel impulse response(CIR),which can reflect the channel variation during one OTFS symbol.Compare with the traditional channel model,the OCR can avoid the IDI problem.After that,the dimension of OCR is reduced by using the basis expansion model(BEM)and the relationship between the time and DD domain channel model,so that we have turned the underdetermined problem into an overdetermined problem.Finally,in terms of sparsity of channel in delay domain,SBL algorithm is used to estimate the basis coefficients in the BEM without any priori information of channel.The simulation results show the effectiveness and superiority of the proposed channel estimation algorithm.
基金supported in part by the National Natural Science Foundation (Nos. U1533107 and U1433105)the Civil Aviation Science and Technology Innovation Foundation (No. MHRD20130217)the Fundamental Research Funds for the Central Universities of CAUC (No. 3122016D003)
文摘L-band digital aeronautical communication system 1(L-DACS1) is a promising candidate data-link for future air-ground communication, but it is severely interfered by the pulse pairs(PPs) generated by distance measure equipment. A novel PP mitigation approach is proposed in this paper. Firstly, a deformed PP detection(DPPD) method that combines a filter bank, correlation detection, and rescanning is proposed to detect the deformed PPs(DPPs) which are caused by multiple filters in the receiver. Secondly, a finite impulse response(FIR) model is used to approximate the overall characteristic of filters, and then the waveform of DPP can be acquired by the original waveform of PP and the FIR model. Finally, sparse representation is used to estimate the position and amplitude of each DPP, and then reconstruct each DPP. The reconstructed DPPs will be subtracted from the contaminated signal to mitigate interference. Numerical experiments show that the bit error rate performance of our approach is about 5 dB better than that of recent works and is closer to interference-free environment.
基金National Natural Science Foundation of China(60604020)
文摘The classic state methods for trajectory estimation in boost phase with multi-range-rate system include method of point-by-point manner and that of spline-model-based manner. Both are deficient in terms of model-approximation accuracy and systematic error determination thus resulting in the estimation errors well beyond the requirements, especially, concerning the maneuvering trajectory. This article proposes a new high-precision estimation approach based on the residual error analysis. The residual error comprises three components, i. e. systematic error, model truncation error and random error. The approach realizes self-adaptive estimation of systematic errors in measurements following the theory of sparse representation of signals to minimize the low-frequency components of residual errors. By taking median- and high-frequency components as indexes, the spline model-approximation is improved by optimizing node sequence of the spline function and the weight selection for data fusion through iteration. Simulation has validated the performances of the proposed method.
基金supported in part by National Natural Science Foundation of China (no. 61571373, no. 61501383, no. U1734209, no. U1709219)in part by Key International Cooperation Project of Sichuan Province (no. 2017HH0002)+2 种基金in part by Marie Curie Fellowship (no. 792406)in part by the National Science and Technology Major Project under Grant 2016ZX03001018-002in part by NSFC China-Swedish project (no. 6161101297)
文摘Polar coded sparse code multiple access(SCMA) system is conceived in this paper. A simple but new iterative multiuser detection framework is proposed, which consists of a message passing algorithm(MPA) based multiuser detector and a soft-input soft-output(SISO) successive cancellation(SC) polar decoder. In particular, the SISO polar decoding process is realized by a specifically designed soft re-encoder, which is concatenated to the original SC decoder. This soft re-encoder is capable of reconstructing the soft information of the entire polar codeword based on previously detected log-likelihood ratios(LLRs) of information bits. Benefiting from the soft re-encoding algorithm, the resultant iterative detection strategy is able to obtain a salient coding gain. Our simulation results demonstrate that significant improvement in error performance is achieved by the proposed polar-coded SCMA in additive white Gaussian noise(AWGN) channels, where the performance of the conventional SISO belief propagation(BP) polar decoder aided SCMA, the turbo coded SCMA and the low-density parity-check(LDPC) coded SCMA are employed as benchmarks.
