Multiple earth observing satellites need to communicate with each other to observe plenty of targets on the Earth together. The factors, such as external interference, result in satellite information interaction delay...Multiple earth observing satellites need to communicate with each other to observe plenty of targets on the Earth together. The factors, such as external interference, result in satellite information interaction delays, which is unable to ensure the integrity and timeliness of the information on decision making for satellites. And the optimization of the planning result is affected. Therefore, the effect of communication delay is considered during the multi-satel ite coordinating process. For this problem, firstly, a distributed cooperative optimization problem for multiple satellites in the delayed communication environment is formulized. Secondly, based on both the analysis of the temporal sequence of tasks in a single satellite and the dynamically decoupled characteristics of the multi-satellite system, the environment information of multi-satellite distributed cooperative optimization is constructed on the basis of the directed acyclic graph（DAG）. Then, both a cooperative optimization decision making framework and a model are built according to the decentralized partial observable Markov decision process（DEC-POMDP）. After that, a satellite coordinating strategy aimed at different conditions of communication delay is mainly analyzed, and a unified processing strategy on communication delay is designed. An approximate cooperative optimization algorithm based on simulated annealing is proposed. Finally, the effectiveness and robustness of the method presented in this paper are verified via the simulation.展开更多
With miscellaneous applications gener-ated in vehicular networks,the computing perfor-mance cannot be satisfied owing to vehicles’limited processing capabilities.Besides,the low-frequency(LF)band cannot further impro...With miscellaneous applications gener-ated in vehicular networks,the computing perfor-mance cannot be satisfied owing to vehicles’limited processing capabilities.Besides,the low-frequency(LF)band cannot further improve network perfor-mance due to its limited spectrum resources.High-frequency(HF)band has plentiful spectrum resources which is adopted as one of the operating bands in 5G.To achieve low latency and sustainable development,a task processing scheme is proposed in dual-band cooperation-based vehicular network where tasks are processed at local side,or at macro-cell base station or at road side unit through LF or HF band to achieve sta-ble and high-speed task offloading.Moreover,a utility function including latency and energy consumption is minimized by optimizing computing and spectrum re-sources,transmission power and task scheduling.Ow-ing to its non-convexity,an iterative optimization algo-rithm is proposed to solve it.Numerical results eval-uate the performance and superiority of the scheme,proving that it can achieve efficient edge computing in vehicular networks.展开更多
To enhance the computational density and energy efficiency of on-chip neuromorphic hardware,this study introduces a novel network architecture for multi-task processing with in-memory optical computing.On-chip optical...To enhance the computational density and energy efficiency of on-chip neuromorphic hardware,this study introduces a novel network architecture for multi-task processing with in-memory optical computing.On-chip optical neural networks are celebrated for their capability to transduce a substantial volume of parameters into optical form while conducting passive computing,yet they encounter challenges in scalability and multitasking.Leveraging the principles of transfer learning,this approach involves embedding the majority of parameters into fixed optical components and a minority into adjustable electrical components.Furthermore,with deep regression algorithm in modeling physical propagation process,a compact optical neural network achieve to handle diverse tasks.In this work,two ultra-compact in-memory diffraction-based chips with integration of more than 60,000 parameters/mm^(2) were fabricated,employing deep neural network model and the hard parameter sharing algorithm,to perform multifaceted classification and regression tasks,respectively.The experimental results demonstrate that these chips achieve accuracies comparable to those of electrical networks while significantly reducing the power-intensive digital computation by 90%.Our work heralds strong potential for advancing in-memory optical computing frameworks and next generation of artificial intelligence platforms.展开更多
Satellite edge computing shows promise in delivering seamless,low-latency services in challenging contexts such as deserts,oceans,and during natural disasters.Previous studies focused on the selection of suitable offl...Satellite edge computing shows promise in delivering seamless,low-latency services in challenging contexts such as deserts,oceans,and during natural disasters.Previous studies focused on the selection of suitable offloading nodes for task processing and result transmission to the original node.However,for end-to-end tasks,exemplified by applications like satellite-based remote sensing and forestry monitoring,the disparity between source and destination nodes emphasizes the importance of routing selection in data transmission.This aspect substantially limits the effectiveness of traditional computation offloading methods.In this paper,we formulate a joint computation offloading,routing,and multiple resource allocation optimization problem for end-to-end tasks in satellite edge computing networks.Moreover,to reduce the complexity of this nondeterministic polynomial-time hard(NP-hard)problem,we decompose it into the multidimensional resource allocation in fixed offloading and routing condition and the computation offloading and routing selection problem.An algorithm based on binary particle swarm optimization for joint computation offloading,routing,and multiple resource allocation optimization problem is proposed.Simulation results are presented to demonstrate the performance of our scheme compared with other baseline schemes.展开更多
基金supported by the National Science Foundation for Young Scholars of China(6130123471401175)
文摘Multiple earth observing satellites need to communicate with each other to observe plenty of targets on the Earth together. The factors, such as external interference, result in satellite information interaction delays, which is unable to ensure the integrity and timeliness of the information on decision making for satellites. And the optimization of the planning result is affected. Therefore, the effect of communication delay is considered during the multi-satel ite coordinating process. For this problem, firstly, a distributed cooperative optimization problem for multiple satellites in the delayed communication environment is formulized. Secondly, based on both the analysis of the temporal sequence of tasks in a single satellite and the dynamically decoupled characteristics of the multi-satellite system, the environment information of multi-satellite distributed cooperative optimization is constructed on the basis of the directed acyclic graph（DAG）. Then, both a cooperative optimization decision making framework and a model are built according to the decentralized partial observable Markov decision process（DEC-POMDP）. After that, a satellite coordinating strategy aimed at different conditions of communication delay is mainly analyzed, and a unified processing strategy on communication delay is designed. An approximate cooperative optimization algorithm based on simulated annealing is proposed. Finally, the effectiveness and robustness of the method presented in this paper are verified via the simulation.
