KCC 2021
Current Result Document :
| 한글제목(Korean Title) |
Tracing Dynamic URLLC Traffics in Vehicular Networks: A Proactive Resource Scheduling |
| 영문제목(English Title) |
Tracing Dynamic URLLC Traffics in Vehicular Networks: A Proactive Resource Scheduling |
| 저자(Author) |
Shashi Raj Pandey
Sabah Suhail
Yan Kyaw Tun
Madyan Alsenwi
Choong Seon Hong
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| 원문수록처(Citation) |
VOL 48 NO. 01 PP. 1246 ~ 1248 (2021. 06) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
Ultra-reliable low-latency communication (URLLC) is a celebrated service of 5G networks that account for sub-millisecond latency with low error rates for reliability. In a vehicular network supporting an autonomous navigation facility, it ensures the exchange of safety messages such as traffic status and controlling data. However, in a typical downlink scenario, the URLLC data packets are multiplexed with pre-scheduled eMBB (Enhanced mobile broadband) traffic that considers services requiring high bandwidth. Therefore, to satisfy the hard latency constraints, the immediate transmission of URLLC data packets over ongoing eMBB data traffic is imperative. Recent works consider “puncturing” as a strategy to perform immediate transmission, however, the challenge to jointly characterize the random traffic dynamics of URLLC load and fulfill the stringent latency requirements persists. In this work, we first develop a resource scheduling optimization problem for a 5G NodeB (gNB) in a vehicular network where the objective is to maximize the average sum rate of vehicular users while satisfying latency deadlines of URLLC users. Then, we develop a proactive resource scheduling strategy where we exploit Kalman estimation to trace dynamic URLLC traffics. Simulation results show that our proposed methodology effectively captures the random dynamics of URLLC traffic load and improve system efficiency in terms of the average sum rate of the users. |
| 키워드(Keyword) |
5G
gNB
URLLC
resource scheduling
Kalman Filter
estimation
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| 파일첨부 |
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