arXiv subjects

Luca Sanguinetti

Publications and source records attributed to Luca Sanguinetti.

At least 19 recordsLinked to original sources

arXiv2026

On the Unilateral Approximation Condition with Linear Arrays in Near-Field NLoS Propagation

This paper investigates the validity of the unilateral approximation in near-field non-line-of-sight (NLoS) propagation for large-scale linear antenna arrays. Utilizing multiport communication theory, the study evaluates whether the electromagnetic interaction from the receiver back to the transmitter can be neglected. The analysis examines both discrete arrays with fixed spacing and continuous arrays with fixed aperture as the number of antennas increases. Results show the approximation is asymptotically satisfied on average for both cases. This provides a theoretical foundation for using correlated Rayleigh fading models in physically consistent next-generation wireless systems.

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arXiv2026

Goal-Oriented Access Optimization for ISAC-Enabled Digital Twins

Digital twins (DTs) of physical systems enable real-time remote tracking, control, and learning, but require to be updated with environmental sensory data to maintain alignment with their physical counterparts. In a network context, integrated sensing and communication (ISAC) capabilities can expand the DT's environmental awareness by linking received updates to the location where wireless sensors acquired them. Integrating localization services, however, increases the complexity of the communication system, and can only be supported through smart access optimization. To tackle this problem, we design a two-step goal-oriented approach: firstly, sensors with a high Value of Information (VoI) inform the network of their resource demands through a push-based random access; then, pull-based scheduled transmissions of the actual sensory data are optimized to satisfy ISAC performance constraints. This design allows to maximize the VoI of the information delivered to the DT while locating the transmitting nodes, significantly outperforming existing schemes.

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arXiv2026

Linear Attention for Joint Power Optimization and User-Centric Clustering in Cell-Free Networks

Optimal AP clustering and power allocation are critical in user-centric cell-free massive MIMO systems. Existing deep learning models lack flexibility to handle dynamic network configurations. Furthermore, many approaches overlook pilot contamination and suffer from high computational complexity. In this paper, we propose a lightweight transformer model that overcomes these limitations by jointly predicting AP clusters and powers solely from spatial coordinates of user devices and AP. Our model is architecture-agnostic to users load, handles both clustering and power allocation without channel estimation overhead, and eliminates pilot contamination by assigning users to AP within a pilot reuse constraint. We also incorporate a customized linear attention mechanism to capture user-AP interactions efficiently and enable linear scalability with respect to the number of users. Numerical results confirm the model's effectiveness in maximizing the minimum spectral efficiency and providing near-optimal performance while ensuring adaptability and scalability in dynamic scenarios.

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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

Channel Estimation for 6G Near-Field Wireless Communications: A Comprehensive Survey

The sixth-generation (6G) wireless systems are expected to adopt extremely large aperture arrays (ELAAs), novel antenna architectures, and operate in extremely high-frequency bands to meet growing data demands. ELAAs significantly increase the number of antennas, enabling finer spatial resolution and improved beamforming. At high frequencies, ELAAs shift communication from the conventional far-field to near-field regime, where spherical wavefronts dominate and the channel response depends on both angle and distance, increasing channel dimensionality. Conventional far-field channel estimation methods, which rely on angular information, struggle in near-field scenarios due to increased pilot overhead and computational complexity. This paper presents a comprehensive survey of recent advances in near-field channel estimation. It first defines the near- and far-field boundary from an electromagnetic perspective and discusses key propagation differences, alongside a brief review of ELAA developments. Then, it introduces mainstream near-field channel models and compares them with far-field models. Major estimation techniques are reviewed under different configurations (single/multi-user, single/multi-carrier), including both direct estimation and RIS-assisted cascaded estimation. These techniques reveal trade-offs among estimation accuracy, complexity, and overhead. This survey aims to provide insights and foundations for efficient and scalable near-field channel estimation in 6G systems, while identifying key challenges and future research directions.

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arXiv2026

Near-Field MMSE Channel Estimation for THz RIS-aided Communications with Electromagnetic Interference

This letter investigates the channel estimation problem in THz wireless communications where a RIS is employed to assist wireless transmission between different devices. Unlike existing studies, we consider a novel scenario where specific devices are all located in the radiative NF region of the RIS. Meanwhile, we also account for the impact on channel estimation of the random electromagnetic interference occurring at the RIS location. A linear minimum mean-square error estimator is employed, where the estimation error is fully determined by the RIS configuration. Optimizing the RIS involves solving a non-convex problem, which is addressed using an alternating optimization approach based on the diagonally scaled gradient descent algorithm. Numerical results in the THz band highlight the importance of leveraging NF channel statistics over far-field approximations and demonstrate that the proposed estimator achieves substantial improvements in normalized mean-square error compared to existing methods.

