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Deep Learning Based Resource Assignment for Wireless Networks
- Source :
- IEEE Communications Letters. 25:3888-3892
- Publication Year :
- 2021
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2021.
-
Abstract
- This paper 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 paper 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.<br />to appear in IEEE Communications Letters
- Subjects :
- FOS: Computer and information sciences
Computer Science - Machine Learning
Artificial neural network
Wireless network
Computer science
business.industry
Information Theory (cs.IT)
Computer Science - Information Theory
Deep learning
Binary number
Permutation matrix
Machine Learning (cs.LG)
Computer Science Applications
Task (project management)
Set (abstract data type)
Modeling and Simulation
Artificial intelligence
Electrical and Electronic Engineering
Projection (set theory)
business
Subjects
Details
- ISSN :
- 23737891 and 10897798
- Volume :
- 25
- Database :
- OpenAIRE
- Journal :
- IEEE Communications Letters
- Accession number :
- edsair.doi.dedup.....c637c661abcc593a3cc5cdf441ae82b8