Recently, device-to-device (D2D) communications have been attracting substantial attention\nbecause they can greatly improve coverage, spectral efficiency, and energy efficiency, compared to\nconventional cellular communications. They are also indispensable for the mobile caching network,\nwhich is an emerging technology for next-generation mobile networks. We investigate a cellular\noverlay D2D network where a dedicated radio resource is allocated for D2D communications to\nremove cross-interference with cellular communications and all D2D devices share the dedicated\nradio resource to improve the spectral efficiency. More specifically, we study a problem of radio\nresource management for D2D networks, which is one of the most challenging problems in D2D\nnetworks, and we also propose a new transmission algorithm for D2D networks based on deep\nlearning with a convolutional neural network (CNN). A CNN is formulated to yield a binary vector\nindicating whether to allow each D2D pair to transmit data. In order to train the CNN and verify the\ntrained CNN, we obtain data samples from a suboptimal algorithm. Our numerical results show that\nthe accuracies of the proposed deep learning based transmission algorithm reach about 85% Approximately 95% in\nspite of its simple structure due to the limitation in computing power.
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