# Experimental Measurements of a Joint 5G-VLC Communication for Future Vehicular Networks
###### Abstract  
One of the main revolutionary features of 5G networks is the ultra-low latency that will enable new services such as those for the future smart vehicles. The 5G technology will be able to support extreme-low latency thanks to new technologies and the wide flexible architecture that integrates new spectra and access technologies. In particular, visible light communication (VLC) is envisaged as a very promising technology for vehicular communications, since the information provided can flow by using the lights (as traffic-lights and car lights). This paper describes one of the first experiments on the joint use of 5G and VLC networks to provide real-time information to cars. The applications span from road safety to emergency alarm. visible light communications; 5G networks; smart vehicles; field trials +
Footnote †: journal: Sensor and Actuator Networks  
0000-0001-5000-0001-5000]D. M.  
In [3; 4], hypotheses on the integration between 5G and VLC are envisioned. In [5], the integration of a VLC segment with 5G backhaul is investigated, while an overall general description of the benefits that the VLC can bring to 5G networks is given in [6; 7]. Apart from the research and standardization processes, launching a new technology requires field trials to define and test the key performance indicators (KPIs) as well as to determine how the users can maximize the exploitation of the new capabilities of the 5G network. Different field trials and tests have been carried out all around the world and are currently on going. Anyway, all these tests are mainly focused on the 5G new radio (NR) technologies: massive multiple input multiple output (mMIMO), 3D-beamforming, carrier aggregation, use of new air interfaces, and advanced solutions for the core network [8; 9]. In contrast, the integration of 5G NR and VLC is not largely tested, only few papers present experimental results. In [10], a performance evaluation is presented where the 5G NR air frame is adapted to the VLC transmission, while in [11], resource allocation in VLC networks is discussed. This paper presents a significant contribute in this context. In fact, it focuses on a field-test that integrates 5G communication capabilities with VLCs in a vehicular scenario. The aim is mainly to evaluate performance that can be actually achieved. To the best of our knowledge this is the first case presenting an effective VLC_5G integrated field-trial in this application scenario. The main goal is to test the end-to-end latency of the communication that can be offered by this integrated network. The Italian Ministry of Economic Development (MiSE) started a 5G field-trial project at the end of 2017 in the cities of Prato e L'Aquila. The entire experimental project lasts four years and includes a pre-commercial experimentation during the last year. Two telecommunications operators, WindTre and OpenFiber, are the coordinators of the project since they both own significant network infrastructure, including both wireless and optical fibers. This paper first describes the main characteristics of the field-trial environment and then details the experiment carried out to deliver road information to vehicles. Information collected by sensors connected to the 5G network, is distributed by the traffic light with near-zero latency thanks to the VLC. This paper is an extension of two previous papers [12; 13]. In particular, [12] presents a general description of the 5G project framework that is here recalled only on its main aspects, while [13] presents the description of the smart mobility use case. This paper focuses on the same use case but provides KPIs measurements and analysis of the performance of the integration between the 5G network and a VLC-based V2I communication system, achieved at the end of the 5G project. In addition, we have included the distribution of the empirical data of the latency for the 5G link and for the overall system (5G link and VLC link), as well as the best fitting distribution, derived by using the Bayesian information criterion (BIC).