文摘recently the indexed modulation(IM)technique in conjunction with the multi-carrier modulation gains an increasing attention.It conveys additional information on the subcarrier indices by activating specific subcarriers in the frequency domain besides the conventional amplitude-phase modulation of the activated subcarriers.Orthogonal frequency division multiplexing(OFDM)with IM(OFDM-IM)is deeply compared with the classical OFDM.It leads to an attractive trade-off between the spectral efficiency(SE)and the energy efficiency(EE).In this paper,the concept of the combinatorial modulation is introduced from a new point of view.The sparsity mapping is suggested intentionally to enable the compressive sensing(CS)concept in the data recovery process to provide further performance and EE enhancement without SE loss.Generating artificial data sparsity in the frequency domain along with naturally embedded channel sparsity in the time domain allows joint data recovery and channel estimation in a double sparsity framework.Based on simulation results,the performance of the proposed approach agrees with the predicted CS superiority even under low signal-to-noise ratio without channel coding.Moreover,the proposed sparsely indexed modulation system outperforms the conventional OFDM system and the OFDM-IM system in terms of error performance,peak-to-average power ratio(PAPR)and energy efficiency under the same spectral efficiency.
文摘Applying the atomic sparse decomposition in the distribution network with harmonics and small current grounding to decompose the transient zero sequence current that appears after the single phase to ground fault occurred. Based on dictionary of Gabor atoms and matching pursuit algorithm, the method extracts the atomic components iteratively from the feature signals and translated them to damped sinusoidal components. Then we can obtain the parametrical and analytical representation of atomic components. The termination condition of decomposing iteration is determined by the threshold of the initial residual energy with the purpose of extract the features more effectively. Accordingly, the proposed method can extract the starting and ending moment of disturbances precisely as well as their magnitudes, frequencies and other features. The numerical examples demonstrate its effectiveness.
文摘In this article algebraic multigrid as preconditioners are designed, with biorthogonal wavelets, as intergrid operators for the Krylov subspace iterative methods. Construction of hierarchy of matrices in algebraic multigrid context is based on lowpass filter version of Wavelet Transform. The robustness and efficiency of this new approach is tested by applying it to large sparse, unsymmetric and ill-conditioned matrices from Tim Davis collection of sparse matrices. Proposed preconditioners have potential in reducing cputime, operator complexity and storage space of algebraic multigrid V-cycle and meet the desired accuracy of solution compared with that of orthogonal wavelets.
基金funded by the State Grid Jilin Economic Research Institute’s 2022 Practical Re-Search Project on the Construction of Long-Term Power Supply Guarantee Mechanism in Provincial Capital Cities under the New Situation,Grant Number SGJLJY00GPJS2200041.
文摘This paper addresses the problem of complex and challenging disturbance localization in the current power system operation environment by proposing a disturbance localization method for power systems based on group sparse representation and entropy weight method.Three different electrical quantities are selected as observations in the compressed sensing algorithm.The entropy weighting method is employed to calculate the weights of different observations based on their relative disturbance levels.Subsequently,by leveraging the topological information of the power system and pre-designing an overcomplete dictionary of disturbances based on the corresponding system parameter variations caused by disturbances,an improved Joint Generalized Orthogonal Matching Pursuit(J-GOMP)algorithm is utilized for reconstruction.The reconstructed sparse vectors are divided into three parts.If at least two parts have consistent node identifiers,the node is identified as the disturbance node.If the node identifiers in all three parts are inconsistent,further analysis is conducted considering the weights to determine the disturbance node.Simulation results based on the IEEE 39-bus system model demonstrate that the proposed method,utilizing electrical quantity information from only 8 measurement points,effectively locates disturbance positions and is applicable to various disturbance types with strong noise resistance.
文摘Broadband wireless channels are often time dispersive and become strongly frequency selective in delay spread domain. Commonly, these channels are composed of a few dominant coefficients and a large part of coefficients are approximately zero or under noise floor. To exploit sparsity of multi-path channels (MPCs), there are various methods have been proposed. They are, namely, greedy algorithms, iterative algorithms, and convex program. The former two algorithms are easy to be implemented but not stable;on the other hand, the last method is stable but difficult to be implemented as practical channel estimation problems be-cause of computational complexity. In this paper, we introduce a novel channel estimation strategy using smooth L0 (SL0) algorithm which combines stable and low complexity. Computer simulations confirm the effectiveness of the introduced algorithm. We also give various simulations to verify the sensing training signal method.
