杂志论文图集-2( IEEE Network Volume: 33 , Issue: 1，Volume: 32 , Issue: 6)

1 Software Defined Space-Terrestrial Integrated Networks: Architecture, Challenges, and Solutions

Figure 1.

The architecture of SD-STIN.

Figure 2.

The logical structure of SD-STIN.

Figure 3.

SDN-based resource management and traffic steering.

2. Virtualized QoS-Driven Spectrum Allocation in Space-Terrestrial Integrated Networks (频谱分配)

Figure 1.

The proposed architecture for STIN.

Figure 2.

The virtual cell construction.

Figure 3.

The reconstruction of a virtual cell.

3.MHCP: Multimedia Hybrid Cloud Computing Protocol and Architecture for Mobile Devices

4. Heterogeneous Space and Terrestrial Integrated Networks for IoT: Architecture and Challenges

5 Label-less Learning for Traffic Control in an Edge Network

Figure 1.

An illustration of traffic control in edge cloud.

Figure 2.

The trade-off among cloud intelligence, data amount, and resource consumption.

Figure 3.

Label-less learning-based traffic control in the edge cloud.

Figure 4.

System testbed.

6. Deep Reinforcement Learning for Mobile Edge Caching: Review, New Features, and Open Issues

7. Improving Traffic Forecasting for 5G Core Network Scalability: A Machine Learning Approach

8. SeDaTiVe: SDN-Enabled Deep Learning Architecture for Network Traffic Control in Vehicular Cyber-Physical Systems

9. 

10. NDN Construction for Big Science: Lessons Learned from Establishing a Testbed

Figure 1.

Data fetching scenario using Interest and Data symmetrical forwarding in NDN network.

Figure 2.

NDN testbed established between continents for climate modeling application.