Antonella Molinaro

Andrea Giovannini, Lorenzo Mario Amorosa, Vittorio Todisco et al.

Connected and automated vehicles (CAVs) are expected to increasingly rely on 5G and future 6G ultra-reliable and low-latency communication (URLLC) services to support safety-critical and time-sensitive applications. Since wireless link conditions can vary rapidly in urban vehicular environments, proactively adapting service parameters based on future channel conditions is essential to maintain service continuity and reliability. In this paper, we investigate the use of machine learning (ML) techniques for channel quality prediction in vehicular URLLC scenarios. Specifically, we evaluate deep neural network (DNN) and long short-term memory (LSTM) models to forecast future channel conditions and enable proactive service adaptation with minimized performance degradation. The analysis is conducted using realistic simulations combining the SUMO traffic simulator and the Sionna-RT ray-tracing framework in a real urban environment reconstructed from OpenStreetMap data. Results show that ML-based prediction significantly outperforms approaches relying solely on past channel measurements and achieves performance close to the ideal case in which future channel conditions are perfectly known in advance. These findings demonstrate the potential of ML-driven prediction techniques to enhance the reliability and robustness of URLLC services for connected vehicular systems.

Waleed Bin Qaim, Aleksandr Ometov, Claudia Campolo et al.

Over the years, significant contributions have been made by the research and industrial sectors to improve wearable devices towards the Internet of Wearable Things (IoWT) paradigm. However, wearables are still facing several challenges. Many stem from the limited battery power and insufficient computation resources available on wearable devices. On the other hand, with the popularity of smart wearables, there is a consistent increase in the development of new computationally intensive and latency-critical applications. In such a context, task offloading allows wearables to leverage the resources available on nearby edge devices to enhance the overall user experience. This paper proposes a framework for Reinforcement Learning (RL)-based task offloading in the IoWT. We formulate the task offloading process considering the tradeoff between energy consumption and task accomplishment time. Moreover, we model the task offloading problem as a Markov Decision Process (MDP) and utilize the Q-learning technique to enable the wearable device to make optimal task offloading decisions without prior knowledge. We evaluate the performance of the proposed framework through extensive simulations for various applications and system configurations conducted in the ns-3 network simulator. We also show how varying the main system parameters of the Q-learning algorithm affects the overall performance in terms of average task accomplishment time, average energy consumption, and percentage of tasks offloaded.

Leonardo Militano, Antonino Orsino, Giuseppe Araniti et al.

In the future standardization of the 5G networks, in Long Term Evolution (LTE) Release 13 and beyond, Device-to-Device communications (D2D) is recognized as one of the key technologies that will support the 5G architecture. In fact, D2D can be exploited for different proximity-based services (ProSe) where the users discover their neighbors and benefit form different services like social applications, advertisement, public safety, and warning messages. In such a scenario, the aim is to manage in a proper way the radio spectrum and the energy consumption to provide high Quality of Experience (QoE) and better Quality of Services (QoS). To reach this goal, in this paper we propose a novel D2D-based uploading scheme in order to decrease the amount of radio resources needed to upload to the eNodeB a certain multimedia content. As a further improvement, the proposed scheme enhances the energy consumption of the users in the network, without affects the content uploading time. The obtained results show that our scheme achieves a gain of about 35\% in term of mean radio resources used with respect to the standard LTE cellular approach. In addition, it is also 40 times more efficient in terms of energy consumption needed to upload the multimedia content.

Giuseppe Araniti, Massimo Condoluci, Antonino Orsino et al.

This paper addresses a multi-criteria decision method properly designed to effectively evaluate the most performing strategy for multicast content delivery in Long Term Evolution (LTE) and beyond systems. We compared the legacy conservative-based approach with other promising strategies in literature, i.e., opportunistic multicasting and subgroup-based policies tailored to exploit different cost functions, such as maximum throughput, proportional fairness and the multicast dissatisfaction index (MDI). We provide a comparison among above schemes in terms of aggregate data rate (ADR), fairness and spectral efficiency. We further design a multi-criteria decision making method, namely TOPSIS, to evaluate through a single mark the overall performance of considered strategies. The obtained results show that the MDI subgrouping strategy represents the most suitable approach for multicast content delivery as it provides the most promising trade-off between the fairness and the throughput achieved by the multicast members.