In this paper,in order to reduce the energy leakage caused by the discretized representation in sparse channel estimation for Orthogonal Frequency Division Multiplexing(OFDM)systems,we systematically have analyzed the...In this paper,in order to reduce the energy leakage caused by the discretized representation in sparse channel estimation for Orthogonal Frequency Division Multiplexing(OFDM)systems,we systematically have analyzed the optimal locations of atoms with discrete delays for each path reconstruction from the perspective of linear fitting theory.Then,we have investigated the adverse effects of the non-ideal inner product function on the iteration in one of the most widely used channel estimation method,Orthogonal Matching Pursuit(OMP).The study shows that the distance between the selected atoms for each path in OMP can be larger than the sampling interval,which prevents OMP-based methods from achieving better performance.To overcome this drawback,the image deblurring-based channel estimation method,in which the channel estimation problem is analogized to one-dimensional image deblurring,was proposed to improve the large compensation distance of traditional OMP.The advantage of the proposed method was validated by the results of numerical simulation and sea trial data decoding.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
A superimposed training （ST） based channel estimation method is presented that provides accurate estimation of a sparse underwater acoustic orthogonal frequency-division multiplexing （OFDM） channel while improving...A superimposed training （ST） based channel estimation method is presented that provides accurate estimation of a sparse underwater acoustic orthogonal frequency-division multiplexing （OFDM） channel while improving bandwidth transmission efficiency. A periodic low power training sequence is superimposed on the information sequence at the transmitter. The channel parameters can be estimated without consuming any extra system bandwidth, but an unknown information sequence can interfere with the ST channel estimation method, so in this paper, an iterative method was adopted to improve estimation performance. An underwater acoustic channel＇s properties include large channel dimensions and a sparse structure, so a matching pursuit （MP） algorithm was used to estimate the nonzero taps, allowing the performance loss caused by additive white Gaussian noise （AWGN） to be reduced. The results of computer simulations show that the proposed method has good channel estimation performance and can reduce the peak-to-average ratio of the OFDM channel as well.展开更多
In this paper,a fast orthogonal matching pursuit(OMP)algorithm based on optimized iterative process is proposed for sparse time-varying underwater acoustic(UWA)channel estimation.The channel estimation consists of cal...In this paper,a fast orthogonal matching pursuit(OMP)algorithm based on optimized iterative process is proposed for sparse time-varying underwater acoustic(UWA)channel estimation.The channel estimation consists of calculating amplitude,delay and Doppler scaling factor of each path using the received multi-path signal.This algorithm,called as OIP-FOMP,can reduce the computationally complexity of the traditional OMP algorithm and maintain accuracy in the presence of severe inter-carrier interference that exists in the time-varying UWA channels.In this algorithm,repeated inner product operations used in the OMP algorithm are removed by calculating the candidate path signature Hermitian inner product matrix in advance.Efficient QR decomposition is used to estimate the path amplitude,and the problem of reconstruction failure caused by inaccurate delay selection is avoided by optimizing the Hermitian inner product matrix.Theoretical analysis and simulation results show that the computational complexity of the OIP-FOMP algorithm is reduced by about 1/4 compared with the OMP algorithm,without any loss of accuracy.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
Working memory plays an important role in human cognition. This study investigated how working memory was encoded by the power of multichannel local field potentials （LFPs） based on sparse non negative matrix factor...Working memory plays an important role in human cognition. This study investigated how working memory was encoded by the power of multichannel local field potentials （LFPs） based on sparse non negative matrix factorization （SNMF）. SNMF was used to extract features from LFPs recorded from the prefrontal cortex of four SpragueDawley rats during a memory task in a Y maze, with 10 trials for each rat. Then the powerincreased LFP components were selected as working memoryrelated features and the other components were removed. After that, the inverse operation of SNMF was used to study the encoding of working memory in the time frequency domain. We demonstrated that theta and gamma power increased significantly during the working memory task. The results suggested that postsynaptic activity was simulated well by the sparse activity model. The theta and gamma bands were meaningful for encoding working memory.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
