Experimental Characterization of Connectivity for ProSe Direct Discovery in Emergency Scenarios for 6G

Abstract

Device-to-device (D2D) enables direct communication between two-user equipment (UEs) with or without the involvement of a base station (BS). D2D communication is a vital paradigm for designing a reliable public safety network (PSN) and supports several services in the sixth generation (6G) such as target monitoring, emergency search and rescue, etc. This paper presents the measurement campaign to characterize the performance of ProSe direct discovery in terms of connectivity. Furthermore, we implement and analyse two supervised learning models: multiple linear regression (MLR) and multiple non-linear regression (MNLR) using the least squares method to predict the probability of direct discovery in PS out-of-coverage scenarios. Experimental data collected in real-time heterogeneous environments has been used to train both models. The comparative analysis indicates that MNLR model is 6 % more efficient compared to MLR model to predict the discovery probability. The impact of this work is that it is possible to deploy the equipment to build reliable connectivity for efficient D2D networks in out-of-coverage emergency scenarios, and can bring intelligence to the UE level to adopt the optimal parameters to establish a stable D2D link.