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http://dx.doi.org/10.6109/jkiice.2018.22.3.527

Neighbor Discovery for Mobile Systems based on Deep Learning  

Lee, Woongsup (Department of Information and Communication Engineering, Institute of Marine Industry, Gyeongsang National University)
Ban, Tae-Won (Department of Information and Communication Engineering, Institute of Marine Industry, Gyeongsang National University)
Kim, Seong Hwan (Department of Information and Communication Engineering, Institute of Marine Industry, Gyeongsang National University)
Ryu, Jongyeol (Department of Information and Communication Engineering, Institute of Marine Industry, Gyeongsang National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.22, no.3, 2018 , pp. 527-533 More about this Journal
Abstract
Recently, the device-to-device (D2D) communication has been conceived as the key technology for the next-generation mobile communication systems. The neighbor discovery in which the nearby users are found, is essential for the proper operation of the D2D communication. In this paper, we propose new neighbor discovery scheme based on deep learning technology which has gained a lot of attention recently. In the proposed scheme, the neighboring users can be found using the uplink pilot transmission of users only, unlike conventional neighbor discovery schemes in which direct pilot communication among users is required, such that the signaling overhead can be greatly reduced in our proposed scheme. Moreover, the neighbors with different proximity can also be classified accordingly which enables more accurate neighbor discovery compared to the conventional schemes. The performance of our proposed scheme is verified through the tensorflow-based computer simulations.
Keywords
Deep learning; Neighbor discovery; Device-to-device communication; Wireless channel correlation;
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