Graph embedding based wireless link scheduling with few training samples
File(s)
Accepted version
Author(s)
Lee, Mengyuan
Yu, Guanding
Li, Geoffrey
Type
Journal Article
Abstract
Link scheduling in device-to-device (D2D) networks is usually formulated as a non-convex combinatorial problem, which is generally NP-hard and difficult to get the optimal solution. Traditional methods to solve this problem are mainly based on mathematical optimization techniques, where accurate channel state information (CSI), usually obtained through channel estimation and feedback, is needed. To overcome the high computational complexity of the traditional methods and eliminate the costly channel estimation stage, machine leaning (ML) has been introduced recently to address the wireless link scheduling problems. In this paper, we propose a novel graph embedding based method for link scheduling in D2D networks. We first construct a fully-connected directed graph for the D2D network, where each D2D pair is a node while interference links among D2D pairs are the edges. Then we compute a low-dimensional feature vector for each node in the graph. The graph embedding process is based on the distances of both communication and interference links, therefore without requiring the accurate CSI. By utilizing a multi-layer classifier, a scheduling strategy can be learned in a supervised manner based on the graph embedding results for each node. We also propose an unsupervised manner to train the graph embedding based method to further reinforce the scalability and develop a K-nearest neighbor graph representation method to reduce the computational complexity. Extensive simulation demonstrates that the proposed method is near-optimal compared with the existing state-of-art methods but is with only hundreds of training network layouts. It is also competitive in terms of scalability and generalizability to more complicated scenarios.
Index Terms—Machine learning, device-to-device communications, graph embedding, link scheduling, combinatorial optimization
Index Terms—Machine learning, device-to-device communications, graph embedding, link scheduling, combinatorial optimization
Date Issued
2021-04-01
Date Acceptance
2020-11-11
Citation
IEEE Transactions on Communications, 2021, 20 (4), pp.2282-2294
ISSN
0090-6778
Publisher
Institute of Electrical and Electronics Engineers
Start Page
2282
End Page
2294
Journal / Book Title
IEEE Transactions on Communications
Volume
20
Issue
4
Copyright Statement
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subjects
Science & Technology
Technology
Engineering, Electrical & Electronic
Telecommunications
Engineering
Device-to-device communication
Wireless communication
Training
Interference
Layout
Scheduling
Optimization
Machine learning
device-to-device communications
graph embedding
link scheduling
combinatorial optimization
graph neural network
POWER-CONTROL
CHANNEL ESTIMATION
NETWORKS
OPTIMIZATION
ALLOCATION
Networking & Telecommunications
0805 Distributed Computing
0906 Electrical and Electronic Engineering
1005 Communications Technologies
Publication Status
Published
Date Publish Online
2021-12-07