Search Results for author: Chengjian Sun

Found 8 papers, 0 papers with code

A Tutorial on Ultra-Reliable and Low-Latency Communications in 6G: Integrating Domain Knowledge into Deep Learning

no code implementations13 Sep 2020 Changyang She, Chengjian Sun, Zhouyou Gu, Yonghui Li, Chenyang Yang, H. Vincent Poor, Branka Vucetic

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 be central for the development of various emerging mission-critical applications.

Decision Making Decision Making Under Uncertainty

Unsupervised Deep Learning for Optimizing Wireless Systems with Instantaneous and Statistic Constraints

no code implementations30 May 2020 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

no code implementations18 May 2020 Chengjian Sun, Jiajun Wu, Chenyang Yang

The samples required to train a DNN after ranking can be reduced by $15 \sim 2, 400$ folds to achieve the same system performance as the counterpart without using prior.

Optimizing Wireless Systems Using Unsupervised and Reinforced-Unsupervised Deep Learning

no code implementations3 Jan 2020 Dong Liu, Chengjian Sun, Chenyang Yang, Lajos Hanzo

If the objective and constraint functions are unavailable, reinforcement learning can be applied to find the solution of a functional optimization problem, which is however not tailored to optimization problems in wireless networks.


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

no code implementations29 Oct 2019 Jiajun Wu, Chengjian Sun, Chenyang Yang

In this paper, we introduce a proactive optimization framework for anticipatory resource allocation, where the future information is implicitly predicted under the same objective with the policy optimization in a single step.

BIG-bench Machine Learning Stochastic Optimization

Model-Free Unsupervised Learning for Optimization Problems with Constraints

no code implementations30 Jul 2019 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 difficult to find.

reinforcement-learning Reinforcement Learning (RL)

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

no code implementations27 May 2019 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.

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

no code implementations26 Apr 2019 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 optimization problems with quality-of-service (QoS) constraints.

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