- Software Defined Networks (SDN) and heterogeneous wireless infrastructures for smart cities.
- Development of Machine Learning Algorithms applied to Wireless Communications (NB-IoT, LoRAWan)
- Intelligent Reflecting Surfaces for In-building Coverage Applications
- Reinforcement Learning Algorithms for UAVs Multiagent Applications
- Transmission protocols for telemedicine applications (WebRTC)
- Comparison between RRH/Small Cell and DAS in 5G-NR Radio Tunnel Applications for Autonomous Driving
The deployment of fifth-generation (5G) cellular radio coverage infrastructures, characterized by reduced latency, represents one of the key enabling scenarios for the implementation of assisted and, in perspective, fully autonomous driving systems. In this context, the network slicing functionalities defined by the 5G standard play a central role, as they allow the creation of virtual network segments dedicated to specific services or applications. However, in order for these capabilities to be effectively implemented, an appropriate configuration of all network layers is required, from the radio interface to the core network.
In the specific case of autonomous driving, the implementation of the so-called radio tunnel is particularly relevant — that is, a dedicated radio coverage along road segments designated for the transit of autonomous vehicles. Although there are existing projects aimed at road coverage, such as the ANAS Smart Road and Wi-Fi in Motion initiatives, these solutions — based mainly on Wi-Fi technologies and on IoT or basic Internet connectivity paradigms — are not suitable to meet the stringent requirements of reliability, continuity, and low latency demanded by autonomous driving applications.
The main aspects to be considered for the implementation of such infrastructures concern the internetworkingarchitecture, that is, the protocol stack and functionalities of the 5G core network, as well as the available radio access technologies. The latter can be implemented through different solutions, such as gNodeB, small cells, Remote Radio Heads (RRH), or Distributed Antenna Systems (DAS). This raises the question of which of these options proves to be the most efficient for the realization of a radio tunnel dedicated to autonomous driving.
A comparative analysis among these different solutions should take into account key parameters such as latency, the minimization of handover events, and the feasibility of deploying a shared infrastructure among multiple Mobile Network Operators (MNOs). In particular, it would be worth investigating whether next-generation DAS systems feature specific characteristics — for instance, in terms of dynamic radio resource management or architectural flexibility — that could facilitate the deployment of such application scenarios.
- Implementation on Digital DAS Systems of a Recognition / Localization Function via IMEI
The topic of geolocation within the cellular domain has long attracted multidisciplinary interest, spanning from applications related to public safety (e.g., emergency services) to commercial purposes such as location-based marketing. Modern smartphone functionalities already enable several positioning methods, including IP address mapping of mobile terminals, positioning through GNSS/GPS systems, and localization via the association of the terminal with specific radio coverage sectors through its unique IMEI code.
The main challenge in determining user location arises in indoor environments, where buildings are served solely by external signals and GNSS reception is unavailable. Under such conditions, it becomes difficult to accurately determine the position of a user within vertically and/or horizontally partitioned structures shielded from external signals. For indoor environments, therefore, dedicated radio coverage systems are required to create localized “spots” within which the presence of mobile terminals can be detected. These spots can be implemented using small cells, low-power Remote Radio Heads (RRHs), or active Distributed Antenna Systems (DAS).
The identification of a mobile terminal within a spot typically relies on recognizing its IMEI code. In the case of small cells and RRHs, this process is straightforward, since these components are integrated parts of the Radio Access Network (RAN) and directly interconnected with the core network, which thus maintains full control over terminal identifiers while also managing security and privacy aspects. DAS systems, by contrast, are often deployed as external and “transparent” infrastructures with respect to information exchange between the radio access network and the mobile terminal; as a result, traditional DAS architectures do not support direct recognition of IMEI codes or their association with specific coverage spots.
However, the evolution toward digital DAS equipped with distributed computing capabilities opens up the possibility of extracting — through appropriate processing modules — information useful for terminal identification, potentially including device identifiers or meta-information. Since DAS infrastructures are frequently deployed in multi-operator environments by tower companies, a question arises as to whether such systems could collect and correlate information from terminals belonging to multiple Mobile Network Operators (MNOs), while ensuring full compliance with data protection regulations. Within this framework, it appears feasible to design a prototype decoding or detection algorithm to be implemented at the DAS architectural level, provided that the associated regulatory, security, and interoperability issues are adequately addressed.
As an alternative to IMEI-based identification, it is worth considering which radio signaling elements remain accessible prior to the application of end-to-end encryption mechanisms and could therefore be leveraged for localization within indoor coverage spots (e.g., signal strength measurements, synchronization parameters, or temporary identifiers transmitted in cleartext). Finally, the presence of a direct O-RAN interface between the DAS and the core network components could simplify this issue by enabling standardized mechanisms for the secure exchange of contextual information and for the integration of localization functionalities — all while respecting regulatory constraints and privacy protection requirements.
