Resilience of Task-Oriented V2X Networks to Incomplete Information Sharing

Luca Lusvarghi; Javier Gozalvez

Home/Archive/arXiv

arXiv · 2602.18620

Resilience of Task-Oriented V2X Networks to Incomplete Information Sharing

Luca Lusvarghi·Javier Gozalvez

View original source ↗Read source document ↗Export metadata ↓

Abstract

Task-oriented Vehicle-to-Everything (V2X) networks have recently been proposed to scalably support the large-scale deployment of connected vehicles. In task-oriented V2X networks, vehicles select the content of the transmitted messages based on its relevance to the intended receivers. However, estimating relevance can be challenging, especially in highly dynamic and complex driving scenarios. Relevance estimation errors may cause a transmitting vehicle to share incomplete information, omitting relevant data that is critical for the intended receivers' situational awareness. This work numerically demonstrates that task-oriented V2X networks exhibit an inherent resilience to incomplete information sharing. We show that such resilience guarantees a consistent delivery of relevant information even under high relevance estimation error probability conditions. Furthermore, we show that the fundamental conditions underpinning such inherent resilience can also be encountered outside of the V2X domain - in particular, in other task-oriented networks where multiple transmitters select the content of their messages based on the task-related requirements of a common set of intended receivers.

Explore related subjects

cs.NI

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Luca Lusvarghi, Javier Gozalvez. 2026-06-08. Resilience of Task-Oriented V2X Networks to Incomplete Information Sharing. https://arxiv.org/abs/2602.18620

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Decoding Delay Guarantees of Space Regulated Multiple Access Random Wireless Networks using Successive Interference Cancellation

This paper is focused on decoding delay guarantees in wireless networks, where messages have a given signal-to-interference-plus-noise ratio threshold $η_0$ to meet in order to be successfully decoded, and where transmissions should occur within some strict time constraints. Its main contribution consists in quantifying the worst-case transmissions decoding delays in the uplink of cellular and cell-free networks using successive interference cancellation. We show how such decoding delay guarantees can be obtained using spatial network calculus, a new tool introduced recently, and in particular spatial regulation. The results rely on the assumption of absence of fading. We nevertheless outline what this approach will lead to in the fading case for cellular networks.

cs.NI↗

arXiv2026

Toward Composable Network Digital Twins: A Subgraph-Based Latency Prediction Study

Modern networks must support changing topologies, configurations, and performance objectives, motivating fast and reliable performance estimation. Network digital twins (NDTs) enable what-if analysis for performance estimation in such network scenarios, however, existing machine learning-based NDT approaches often rely on entire topology representations, which are inherently monolithic and lack reusability under topological or traffic changes in the network. This paper introduces a composable NDT approach that decomposes networks into subgraphs represented by reusable unit twins that capture subgraph structure, configuration and traffic behaviours. A lightweight composer aggregates unit twin combinations to create NDTs that predict per-route end-to-end latency through an overall topology. Evaluation across controlled synthetic topologies and diverse traffic scenarios, real-world Topology Zoo topologies, and a public NDT challenge dataset demonstrates that the composable NDTs achieve high in-distribution accuracy while remaining stable under out-of-distribution scenarios. Comparison with monolithic full topology NDTs demonstrates that our composable approach achieves reusability, while achieving comparable or superior accuracy.

cs.NI↗

arXiv2026

Pattern-Aware Virtual Network Embedding Optimization for Cloud Data Centers

The network virtualization (NV) technology has enabled the sharing of multiple resources among virtual networks (VNs) in cloud data centers. One of the key challenges is to allocate resources in real-time for virtual network request (VNR), which is known as online virtual network embedding (VNE). However, the existing online VNE methods do not exploit the multi-dimensional complementary relationship among diverse VNRs, resulting in the fragmentation and waste of substrate resources. In this paper, we propose the pattern matching based online VNE approach by constructing appropriate matching rules among observed patterns to maximize resources utilization. We devise the clustering based VNRs quantization method and conduct rigorous study on the pattern combination filtering problem. Then, we utilize the column generation to solve it and construct the pattern matching rules. Based on the rules, we propose an online pattern matching VNE algorithm with linear worst-case complexity. Evaluation on a 106-server testbed using Alibaba production cluster trace dataset shows that our algorithm achieves close-to-offline performance and more accepted workloads that outperforms traditional designs by 25%-30%.

cs.NI↗