Toward massive machine type communications in ultra-dense cellular IoT networks: Current issues and machine learning-assisted solutions
The ever-increasing number of resource-constrained machine-type communication (MTC)
devices is leading to the critical challenge of fulfilling diverse communication requirements …
devices is leading to the critical challenge of fulfilling diverse communication requirements …
Channel state information prediction for 5G wireless communications: A deep learning approach
Channel state information (CSI) estimation is one of the most fundamental problems in
wireless communication systems. Various methods, so far, have been developed to conduct …
wireless communication systems. Various methods, so far, have been developed to conduct …
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 …
Mobility-aware edge caching and computing in vehicle networks: A deep reinforcement learning
RQ Hu - IEEE Transactions on Vehicular Technology, 2018 - ieeexplore.ieee.org
This paper studies the joint communication, caching and computing design problem for
achieving the operational excellence and the cost efficiency of the vehicular networks …
achieving the operational excellence and the cost efficiency of the vehicular networks …
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 …
LightAMC: Lightweight automatic modulation classification via deep learning and compressive sensing
Automatic modulation classification (AMC) is an promising technology for non-cooperative
communication systems in both military and civilian scenarios. Recently, deep learning (DL) …
communication systems in both military and civilian scenarios. Recently, deep learning (DL) …
Fast beamforming design via deep learning
H Huang, Y Peng, J Yang, W Xia… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Beamforming is considered as one of the most important techniques for designing advanced
multiple-input and multiple-output (MIMO) systems. Among existing design criterions, sum …
multiple-input and multiple-output (MIMO) systems. Among existing design criterions, sum …
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 …
Deep cognitive perspective: Resource allocation for NOMA-based heterogeneous IoT with imperfect SIC
The Internet of Things (IoT) has attracted significant attentions in the fifth generation mobile
networks and the smart cities. However, considering the large numbers of connectivity …
networks and the smart cities. However, considering the large numbers of connectivity …