基金supported in part by National Natural Science Foundation of China(No.62071393)Fundamental Research Funds for the Central Universities(2682023ZTPY058).
文摘With miscellaneous applications gener-ated in vehicular networks,the computing perfor-mance cannot be satisfied owing to vehicles’limited processing capabilities.Besides,the low-frequency(LF)band cannot further improve network perfor-mance due to its limited spectrum resources.High-frequency(HF)band has plentiful spectrum resources which is adopted as one of the operating bands in 5G.To achieve low latency and sustainable development,a task processing scheme is proposed in dual-band cooperation-based vehicular network where tasks are processed at local side,or at macro-cell base station or at road side unit through LF or HF band to achieve sta-ble and high-speed task offloading.Moreover,a utility function including latency and energy consumption is minimized by optimizing computing and spectrum re-sources,transmission power and task scheduling.Ow-ing to its non-convexity,an iterative optimization algo-rithm is proposed to solve it.Numerical results eval-uate the performance and superiority of the scheme,proving that it can achieve efficient edge computing in vehicular networks.
基金supported by the National Key R&D Plan of China(2024YFE0203600)the National Natural Science Foundation of China(62135009).
文摘To enhance the computational density and energy efficiency of on-chip neuromorphic hardware,this study introduces a novel network architecture for multi-task processing with in-memory optical computing.On-chip optical neural networks are celebrated for their capability to transduce a substantial volume of parameters into optical form while conducting passive computing,yet they encounter challenges in scalability and multitasking.Leveraging the principles of transfer learning,this approach involves embedding the majority of parameters into fixed optical components and a minority into adjustable electrical components.Furthermore,with deep regression algorithm in modeling physical propagation process,a compact optical neural network achieve to handle diverse tasks.In this work,two ultra-compact in-memory diffraction-based chips with integration of more than 60,000 parameters/mm^(2) were fabricated,employing deep neural network model and the hard parameter sharing algorithm,to perform multifaceted classification and regression tasks,respectively.The experimental results demonstrate that these chips achieve accuracies comparable to those of electrical networks while significantly reducing the power-intensive digital computation by 90%.Our work heralds strong potential for advancing in-memory optical computing frameworks and next generation of artificial intelligence platforms.
基金supported in part by the National Key Research and Development Program of China(no.2021YFB2900600)in part by the National Natural Science Foundation of China under grant 62371045.
文摘Satellite edge computing shows promise in delivering seamless,low-latency services in challenging contexts such as deserts,oceans,and during natural disasters.Previous studies focused on the selection of suitable offloading nodes for task processing and result transmission to the original node.However,for end-to-end tasks,exemplified by applications like satellite-based remote sensing and forestry monitoring,the disparity between source and destination nodes emphasizes the importance of routing selection in data transmission.This aspect substantially limits the effectiveness of traditional computation offloading methods.In this paper,we formulate a joint computation offloading,routing,and multiple resource allocation optimization problem for end-to-end tasks in satellite edge computing networks.Moreover,to reduce the complexity of this nondeterministic polynomial-time hard(NP-hard)problem,we decompose it into the multidimensional resource allocation in fixed offloading and routing condition and the computation offloading and routing selection problem.An algorithm based on binary particle swarm optimization for joint computation offloading,routing,and multiple resource allocation optimization problem is proposed.Simulation results are presented to demonstrate the performance of our scheme compared with other baseline schemes.
