The transition from early non-standalone 5G deployments to 5G Standalone and, more recently, 5G-Advanced has turned mobile networks into flexible, programmable infrastructures capable of supporting private, industrial, and research-oriented deployments for the development of beyond-5G applications and architectures. Evaluating these networks’ capabilities, however, remains challenging because commercial platforms often provide limited access to internal interfaces, radio parameters, and network measurements. This paper presents an open-source private 5G SA testbed for beyond-5G application validations built using Open5GS, srsRAN, Ettus USRP N310 software-defined radio, programmable SIM cards, and commercial 5G customer-premise equipment. The platform is deployed in a semi-anechoic chamber. End-to-end operation is validated through subscriber registration, authentication, PDU session establishment, and external data connectivity. The performance of the implemented 5G network is evaluated using throughput, block error rate, modulation and coding scheme, and gNB trace logs. Unlike previous open-source 5G testbeds that primarily use RF waveguides, individual network components, or a limited set of radio configurations, the proposed platform combines COTS SIM-based UE operation with a controlled over-the-air evaluation of FDD/TDD and multiple antenna configurations and correlates application-level throughput with internal gNB radio metrics. For FDD downlink operation, the average throughput increased by approximately 74% from 1 × 1 to 2 × 2 and by a further 57% from 2 × 2 to 4 × 4, although the additional peak-throughput gain from 2 × 2 to 4 × 4 remained limited. The platform provides a reproducible environment for validating beyond-5G mechanisms, comparing network configurations, and studying the behavior of future open-source 5G SA systems under controlled conditions.
V. Popa, A. Petrariu, Alexandru A. Maftei et al.· Italian National Conference...· 0 citations
The rapid growth in the use of unmanned aerial vehicles (UAVs) in commercial and military applications, along with the increasing accessibility of these technologies, has created new challenges for critical infrastructure security, airspace protection, and public safety. In this context, the development of effective methods for detecting UAVs has become crucial for security and defense applications. This paper proposes an artificial intelligence-based framework for UAV detection using radio spectrum sensing methods. The training and evaluation pipeline for the AI model uses a custom dataset consisting of 36,000 RF spectrograms in the time-frequency domain. The dataset was divided into two classes: drone, which includes RF signals from 6 commercial UAVs acquired in different operating modes, and no drone, which includes signals acquired in indoor and outdoor environments. The system has been tested in real-world dynamic scenarios at various distances in congested wireless environments characterized by high RF traffic. The experimental results achieved an accuracy of over 94% in real-world operating scenarios. The obtained results show a high level of performance, highlighting the system’s potential for real-world applications in live RF monitoring and UAV sensing.
Alexandrin Gutu, A. Lavric, Valentin Popa et al.· IEEE Access· 0 citations
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