Deep Learning Based Resource Assignment for Wireless Networks

被引：7

作者：

Kim, Minseok ^{[1
]}

Lee, Hoon ^{[2
]}

Lee, Hongju ^{[1
]}

Lee, Inkyu ^{[1
]}

机构：

[1] Korea Univ, Sch Elect Engn, Seoul 02841, South Korea

[2] Pukyong Natl Univ, Dept Informat & Commun Engn, Busan 48513, South Korea

来源：

IEEE COMMUNICATIONS LETTERS | 2021年 / 25卷 / 12期

基金：

新加坡国家研究基金会;

关键词：

Training; Cost function; Task analysis; Deep learning; Supervised learning; Neural networks; Wireless networks; Sinkhorn operator; assignment problem;

D O I：

10.1109/LCOMM.2021.3116233

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

This letter studies a deep learning approach for binary assignment problems in wireless networks, which identifies binary variables for permutation matrices. This poses challenges in designing a structure of a neural network and its training strategies for generating feasible assignment solutions. To this end, this letter develop a new Sinkhorn neural network which learns a non-convex projection task onto a set of permutation matrices. An unsupervised training algorithm is proposed where the Sinkhorn neural network can be applied to network assignment problems. Numerical results demonstrate the effectiveness of the proposed method in various network scenarios.

引用

页码：3888 / 3892

页数：5

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