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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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arXiv2025

Tree Meets Transformer: A Hybrid Architecture for Scalable Power Allocation in Cell-Free Networks

Power allocation remains a fundamental challenge in wireless communication networks, particularly under dynamic user loads and large-scale deployments. While Transformerbased models have demonstrated strong performance, their computational cost scales poorly with the number of users. In this work, we propose a novel hybrid Tree-Transformer architecture that achieves scalable per-user power allocation. Our model compresses user features via a binary tree into a global root representation, applies a Transformer encoder solely to this root, and decodes per-user uplink and downlink powers through a shared decoder. This design achieves logarithmic depth and linear total complexity, enabling efficient inference across large and variable user sets without retraining or architectural changes. We evaluate our model on the max-min fairness problem in cellfree massive MIMO systems and demonstrate that it achieves near-optimal performance while significantly reducing inference time compared to full-attention baselines.

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arXiv2025

Transformer-Based Power Optimization for Max-Min Fairness in Cell-Free Massive MIMO

Power allocation is an important task in wireless communication networks. Classical optimization algorithms and deep learning methods, while effective in small and static scenarios, become either computationally demanding or unsuitable for large and dynamic networks with varying user loads. This letter explores the potential of transformer-based deep learning models to address these challenges. We propose a transformer neural network to jointly predict optimal uplink and downlink power using only user and access point positions. The max-min fairness problem in cell-free massive multiple input multiple output systems is considered. Numerical results show that the trained model provides near-optimal performance and adapts to varying numbers of users and access points without retraining, additional processing, or updating its neural network architecture. This demonstrates the effectiveness of the proposed model in achieving robust and flexible power allocation for dynamic networks.

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arXiv2025

Uplink Cell-Free Massive MIMO OFDM with Phase Noise-Aware Channel Estimation: Separate and Shared Local Oscillators

Cell-free massive multiple-input multiple-output (mMIMO) networks enhance coverage and spectral efficiency (SE) by distributing antennas across access points (APs) with phase coherence between APs. However, the use of cost-efficient local oscillators (LOs) introduces phase noise (PN) that compromises phase coherence, even with centralized processing. Sharing an LO across APs can reduce costs in specific configurations but cause correlated PN between APs, leading to correlated interference that affects centralized combining. This can be improved by exploiting the PN correlation in channel estimation. This paper presents an uplink orthogonal frequency division multiplexing (OFDM) signal model for PN-impaired cell-free mMIMO, addressing gaps in single-carrier signal models. We evaluate mismatches from applying single-carrier methods to OFDM systems, showing how they underestimate the impact of PN and produce over-optimistic achievable SE predictions. Based on our OFDM signal model, we propose two PN-aware channel and common phase error estimators: a distributed estimator for uncorrelated PN with separate LOs and a centralized estimator with shared LOs. We introduce a deep learning-based channel estimator to enhance the performance and reduce the number of iterations of the centralized estimator. The simulation results show that the distributed estimator outperforms mismatched estimators with separate LOs, whereas the centralized estimator enhances distributed estimators with shared LOs.

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arXiv2025

Coherent and Noncoherent Detection in Dense Arrays: Can We Ignore Mutual Coupling?

This paper investigates the impact of mutual coupling on MIMO systems with densely deployed antennas. Leveraging multiport communication theory, we analyze both coherent and noncoherent detection approaches in a single-user uplink scenario where the receiver ignores mutual coupling effects. Simulation results indicate that while coherent detection is generally more accurate, it is highly sensitive to mismatches in the coupling model, leading to severe performance degradation when antennas are closely spaced, to the point of becoming unusable. Noncoherent detection, on the other hand, exhibits a higher error probability but is more robust to coupling model mismatches.

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arXiv2025

Harnessing Wavefront Curvature and Spatial Correlation in Noncoherent MIMO Communications

Noncoherent communication systems have regained interest due to the growing demand for high-mobility and low-latency applications. Most existing studies using large antenna arrays rely on the far-field approximation, which assumes locally plane wavefronts. This assumption becomes inaccurate at higher frequencies and shorter ranges, where wavefront curvature plays a significant role and antenna arrays may operate in the radiative near field. In this letter, we adopt a model for the channel spatial correlation matrix that remains valid in both near and far field scenarios. Using this model, we demonstrate that energy-based noncoherent systems can leverage the benefits of wavefront spherical curvature, even beyond the Fraunhofer distance, revealing that the classical far-field approximation may significantly underestimate system performance. Moreover, we show that large antenna arrays enable the multiplexing of various users even with a noncoherent processing, as well as permitting near-optimal detection with low computational complexity.

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arXiv2025

Parametric Near-Field MMSE Channel Estimation for sub-THz XL-MIMO Systems

Accurate channel estimation is essential for reliable communication in sub-THz extremely large (XL) MIMO systems. Deploying XL-MIMO in high-frequency bands not only increases the number of antennas, but also fundamentally alters channel propagation characteristics, placing the user equipments (UE) in the radiative near-field of the base station. This paper proposes a parametric estimation method using the multiple signal classification (MUSIC) algorithm to extract UE location data from uplink pilot signals. These parameters are used to reconstruct the spatial correlation matrix, followed by an approximation of the minimum mean square error (MMSE) channel estimator. Numerical results show that the proposed method outperforms the least-squares (LS) estimator in terms of the normalized mean-square error (NMSE), even without prior UE location knowledge.

