no code implementations • 22 Dec 2022 • ShengJie Liu, Jia Guo, Chenyang Yang
Based on the observation that the mismatched permutation property from the policies and the information loss during the update of hidden representations have large impact on the learning performance and efficiency, in this paper we propose a unified framework to learn permutable wireless policies with multidimensional GNNs.
no code implementations • 1 Dec 2022 • Jia Guo, Chenyang Yang
Simulation results show that the proposed GNN can well learn spectral efficient and energy efficient precoding policies in single- and multi-cell multi-user multi-antenna systems with low training complexity, and can be well generalized to the numbers of users.
no code implementations • 27 Nov 2022 • Baichuan Zhao, Jia Guo, Chenyang Yang
Learning-based precoding has been shown able to be implemented in real-time, jointly optimized with channel acquisition, and robust to imperfect channels.
no code implementations • 11 Nov 2022 • Chenyang Yang, Rachel Brower-Sinning, Grace A. Lewis, Christian Kästner, Tongshuang Wu
In spite of machine learning's rapid growth, its engineering support is scattered in many forms, and tends to favor certain engineering stages, stakeholders, and evaluation preferences.
no code implementations • 8 Mar 2022 • Jianyu Zhao, Chenyang Yang
Allocating resources with future channels can save resource to ensure quality-of-service of video streaming.
no code implementations • 28 Oct 2021 • Dong Liu, Jingjing Cui, Jiankang Zhang, Chenyang Yang, Lajos Hanzo
Data packet routing in aeronautical ad-hoc networks (AANETs) is challenging due to their high-dynamic topology.
no code implementations • 10 Feb 2021 • Jianyu Zhao, Chenyang Yang
Deep reinforcement learning has been applied for a variety of wireless tasks, which is however known with high training and inference complexity.
no code implementations • 18 Jan 2021 • Kaiqiang Qi, Tingting Liu, Chenyang Yang
Proactive handover can avoid frequent handovers and reduce handover delay, which plays an important role in maintaining the quality of service (QoS) for mobile users in millimeter-wave vehicular networks.
1 code implementation • 7 Dec 2020 • Yinghan Li, Shengqian Han, Chenyang Yang
In the paper we study a deep learning based method to solve the multicell power control problem for sum rate maximization subject to per-user rate constraints and per-base station (BS) power constraints.
no code implementations • 6 Nov 2020 • Jia Guo, Chenyang Yang
In this paper, we show that the power control policy has a combination of different PI and PE properties, and existing HetGNN does not satisfy these properties.
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 • 21 Mar 2020 • Dong Liu, Jianyu Zhao, Chenyang Yang, Lajos Hanzo
Predictive power allocation is conceived for energy-efficient video streaming over mobile networks using deep reinforcement learning.
no code implementations • 22 Feb 2020 • Changyang She, Rui Dong, Zhouyou Gu, Zhanwei Hou, Yonghui Li, Wibowo Hardjawana, Chenyang Yang, Lingyang Song, Branka Vucetic
In this article, we first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC, and discuss some open problems of these methods.
no code implementations • 29 Jan 2020 • Jia Guo, Chenyang Yang
In this paper, we show that two kinds of permutation invariant properties widely existed in wireless tasks can be harnessed to reduce the number of model parameters and hence the sample and computational complexity for training.
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 • 30 Oct 2019 • 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.
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.
no code implementations • 17 May 2018 • Rongpeng Li, Zhifeng Zhao, Qi Sun, Chi-Lin I, Chenyang Yang, Xianfu Chen, MinJian Zhao, Honggang Zhang
Network slicing is born as an emerging business to operators, by allowing them to sell the customized slices to various tenants at different prices.
no code implementations • 22 Jan 2018 • Dong Liu, Chenyang Yang
We then formulate a joint caching and recommendation problem maximizing the successful offloading probability, which is a mixed integer programming problem.