Search Results for author: Chenyang Yang

Found 24 papers, 1 papers with code

Multidimensional Graph Neural Networks for Wireless Communications

no code implementations22 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.

A Model-based GNN for Learning Precoding

no code implementations1 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.

Understanding the Performance of Learning Precoding Policy with GNN and CNNs

no code implementations27 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.

Capabilities for Better ML Engineering

no code implementations11 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.

Graph Reinforcement Learning for Predictive Power Allocation to Mobile Users

no code implementations8 Mar 2022 Jianyu Zhao, Chenyang Yang

Allocating resources with future channels can save resource to ensure quality-of-service of video streaming.

reinforcement-learning Reinforcement Learning (RL)

Deep Reinforcement Learning Aided Packet-Routing For Aeronautical Ad-Hoc Networks Formed by Passenger Planes

no code implementations28 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.

Deep Reinforcement Learning with Symmetric Prior for Predictive Power Allocation to Mobile Users

no code implementations10 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.

Reinforcement Learning (RL)

Federated Learning Based Proactive Handover in Millimeter-wave Vehicular Networks

no code implementations18 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.

Federated Learning

Multicell Power Control under Rate Constraints with Deep Learning

1 code implementation7 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.

Learning Power Control for Cellular Systems with Heterogeneous Graph Neural Network

no code implementations6 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.

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.

Accelerating Deep Reinforcement Learning With the Aid of Partial Model: Energy-Efficient Predictive Video Streaming

no code implementations21 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.

Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks

no code implementations22 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.

Edge-computing Federated Learning +1

Constructing Deep Neural Networks with a Priori Knowledge of Wireless Tasks

no code implementations29 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.

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.


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

no code implementations30 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.

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.

Deep Reinforcement Learning for Resource Management in Network Slicing

no code implementations17 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.

Management reinforcement-learning +1

A Learning-based Approach to Joint Content Caching and Recommendation at Base Stations

no code implementations22 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.

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