activity
20122026
most citedDeep Reinforcement Learning Aided Packet-Routing For Aeronautical Ad-Hoc Networks Formed by Passenger Planes

31 citations · 69 across the 27 of their papers we have counts for

collaborators
Showing 2019Show all

5 papers · 1 filter

cs.LG2019

Structure of Deep Neural Networks with a Priori Information in Wireless Tasks

Jia Guo, Chenyang Yang

Deep neural networks (DNNs) have been employed for designing wireless networks in many aspects, such as transceiver optimization, resource allocation, and information prediction. E…

eess.SY2019

Proactive Optimization with Machine Learning: Femto-caching with Future Content Popularity

Jiajun Wu, Chengjian Sun, Chenyang Yang

Optimizing resource allocation with predicted information has shown promising gain in boosting network performance and improving user experience. Earlier research efforts focus on…

cs.LG2019

Model-Free Unsupervised Learning for Optimization Problems with Constraints

Chengjian Sun, Dong Liu, Chenyang Yang

In many optimization problems in wireless communications, the expressions of objective function or constraints are hard or even impossible to derive, which makes the solutions diff…

cs.NI20192 cited

Unsupervised Deep Learning for Ultra-reliable and Low-latency Communications

Chengjian Sun, Chenyang Yang

In this paper, we study how to solve resource allocation problems in ultra-reliable and low-latency communications by unsupervised deep learning, which often yield functional optim…

cs.LG20191 cited

Learning to Optimize with Unsupervised Learning: Training Deep Neural Networks for URLLC

Chengjian Sun, Chenyang Yang

Learning the optimized solution as a function of environmental parameters is effective in solving numerical optimization in real time for time-sensitive applications. Existing work…