Generative neural network based spectrum sharing using linear sum assignment problems
Spectrum management and resource allocation (RA) problems are challenging and critical in a vast number of research areas such as wireless communications and computer networks. The traditional approaches for solving such problems usually consume time and memory, especially for large-size problems. R...
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| Published in: | China communications Vol. 17; no. 2; pp. 14 - 29 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
China Institute of Communications
01.02.2020
Benha University,Benha,13511,Egypt Big Data Institute,Shenzhen University,518060,China Benha University,Benha,13511,Egypt%Big Data Institute,Shenzhen University,518060,China%Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ),Shenzhen University,Shenzhen 518060,China PCL Research Center of Networks and Communications,Peng Cheng Laboratory,Shenzhen 518060,China%Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ),Shenzhen University,Shenzhen 518060,China |
| Subjects: | |
| ISSN: | 1673-5447 |
| Online Access: | Get full text |
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| Abstract | Spectrum management and resource allocation (RA) problems are challenging and critical in a vast number of research areas such as wireless communications and computer networks. The traditional approaches for solving such problems usually consume time and memory, especially for large-size problems. Recently different machine learning approaches have been considered as potential promising techniques for combinatorial optimization problems, especially the generative model of the deep neural networks. In this work, we propose a resource allocation deep autoencoder network, as one of the promising generative models, for enabling spectrum sharing in underlay device-to-device (D2D) communication by solving linear sum assignment problems (LSAPs). Specifically, we investigate the performance of three different architectures for the conditional variational autoencoders (CVAE). The three proposed architecture are the convolutional neural network (CVAE-CNN) autoencoder, the feed-forward neural network (CVAE-FNN) autoencoder, and the hybrid (H-CVAE) autoencoder. The simulation results show that the proposed approach could be used as a replacement of the conventional RA techniques, such as the Hungarian algorithm, due to its ability to find solutions of LASPs of different sizes with high accuracy and very fast execution time. Moreover, the simulation results reveal that the accuracy of the proposed hybrid autoencoder architecture outperforms the other proposed architectures and the state-of-the-art DNN techniques. |
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| AbstractList | Spectrum management and resource allocation(RA)problems are challenging and critical in a vast number of research areas such as wireless communications and computer networks.The traditional approaches for solv-ing such problems usually consume time and memory,especially for large-size problems.Recently different machine learning approach-es have been considered as potential promis-ing techniques for combinatorial optimization problems,especially the generative model of the deep neural networks.In this work,we propose a resource allocation deep autoen-coder network,as one of the promising gener-ative models,for enabling spectrum sharing in underlay device-to-device(D2D)communi-cation by solving linear sum assignment prob-lems(LSAPs).Specifically,we investigate the performance of three different architectures for the conditional variational autoencoders(CVAE).The three proposed architecture are the convolutional neural network(CVAE-CNN)autoencoder,the feed-forward neural network(CVAE-FNN)autoencoder,and the hybrid(H-CVAE)autoencoder.The simula-tion results show that the proposed approach could be used as a replacement of the conven-tional RA techniques,such as the Hungarian algorithm,due to its ability to find solutions of LASPs of different sizes with high accuracy and very fast execution time.Moreover,the simulation results reveal that the accuracy of the proposed hybrid autoencoder architecture outperforms the other proposed architectures and the state-of-the-art DNN techniques. Spectrum management and resource allocation (RA) problems are challenging and critical in a vast number of research areas such as wireless communications and computer networks. The traditional approaches for solving such problems usually consume time and memory, especially for large-size problems. Recently different machine learning approaches have been considered as potential promising techniques for combinatorial optimization problems, especially the generative model of the deep neural networks. In this work, we propose a resource allocation deep autoencoder network, as one of the promising generative models, for enabling spectrum sharing in underlay device-to-device (D2D) communication by solving linear sum assignment problems (LSAPs). Specifically, we investigate the performance of three different architectures for the conditional variational autoencoders (CVAE). The three proposed architecture are the convolutional neural network (CVAE-CNN) autoencoder, the feed-forward neural network (CVAE-FNN) autoencoder, and the hybrid (H-CVAE) autoencoder. The simulation results show that the proposed approach could be used as a replacement of the conventional RA techniques, such as the Hungarian algorithm, due to its ability to find solutions of LASPs of different sizes with high accuracy and very fast execution time. Moreover, the simulation results reveal that the accuracy of the proposed hybrid autoencoder architecture outperforms the other proposed architectures and the state-of-the-art DNN techniques. |
| Author | ElHalawany, Basem M. Zaky, Ahmed B. Wu, Kaishun Huang, Joshua Zhexue |
| AuthorAffiliation | Big Data Institute,Shenzhen University,518060,China;Benha University,Benha,13511,Egypt%Big Data Institute,Shenzhen University,518060,China%Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ),Shenzhen University,Shenzhen 518060,China;PCL Research Center of Networks and Communications,Peng Cheng Laboratory,Shenzhen 518060,China%Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ),Shenzhen University,Shenzhen 518060,China;Benha University,Benha,13511,Egypt |
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| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| Keywords | generative models autoencoder resource allocation linear sum assignment problems |
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| Snippet | Spectrum management and resource allocation (RA) problems are challenging and critical in a vast number of research areas such as wireless communications and... Spectrum management and resource allocation(RA)problems are challenging and critical in a vast number of research areas such as wireless communications and... |
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| SubjectTerms | autoencoder Computer architecture Device-to-device communication generative models linear sum assignment problems Machine learning Neural networks Optimization resource allocation Resource management Wireless communication |
| Title | Generative neural network based spectrum sharing using linear sum assignment problems |
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