Characterizing and Taming the Tail inURLLC

Abstract

Chapter 3 Characterizing and Taming the Tail in URLLC Book Editor(s):Trung Duong, Trung DuongSearch for more papers by this authorSaeed Khosravirad, Saeed KhosraviradSearch for more papers by this authorChangyang She, Changyang SheSearch for more papers by this authorPetar Popovski, Petar PopovskiSearch for more papers by this authorMehdi Bennis, Mehdi BennisSearch for more papers by this authorTony Quek, Tony QuekSearch for more papers by this author First published: 17 March 2023 https://doi.org/10.1002/9781119818366.ch3 AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Abstract In this chapter, we go beyond the average-based system design for ultra-reliable low latency communication (URLLC). We first motivate the need of considering the tail distribution, higher-order statistics, extreme events with very low occurrence probabilities, worst-case metrics, and reliability/latency-affected metrics, e.g., age of information, for URLLC. To investigate these metrics, we introduce the entropic risk measure in financial mathematics as well as the generalized extreme value (GEV) distribution and generalized Pareto distribution (GPD) in extreme value theory. Subsequently, statistical learning methods with gradient descent are explored for characterizing the parameters/models of the GEV distribution and GPD. We further invoke the notion of federated learning in order to tackle the data shortage issues of model training in URLLC regimes. Finally, the performance of beyond-average metrics and various tradeoffs are evaluated and investigated accordingly. Ultra‐Reliable and Low‐Latency Communications (URLLC) Theory and Practice: Advances in 5G and Beyond RelatedInformation