arXiv subjects

Sara Garcia Sanchez

Publications and source records attributed to Sara Garcia Sanchez.

4 recordsLinked to original sources

arXiv2026

Tutorial on Closed-Loop and Application-Aware Digital Twins for Future Wireless Networks

To support emerging applications such as extended reality, autonomous systems, and large-scale cyber-physical infrastructures, next-generation wireless networks must evolve beyond traditional communication paradigms toward tightly integrated and closed-loop systems. Digital Twin Networks (DTNs) have recently emerged as a key enabler of this transformation by bridging the physical and digital worlds through real-time, bidirectional interactions and adaptive control mechanisms. In this tutorial, we provide a comprehensive overview of DTN design and optimization across multiple system dimensions. Specifically, we first introduce the fundamental design principles of DTN creation, including high-fidelity twin modeling, telemetry integration, and cyber-physical synchronization. We then present resource optimization and semantic-driven DTNs, where application-aware objectives and semantic abstractions guide efficient data processing, communication, and decision-making. Subsequently, we explore physical-layer optimization techniques for DTNs, emphasizing joint sensing, communication, and adaptive wireless resource control. Furthermore, we examine DTN-guided network management and feedback optimization, focusing on closed-loop learning, real-time control, and reinforcement-driven adaptation between digital twins and physical networks. Finally, we review ongoing standardization efforts and enabling tools that support the practical deployment of DTNs in real-world systems. This tutorial aims to provide a unified perspective on DTNs as an emerging paradigm for application-aware, AI-native, and closed-loop wireless systems, and to highlight key challenges and future research directions in this rapidly evolving field.

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arXiv2026

Single-Voxel Wireless NeRF for Spatial Spectrum Prediction

Wireless channel measurements across multiple spatial directions are crucial for AI-driven applications, such as RF digital twins and integrated communication and sensing. However, collecting channel data across large scenes is labor-intensive. Wireless NeRFs address this challenge by learning propagation behavior from sparse measurements and synthesizing channel spatial spectrum magnitude at unseen locations. However, existing wireless NeRFs inherit dense volumetric sampling from vision NeRFs, which requires substantial computation. This paper asks whether such dense sampling is necessary for predicting magnitudes of the wireless spatial spectrum. We empirically show that wireless NeRFs are over-parameterized for this task and introduce SV-INGP, a sparse volumetric sampling variant of Instant Neural Graphics Primitives (INGP). Across real-world and simulated datasets, SV-INGP matches the median Structural Similarity Index Measure (SSIM) of the NeRF2 baseline while reducing training time by 184x. These results generalize across LoS and NLoS scenes, sub-6 and millimeter-wave frequencies, and antenna array tapering configurations, suggesting a simpler and more efficient design path for RF digital twins

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arXiv2022

AirNN: Neural Networks with Over-the-Air Convolution via Reconfigurable Intelligent Surfaces

Over-the-air analog computation allows offloading computation to the wireless environment through carefully constructed transmitted signals. In this paper, we design and implement the first-of-its-kind over-the-air convolution and demonstrate it for inference tasks in a convolutional neural network (CNN). We engineer the ambient wireless propagation environment through reconfigurable intelligent surfaces (RIS) to design such an architecture, which we call 'AirNN'. AirNN leverages the physics of wave reflection to represent a digital convolution, an essential part of a CNN architecture, in the analog domain. In contrast to classical communication, where the receiver must react to the channel-induced transformation, generally represented as finite impulse response (FIR) filter, AirNN proactively creates the signal reflections to emulate specific FIR filters through RIS. AirNN involves two steps: first, the weights of the neurons in the CNN are drawn from a finite set of channel impulse responses (CIR) that correspond to realizable FIR filters. Second, each CIR is engineered through RIS, and reflected signals combine at the receiver to determine the output of the convolution. This paper presents a proof-of-concept of AirNN by experimentally demonstrating over-the-air convolutions. We then validate the entire resulting CNN model accuracy via simulations for an example task of modulation classification.

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arXiv2021

Machine Learning on Camera Images for Fast mmWave Beamforming

Perfect alignment in chosen beam sectors at both transmit- and receive-nodes is required for beamforming in mmWave bands. Current 802.11ad WiFi and emerging 5G cellular standards spend up to several milliseconds exploring different sector combinations to identify the beam pair with the highest SNR. In this paper, we propose a machine learning (ML) approach with two sequential convolutional neural networks (CNN) that uses out-of-band information, in the form of camera images, to (i) rapidly identify the locations of the transmitter and receiver nodes, and then (ii) return the optimal beam pair. We experimentally validate this intriguing concept for indoor settings using the NI 60GHz mmwave transceiver. Our results reveal that our ML approach reduces beamforming related exploration time by 93% under different ambient lighting conditions, with an error of less than 1% compared to the time-intensive deterministic method defined by the current standards.

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