8 papers
Fast Transmission Control Adaptation for URLLC via Channel Knowledge Map and Meta-Learning
Hongsen Peng, Tobias Kallehauge, Meixia Tao +1
This paper considers methods for delivering ultra reliable low latency communication (URLLC) to enable mission-critical Internet of Things (IoT) services in wireless environments w…
Prediction of Rare Channel Conditions using Bayesian Statistics and Extreme Value Theory
Tobias Kallehauge, Anders E. Kalør, Pablo Ramírez-Espinosa +2
Estimating the probability of rare channel conditions is a central challenge in ultra-reliable wireless communication, where random events, such as deep fades, can cause sudden var…
Experimental Study of Spatial Statistics for Ultra-Reliable Communications
Tobias Kallehauge, Anders E. Kalør, Fengchun Zhang +1
This paper presents an experimental validation for prediction of rare fading events using channel distribution information (CDI) maps that predict channel statistics from measureme…
On the Statistical Relation of Ultra-Reliable Wireless and Location Estimation
Tobias Kallehauge, Martin Voigt Vejling, Pablo Ramìrez-Espinosa +3
Location information is often used as a proxy to guarantee the performance of a wireless communication link. However, localization errors can result in a significant mismatch with…
Delivering Ultra-Reliable Low-Latency Communications via Statistical Radio Maps
Tobias Kallehauge, Anders E. Kalør, Pablo Ramírez-Espinosa +2
High reliability guarantees for Ultra-Reliable Low-Latency Communications (URLLC) require accurate knowledge of channel statistics, used as an input for rate selection. Exploiting…
Bayesian Inference for Non-Parametric Extreme Value Theory
Tobias Kallehauge
Statistical inference for extreme values of random events is difficult in practice due to low sample sizes and inaccurate models for the studied rare events. If prior knowledge for…