ETRI-Knowledge Sharing Plaform

ENGLISH

성과물

논문 검색
구분 SCI
연도 ~ 키워드

상세정보

학술지 DL-TCP: Deep Learning-Based Transmission Control Protocol for Disaster 5G mmWave Networks
Cited 11 time in scopus Download 8 time Share share facebook twitter linkedin kakaostory
저자
나웅수, 배병준, 조숙희, 김나연
발행일
201910
출처
IEEE Access, v.7, pp.145134-145144
ISSN
2169-3536
출판사
IEEE
DOI
https://dx.doi.org/10.1109/ACCESS.2019.2945582
협약과제
19HR3500, 재난피해 저감을 위한 지상파 UHD기반 재난방송 서비스, 배병준
초록
The 5G mobile communication system is attracting attention as one of the most suitable communication models for broadcasting and managing disaster situations, owing to its large capacity and low latency. High-quality videos taken by a drone, which is an embedded IoT device for shooting in a disaster environment, play an important role in managing the disaster. However, the 5G mmWave frequency band is susceptible to obstacles and has beam misalignment problems, severing the connection and greatly affecting the degradation of TCP performance. This problem becomes even more serious in high-mobility drones and disaster sites with many obstacles. To solve this problem, we propose a deep-learning-based TCP (DL-TCP) for a disaster 5G mmWave network. DL-TCP learns the node's mobility information and signal strength, and adjusts the TCP congestion window by predicting when the network is disconnected and reconnected. As a result of the experiment, DL-TCP provides better network stability and higher network throughput than the existing TCP NewReno, TCP Cubic, and TCP BBR.
키워드
5G, Deep-learning, mmWave, supervised-learning, TCP
KSP 제안 키워드
5G mobile communication system, Beam misalignment, Communication Model, Congestion Window, Disaster situations, High Mobility, High-quality, IoT Devices, Large capacity, Learning-based, Low latency