Resource Allocation Based on Deep Neural Networks for Cognitive Radio Networks
| Autor: | Fuhui Zhou, Apostolos Papathanassiou, Rose Qingyang Hu, Weixiao Meng, Xiongjian Zhang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Artificial neural network Computer science Information Theory (cs.IT) Quality of service Distributed computing Computer Science - Information Theory 020206 networking & telecommunications 020302 automobile design & engineering 02 engineering and technology Interference (wave propagation) Cognitive radio 0203 mechanical engineering Channel state information 0202 electrical engineering electronic engineering information engineering Resource allocation Resource management |
| Zdroj: | ICCC |
| Popis: | Resource allocation is of great importance in the next generation wireless communication systems, especially for cognitive radio networks (CRNs). Many resource allocation strategies have been proposed to optimize the performance of CRNs. However, it is challenging to implement these strategies and achieve real-time performance in wireless systems since most of them need accurate and timely channel state information and/or other network statistics. In this paper a resource allocation strategy based on deep neural networks (DNN) is proposed and the training method is presented to train the neural networks. Simulation results show that our proposed strategy based on DNN is efficient in terms of the computation time compared with the conventional resource allocation schemes. This paper has been accepted by IEEE ICCC 2018 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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