Fog Radio Access Networks(F-RANs)have been considered a groundbreaking technique to support the services of Internet of Things by leveraging edge caching and edge computing.However,the current contributions in computa...Fog Radio Access Networks(F-RANs)have been considered a groundbreaking technique to support the services of Internet of Things by leveraging edge caching and edge computing.However,the current contributions in computation offloading and resource allocation are inefficient;moreover,they merely consider the static communication mode,and the increasing demand for low latency services and high throughput poses tremendous challenges in F-RANs.A joint problem of mode selection,resource allocation,and power allocation is formulated to minimize latency under various constraints.We propose a Deep Reinforcement Learning(DRL)based joint computation offloading and resource allocation scheme that achieves a suboptimal solution in F-RANs.The core idea of the proposal is that the DRL controller intelligently decides whether to process the generated computation task locally at the device level or offload the task to a fog access point or cloud server and allocates an optimal amount of computation and power resources on the basis of the serving tier.Simulation results show that the proposed approach significantly minimizes latency and increases throughput in the system.展开更多
Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emi...Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emitters and complicate the procedures of identification.In this paper,we propose a deep SEI approach via multidimensional feature extraction for radio frequency fingerprints(RFFs),namely,RFFsNet-SEI.Particularly,we extract multidimensional physical RFFs from the received signal by virtue of variational mode decomposition(VMD)and Hilbert transform(HT).The physical RFFs and I-Q data are formed into the balanced-RFFs,which are then used to train RFFsNet-SEI.As introducing model-aided RFFs into neural network,the hybrid-driven scheme including physical features and I-Q data is constructed.It improves physical interpretability of RFFsNet-SEI.Meanwhile,since RFFsNet-SEI identifies individual of emitters from received raw data in end-to-end,it accelerates SEI implementation and simplifies procedures of identification.Moreover,as the temporal features and spectral features of the received signal are both extracted by RFFsNet-SEI,identification accuracy is improved.Finally,we compare RFFsNet-SEI with the counterparts in terms of identification accuracy,computational complexity,and prediction speed.Experimental results illustrate that the proposed method outperforms the counterparts on the basis of simulation dataset and real dataset collected in the anechoic chamber.展开更多
This note introduces the newly developed working modes, i.e. one-dimensional meter-wave radio heliograph (MRH) and interplanetary scintillation (IPS) telescope, of meter-wave aperture synthesis radio telescope (MSRT) ...This note introduces the newly developed working modes, i.e. one-dimensional meter-wave radio heliograph (MRH) and interplanetary scintillation (IPS) telescope, of meter-wave aperture synthesis radio telescope (MSRT) at Beijing Astronomical Observatory (BAO). The note describes briefly the scientific objectives, configurations of the hardware and software, and functions of the system. It presents the examples of observations on solar meter-wave bursts and IPS with the two new working modes. The results indicated that new modes not only can provide the information on the evolution of solar activities with space and time, but also can trace and monitor the propagation and spatial distribution of interplanetary plasma shock resulting from solar activities and the instability of the ionosphere, etc. Both modes are new facilities that could fill the gaps in scientific frontiers.展开更多
The characteristics of a stable discharge at atmospheric pressure is investigated. The plasma source consisted of two closely spaced parallel-plated perforated electrodes, driven by a radio frequency power to generate...The characteristics of a stable discharge at atmospheric pressure is investigated. The plasma source consisted of two closely spaced parallel-plated perforated electrodes, driven by a radio frequency power to generate a uniform cold plasma in Helium at atmospheric pressure. Both alpha and gamma modes were clearly observed. The hollow cathode effects were found in the discharge. The influence of the dielectric barrier on the discharge was also investigated by utilizing a surface-anodized aluminium electrode as the anode.展开更多
文摘Fog Radio Access Networks(F-RANs)have been considered a groundbreaking technique to support the services of Internet of Things by leveraging edge caching and edge computing.However,the current contributions in computation offloading and resource allocation are inefficient;moreover,they merely consider the static communication mode,and the increasing demand for low latency services and high throughput poses tremendous challenges in F-RANs.A joint problem of mode selection,resource allocation,and power allocation is formulated to minimize latency under various constraints.We propose a Deep Reinforcement Learning(DRL)based joint computation offloading and resource allocation scheme that achieves a suboptimal solution in F-RANs.The core idea of the proposal is that the DRL controller intelligently decides whether to process the generated computation task locally at the device level or offload the task to a fog access point or cloud server and allocates an optimal amount of computation and power resources on the basis of the serving tier.Simulation results show that the proposed approach significantly minimizes latency and increases throughput in the system.
基金supported by the National Natural Science Foundation of China(62061003)Sichuan Science and Technology Program(2021YFG0192)the Research Foundation of the Civil Aviation Flight University of China(ZJ2020-04,J2020-033)。
文摘Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emitters and complicate the procedures of identification.In this paper,we propose a deep SEI approach via multidimensional feature extraction for radio frequency fingerprints(RFFs),namely,RFFsNet-SEI.Particularly,we extract multidimensional physical RFFs from the received signal by virtue of variational mode decomposition(VMD)and Hilbert transform(HT).The physical RFFs and I-Q data are formed into the balanced-RFFs,which are then used to train RFFsNet-SEI.As introducing model-aided RFFs into neural network,the hybrid-driven scheme including physical features and I-Q data is constructed.It improves physical interpretability of RFFsNet-SEI.Meanwhile,since RFFsNet-SEI identifies individual of emitters from received raw data in end-to-end,it accelerates SEI implementation and simplifies procedures of identification.Moreover,as the temporal features and spectral features of the received signal are both extracted by RFFsNet-SEI,identification accuracy is improved.Finally,we compare RFFsNet-SEI with the counterparts in terms of identification accuracy,computational complexity,and prediction speed.Experimental results illustrate that the proposed method outperforms the counterparts on the basis of simulation dataset and real dataset collected in the anechoic chamber.
基金This work was supported by theUnited Laboratory of Radio Astronomy, National Natural Science Foundation of China (Grant No. 19773017) Beijing Astronomical Observatory, and headquarters of Meridian Project.
文摘This note introduces the newly developed working modes, i.e. one-dimensional meter-wave radio heliograph (MRH) and interplanetary scintillation (IPS) telescope, of meter-wave aperture synthesis radio telescope (MSRT) at Beijing Astronomical Observatory (BAO). The note describes briefly the scientific objectives, configurations of the hardware and software, and functions of the system. It presents the examples of observations on solar meter-wave bursts and IPS with the two new working modes. The results indicated that new modes not only can provide the information on the evolution of solar activities with space and time, but also can trace and monitor the propagation and spatial distribution of interplanetary plasma shock resulting from solar activities and the instability of the ionosphere, etc. Both modes are new facilities that could fill the gaps in scientific frontiers.
文摘The characteristics of a stable discharge at atmospheric pressure is investigated. The plasma source consisted of two closely spaced parallel-plated perforated electrodes, driven by a radio frequency power to generate a uniform cold plasma in Helium at atmospheric pressure. Both alpha and gamma modes were clearly observed. The hollow cathode effects were found in the discharge. The influence of the dielectric barrier on the discharge was also investigated by utilizing a surface-anodized aluminium electrode as the anode.
