arXiv subjects

Luca Antonelli

Publications and source records attributed to Luca Antonelli.

5 recordsLinked to original sources

arXiv2026

Joint Channel Scanning and Geometry-Aware Codebook Design for Movable Antenna Arrays

Multiple-input multiple-output (MIMO) systems rely on spatial degrees of freedom, but fixed antenna geometries cannot adapt to propagation environments. Movable antenna (MA) systems address this by adapting antenna positions to the channel. In discrete MA architectures, active antennas are selected from candidate positions, requiring channel state information (CSI) at those positions. With limited radio-frequency (RF) chains, CSI is acquired through successive pilot rounds, or antenna scanning, whose overhead can offset reconfiguration gains. This paper studies discrete MA geometry optimization when only a fraction of candidate positions is scanned. We show that a geometry-aware (GA) steering-vector codebook, constructed from the actual antenna coordinates, reconstructs channels more accurately than a conventional discrete Fourier transform (DFT) codebook when the optimized array departs from a uniform grid. We then show that the scanning fraction required to approach full-scan sum-rate performance depends on the CSI acquisition method: least-squares (LS) estimation degrades sharply under sparse scanning, while codebook-based acquisition remains robust. We also propose an adaptive scanning procedure that probes candidates in batches and stops automatically using the acquired pilots, avoiding a preset scanning budget. Finally, we quantify sum-rate losses as users move from their initial positions and evaluate recovery through re-scanning and geometry re-selection, relative to fixed-aperture operation with outdated or fresh CSI. Numerical results show that adaptive scanning achieves about 99.5% of the full-scan sum rate while scanning only 11% of candidate positions.

eess.SP↗

arXiv2026

A Novel Near-Field Dictionary Design for Hybrid MIMO with Uniform Planar Arrays

Near-field ultra-massive MIMO (U-MIMO) systems provide enhanced spatial resolution but present challenges for channel estimation, particularly when hybrid architectures are employed. Within this framework, dictionary-based channel estimation schemes are needed to achieve accurate reconstruction from a reduced set of measurements. However, existing near-field dictionaries generally provide full three-dimensional coverage, which is unnecessary when user equipments are primarily located on the ground. In this paper, we propose a novel near-field grid design tailored to this common scenario. Specifically, grid points lie on a reference plane located at an arbitrary height with respect to the U-MIMO system, equipped with a uniform planar array. Furthermore, a channel accuracy metric is used to improve codebook performance, and to remark the limitations of the traditional far-field angular sampling in the near field. Results show that, as long as user equipments are not far from the reference plane, the proposed grid outperforms state-of-the-art designs in both channel estimation accuracy and spectral efficiency.

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arXiv2026

An Enhanced Polar-Domain Dictionary Design for Elevated BSs in Near-Field U-MIMO

Near-field U-MIMO communications require carefully optimized sampling grids in both angular and distance domains. However, most existing grid design methods neglect the influence of base station height, assuming instead that the base station is positioned at ground level - a simplification that rarely reflects real-world deployments. To overcome this limitation, we propose a generalized grid design framework that accommodates arbitrary base station locations. Unlike conventional correlation-based approaches, our method optimizes the grid based on the minimization of the optimal normalized mean squared error, leading to more accurate channel representation. We evaluate the performance of a hybrid U-MIMO system operating at sub-THz frequencies, considering the P-SOMP algorithm for channel estimation. Analytical and numerical results show that the proposed design enhances both channel estimation accuracy and spectral efficiency compared to existing alternatives.

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arXiv2023

Measurement of magnetic cavitation driven by heat flow in a plasma

We describe the direct measurement of the expulsion of a magnetic field from a plasma driven by heat flow. Using a laser to heat a column of gas within an applied magnetic field, we isolate Nernst advection and show how it changes the field over a nanosecond timescale. Reconstruction of the magnetic field map from proton radiographs demonstrates that the field is advected by heat flow in advance of the plasma expansion. This changes the dynamics of high energy density plasmas, in which heat flows and fields are strongly coupled, and may disrupt magnetised inertial confinement fusion schemes.

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arXiv2021

The data-driven future of high energy density physics

The study of plasma physics under conditions of extreme temperatures, densities and electromagnetic field strengths is significant for our understanding of astrophysics, nuclear fusion and fundamental physics. These extreme physical systems are strongly non-linear and very difficult to understand theoretically or optimize experimentally. Here, we argue that machine learning models and data-driven methods are in the process of reshaping our exploration of these extreme systems that have hitherto proven far too non-linear for human researchers. From a fundamental perspective, our understanding can be helped by the way in which machine learning models can rapidly discover complex interactions in large data sets. From a practical point of view, the newest generation of extreme physics facilities can perform experiments multiple times a second (as opposed to ~daily), moving away from human-based control towards automatic control based on real-time interpretation of diagnostic data and updates of the physics model. To make the most of these emerging opportunities, we advance proposals for the community in terms of research design, training, best practices, and support for synthetic diagnostics and data analysis.

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