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- Title
Bidirectional Long Short-Term Memory Neural Networks for Linear Sum Assignment Problems.
- Authors
Minh-Tuan, Nguyen; Kim, Yong-Hwa
- Abstract
Many resource allocation problems can be modeled as a linear sum assignment problem (LSAP) in wireless communications. Deep learning techniques such as the fully-connected neural network and convolutional neural network have been used to solve the LSAP. We herein propose a new deep learning model based on the bidirectional long short-term memory (BDLSTM) structure for the LSAP. In the proposed method, the LSAP is divided into sequential sub-assignment problems, and BDLSTM extracts the features from sequential data. Simulation results indicate that the proposed BDLSTM is more memory efficient and achieves a higher accuracy than conventional techniques.
- Subjects
ASSIGNMENT problems (Programming); SHORT-term memory; ARTIFICIAL neural networks; DEEP learning; RESOURCE allocation
- Publication
Applied Sciences (2076-3417), 2019, Vol 9, Issue 17, p3470
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app9173470