Towards adversary-resilient interference management in O-RAN :
Sneihil Gopal, Wesley D Garey, Richard A Rouil, Sridhar Kowdley · 2025
The Open Radio Access Network (RAN) (O-RAN) Alliance seeks to establish a more open, intelligent, and flexible Radio Access Network (RAN) architecture for Fourth Generation (4G), Fifth Generation (5G), and future Sixth Generation (6G) networks. By leveraging O-RAN's standardized ecosystem, along with Artificial Intelligence (AI) and Machine Learning (ML) technologies, network operators can address complex challenges like interference management in 4GLong Term Evolution (LTE) and 5GNew Radio (NR), with future applicability to 6G. A key innovation within O-RAN is its potential for enhanced interference management, particularly through the "Interference Detection, Prediction, and Optimization" use case, which builds on legacy Inter-Cell Interference Coordination (ICIC) techniques from 4G. This promises more granular control and real-time optimization in coordinated environments. However, public safety entities such as the U.S. Department of Homeland Security (DHS) and Department of Defense (DoD) face unique interference challenges, including uncoordinated interference from external or malicious sources. This paper examines state-of-the-art interference management techniques in cellular networks and explores whether O-RAN's current capabilities can effectively manage interference in coordinated scenarios, as well as whether they can be extended to address non-ideal situations involving uncoordinated entities, faulty equipment, or malicious actors. Through a gap analysis, we com-pare existing O-RAN specifications with real-world interference scenarios. Our findings underscore the need for enhanced O-RAN-based solutions to manage diverse interference cases, critical for both commercial and public safety networks.