no code implementations • 13 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.
no code implementations • 30 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.
no code implementations • 18 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.
no code implementations • 3 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.
no code implementations • 29 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.
no code implementations • 30 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.
no code implementations • 27 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.
no code implementations • 26 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.