Deep Autoencoder Design for RF Anomaly Detection in 5G O-RAN Near-RT RIC via xApps

The agile and rapid management of the operations that take place in 5G radio access networks (RAN) including the monitoring of RF fluctuations, throughput suffering, and handover issues due to mobility on the user equipment (UE) side is becoming critical. Therefore, it needs to be managed by a near real-time RAN intelligent controller (RIC) in the context of O-RAN concept where O-RAN aims at the democratization of 5G RAN components to provide flexibility and compatibility to the vendors in the 5G market. Accordingly, in this paper, we present a deep learning (DL)-based autoencoder design for detecting the RF anomalies at the UE side through the extended applications (xApps) running on 5G near real-time RIC, thus, providing better and seamless service continuity. Simulation results demonstrate that the proposed autoencoder is able to achieve better performance on RF anomaly detection compared to the existing models such as random forest and isolation forest. Compared to the isolation forest algorithm, the deep-autoencoder model gives 10% better results in terms of overall accuracy score.