- Volume 22 Issue 3
DOI QR Code
Neighbor Discovery for Mobile Systems based on Deep Learning
딥러닝을 이용한 주변 무선단말 파악방안
- 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)
- Received : 2018.01.19
- Accepted : 2018.02.20
- Published : 2018.03.28
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.
최근 단말-대-단말(Device-to-device, D2D) 통신기술이 차세대 무선통신시스템의 핵심기술로 큰 관심을 받고 있다. 이러한 단말간 통신에서는 자신의 주변에 어떠한 단말이 있는지 파악하는 주변단말 탐색(Neighbor discovery)이 매우 중요하다. 본 논문에서는 최근 큰 관심을 받고 있는 딥러닝(Deep learning) 기술을 활용하여 단말간 통신에서 주변단말을 파악하는 방안에 대해서 제안한다. 제안 방안은 기존의 방안과 달리 무선채널의 공간적 연관성을 이용하여 단말간의 신호 전송 없이 단말이 기지국으로 전송하는 상향링크 파일럿 신호를 기반으로 주변 단말을 찾고 따라서 기존의 방식에 비해 신호전송 복잡도(signaling complexity)를 크게 줄일 수 있다. 또한 제안 방안에서는 떨어져 있는 거리에 따라서 주변 단말을 분류 가능하여 기존 방안에 비해서 좀 더 세밀한 단말 탐색이 가능하다. 마지막으로 본 논문에서는 tensorflow를 이용한 컴퓨터 시뮬레이션을 통해 제안 방안의 성능을 검증하였다.
Supported by : Rural Development Administration(RDA)
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