The disaggregated Open RAN (O-RAN) architecture requires stringent Service Level Agreements (SLAs) for network slices, particularly for ultra-reliable low-latency communications (URLLC). While Proofs of Retrievability (PoR) can verify data integrity in the O-Cloud, they provide no guarantees on data access latency. In this paper, we introduce Proof of Latency (PoLa), a novel protocol that extends cryptographic PoR audits to enable verifiable latency enforcement in O-RAN. By timing a non-trivial, data-dependent PoR challenge-response, PoLa allows an xApp to verify whether a storage provider can access the required data within a slice-specific latency budget $(\kappa_{d})$. PoLa builds on a lightweight homomorphic PoR construction to achieve a dual guarantee of integrity and performance. Our analysis shows that PoLa incurs minimal and predictable overhead, making it suitable for enforcing latency-sensitive SLAs in O-RAN deployments.
Youssef Aly, Ahmed Badawy· International Conference on...· 0 citations
The rapid advancements of next-generation vehicular networks require intelligent, low-latency, and efficient resource management to support heterogeneous services. In this work, we propose a Traffic-aware Dynamic Resource Allocation (TADRA) architecture for UAV-assisted vehicular O-RAN to address the challenges of dynamic traffic conditions, infrastructure failures, and stringent quality of service (QoS) requirements. Due to the dynamic mobility and flexible deployment characteristics, UAV Open Radio Units (O-RUs) in the TADRA architecture support the terrestrial infrastructure under overload or failure conditions, dynamically extending coverage, balancing traffic loads, and restoring service to maintain uninterrupted QoS across diverse and heterogeneous traffic demands. Unlike existing static or single-layer solutions, our proposed TADRA integrates RAN Intelligent Controllers (RICs) with a Hierarchical Traffic-Aware Multi-Agent Twin-Delayed (TMT) algorithm to optimize the allocation of computation and radio resources. This joint optimization problem is NP-hard, highly dynamic, and coupled across agents, making TMT a tractable and adaptive alternative. This hierarchical framework performs traffic prioritization at the upper (application) layer and resource allocation at the lower (MAC) layer, facilitating adaptive decision-making under diverse vehicular traffic patterns. Numerical results demonstrate that our solution provides substantial gains over MATD3, MADDPG, and GA, achieving 17% lower latency, 10% higher throughput, 14% lower energy consumption, and 6.5% higher reliability.
Hayla Nahom Abishu, Ahmed Badawy, Amr Mohamed et al.· IEEE Transactions on Network...· 0 citations