Survey on machine learning for intelligent end-to-end communication toward 6G: From network access, routing to traffic control and streaming adaption
The end-to-end quality of service (QoS) and quality of experience (QoE) guarantee is quite
important for network optimization. The current 5G and conceived 6G network in the future …
important for network optimization. The current 5G and conceived 6G network in the future …
Deep learning for an effective nonorthogonal multiple access scheme
Nonorthogonal multiple access (NOMA) has been considered as an essential multiple
access technique for enhancing system capacity and spectral efficiency in future …
access technique for enhancing system capacity and spectral efficiency in future …
Smart resource allocation for mobile edge computing: A deep reinforcement learning approach
The development of mobile devices with improving communication and perceptual
capabilities has brought about a proliferation of numerous complex and computation …
capabilities has brought about a proliferation of numerous complex and computation …
State-of-the-art deep learning: Evolving machine intelligence toward tomorrow's intelligent network traffic control systems
Currently, the network traffic control systems are mainly composed of the Internet core and
wired/wireless heterogeneous backbone networks. Recently, these packet-switched …
wired/wireless heterogeneous backbone networks. Recently, these packet-switched …
Machine learning for networking: Workflow, advances and opportunities
Recently, machine learning has been used in every possible field to leverage its amazing
power. For a long time, the networking and distributed computing system is the key …
power. For a long time, the networking and distributed computing system is the key …
Intelligent edge computing based on machine learning for smart city
To alleviate the huge computing pressure caused by the single mobile edge server
computing mode as the amount of data increases, in this research, we propose a method to …
computing mode as the amount of data increases, in this research, we propose a method to …
Optimizing space-air-ground integrated networks by artificial intelligence
It is widely acknowledged that the development of traditional terrestrial communication
technologies cannot provide all users with fair and high quality services due to scarce …
technologies cannot provide all users with fair and high quality services due to scarce …
Unsupervised machine learning for networking: Techniques, applications and research challenges
While machine learning and artificial intelligence have long been applied in networking
research, the bulk of such works has focused on supervised learning. Recently, there has …
research, the bulk of such works has focused on supervised learning. Recently, there has …
RouteNet: Leveraging graph neural networks for network modeling and optimization in SDN
Network modeling is a key enabler to achieve efficient network operation in future self-
driving Software-Defined Networks. However, we still lack functional network models able to …
driving Software-Defined Networks. However, we still lack functional network models able to …
Deep reinforcement learning for dynamic uplink/downlink resource allocation in high mobility 5G HetNet
Recently, the 5G is widely deployed for supporting communications of high mobility nodes
including train, vehicular and unmanned aerial vehicles (UAVs) largely emerged as the …
including train, vehicular and unmanned aerial vehicles (UAVs) largely emerged as the …