This paper presents a novel application for V2X communication, namely a Mountain-Bike-to-Hiker Warning System (or short MBHWS) which should ease the ongoing conflict between hikers and mountain bikers in many alpine regions where both use shared trails. Fast approaching and overtaking bikers can scare hikers, especially when the latter do not recognize the bikers early enough, and in consequence conflict-ridden or dangerous situations regularly occur in mountains. Building on recent technology advances and inspired by recent works on Vulnerable-Road-User protection, we analyze the MBHWS use-case and its requirements, derive and implement a system design to show the general feasibility, conduct technical tests of the system prototype in a realistic outdoor environment in the Italian Alps, and also perform a user-study to analyze user acceptance and preferences. We conclude that a MBHWS is technically feasible based on widely available consumer-grade equipment and our user study gives indications about high user-demand and preferences of different user groups.
The definition and deployment of next-generation mobile networks must incorporate considerations of sustainability and environmental impact. In this context, estimating the energy consumption of a mobile network deployment across a city or region is of utmost importance. Nationwide historical aggregate values are informative but do not allow to understand the underlying dynamics or to perform prospective studies. There is hence a need for bottom-up approaches to assess the energy consumption of mobile networks. However, this is a complex task, as it depends on numerous factors, including the number and spatial distribution of base stations, the underlying technologies and their configuration, as well as user demand patterns and their geographic distribution. This paper thus introduces a simulation framework aimed at estimating the energy consumption of 5G networks at urban, regional or national scale. The framework integrates radio propagation models operating in the 3.5 GHz band with publicly available datasets describing user and base station locations, as well as network traffic volumes. As a case study, we consider the 5G deployment in France and examine the spatial distribution of network load and the resulting energy consumption. The source code and datasets are publicly available, ensuring that the simulator is fully reproducible and easily adaptable to other use cases, countries, or regions.
Network slicing enables 5G and beyond networks to concurrently support diverse services, including emerging IoT applications, over a shared, softwarized infrastructure. Traditional resource management approaches are often reactive, resulting in suboptimal utilization and potential Service Level Agreement (SLA) violations under dynamic traffic conditions. We present Proactive Resource OPtimization for Heterogeneous nETwork Slicing (PROPHET), a framework for Proactive Resource Optimization in heterogeneous slice-enabled networks. PROPHET integrates an attention-based traffic forecasting model with a deep reinforcement learning (DRL) resource allocator based on Proximal Policy Optimization (PPO) to anticipate future slice demands and proactively adjust configurations. Evaluated using real LTE traffic traces representing enhanced Mobile Broadband (eMBB), ultra-Reliable and Low-Latency Communication (uRLLC)-like, and best-effort services, PROPHET improves SLA compliance and resource efficiency compared to reactive baselines. The proposed framework is equally applicable to IoTdriven slices, supporting massive connectivity and ultra-reliable low-latency requirements envisioned for next-generation smart environments.
Physical layer security (PLS) deals with implementing security mechanisms directly at the physical layer, potentially at the signal level. The recent advances on reconfigurable intelligent surfaces (RISs) make it possible to exploit their manipulation capabilities to make a signal decodable at specific locations and unintelligible at other ones, adding additional security and privacy mechanisms on top of existing ones. In previous work, we devised a mathematical framework to achieve multi-receiver PLS in the presence of multiple RISs, showing its effectiveness by means of spatial bit error rate (BER) maps. Our analyses, however, modeled each reception point in the BER map as affected by an independent Rice fading realization on top of path loss, making it impossible to study the size of the secured area around intended receivers. In this work, we extend our analyses by exploiting the Sionna ray tracing (RT) software which, by considering 3D scenarios, can properly estimate the effects of fading by considering realistic reflections and multi-path propagation. The results confirm the applicability of the methodology, but raise several interesting points regarding spatial correlation and undesired signal attenuation effects.
Beyond-5G and 6G networks must support diverse QoS, from ultra-reliable low latency communications to enhanced mobile broadband. This requires joint, efficient resource management that combines network slicing and dynamic beam management. This paper addresses the complexity of optimizing resource allocation across users and slices served by a set of beams. We propose SBQF, a high-performance technique that optimizes resource allocation at both the MAC and the physical layers. SBQF is designed to maximize the overall user utility and is in particular efficient in improving the performance of users requiring latency-critical services. Validated using real-world vehicular traces, SBQF significantly improves latency, user utility, and data rates over existing solutions.
