Reliable UAV Detection with ISAC

Unmanned Aerial Vehicle (UAV) detection is one prominent use case of Integrated Sensing and Communication (ISAC) systems in 5G-Advanced and future 6G networks. In this paper, we present experimental results for the detection of a small UAV using unmodified commercial 5G hardware for mono-static Orthogonal Frequency-Division Multiplexing (OFDM) radar and compare them with the expected performance based on models for link budget and hardware impairments. We show that reliable detection with sub-meter accuracy is still possible in over 500 meters distance in a challenging radio environment rich of strong clutter.

Reinforcement learning for Admission Control in 5G Wireless Networks

The key challenge in admission control in wireless networks is to strike an optimal trade-off between the blocking probability for new requests while minimizing the dropping probability of ongoing requests. We consider two approaches for solving the admission control problem: i) the typically adopted threshold policy and ii) our proposed policy relying on reinforcement learning with neural networks. Extensive simulation experiments are conducted to analyze the performance of both policies. The results show that the reinforcement learning policy outperforms the threshold-based policies in the scenario with heterogeneous time-varying arrival rates and multiple user equipment types, proving its applicability in realistic wireless network scenarios.