7 papers
Data Sourcing Random Access using Semantic Queries for Massive IoT Scenarios
Anders E. Kalør, Petar Popovski, Kaibin Huang
Efficiently retrieving relevant data from massive Internet of Things (IoT) networks is essential for downstream tasks such as machine learning. This paper addresses this challenge…
Ultra-Low-Latency Edge Inference for Distributed Sensing
Zhanwei Wang, Anders E. Kalør, You Zhou +2
There is a broad consensus that artificial intelligence (AI) will be a defining component of the sixth-generation (6G) networks. As a specific instance, AI-empowered sensing will g…
Black-Box Edge AI Model Selection with Conformal Latency and Accuracy Guarantees
Anders E. Kalør, Tomoaki Ohtsuki
Edge artificial intelligence (AI) will be a central part of 6G, with powerful edge servers supporting devices in performing machine learning (ML) inference. However, it is challeng…
Content-based Wake-up for Energy-efficient and Timely Top-k IoT Sensing Data Retrieval
Junya Shiraishi, Anders E. Kalør, Israel Leyva-Mayorga +3
Energy efficiency and information freshness are key requirements for sensor nodes serving Industrial Internet of Things (IIoT) applications, where a sink node collects informative…
Learning-Based Rich Feedback HARQ for Energy-Efficient Uplink Short Packet Transmission
Martin Voigt Vejling, Federico Chiariotti, Anders Ellersgaard Kalør +3
The trade-off between reliability, latency, and energy efficiency is a central problem in communication systems. Advanced hybrid automated repeat request (HARQ) techniques reduce r…
Wireless 6G Connectivity for Massive Number of Devices and Critical Services
Anders E. Kalør, Giuseppe Durisi, Sinem Coleri +4
Compared to the generations up to 4G, whose main focus was on broadband and coverage aspects, 5G has expanded the scope of wireless cellular systems towards embracing two new types…