A sparse channel estimation method is proposed for doubly selective channels in multiple- input multiple-output （ MIMO ） orthogonal frequency division multiplexing （ OFDM ） systems. Based on the basis expansion mo...A sparse channel estimation method is proposed for doubly selective channels in multiple- input multiple-output （ MIMO ） orthogonal frequency division multiplexing （ OFDM ） systems. Based on the basis expansion model （BEM） of the channel, the joint-sparsity of MIMO-OFDM channels is described. The sparse characteristics enable us to cast the channel estimation as a distributed compressed sensing （DCS） problem. Then, a low complexity DCS-based estimation scheme is designed. Compared with the conventional compressed channel estimators based on the compressed sensing （CS） theory, the DCS-based method has an improved efficiency because it reconstructs the MIMO channels jointly rather than addresses them separately. Furthermore, the group-sparse structure of each single channel is also depicted. To effectively use this additional structure of the sparsity pattern, the DCS algorithm is modified. The modified algorithm can further enhance the estimation performance. Simulation results demonstrate the superiority of our method over fast fading channels in MIMO-OFDM systems.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
The deep convolutional neural network(CNN)is exploited in this work to conduct the challenging channel estimation for mmWave massive multiple input multiple output(MIMO)systems.The inherent sparse features of the mmWa...The deep convolutional neural network(CNN)is exploited in this work to conduct the challenging channel estimation for mmWave massive multiple input multiple output(MIMO)systems.The inherent sparse features of the mmWave massive MIMO channels can be extracted and the sparse channel supports can be learnt by the multi-layer CNN-based network through training.Then accurate channel inference can be efficiently implemented using the trained network.The estimation accuracy and spectrum efficiency can be further improved by fully utilizing the spatial correlation among the sparse channel supports of different antennas.It is verified by simulation results that the proposed deep CNN-based scheme significantly outperforms the state-of-the-art benchmarks in both accuracy and spectrum efficiency.展开更多
The existence of a multi-path channel under the water greatly decreases the accuracy of the short baseline positioning system.In this paper,the application of a time reversal mirror to the short baseline positioning s...The existence of a multi-path channel under the water greatly decreases the accuracy of the short baseline positioning system.In this paper,the application of a time reversal mirror to the short baseline positioning system was investigated.The time reversal mirror technique allowed the acoustic signal to better focus in an unknown environment,which effectively reduced the expansion of multi-path acoustic signals as well as improved the signal focusing.The signal-to-noise ratio（SNR） of the time reversal operator greatly increased and could be obtained by ensonifying the water.The technique was less affected by the environment and therefore more applicable to a complex shallow water environment.Numerical simulations and pool experiments were used to demonstrate the efficiency of this technique.展开更多
Color image super-resolution reconstruction based on the sparse representation model usually adopts the regularization norm(e.g.,L1 or L2).These methods have limited ability to keep image texture detail to some extent...Color image super-resolution reconstruction based on the sparse representation model usually adopts the regularization norm(e.g.,L1 or L2).These methods have limited ability to keep image texture detail to some extent and are easy to cause the problem of blurring details and color artifacts in color reconstructed images.This paper presents a color super-resolution reconstruction method combining the L2/3 sparse regularization model with color channel constraints.The method converts the low-resolution color image from RGB to YCbCr.The L2/3 sparse regularization model is designed to reconstruct the brightness channel of the input low-resolution color image.Then the color channel-constraint method is adopted to remove artifacts of the reconstructed highresolution image.The method not only ensures the reconstruction quality of the color image details,but also improves the removal ability of color artifacts.The experimental results on natural images validate that our method has improved both subjective and objective evaluation.展开更多
As antennas are inherently included recommended in Over-The-Air (OTA) testing, it is important to also consider realistic channel models for the multiple-input multiple-output (MIMO) device performance evaluation. Thi...As antennas are inherently included recommended in Over-The-Air (OTA) testing, it is important to also consider realistic channel models for the multiple-input multiple-output (MIMO) device performance evaluation. This paper aims to emulate realistic multi-Path propagation channels in terms of angles of arrivals (AoA) and cross-polarization ratio (XPR) with Rayleigh fading, inside an anechoic chamber, for antenna diversity measurements. In this purpose, a practical multi-probe anechoic chamber measurement system (MPAC) with 24 probe antennas (SATIMO SG24) has been used. However, the actual configuration of this system is not able to reproduce realistic channels. Therefore, a new method based on the control of the SG24 probes has been developed. At first time, this method has been validated numerically through the comparison of simulated and analytical AoA probability density distributions. At the second time, the performance of an antenna diversity system inside the SG24 has been performed in terms of the correlation coefficient and diversity gain (DG) using an antenna reference system. Simulated and measurements results have shown a good agreement.展开更多
基金supported by the Fundamental Research Funds for the Central Universities under Grant 20720200092the National Natural Science Foundation of China under Grant 62171394,U21A20444,61771152,62071402+2 种基金the Sustainable Funding of the Key Laboratory of Underwater Acoustic Technology under Grant JCKYS2022604SSJS001Key Laboratory of Universal Wireless Communications(BUPT)Ministry of Education,P.R.China under Grant KFKT-2022103.
