Efficient Resource Management for Secure and Low-Latency O-RAN Communication

TL;DR

The paper tackles secure, low-latency O-RAN resource management in a distributed, multi-vendor setting with heterogeneous UEs and O-RUs. It models UE-O-RU association, encryption algorithm choice, and adaptive key lengths as a Multi-objective Optimization Problem that minimizes latency while maximizing security, with objectives guided by $L_{i,t}$ and $S_{i,t}$ and weighted by $\alpha$. To address NP-hardness, it offers two sub-optimal methods: a one-shot convex relaxation using an exponential transformation and variable consolidation, and an iterative alternating-optimization approach, both evaluated against exhaustive search. Results show near-optimal performance with substantially reduced computation times, highlighting trade-offs between latency, security, and complexity and supporting deployment in online, resource-constrained O-RAN environments. The framework thus provides a practical, scalable path to securely and efficiently manage open, cloud-based O-RAN networks.

Abstract

Open Radio Access Networks (O-RAN) are transforming telecommunications by shifting from centralized to distributed architectures, promoting flexibility, interoperability, and innovation through open interfaces and multi-vendor environments. However, O-RAN's reliance on cloud-based architecture and enhanced observability introduces significant security and resource management challenges. Efficient resource management is crucial for secure and reliable communication in O-RAN, within the resource-constrained environment and heterogeneity of requirements, where multiple User Equipment (UE) and O-RAN Radio Units (O-RUs) coexist. This paper develops a framework to manage these aspects, ensuring each O-RU is associated with UEs based on their communication channel qualities and computational resources, and selecting appropriate encryption algorithms to safeguard data confidentiality, integrity, and authentication. A Multi-objective Optimization Problem (MOP) is formulated to minimize latency and maximize security within resource constraints. Different approaches are proposed to relax the complexity of the problem and achieve near-optimal performance, facilitating trade-offs between latency, security, and solution complexity. Simulation results demonstrate that the proposed approaches are close enough to the optimal solution, proving that our approach is both effective and efficient.

Figures (2)

• Figure 1: System Model
• Figure 2: Performance of our approaches in terms of latency, security, and battery level