29 citations · 38 across the 11 of their papers we have counts for
12 papers
Optimizing QoE-Privacy Tradeoff for Proactive VR Streaming
Xing Wei, Shengqian Han, Chenyang Yang +1
Proactive virtual reality (VR) streaming requires users to upload viewpoint-related information, raising significant privacy concerns. Existing strategies preserve privacy by intro…
Size Generalization for Resource Allocation with Graph Neural Networks
Wu Jiajun, Sun Chengjian, Yang Chenyang
Size generalization is important for learning wireless policies, which are often with dynamic sizes, say caused by time-varying number of users. Recent works of learning to optimiz…
Privacy Leakage in Proactive VR Streaming: Modeling and Tradeoff
Xing Wei, Chenyang Yang, Chengjian Sun
Proactive tile-based virtual reality (VR) video streaming employs the viewpoint of a user to predict the tiles to be requested, renders and delivers the predicted tiles before play…
A Tutorial on Ultra-Reliable and Low-Latency Communications in 6G: Integrating Domain Knowledge into Deep Learning
Changyang She, Chengjian Sun, Zhouyou Gu +4
As one of the key communication scenarios in the 5th and also the 6th generation (6G) of mobile communication networks, ultra-reliable and low-latency communications (URLLC) will b…
Unsupervised Deep Learning for Optimizing Wireless Systems with Instantaneous and Statistic Constraints
Chengjian Sun, Changyang She, Chenyang Yang
Deep neural networks (DNNs) have been introduced for designing wireless policies by approximating the mappings from environmental parameters to solutions of optimization problems.…
Improving Learning Efficiency for Wireless Resource Allocation with Symmetric Prior
Chengjian Sun, Jiajun Wu, Chenyang Yang
Improving learning efficiency is paramount for learning resource allocation with deep neural networks (DNNs) in wireless communications over highly dynamic environments. Incorporat…
Optimizing Wireless Systems Using Unsupervised and Reinforced-Unsupervised Deep Learning
Dong Liu, Chengjian Sun, Chenyang Yang +1
Resource allocation and transceivers in wireless networks are usually designed by solving optimization problems subject to specific constraints, which can be formulated as variable…
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…
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…
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…
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…
Energy-Efficient Resource Allocation for Ultra-reliable and Low-latency Communications
Chengjian Sun, Changyang She, Chenyang Yang
Ultra-reliable and low-latency communications (URLLC) is expected to be supported without compromising the resource usage efficiency. In this paper, we study how to maximize energy…