4.6 Article

Guaranteed Dynamic Scheduling of Ultra-Reliable Low-Latency Traffic via Conformal Prediction

期刊

IEEE SIGNAL PROCESSING LETTERS
卷 30, 期 -, 页码 473-477

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2023.3264939

关键词

Ultra reliable low latency communication; Reliability; Resource management; Predictive models; Europe; Reliability engineering; Dynamic scheduling; URLLC; eMBB; 5G; 6G; conformal prediction; scheduling

向作者/读者索取更多资源

The dynamic scheduling of ultra-reliable and low-latency communication traffic (URLLC) in the uplink can enhance coexisting services by only allocating resources when necessary. The main challenge is the uncertainty in URLLC packet generation, which requires predictors. This letter introduces a novel scheduler for URLLC packets that guarantees reliability and latency regardless of the prediction quality.
The dynamic scheduling of ultra-reliable and low-latency communication traffic (URLLC) in the uplink can significantly enhance the efficiency of coexisting services, such as enhanced mobile broadband (eMBB) devices, by only allocating resources when necessary. The main challenge is posed by the uncertainty in the process of URLLC packet generation, which mandates the use of predictors for URLLC traffic in the coming frames. In practice, such prediction may overestimate or underestimate the amount of URLLC data to be generated, yielding either an excessive or an insufficient amount of resources to be pre-emptively allocated for URLLC packets. In this letter, we introduce a novel scheduler for URLLC packets that provides formal guarantees on reliability and latency irrespective of the quality of the URLLC traffic predictor. The proposed method leverages recent advances in online conformal prediction (CP), and follows the principle of dynamically adjusting the amount of allocated resources so as to meet reliability and latency requirements set by the designer.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据