文摘We consider the sparse identification of multivariate ARX systems, i.e., to recover the zero elements of the unknown parameter matrix. We propose a two-step algorithm, where in the first step the stochastic gradient (SG) algorithm is applied to obtain initial estimates of the unknown parameter matrix and in the second step an optimization criterion is introduced for the sparse identification of multivariate ARX systems. Under mild conditions, we prove that by minimizing the criterion function, the zero elements of the unknown parameter matrix can be recovered with a finite number of observations. The performance of the algorithm is testified through a simulation example.
基金supported by Comunidad de Madrid within the framework of the Multiannual Agreement with Universidad Politécnica de Madrid to encourage research by young doctors(PRINCE).
文摘Cyber-Physical Systems are very vulnerable to sparse sensor attacks.But current protection mechanisms employ linear and deterministic models which cannot detect attacks precisely.Therefore,in this paper,we propose a new non-linear generalized model to describe Cyber-Physical Systems.This model includes unknown multivariable discrete and continuous-time functions and different multiplicative noises to represent the evolution of physical processes and randomeffects in the physical and computationalworlds.Besides,the digitalization stage in hardware devices is represented too.Attackers and most critical sparse sensor attacks are described through a stochastic process.The reconstruction and protectionmechanisms are based on aweighted stochasticmodel.Error probability in data samples is estimated through different indicators commonly employed in non-linear dynamics(such as the Fourier transform,first-return maps,or the probability density function).A decision algorithm calculates the final reconstructed value considering the previous error probability.An experimental validation based on simulation tools and real deployments is also carried out.Both,the new technology performance and scalability are studied.Results prove that the proposed solution protects Cyber-Physical Systems against up to 92%of attacks and perturbations,with a computational delay below 2.5 s.The proposed model shows a linear complexity,as recursive or iterative structures are not employed,just algebraic and probabilistic functions.In conclusion,the new model and reconstructionmechanism can protect successfully Cyber-Physical Systems against sparse sensor attacks,even in dense or pervasive deployments and scenarios.
基金This work was supported in part by the National Natural Science Foundation of PRC (No.60425310)the Teaching and Research Award Program for Outstanding Young Teachers in Higher Education Institutions of MOE,PRC.
文摘LDL-factorization is an efficient way of solving Ax = b for a large symmetric positive definite sparse matrix A. This paper presents a new method that further improves the efficiency of LDL-factorization. It is based on the theory of elimination trees for the factorization factor. It breaks the computations involved in LDL-factorization down into two stages： 1） the pattern of nonzero entries of the factor is predicted, and 2） the numerical values of the nonzero entries of the factor are computed. The factor is stored using the form of an elimination tree so as to reduce memory usage and avoid unnecessary numerical operations. The calculation results for some typical numerical examples demonstrate that this method provides a significantly higher calculation efficiency for the one-to-one marketing optimization algorithm.
基金Supported by the National Natural Science Foundation of China(61571368)the Ministerial Level Advanced Research Foundation(950303HK,C9149C0511)
文摘In order to improve the performance of linear time-varying（LTV）channel estimation,based on the sparsity of channel taps in time domain,a sparse recovery method of LTV channel in orthogonal frequency division multiplexing（OFDM）system is proposed.Firstly,based on the compressive sensing theory,the average of the channel taps over one symbol duration in the LTV channel model is estimated.Secondly,in order to deal with the inter-carrier interference（ICI）,the group-pilot design criterion is used based on the minimization of mutual coherence of the measurement.Finally,an efficient pilot pattern optimization algorithm is proposed by a dual layer loops iteration.The simulation results show that the new method uses less pilots,has a smaller bit error ratio（BER）,and greater ability to deal with Doppler frequency shift than the traditional method does.