文摘In this paper,in order to reduce the energy leakage caused by the discretized representation in sparse channel estimation for Orthogonal Frequency Division Multiplexing(OFDM)systems,we systematically have analyzed the optimal locations of atoms with discrete delays for each path reconstruction from the perspective of linear fitting theory.Then,we have investigated the adverse effects of the non-ideal inner product function on the iteration in one of the most widely used channel estimation method,Orthogonal Matching Pursuit(OMP).The study shows that the distance between the selected atoms for each path in OMP can be larger than the sampling interval,which prevents OMP-based methods from achieving better performance.To overcome this drawback,the image deblurring-based channel estimation method,in which the channel estimation problem is analogized to one-dimensional image deblurring,was proposed to improve the large compensation distance of traditional OMP.The advantage of the proposed method was validated by the results of numerical simulation and sea trial data decoding.
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 by the National Natural Science Foundation of China under Grant No.60572039
文摘A superimposed training （ST） based channel estimation method is presented that provides accurate estimation of a sparse underwater acoustic orthogonal frequency-division multiplexing （OFDM） channel while improving bandwidth transmission efficiency. A periodic low power training sequence is superimposed on the information sequence at the transmitter. The channel parameters can be estimated without consuming any extra system bandwidth, but an unknown information sequence can interfere with the ST channel estimation method, so in this paper, an iterative method was adopted to improve estimation performance. An underwater acoustic channel＇s properties include large channel dimensions and a sparse structure, so a matching pursuit （MP） algorithm was used to estimate the nonzero taps, allowing the performance loss caused by additive white Gaussian noise （AWGN） to be reduced. The results of computer simulations show that the proposed method has good channel estimation performance and can reduce the peak-to-average ratio of the OFDM channel as well.
基金supported in part by the National Natural Science Foundation of China(NSFC)(No.U1806201,61671261)Project of Shandong Province Higher Educational Science and Technology Program(No.J17KA058,J17KB154).
文摘In this paper,a fast orthogonal matching pursuit(OMP)algorithm based on optimized iterative process is proposed for sparse time-varying underwater acoustic(UWA)channel estimation.The channel estimation consists of calculating amplitude,delay and Doppler scaling factor of each path using the received multi-path signal.This algorithm,called as OIP-FOMP,can reduce the computationally complexity of the traditional OMP algorithm and maintain accuracy in the presence of severe inter-carrier interference that exists in the time-varying UWA channels.In this algorithm,repeated inner product operations used in the OMP algorithm are removed by calculating the candidate path signature Hermitian inner product matrix in advance.Efficient QR decomposition is used to estimate the path amplitude,and the problem of reconstruction failure caused by inaccurate delay selection is avoided by optimizing the Hermitian inner product matrix.Theoretical analysis and simulation results show that the computational complexity of the OIP-FOMP algorithm is reduced by about 1/4 compared with the OMP algorithm,without any loss of accuracy.
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.
基金supported by the National Natural Science Foundation of China (61074131 and 91132722)the Doctoral Fund of the Ministry of Education of China (21101202110007)
文摘Working memory plays an important role in human cognition. This study investigated how working memory was encoded by the power of multichannel local field potentials （LFPs） based on sparse non negative matrix factorization （SNMF）. SNMF was used to extract features from LFPs recorded from the prefrontal cortex of four SpragueDawley rats during a memory task in a Y maze, with 10 trials for each rat. Then the powerincreased LFP components were selected as working memoryrelated features and the other components were removed. After that, the inverse operation of SNMF was used to study the encoding of working memory in the time frequency domain. We demonstrated that theta and gamma power increased significantly during the working memory task. The results suggested that postsynaptic activity was simulated well by the sparse activity model. The theta and gamma bands were meaningful for encoding working memory.
