As a part of its training events, the ITN WindMill Project invites scholars and experts in the field of machine learning and wireless communications to hold lectures for the ESRs. The abstracts of these are available on this page, under the tab of the corresponding training event.
In this lecture, we will provide an overview of the URLLC-related 3GPP standardization in 5G releases 15, 16 and 17. We will discuss the URLLC use-cases and their requirements, as well as the proposed enhancements of mobile cellular standards addressing these requirements. In closing, we will discuss the potential applications of ML techniques for enabling URLLC connectivity in 3GPP approaches.
The next frontier towards truly ubiquitous connectivity is the use of Low Earth Orbit (LEO) small-satellite constellations to support 5G and Beyond-5G (B5G) networks. LEO constellations can support ultra-reliable communications (URC) with relaxed latency requirements of a few tens of milliseconds. However, small-satellite impairments and the use of low orbits pose major challenges to the design and performance of these networks. This session provides a comprehensive overview of communications with LEO constellations, and insights of the role of ML to optimize the resource utilization, performance and maintenance of the network.
This presentation gives an introduction to the principles of Multi-Connectivity both from a 3GPP perspective and from a generic perspective. Furthermore, a selection of recent work will be presented covering both classical as well as novel use cases such as UAVs.
URLLC use cases pose significant challenges in the wireless access. Their stringent latency requirements hinder the use of traditional grant-based access schemes and limit the utility of feedback mechanisms. At the same time, the extremely high reliability requirements are oftentimes guaranteed through naive resource over-provisioning. In this session, we explore potential solutions based on grant-free random access protocols and non-orthogonal multiple access (NOMA) for resource-efficient URLLC communications.