arXiv subjects

Claudia Campolo

Publications and source records attributed to Claudia Campolo.

3 recordsLinked to original sources

arXiv2026

Proactive URLLC Adaptation for Connected Vehicles Through ML-Based Channel Prediction

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.

cs.NI↗

arXiv2026

Reinforcement Learning-based Task Offloading in the Internet of Wearable Things

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.

cs.LG↗

arXiv2026

Exploiting Repetitions and Interference Cancellation for the 6G-V2X Sidelink Autonomous Mode

In recent years, the Third Generation Partnership Project (3GPP) has developed the new radio-vehicle-to-everything (NR-V2X) sidelink standard, to enable direct communication between connected and autonomous vehicles (CAVs). Users can autonomously select radio resources for their transmissions with the Mode 2 channel access scheme, which can also operate under out-of-coverage conditions. However, Mode 2 performance is hindered by interference and packet collisions arising from dynamic mobile environments and limitations in assessing radio resource availability. The 3GPP specifications allow transmitting multiple copies of the same packet to improve reliability, though at the cost of increased channel congestion. This paper proposes to leverage receivers equipped with successive interference cancellation (SIC) capabilities, to exploit packet repetitions. Specifically, once a packet is successfully decoded the interfering contribution carried by repetitions can be cancelled from future or past received signals, enabling the decoding of new packets. Extensive highway scenario simulations demonstrate that the proposed solution significantly outperforms the legacy Mode 2 scheme, especially under high interference conditions, achieving improvements exceeding 100% in some cases.

cs.NI↗