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Supported by the National Natural Science Foundation of China(61077022)
文摘A sparse channel estimation method is proposed for doubly selective channels in multiple- input multiple-output （ MIMO ） orthogonal frequency division multiplexing （ OFDM ） systems. Based on the basis expansion model （BEM） of the channel, the joint-sparsity of MIMO-OFDM channels is described. The sparse characteristics enable us to cast the channel estimation as a distributed compressed sensing （DCS） problem. Then, a low complexity DCS-based estimation scheme is designed. Compared with the conventional compressed channel estimators based on the compressed sensing （CS） theory, the DCS-based method has an improved efficiency because it reconstructs the MIMO channels jointly rather than addresses them separately. Furthermore, the group-sparse structure of each single channel is also depicted. To effectively use this additional structure of the sparsity pattern, the DCS algorithm is modified. The modified algorithm can further enhance the estimation performance. Simulation results demonstrate the superiority of our method over fast fading channels in MIMO-OFDM systems.
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.
基金This work is supported in part by the National Natural Science Foundation of China under grants 61901403,61971366 and 61971365in part by the Youth Innovation Fund of Xiamen under grant 3502Z20206039in part by the Natural Science Foundation of Fujian Province of China under grant 2019J05001.
文摘The deep convolutional neural network(CNN)is exploited in this work to conduct the challenging channel estimation for mmWave massive multiple input multiple output(MIMO)systems.The inherent sparse features of the mmWave massive MIMO channels can be extracted and the sparse channel supports can be learnt by the multi-layer CNN-based network through training.Then accurate channel inference can be efficiently implemented using the trained network.The estimation accuracy and spectrum efficiency can be further improved by fully utilizing the spatial correlation among the sparse channel supports of different antennas.It is verified by simulation results that the proposed deep CNN-based scheme significantly outperforms the state-of-the-art benchmarks in both accuracy and spectrum efficiency.
基金Supported by the National Defense Basic Foundation of China B2420710007
文摘The existence of a multi-path channel under the water greatly decreases the accuracy of the short baseline positioning system.In this paper,the application of a time reversal mirror to the short baseline positioning system was investigated.The time reversal mirror technique allowed the acoustic signal to better focus in an unknown environment,which effectively reduced the expansion of multi-path acoustic signals as well as improved the signal focusing.The signal-to-noise ratio（SNR） of the time reversal operator greatly increased and could be obtained by ensonifying the water.The technique was less affected by the environment and therefore more applicable to a complex shallow water environment.Numerical simulations and pool experiments were used to demonstrate the efficiency of this technique.
基金supported by the National Natural Science Foundation of China(61761028)。
文摘Color image super-resolution reconstruction based on the sparse representation model usually adopts the regularization norm(e.g.,L1 or L2).These methods have limited ability to keep image texture detail to some extent and are easy to cause the problem of blurring details and color artifacts in color reconstructed images.This paper presents a color super-resolution reconstruction method combining the L2/3 sparse regularization model with color channel constraints.The method converts the low-resolution color image from RGB to YCbCr.The L2/3 sparse regularization model is designed to reconstruct the brightness channel of the input low-resolution color image.Then the color channel-constraint method is adopted to remove artifacts of the reconstructed highresolution image.The method not only ensures the reconstruction quality of the color image details,but also improves the removal ability of color artifacts.The experimental results on natural images validate that our method has improved both subjective and objective evaluation.
文摘As antennas are inherently included recommended in Over-The-Air (OTA) testing, it is important to also consider realistic channel models for the multiple-input multiple-output (MIMO) device performance evaluation. This paper aims to emulate realistic multi-Path propagation channels in terms of angles of arrivals (AoA) and cross-polarization ratio (XPR) with Rayleigh fading, inside an anechoic chamber, for antenna diversity measurements. In this purpose, a practical multi-probe anechoic chamber measurement system (MPAC) with 24 probe antennas (SATIMO SG24) has been used. However, the actual configuration of this system is not able to reproduce realistic channels. Therefore, a new method based on the control of the SG24 probes has been developed. At first time, this method has been validated numerically through the comparison of simulated and analytical AoA probability density distributions. At the second time, the performance of an antenna diversity system inside the SG24 has been performed in terms of the correlation coefficient and diversity gain (DG) using an antenna reference system. Simulated and measurements results have shown a good agreement.
