Heiko Lehmann

Marcin Hoffmann, Marcin Dryjański, Adrian Kliks et al.

Energy efficiency (EE) is one of the key challenges for contemporary and future mobile networks, including within the Open Radio Access Network (O-RAN) architecture. However, there is a significant gap in common procedures for comparing the EE of both hardware (HW) and software (SW) solutions offered by various vendors. Usually, EE improvements of both SW and HW solutions are demonstrated in a specific scenario defined by individual vendors avoiding comparisons and benchmarking under various network conditions. This paper outlines the need for unified end-to-end (E2E) EE testing for O-RAN. First, it analyzes the standards to identify missing parts. Based on the analysis, a novel O-RAN E2E EE Testing framework is proposed. The framework aims to test the EE of the xApp/rApp pair cooperating on the cell on/off switching using a commercial RAN emulator and real-world network topology data from a mobile network operator (MNO). The test results show up to 57% improvement in EE compared to the baseline.

Paweł Kryszkiewicz, Łukasz Kułacz, Marcin Pakuła et al.

With growing mobile-network complexity, management and optimization have become increasingly difficult. Centralized algorithms face high control-data overhead and computational load, while distributed approaches often perform far from optimally. The O-RAN architecture introduces two tiers of RAN Intelligent Controllers (RICs), enabling hierarchical network-management schemes. This work proposes Tandem Apps: a pair of tightly coupled optimization mechanisms running on both controllers. We show how to design Tandem Apps through architectural and functional splitting to achieve an agile, low-complexity solution that still preserves a global network view. As an example, we implement Tandem Apps for cell on/off switching and evaluate them in a large heterogeneous network using real network data. Although the Tandem Apps concept is new, it remains fully compliant with the O-RAN standard, as validated using commercial network software.

Edan Habler, Ron Bitton, Dan Avraham et al.

O-RAN is a new, open, adaptive, and intelligent RAN architecture. Motivated by the success of artificial intelligence in other domains, O-RAN strives to leverage machine learning (ML) to automatically and efficiently manage network resources in diverse use cases such as traffic steering, quality of experience prediction, and anomaly detection. Unfortunately, it has been shown that ML-based systems are vulnerable to an attack technique referred to as adversarial machine learning (AML). This special kind of attack has already been demonstrated in recent studies and in multiple domains. In this paper, we present a systematic AML threat analysis for O-RAN. We start by reviewing relevant ML use cases and analyzing the different ML workflow deployment scenarios in O-RAN. Then, we define the threat model, identifying potential adversaries, enumerating their adversarial capabilities, and analyzing their main goals. Next, we explore the various AML threats associated with O-RAN and review a large number of attacks that can be performed to realize these threats and demonstrate an AML attack on a traffic steering model. In addition, we analyze and propose various AML countermeasures for mitigating the identified threats. Finally, based on the identified AML threats and countermeasures, we present a methodology and a tool for performing risk assessment for AML attacks for a specific ML use case in O-RAN.

Dudu Mimran, Ron Bitton, Yehonatan Kfir et al.

The Open Radio Access Network (O-RAN) is a promising RAN architecture, aimed at reshaping the RAN industry toward an open, adaptive, and intelligent RAN. In this paper, we conducted a comprehensive security analysis of Open Radio Access Networks (O-RAN). Specifically, we review the architectural blueprint designed by the O-RAN alliance -- A leading force in the cellular ecosystem. Within the security analysis, we provide a detailed overview of the O-RAN architecture; present an ontology for evaluating the security of a system, which is currently at an early development stage; detect the primary risk areas to O-RAN; enumerate the various threat actors to O-RAN; and model potential threats to O-RAN. The significance of this work is providing an updated attack surface to cellular network operators. Based on the attack surface, cellular network operators can carefully deploy the appropriate countermeasure for increasing the security of O-RAN.