Photonic-aware Neural Networks for Packet Classification in URLLC scenarios

Ultra Reliable Low Latency Communications (URLLC) scenarios require very low latency and high reliability, imposing an optimization of every aspect of 5G data processing, transmission, and networking. Artificial Intelligence (AI)-based tools can be helpful resources in this context, enhancing multiple functionalities, from network resource allocation to network security. In this paper we propose a solution placed at the next generation eNB (gNB)-Central Unit (CU) level, relying on Neural Networks (NNs), capable of classifying incoming packets. The developed system increases the security of 5G and B5G architectures, protecting the 5G Core (5GC) from potential attacks. To comply with URLLC requirements on latency, we present an architecture leveraging photonic hardware to speed-up NN computations. The proposed solution, namely Photonic-Aware Neural Network (PANN), complies with physical layer constraints raised by photonic analog computing and can achieve high throughput and time-of-flight latency. The classification performance of the devised PANN model has been assessed through simulation on the distilled Kitsune dataset, suited for 5G scenarios. The experiments proved that PANN significantly lowers the chance of transmitting malicious packets to the 5GC with a classification performance increasing with the bit resolution supported by the analog photonic physical layer.