Search arXivSearch

arXiv · 2607.28756

Sovereign Cognitive Digital Twins: Fusing 6G ISAC, AI-RAN, and Zero-Trust Edge Grids for National Resilience in the Global South

Abstract

Small-island developing states face accelerating sea-level rise, intensifying cyclones, and storm surge, while suffering the sparse ground instrumentation that makes timely hazard perception difficult. This paper argues for a shift from passive cellular connectivity to the Network as a Sensor, realized through a Sovereign Cognitive Digital Twin (S-CDT): a federated national digital twin whose perceptual substrate is the 6G radio interface itself. Instead of disjoint sensing systems, a nation can reuse the Integrated Sensing and Communication (ISAC) waveforms of its own network as a distributed radar mesh. We specify a six-layer S-CDT stack in which ISAC collapses the boundary between the dynamic-data and communication layers; we map the physical layer to the ETSI GR ISC 001 and 3GPP Release 19 sensing frameworks; and we formulate a belief-state control loop, an Extended Kalman Filter feeding a Proximal Policy Optimization agent, designed to absorb O-RAN telemetry delay. That loop is specified but not evaluated here. Beyond the reference architecture, we implement a reproducible, CPU-only geodata-to-ray-tracing pipeline over a 2 km study area at the Barbados Heritage District, Newton Plantation: 576 LiDAR-height buildings, a 70x70 terrain grid, and the government tower register become a Sionna RT scene. On identical geometry, median best-server path gain falls from -106 dB at 1.8 GHz to -122 dB at 10 GHz; concrete-only 28/60 GHz runs yield -130/-136 dB, with coverage contracting to line-of-sight lobes. These uncalibrated, 1x1 V-polarized simulations are framed as a towards-6G site model of the background channel, not an operational ISAC deployment. We treat data sovereignty and physical-layer zero-trust security as first-order design constraints. Barbados (166 km2, ~280k population) is the reference deployment, with the Philippines as an archipelagic generalization.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Zoe Aiyanna M. Cayetano, George M. Gichuru, Taijuo T. Morris. 2026-07-30. Sovereign Cognitive Digital Twins: Fusing 6G ISAC, AI-RAN, and Zero-Trust Edge Grids for National Resilience in the Global South. https://arxiv.org/abs/2607.28756

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

KEEP EXPLORING

Related papers

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

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

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