Optical networks are evolving toward ultrawide bandwidth and autonomous operation.In this scenario,it is crucial to accurately model and control optical power evolutions(OPEs)through optical amplifiers(OAs),as they di...Optical networks are evolving toward ultrawide bandwidth and autonomous operation.In this scenario,it is crucial to accurately model and control optical power evolutions(OPEs)through optical amplifiers(OAs),as they directly affect the signal-to-noise ratio and fiber nonlinearities.However,a fundamental contradiction arises between the complex physical phenomena in optical transmission and the required precision in network control.Traditional theoretical methods underperform due to ideal assumptions,while data-driven approaches entail exorbitant costs associated with acquiring massive amounts of data to achieve the desired level of accuracy.In this work,we propose a Bayesian inference framework(BIF)to construct the digital twin of OAs and control OPE in a data-efficient manner.Only the informative data are collected to balance the exploration and exploitation of the data space,thus enabling efficient autonomous-driving optical networks(ADONs).Simulations and experiments demonstrate that the BIF can reduce the data size for modeling erbium-doped fiber amplifiers by 80%and Raman amplifiers by 60%.Within 30 iterations,the optimal controlling performance can be achieved to realize target signal/gain profiles in links with different types of OAs.The results show that the BIF paves the way to accurately model and control OPE for future ADONs.展开更多
Self-driving vehicles require a number of tests to prevent fatal accidents and ensure their appropriate operation in the physical world.However,conducting vehicle tests on the road is difficult because such tests are ...Self-driving vehicles require a number of tests to prevent fatal accidents and ensure their appropriate operation in the physical world.However,conducting vehicle tests on the road is difficult because such tests are expensive and labor intensive.In this study,we used an autonomous-driving simulator,and investigated the three-dimensional environmental perception problem of the simulated system.Using the open-source CARLA simulator,we generated a CarlaSim from unreal traffic scenarios,comprising 15000 camera-LiDAR(Light Detection and Ranging)samples with annotations and calibration files.Then,we developed Multi-Sensor Fusion Perception(MSFP)model for consuming two-modal data and detecting objects in the scenes.Furthermore,we conducted experiments on the KITTI and CarlaSim datasets;the results demonstrated the effectiveness of our proposed methods in terms of perception accuracy,inference efficiency,and generalization performance.The results of this study will faciliate the future development of autonomous-driving simulated tests.展开更多
基金supported by the Shanghai Pilot Program for Basic Research-Shanghai Jiao Tong University(Grant No.21TQ1400213)the National Natural Science Foundation of China(Grant No.62175145)
文摘Optical networks are evolving toward ultrawide bandwidth and autonomous operation.In this scenario,it is crucial to accurately model and control optical power evolutions(OPEs)through optical amplifiers(OAs),as they directly affect the signal-to-noise ratio and fiber nonlinearities.However,a fundamental contradiction arises between the complex physical phenomena in optical transmission and the required precision in network control.Traditional theoretical methods underperform due to ideal assumptions,while data-driven approaches entail exorbitant costs associated with acquiring massive amounts of data to achieve the desired level of accuracy.In this work,we propose a Bayesian inference framework(BIF)to construct the digital twin of OAs and control OPE in a data-efficient manner.Only the informative data are collected to balance the exploration and exploitation of the data space,thus enabling efficient autonomous-driving optical networks(ADONs).Simulations and experiments demonstrate that the BIF can reduce the data size for modeling erbium-doped fiber amplifiers by 80%and Raman amplifiers by 60%.Within 30 iterations,the optimal controlling performance can be achieved to realize target signal/gain profiles in links with different types of OAs.The results show that the BIF paves the way to accurately model and control OPE for future ADONs.
基金supported by the National Natural Science Foundation of China(Nos.61822101 and 62061130221)the Beijing Municipal Key Research and Development Program(No.Z181100004618006)+1 种基金the Beijing Municipal Natural Science Foundation(No.L191001),the Zhuoyue Program of Beihang University(Postdoctoral Fellowship)(No.262716)the China Postdoctoral Science Foundation(No.2020M680299)。
文摘Self-driving vehicles require a number of tests to prevent fatal accidents and ensure their appropriate operation in the physical world.However,conducting vehicle tests on the road is difficult because such tests are expensive and labor intensive.In this study,we used an autonomous-driving simulator,and investigated the three-dimensional environmental perception problem of the simulated system.Using the open-source CARLA simulator,we generated a CarlaSim from unreal traffic scenarios,comprising 15000 camera-LiDAR(Light Detection and Ranging)samples with annotations and calibration files.Then,we developed Multi-Sensor Fusion Perception(MSFP)model for consuming two-modal data and detecting objects in the scenes.Furthermore,we conducted experiments on the KITTI and CarlaSim datasets;the results demonstrated the effectiveness of our proposed methods in terms of perception accuracy,inference efficiency,and generalization performance.The results of this study will faciliate the future development of autonomous-driving simulated tests.
