The 2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall) was held 7-10 October 2024 in Washington DC, USA. The semi-annual flagship conference brings together individuals from academia, government, and industry to discuss and exchange ideas in the fields of wireless, mobile, and vehicular technology.
IEEE VTC2024-Fall featured world-class plenary speakers, tutorials, technical and application sessions, and an innovative Industry Track with panels and presentations from industry leaders sharing their perspectives on the latest technologies. PREDICT-6G was part of the high-level programme with two papers presented in the Technical Papers section.
The first paper, entitled ‘Slice-aware Resource Allocation and Admission Control for Smart Factory Wireless Networks’, was written by Regina Ochonu and Josep Vidal, researchers at the Universitat Politècnica de Catalunya – a partner of the consortium. This paper presents a novel solution for slice-aware radio resource allocation based on a convex optimisation and control framework for applications in smart factory wireless networks. The proposed framework dynamically allocates minimum power and sub-channels to downlink mixed service type industrial users categorised into three slices: Capacity Limited (CL), Ultra Reliable Low Latency Communication (URLLC), and Time Sensitive (TS) slices. Read it here.
The second paper, titled ‘Reinforcement Learning-based UL/DL Splitter for Latency Reduction in Wireless TSN Networks’ was authored by Margarita Cabrera-Bean, Wenli Pan and Josep Vidal, also researchers at the Universitat Politècnica de Catalunya. In this paper researchers present and compare a set of approaches to scheduling time slots within a wireless frame for communication between the uplink (UL) and downlink (DL) in a TSN network. The primary objective is to reduce latency in wireless transmissions, particularly in scenarios with stringent timing requirements. By optimizing the allocation of time slots between UL and DL, our proposed scheduling algorithm aims to minimize queueing delays while ensuring efficient utilization of network resources. The results highlight the significant reduction achieved in terms of queueing latency and packet loss through our scheduling strategy, thereby enhancing the reliability and timeliness of wireless links in TSN networks. Read it here.
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