Motivated by the study of regularization for sparse problems,we propose a new regularization method for sparse vector recovery.We derive sufficient conditions on the well-posedness of the new regularization,and design...Motivated by the study of regularization for sparse problems,we propose a new regularization method for sparse vector recovery.We derive sufficient conditions on the well-posedness of the new regularization,and design an iterative algorithm,namely the iteratively reweighted algorithm(IR-algorithm),for efficiently computing the sparse solutions to the proposed regularization model.The convergence of the IR-algorithm and the setting of the regularization parameters are analyzed at length.Finally,we present numerical examples to illustrate the features of the new regularization and algorithm.展开更多
Sparse vector coding(SVC)is emerging as a potential technology for short packet communications.To further improve the block error rate(BLER)performance,a uniquely decomposable constellation group-based SVC(UDCG-SVC)is...Sparse vector coding(SVC)is emerging as a potential technology for short packet communications.To further improve the block error rate(BLER)performance,a uniquely decomposable constellation group-based SVC(UDCG-SVC)is proposed in this article.Additionally,in order to achieve an optimal BLER performance of UDCG-SVC,a problem to optimize the coding gain of UDCG-based superimposed constellation is formulated.Given the energy of rotation constellations in UDCG,this problem is solved by converting it into finding the maximized minimum Euclidean distance of the superimposed constellation.Simulation results demonstrate the validness of our derivation.We also find that the proposed UDCGSVC has better BLER performance compared to other SVC schemes,especially under the high order modulation scenarios.展开更多
Recent advances in AC/DC hybrid power distribution systems have enhanced convenience in daily life.However,DC distribution introduces significant power quality challenges.To address the identification and classificati...Recent advances in AC/DC hybrid power distribution systems have enhanced convenience in daily life.However,DC distribution introduces significant power quality challenges.To address the identification and classification of DC power quality disturbances,this paper proposes a novel methodology integrating Compressed Sensing(CS)with an enhanced Stacked Denoising Autoencoder(SDAE).The proposed approach first employs MATLAB/SIMULINK to model the DC distribution network and generate DC power quality disturbance signals.The measured original signals are then reconstructed using the compressive sensing-based generalized orthogonal matching pursuit(GOMP)algorithm to obtain sparse vectors as the final dataset.Subsequently,a Stacked Denoising Autoencoder model is constructed.The Root Mean Square Propagation(RMSprop)optimization algorithm is introduced to finetune network parameters,thereby reducing the probability of convergence to local optima.Finally,simulation analyses are conducted on five common types of DC power quality disturbance signals.Both raw signals and sparse vectors are utilized as datasets and fed into the encoder model.The results indicate that this method effectively reduces the feature dimensionality for DC power quality disturbance classification while improving both recognition efficiency and accuracy,with additional advantages in noise resistance.展开更多
基金Project supported by the National Natural Science Foundation of China(No.61603322)the Research Foundation of Education Bureau of Hunan Province of China(No.16C1542)
文摘Motivated by the study of regularization for sparse problems,we propose a new regularization method for sparse vector recovery.We derive sufficient conditions on the well-posedness of the new regularization,and design an iterative algorithm,namely the iteratively reweighted algorithm(IR-algorithm),for efficiently computing the sparse solutions to the proposed regularization model.The convergence of the IR-algorithm and the setting of the regularization parameters are analyzed at length.Finally,we present numerical examples to illustrate the features of the new regularization and algorithm.
基金supported by the National Science Fundation of China(NSFC)under grant 62001423the Henan Provincial Key Research,Development and Promotion Project under grant 212102210175the Henan Provincial Key Scientific Research Project for College and University under grant 21A510011.
文摘Sparse vector coding(SVC)is emerging as a potential technology for short packet communications.To further improve the block error rate(BLER)performance,a uniquely decomposable constellation group-based SVC(UDCG-SVC)is proposed in this article.Additionally,in order to achieve an optimal BLER performance of UDCG-SVC,a problem to optimize the coding gain of UDCG-based superimposed constellation is formulated.Given the energy of rotation constellations in UDCG,this problem is solved by converting it into finding the maximized minimum Euclidean distance of the superimposed constellation.Simulation results demonstrate the validness of our derivation.We also find that the proposed UDCGSVC has better BLER performance compared to other SVC schemes,especially under the high order modulation scenarios.
基金funded by the National Natural Science Foundation of China(52177074).
文摘Recent advances in AC/DC hybrid power distribution systems have enhanced convenience in daily life.However,DC distribution introduces significant power quality challenges.To address the identification and classification of DC power quality disturbances,this paper proposes a novel methodology integrating Compressed Sensing(CS)with an enhanced Stacked Denoising Autoencoder(SDAE).The proposed approach first employs MATLAB/SIMULINK to model the DC distribution network and generate DC power quality disturbance signals.The measured original signals are then reconstructed using the compressive sensing-based generalized orthogonal matching pursuit(GOMP)algorithm to obtain sparse vectors as the final dataset.Subsequently,a Stacked Denoising Autoencoder model is constructed.The Root Mean Square Propagation(RMSprop)optimization algorithm is introduced to finetune network parameters,thereby reducing the probability of convergence to local optima.Finally,simulation analyses are conducted on five common types of DC power quality disturbance signals.Both raw signals and sparse vectors are utilized as datasets and fed into the encoder model.The results indicate that this method effectively reduces the feature dimensionality for DC power quality disturbance classification while improving both recognition efficiency and accuracy,with additional advantages in noise resistance.
