Wolfgang Kellerer

The introduction of Open RAN (O-RAN) promises to enable network operators to integrate components of different vendors into their Radio Access Network (RAN). Additionally, O-RAN allows the deployment of specific and dynamic applications with further softwarized RAN management. However, opening the formerly proprietary and closed RAN architecture also broadens the attack surface due to a higher number of i) interfaces, components and protocols as well as ii) involved vendors with different implementations posing different security flaws. In this paper, we analyze possible attacks on different compromised O-RAN components. We first describe the protocol stack and highlight the security mechanisms used in O-RAN. Based thereon, we provide a survey of possible known attacks for each component if compromised by an attacker. We extend our analysis to possible security threats for the interconnected interfaces and the usage of Fake Base Stations (FBSs), in particular. Concluding our survey, we provide a discussion of the security vulnerabilities in O-RAN and possible research directions.

A network is composed of interconnected physical network elements, namely, nodes and links. A network operator must identify the network’s critical elements to maintain seamless network connectivity even in the case of failures, disasters, or attacks. In this work, we introduce the k-score, a novel metric to measure a network element’s importance in the network based on minimal cut sets (MCSs), and compare it with centrality measures. Furthermore, we demonstrate that the k-score is strongly related to the network’s resilience.

Multiple access (MA) schemes can enable cooperation between multiple nodes in future diffusion-based molecular communication (DBMC) networks. Non-orthogonal MA for DBMC networks (DBMC-NOMA) is a promising option for efficient simultaneous MA using a single molecule type. This paper studies parameter optimization and bit error probability (BEP) reduction for asynchronous DBMC-NOMA. First, we analytically derive the associated BEP and compare DBMC-NOMA with time-division and molecule-division MA. We show that asynchronous offsets can improve performance, and the upper-bound performance can be approached under almost all considered conditions by avoiding a small set of worst-case offset configurations, for which we propose and characterize a dedicated avoidance mechanism. We then propose DBMC-aNOMAly, a pilot-symbol-based optimization protocol for asynchronous DBMC-NOMA, and evaluate it using Monte Carlo simulations. DBMC-aNOMAly provides robust BEP reduction across different network sizes and noise levels, under sampling jitter, and under changing runtime conditions, outperforming protocols from previous work. An end-to-end efficiency analysis further shows that these gains translate into increased net throughput after compensating for the pilot overhead. DBMC-aNOMAly uses simple operations such as comparisons and additions that are compatible with chemical reaction networks, motivating future realistic modeling of the protocol.

The deployment of 5G networks, which are characterized by high-frequency bands, dense cell deployments, and users with diverse application demands, presents significant challenges for efficient mobility management. Frequent handovers lead to transmission interruptions and reduced network capacity, affecting both the user experience and overall network performance. The current standard handover algorithm selects target base stations solely based on signal strength, without considering the availability of resources, which can result in overloaded cells. To address these challenges, in this paper, we formulate a multi-objective optimization problem that reliably captures the network support for dual connectivity. The objective is to maximize network throughput (of interest to the operator), and to minimize service interruptions (of interest to users). Given the computational complexity of the optimization problem, we develop a Deep Reinforcement Learning (DRL)-based algorithm to perform user-to-BS assignment and wireless resource allocation. We evaluate our approach through realistic simulations in two scenarios: an indoor factory floor that uses Frequency Range (FR) 2 and a two-tier outdoor network that operates in both FR1 and FR2. The proposed algorithm demonstrates significant improvements over state-of-the-art methods, achieving better sum throughput and zero handover rates while operating within 10% of the optimum. Additionally, our algorithm ensures a 100% user rate satisfaction. Instead of performing handovers, it utilizes dual connectivity initiations and keeps their rate 20−30% lower than the handover rate.

A network is represented as a graph consisting of nodes and edges. A cut set for a source-destination pair in a network is a set of elements that, when failed, cause the source-destination pair to lose connectivity. A Minimal Cut Set (MCS) is a cut set that cannot be further reduced while maintaining its status as a cut set. MCSs are crucial in identifying the critical elements in the network that have the most significant impact on failure. This work introduces Fast-MCS, an open-source, scalable tool for evaluating MCSs in large, complex networks. Additionally, we compare the computation time of Fast-MCS with the state-of-the-art.

The development of 5G networks has enabled diverse applications with stringent communication and computation requirements. Ensuring timely operation for these applications requires proximity to compute and network resources, such as edge servers and User Plane Functions. With control over network resources and partial ownership of compute resources, Mobile Network Operators (MNOs) can assume a larger role in service orchestration within a multi-owner edge infrastructure. However, the increasing demand for edge applications often exceeds MNO capacity. To address this, some user requests can be offloaded to third-party cloud operators, expanding capacity but incurring costs and reducing profitability. Beyond profit, energy consumption is a key concern, particularly as we transition toward 6G. Addressing high energy consumption adds complexity to resource orchestration, as it directly conflicts with the goal of maximizing profit. Moreover, in dynamic edge environments, frequent service migrations increase orchestration overhead and can negatively impact user experience. Navigating these trade-offs requires an effective optimization approach. To this end, we formulate a multi-objective optimization problem for demand placement and routing, aiming to maximize MNO profit while minimizing energy consumption and migration overhead. Given its NP-hard nature, we propose a ranked greedy heuristic to effectively balance these objectives. Simulation results show that our approach outperforms benchmark algorithms in both static and dynamic scenarios, achieving near-optimal performance and deviating less than 7% from the optimal solution.