arXiv subjects

J. Xavier Salvat Lozano

Publications and source records attributed to J. Xavier Salvat Lozano.

5 recordsLinked to original sources

arXiv2026

Optimizing Entanglement Distribution Protocols: Maximizing Classical Information in Quantum Networks

Large-scale quantum networks depend on the efficient distribution of entangled pairs between distant nodes. However, state-of-the-art solutions rely on restrictive operational assumptions, incur prohibitive computational complexity, and optimize metrics misaligned with application needs. This paper addresses the entanglement distribution problem introducing four pivotal advances. First, since the primary application of quantum communication is private information transmission, we derive the Ensemble Capacity, a novel metric that quantifies the private classical information enabled by entanglement distribution. Second, we propose a generalized formulation of the Entanglement Distribution Problem that allows arbitrary sequencing of entanglement swapping and purification, removing the structural restrictions of prior models. Third, to navigate the resulting combinatorial space, we introduce a dynamic programming-based hypergraph generation algorithm that preserves exact, continuous fidelities, avoiding quantization, while pruning sub-optimal sequences in advance. Finally, we integrate these solutions into CODE, a two-tier orchestration framework enabling near-real-time network operation. Extensive evaluations show that our approach achieves higher private information capacity and significantly lower computational complexity than existing solutions, while meeting the sub-second latency requirements of dynamic quantum network operation.

quant-ph↗

arXiv2026

C-RE-ACT: Causal RE-ACTing Agent for O-RAN Forensic Triage

The shift to O-RAN architectures marks a turning point in cellular security, where increased openness and modularity directly translate into a broader attack surface. Among the security threats cataloged by the O-RAN Alliance Working Group 11, performance-degradation attacks constitute the largest class. These attacks induce packet losses and latency spikes that are hard to distinguish from operational events such as misconfigurations, transient congestion, or software regressions. Consequently, upon an adverse incident detection, support engineers must rapidly determine whether to route the corresponding incident ticket to network maintenance or escalate it to security operations. This triage phase represents a critical human-in-the-loop bottleneck in the incident response lifecycle. To address this vulnerability, we introduce C-RE-ACT (Causal RE-ACTing agent), an automated agentic triage framework designed to generate actionable incident reports. C-RE-ACT starts constructing a Weighted Directed Acyclic Graph (WDAG) over O-RAN metrics using the Structural Agnostic Model (SAM). The resulting causal topology is encoded into a continuous soft token via a Graph Isomorphism Network (GIN) aligned with the language space of the Large Language Model (LLM) powering a ReAct agent. We evaluate C-RE-ACT on a physical, O-RAN-compliant testbed across 140 distinct performance-degradation experiments. Empirical results demonstrate the causal ranking isolates the correct root cause within the top three candidates in 89% of instances. Furthermore, graph soft-prompting improves LLM accuracy on causal-topology queries from 0.22 (text-only baseline) to 0.72. The autonomous agent achieves anomaly classification accuracies of 83% for delay anomalies and 84% for packet-loss anomalies.

cs.NI↗

arXiv2025

Kairos: Energy-Efficient Radio Unit Control for O-RAN via Advanced Sleep Modes

The high energy footprint of 5G base stations, particularly the radio units (RUs), poses a significant environmental and economic challenge. We introduce Kairos, a novel approach to maximize the energy-saving potential of O-RAN's Advanced Sleep Modes (ASMs). Unlike state-of-the-art solutions, which often rely on complex ASM selection algorithms unsuitable for time-constrained base stations and fail to guarantee stringent QoS demands, Kairos offers a simple yet effective joint ASM selection and radio scheduling policy capable of real-time operation. This policy is then optimized using a data-driven algorithm within an xApp, which enables several key innovations: (i) a dimensionality-invariant encoder to handle variable input sizes (e.g., time-varying network slices), (ii) distributional critics to accurately model QoS metrics and ensure constraint satisfaction, and (iii) a single-actor-multiple-critic architecture to effectively manage multiple constraints. Through experimental analysis on a commercial RU and trace-driven simulations, we demonstrate Kairos's potential to achieve energy reductions ranging between 15% and 72% while meeting QoS requirements, offering a practical solution for cost- and energy-efficient 5G networks.

cs.NI↗

arXiv2024

MemorAI: Energy-Efficient Last-Level Cache Memory Optimization for Virtualized RANs

The virtualization of Radio Access Networks (vRAN) is well on its way to become a reality, driven by its advantages such as flexibility and cost-effectiveness. However, virtualization comes at a high price - virtual Base Stations (vBSs) sharing the same computing platform incur a significant computing overhead due to in extremis consumption of shared cache memory resources. Consequently, vRAN suffers from increased energy consumption, which fuels the already high operational costs in 5G networks. This paper investigates cache memory allocation mechanisms' effectiveness in reducing total energy consumption. Using an experimental vRAN platform, we profile the energy consumption and CPU utilization of vBS as a function of the network state (e.g., traffic demand, modulation scheme). Then, we address the high dimensionality of the problem by decomposing it per vBS, which is possible thanks to the Last-Level Cache (LLC) isolation implemented in our system. Based on this, we train a vBS digital twin, which allows us to train offline a classifier, avoiding the performance degradation of the system during training. Our results show that our approach performs very closely to an offline optimal oracle, outperforming standard approaches used in today's deployments.

cs.NI↗

arXiv2023

AIRIC: Orchestration of Virtualized Radio Access Networks with Noisy Neighbours

Radio Access Networks virtualization (vRAN) is on its way becoming a reality driven by the new requirements in mobile networks, such as scalability and cost reduction. Unfortunately, there is no free lunch but a high price to be paid in terms of computing overhead introduced by noisy neighbors problem when multiple virtualized base station instances share computing platforms. In this paper, first, we thoroughly dissect the multiple sources of computing overhead in a vRAN, quantifying their different contributions to the overall performance degradation. Second, we design an AI-driven Radio Intelligent Controller (AIRIC) to orchestrate vRAN computing resources. AIRIC relies upon a hybrid neural network architecture combining a relation network (RN) and a deep Q-Network (DQN) such that: (i) the demand of concurrent virtual base stations is satisfied considering the overhead posed by the noisy neighbors problem while the operating costs of the vRAN infrastructure is minimized; and (ii) dynamically changing contexts in terms of network demand, signal-to-noise ratio (SNR) and the number of base station instances are efficiently supported. Our results show that AIRIC performs very closely to an offline optimal oracle, attaining up to 30% resource savings, and substantially outperforms existing benchmarks in service guarantees.

cs.NI↗