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arXiv2025

Optimizing RIS Impairments through Semantic Communication

This paper investigates how semantic communication can effectively influence and potentially redefine the limitations imposed by physical layer settings. Reconfigurable Intelligent Surfaces (RIS) enable the intelligent configuration of the physical layer of communication systems. However, its practical implementation is hampered by several limitations. The Semantic Communication (SemCom) paradigm introduces additional degrees of freedom that can be exploited to improve the robustness of communication against physical layer impairments. In essence, SemCom ensures that the data representation remains robust even under adverse physical conditions by emphasizing the transmission of meaningful information in a manner that is less susceptible to degradation. Through the use of SemCom, potential RIS gains are demonstrated in terms of RIS area size and the phase shift precision of its active elements.

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arXiv2024

Optimal Dual-Polarized Planar Arrays for Massive Capacity Over Point-to-Point MIMO Channels

Future wireless networks must provide ever higher data rates. The available bandwidth increases roughly linearly as we increase the carrier frequency, but the range shrinks drastically. This paper explores if we can instead reach massive capacities using spatial multiplexing over multiple-input multiple-output (MIMO) channels. In line-of-sight (LOS) scenarios, therank of the MIMO channel matrix depends on the polarization and antenna arrangement. We optimize the rank and condition number by identifying the optimal antenna spacing in dual-polarized planar antenna arrays with imperfect isolation. The result is sparely spaced antenna arrays that exploit radiative near-field properties. We further optimize the array geometry for minimum aperture length and aperture area, which leads to different configurations. Moreover, we prove analytically that for fixed-sized arrays, the MIMO rank grows quadratically with the carrier frequency in LOS scenarios, if the antennas are appropriately designed. Hence, MIMO technology contributes more to the capacity growth than the bandwidth. The numerical results show that massive data rates, far beyond 1 Tbps, can be reached both over fixed point-to-point links. It is also possible for a large base station to serve a practically-sized mobile device.

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arXiv2024

Efficient Channel Estimation With Shorter Pilots in RIS-Aided Communications: Using Array Geometries and Interference Statistics

Accurate estimation of the cascaded channel from a user equipment (UE) to a base station (BS) via each reconfigurable intelligent surface (RIS) element is critical to realizing the full potential of the RIS's ability to control the overall channel. The number of parameters to be estimated is equal to the number of RIS elements, requiring an equal number of pilots unless an underlying structure can be identified. In this paper, we show how the spatial correlation inherent in the different RIS channels provides this desired structure. We first optimize the RIS phase-shift pattern using a much-reduced pilot length (determined by the rank of the spatial correlation matrices) to minimize the mean square error (MSE) in the channel estimation under electromagnetic interference. In addition to considering the linear minimum MSE (LMMSE) channel estimator, we propose a novel channel estimator that requires only knowledge of the array geometry while not requiring any user-specific statistical information. We call this the reduced-subspace least squares (RS-LS) estimator and optimize the RIS phase-shift pattern for it. This novel estimator significantly outperforms the conventional LS estimator. For both the LMMSE and RS-LS estimators, the proposed optimized RIS configurations result in significant channel estimation improvements over the benchmarks.

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arXiv2024

MMSE Channel Estimation in Large-Scale MIMO: Improved Robustness with Reduced Complexity

Large-scale MIMO systems with a massive number N of individually controlled antennas pose significant challenges for minimum mean square error (MMSE) channel estimation, based on uplink pilots. The major ones arise from the computational complexity, which scales with $N^3$, and from the need for accurate knowledge of the channel statistics. This paper aims to address both challenges by introducing reduced-complexity channel estimation methods that achieve the performance of MMSE in terms of estimation accuracy and uplink spectral efficiency while demonstrating improved robustness in practical scenarios where channel statistics must be estimated. This is achieved by exploiting the inherent structure of the spatial correlation matrix induced by the array geometry. Specifically, we use a Kronecker decomposition for uniform planar arrays and a well-suited circulant approximation for uniform linear arrays. By doing so, a significantly lower computational complexity is achieved, scaling as $N\sqrt{N}$ and $N\log N$ for squared planar arrays and linear arrays, respectively.

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arXiv2024

Holographic MIMO Communications: What is the benefit of closely spaced antennas?

Holographic MIMO refers to a (possibly large) array with a large number of individually controlled and densely deployed antennas. The objective of this paper is to provide further insight into the use of closely spaced antennas in the uplink and downlink of a multi-user Holographic MIMO system. To this end, we utilize multiport communication theory, which ensures physically consistent uplink and downlink models. We first consider a simple uplink scenario with two side-by-side half-wavelength dipoles, two users, and single-path line-of-sight propagation, and show both analytically and numerically that the array gain and average spectral efficiency strongly depend on the directions from which the signals are received and on the array matching network used. The numerical results are then used to extend the analysis to more practical scenarios involving larger arrays of dipoles (arranged in a uniform linear array) and a larger number of users. The case where the antennas are densely packed in a space-constrained factor form is also considered. It is found that the spectral efficiency increases with decreasing antenna spacing only for arrays of moderate size, e.g. in the order of a few wavelengths. In comparison, larger arrays with closely spaced antennas show only marginal improvements in spectral efficiency compared to half-wavelength arrays.

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