基金This work was supported in part by the National Key Research and Development Program 2018YFA0701602the National Science Foundation of China(NSFC)for Distinguished Young Scholars with Grant 61625106,and the NSFC under Grant 61941104.
文摘Recently,Reconfigurable Intelligent Surfaces(RISs)have drawn intensive attention in the realization of the smart radio environment.However,existing works mainly consider the RIS as a whole uniform plane,which may be unrealistic to be installed on the facade of buildings when the RIS is extremely large.In contrast,this paper investigates a practical Sparse Array of Sub-surface(SAoS)deployment of the RIS for uplink multi-user millimeter Wave(mmWave)communication systems,in which the Mobile Stations(MSs)are distributed in the blind coverage area due to the blockage.In order to exploit the benefits of the sparse deployment,the correlation of the effective channel is firstly investigated.Then the approximation and lower bounds of the ergodic spectral efficiency are derived under frequency and spatial multiplexing scenarios,respectively.Based on the autocorrelation of the effective channel,we obtain an optimal reflect coefficient design as well as the deployment guidelines of RIS tiles.Moreover,the RIS tile scheduling algorithms are also proposed.Numerical results show that the ergodic spectral efficiency approximation matches well with the Monte Carlo result under frequency multiplexing scenarios,and the lower bound is tight under spatial multiplexing scenarios only when the effective channel is strongly correlated.On the basis of the RIS tile scheduling algorithm and the reflect coefficient design,the system performance can be significantly improved under frequency multiplexing scenarios.On the other hand,by deploying more sparse RIS tiles,we can increase the multiplexing gain under spatial multiplexing scenarios.
基金Supported by the Starting Fund for Science Research of NJUST (AB41947)the Open Research Fund of National Mobile Communications Research Laboratory (N200609)Science Research Developing Fund of NJUST (XKF07023)
文摘A channel estimator used in sparse muhipath fading channel for orthogonal frequency division multiplexing （OFDM） system is proposed. The dimension of signal subspace can be reduced to improve the performance of channel estimation. The simplified version of original subspace fitting algorithm is employed to derive the sparse multipaths. In order to overcome the difficulty of termination condition, we consider it as a model identification problem and the set of nonzero paths is found under the generalized Akaike information criterion （GAIC）. The computational complexity can be kept very low under proper training design. Our proposed method is superior to other related schemes due to combining the procedure of selecting the most probable taps with GAIC model selection. Simulation in hilly terrain （HT） channel shows that the proposed method has an outstanding performance.
文摘Millimeter-wave(mmWave)Non-Orthogonal Multiple Access(NOMA)with random beamforming is a promising technology to guarantee massive connectivity and low latency transmissions of future generations of mobile networks.In this paper,we introduce a cost-effective and energy-efficient mmWave-NOMA system that exploits sparse antenna arrays in the transmitter.Our analysis shows that utilizing low-weight and small-sized sparse antennas in the Base Station(BS)leads to better outage probability performance.We also introduce an optimum low complexity Equilibrium Optimization(EO)-based algorithm to further improve the outage probability.The simulation and analysis results show that the systems equipped with sparse antenna arrays making use of optimum beamforming vectors outperform the conventional systems with uniform linear arrays in terms of outage probability and sum rates.
文摘LetΩbe homogeneous of degree zero,integrable on S^(d−1) and have vanishing moment of order one,a be a function on R^(d) such that ∇a∈L^(∞)(R^(d)).Let T*_(Ω,a) be the maximaloperator associated with the d-dimensional Calder´on commutator defined by T*_(Ωa)f(x):=sup_(ε>0)|∫_(|x-y|>ε)^Ω(x-y)/|x-y|^(d+1)(a(x)-a(y))f(y)dy.In this paper,the authors establish bilinear sparse domination for T*_(Ω,a) under the assumption Ω∈L∞(Sd−1).As applications,some quantitative weighted bounds for T*_(Ω,a) are obtained.
