The mobile network operation in the unlicensed spectrum offers opportunities for non-public connectivity services without the financial burden of spectrum licensing. Previous studies regarding unlicensed spectrum operation have mainly relied on analysis or simulation without addressing practical constraints. In this work, we designed, implemented, and evaluated the listen-before-talk protocol as a co-existence medium access mechanism on top of the 5G-NR physical layer. We propose different TDD configurations to enable different sensing frequencies. We enabled OpenAirInterface operation in the n46/n47 bands, a European band dedicated to connected intelligent transportation services. We evaluate the performance of unlicensed 5G-NR under bursty cross-traffic from a different device. The results show that the frequency of cross-traffic bursts affects BLER (Block Error Rate) and throughput more than their duration. The design of the TDD frame to offer more frequent sensing opportunities allows for reduced BLER but negatively impacts achievable throughput in the current setup.

The future of goods transportation will rely on increased efficiency, lower risks, and diminished delays through the use of vehicle platoons that benefit from vehicular connectivity using V2X (Vehicle to Everything) applications. This article describes a system that offers the aforementioned vehicular connectivity to platoons, based on AI-enhanced 5G for resource allocation in wireless platoon intra-communications under three scenarios (latency emergency braking, platoon wireless resource management in tunnels, V2X communications interference in a traffic congestion). Demos are described for each of the scenarios, targeting different layers, starting by the PHY (physical) layer where propagation models are implemented, then a simulation-based MAC (medium access control) layer that allows the allocation of resources to the connected User Equipments (UE) and finally a management and orchestration layer capable of monitoring and managing the radio network, offering features such as network slicing management using O-RAN (Open Radio Access Network) standards.

The Fifth Generation Mobile Networks has transformed the paradigm of mobile network communications. In Beyond Fifth Generation Networks networks, Machine Learning (ML) and Artificial Intelligence (AI) are crucial components, optimizing network resource management to improve the network performance as well as end-users Quality of Service while lowering the network operating costs. This work makes use of an End-to-End 5G architecture to validate three demonstrations: 1) Radio Access Network monitoring using a Flexible RIC’s xApp; 2) 5G Core Network’s metrics collection via Capgemini Engineering’s Network Data Analytics Function; 3) Analysis of the Core Network’s collected data to predict Network Function load and Network Slice Instance load through the Capgemini Engineering’s NetAnticipate AI/ML engine.

Results of measurement campaigns carried out aiming at the development of a tool for design, deployment, and analysis of RFID systems are shown. Particularly, in this paper, the radio path loss from an RFID reader towards the test tag and back to the reader is characterized at the 915 MHz band. The path loss is estimated based on the received signal strength measured at the reader, for different reader-tag separation distances and different antenna tag heights. Several propagation scenarios have been considered, including outdoor and indoor environments for which an extensive number of typical real manufacturing plants have been chosen. The field data are then compared to a proposed novel, improved version of the classical 2-ray path loss model, adjusted to also include the non-omnidirectional antenna radiation patterns at the reader and tag sides. To the best of the authors' knowledge, the scope and reach of the measurements as well as the proposed path loss model are new.

This article investigates VoIP performance over WLAN (WVoIP) through qualitative and quantitative point of view, considering degradation with the mobility. The results was obtained through a channel emulator to produce the Weibull distribution. Measurements results with a robot showed that this distribution is adequate to represent WLAN environment instability. The test utilized the CODECs G711 and GSM. The article presents preliminary results that indicate the effect of the instability of wireless environment in the VoIP performance. The results indicate a significant variation of voice quality, considering the severity of the phenomena emulated. The behaviors of the CODECs are evaluated considering the different characteristics of bandwidth and robustness.

WLAN 802.11 operating in 2,4 GHz are being intensely implanted mainly in public environments. In these environments mobility is always a present characteristic. The objective of this work is to analyze the behavior of 802.11 MAC with flat fading. To reach this objective an emulation system was developed to create the flat fading channel phenomenon. With a WLAN and the emulator was possible to show the cross layer effect, evaluating the WLAN performance analyzes in mobility conditions. The experiments evaluated: rate of tansmission in each modulation level, data rate in the transport layer, network efficiency, jitter and throughput. The network information was obtained through SNMP. The results show that the 802.11 MAC had problems to support services that need high performance, as VoIP and Streaming. Therefore when the receiver is in movement the degradation does not allow the quality required by these types of services.