A Deep Learning Based Transmission Algorithm for Mobile Device-to-Device Networksopen access
- Ban, Tae-Won; Lee, Woongsup
- Issue Date
- D2D; mobile caching; CNN; deep learning; transmission algorithm
- ELECTRONICS, v.8, no.11
- Journal Title
- Recently, device-to-device (D2D) communications have been attracting substantial attention because they can greatly improve coverage, spectral efficiency, and energy efficiency, compared to conventional cellular communications. They are also indispensable for the mobile caching network, which is an emerging technology for next-generation mobile networks. We investigate a cellular overlay D2D network where a dedicated radio resource is allocated for D2D communications to remove cross-interference with cellular communications and all D2D devices share the dedicated radio resource to improve the spectral efficiency. More specifically, we study a problem of radio resource management for D2D networks, which is one of the most challenging problems in D2D networks, and we also propose a new transmission algorithm for D2D networks based on deep learning with a convolutional neural network (CNN). A CNN is formulated to yield a binary vector indicating whether to allow each D2D pair to transmit data. In order to train the CNN and verify the trained CNN, we obtain data samples from a suboptimal algorithm. Our numerical results show that the accuracies of the proposed deep learning based transmission algorithm reach about 85%similar to 95% in spite of its simple structure due to the limitation in computing power.
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- 해양과학대학 > 지능형통신공학과 > Journal Articles
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