Starting from the extended nonlinear Schrodinger equation in which the self-steepening effect is included, the evolution and the splitting processes of continuous optical wave whose amplitude is perturbed into time re...Starting from the extended nonlinear Schrodinger equation in which the self-steepening effect is included, the evolution and the splitting processes of continuous optical wave whose amplitude is perturbed into time related ultra-short optical pulse trains in an optical fibre are numerically simulated by adopting the split-step Fourier algorithm. The results show that the self-steepening effect can cause the characteristic of the pulse trains to vary with time, which is different from the self-steepening-free case where the generated pulse trains consist of single pulses which are identical in width, intensity, and interval, namely when pulses move a certain distance, they turn into the pulse trains within a certain time range. Moreover, each single pulse may split into several sub-pulses. And as time goes on, the number of the sub-pulses will decrease gradually and the pulse width and the pulse intensity will change too. With the increase of the self-steepening parameter, the distance needed to generate time-dependent pulse trains will shorten. In addition, for a large self-steepening parameter and at the distance where more sub-pulses appear, the corresponding frequency spectra of pulse trains are also wider.展开更多
State-of-the-art model-driven Direction-Of-Arrival(DOA)estimation methods for multipath signals face great challenges in practical application because of the dependence on the precise multipath model.In this paper,we ...State-of-the-art model-driven Direction-Of-Arrival(DOA)estimation methods for multipath signals face great challenges in practical application because of the dependence on the precise multipath model.In this paper,we introduce a framework,based on deep learning,for synchronizing perturbation auto-elimination with effective DOA estimation in multipath environment.Firstly,a signal selection mechanism is introduced to roughly locate specific signals to spatial subregion via frequency domain filters and compressive sensing-based method.Then,we set the mean of the correlation matrix’s row vectors as the input feature to construct the spatial spectrum by the corresponding single network within the parallel deep capsule networks.The proposed method enhances the generalization capability to untrained scenarios and the adaptability to non-ideal conditions,e.g.,lower SNRs,smaller snapshots,unknown reflection coefficients and perturbational steering vectors,which make up for the defects of the previous model-driven methods.Simulations are carried out to demonstrate the superiority of the proposed method.展开更多
基金supported by Key Program of Natural Science Foundation of Educational Commission of Sichuan Province, China (GrantNo 2006A124)the Fundamental Application Research Project of the Department of Science and Technology of Sichuan Province,China (Grant No 05JY029-084)the Foundation of Science and Technology Development of Chengdu University of Information Technology, China (Grant No KYTZ20060604)
文摘Starting from the extended nonlinear Schrodinger equation in which the self-steepening effect is included, the evolution and the splitting processes of continuous optical wave whose amplitude is perturbed into time related ultra-short optical pulse trains in an optical fibre are numerically simulated by adopting the split-step Fourier algorithm. The results show that the self-steepening effect can cause the characteristic of the pulse trains to vary with time, which is different from the self-steepening-free case where the generated pulse trains consist of single pulses which are identical in width, intensity, and interval, namely when pulses move a certain distance, they turn into the pulse trains within a certain time range. Moreover, each single pulse may split into several sub-pulses. And as time goes on, the number of the sub-pulses will decrease gradually and the pulse width and the pulse intensity will change too. With the increase of the self-steepening parameter, the distance needed to generate time-dependent pulse trains will shorten. In addition, for a large self-steepening parameter and at the distance where more sub-pulses appear, the corresponding frequency spectra of pulse trains are also wider.
基金supported by the Program for Innovative Research Groups of the Hunan Provincial Natural Science Foundation of China(No.2019JJ10004)。
文摘State-of-the-art model-driven Direction-Of-Arrival(DOA)estimation methods for multipath signals face great challenges in practical application because of the dependence on the precise multipath model.In this paper,we introduce a framework,based on deep learning,for synchronizing perturbation auto-elimination with effective DOA estimation in multipath environment.Firstly,a signal selection mechanism is introduced to roughly locate specific signals to spatial subregion via frequency domain filters and compressive sensing-based method.Then,we set the mean of the correlation matrix’s row vectors as the input feature to construct the spatial spectrum by the corresponding single network within the parallel deep capsule networks.The proposed method enhances the generalization capability to untrained scenarios and the adaptability to non-ideal conditions,e.g.,lower SNRs,smaller snapshots,unknown reflection coefficients and perturbational steering vectors,which make up for the defects of the previous model-driven methods.Simulations are carried out to demonstrate the superiority of the proposed method.
