arXiv subjects

Giuseppe Thadeu Freitas de Abreu

Publications and source records attributed to Giuseppe Thadeu Freitas de Abreu.

At least 37 records · Page 2Linked to original sources

arXiv2026

Joint Synchronization and Radar Parameter Estimation for OFDM-based DISAC Systems

We propose a novel approach to the synchronization paradigm in distributed ISAC (DISAC) systems in doubly-dispersive (DD) channel environments via a joint synchronization and radar parameter estimation framework. The proposed method exploits the structure of the system model, which can be linearized in order to apply a bivariate Gaussian belief propagation (GaBP) algorithm that jointly estimates the time offset (TO) and carrier frequency offset (CFO) of each base station (BS), as well as the delay and Doppler parameters of the DD channel in conventional orthogonal frequency division multiplexing (OFDM) systems. Simulation results demonstrate the effectiveness of the proposed algorithm, showing that the radar parameter estimates (i.e., range and velocity) and synchronization parameter estimates (i.e., TO and CFO) approach the Cramér Rao lower bound (CRLB) even at moderate-to-high signal-to-noise ratio (SNR) regimes.

eess.SP↗

arXiv2026

Low-complexity Leakage Minimization Beamforming for Large-scale Multi-user Cell-Free Massive MIMO

We propose a low-complexity beamforming (BF) scheme for secrecy-rate maximization in multi-user (MU) cell-free massive multiple-input multiple-output (CF-mMIMO) systems, where legitimate users may act as non-colluding eavesdroppers of one another. To this end, we formulate an information leakage minimization problem and cast it into a tractable difference-of-convex algorithmic (DCA) form by leveraging fractional programming (FP). The resulting non-convex problem is solved through a concave-convex procedure (CCP)-based beamformer update, and an additional row-wise coordinate descent method (CDM) implementation is introduced to avoid explicit matrix inversion in the dominant linear-solve step. Additionally, we consider both direct transmit (TX)-BF and beyond-diagonal reconfigurable intelligent surface (BD-RIS)-assisted operation by defining an equivalent channel between each access point and user that combines the direct and reflective intelligent surface (RIS)-assisted propagation components. Simulation results show that the proposed secrecy-enhancement via leakage minimization (SecLM)-BF framework achieves secrecy and sum-rate performance close to state-of-the-art (SotA) semidefinite programming (SDP)-based benchmarks, as well as FP-based benchmarks, while providing a scalable inversion-free implementation for large-scale secure CF-mMIMO deployments.

eess.SP↗

arXiv2026

Learning to Compute on Dirty Paper

We propose a fully learning-based approach to integrated communication and computing (ICC) that combines dirty paper coding (DPC) with over-the-air computation. Each user employs a neural encoder with sinusoidal activations that learns to pre-cancel its own computing symbol as non-causally known interference, recovering modulo-like periodic structures consistent with lattice-based DPC schemes. A joint neural decoder recovers all users' messages from the received signal, while a separate neural AirComp estimator exploits a multi-slot block structure to estimate a target function of the computing symbols after the encoder-decoder network converges. To our knowledge, this is the first fully learning-based approach to jointly address DPC-based interference pre-cancellation and over-the-air computation in a unified framework.

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arXiv2026

Manifold Optimization-based Pilot Allocation for Cell-Free Massive MIMO ISAC Systems

We address the challenge of pilot design in cell-free massive multiple input multiple output (CF-mMIMO) integrated sensing and communications (ISAC) systems. We propose a novel pilot allocation framework based on manifold optimization that maximizes the system sum rate by minimizing coherence among pilot sequences, while enforcing unimodularity constraints in the frequency domain to ensure pilots are suitable for both communication and sensing tasks. Simulation results demonstrate that the proposed pilot design achieves communication performance comparable to state-of-the-art (SotA) algorithms, while delivering superior sensing capabilities due to its unimodular structure. These results highlight the potential of manifold-based pilot design for practical CF-mMIMO ISAC deployment.

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arXiv2026

Fractional Programming and Manifold Optimization for Reciprocal BD-RIS Scattering Matrix Design

We investigate the problem of maximizing the sum-rate performance of a beyond-diagonal reconfigurable intelligent surface (BD-RIS)-aided multi-user (MU)-multiple-input single-output (MISO) system using fractional programming (FP) techniques. More specifically, we leverage the Lagrangian Dual Transform (LDT) and Quadratic Transform (QT) to derive an equivalent objective function which is then solved iteratively via a manifold optimization framework. It is shown that these techniques reduce the complexity of the optimization problem for the scattering matrix solution, while also providing notable performance gains compared to state-of-the-art (SotA) methods under the same system conditions. Simulation results confirm the effectiveness of the proposed method in improving sum-rate performance.

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arXiv2026

Reciprocal Beyond Diagonal Reconfigurable Intelligent Surface: Distributed Scattering Matrix Design and MIMO Beamforming via Fractional Programming and Manifold Optimization

We consider the optimization of beyond diagonal reconfigurable intelligent surface (BD-RIS)-aided multi-user (MU) cell-free (CF)-massive multiple-input multiple-output (mMIMO) systems, where the propagation environment design achieved scattering matrix optimization is complemented by developing an efficient base station (BS) beamforming (BF) scheme that effectively exploits the latter ``engineered'' channel. In particular, we describe a fractional programming (FP) method, which based on the equivalent channel incorporating a reciprocal BD-RIS (RBD-RIS) parameterized by existing scattering matrix design methods, yielding the correspondingly optimized multiple-input multiple-output (MIMO) BF weights. The proposed approach decomposes the transmit (TX) beamformer into multiple sum-rate maximization (SRM) sub-beamformers, each satisfying an independent power-constraint, such that distributed MIMO-BF scenarios can be optimally handled. Although the proposed SRM-MIMO-BF framework is independent of the specific scattering matrix design, extending the BD-RIS-aided system model to the CF-mMIMO setting requires the design of a corresponding beamforming matrix. In this context, this work investigates the impact of beamforming in reconfigurable intelligent surface (RIS)-aided systems. Simulation results demonstrate that the proposed method for designing the MIMO-BF weights, when combined with the previously developed design of reciprocal BD-RIS (RBD-RIS) scattering matrices, outperforms existing BD-RIS-aided state-of-the-art (SotA) schemes employing existing MIMO-BF techniques, indicating that the whole contribution is more than the sum of the parts.

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arXiv2026

Electromagnetic Signal and Information Theory: A Continuous-Aperture Array Perspective

Emerging wireless systems are evolving toward larger, denser, higher-frequency, and more reconfigurable apertures, which motivates the study of continuous-aperture arrays (CAPAs). Unlike conventional spatially discrete arrays (SPDAs), CAPAs are more naturally modeled as spatially continuous electromagnetic apertures and therefore call for a fundamental shift in both signal processing and information-theoretic analysis. In particular, the underlying channels, signals, and beamformers are no longer finite-dimensional vectors and matrices, but continuous fields and operators governed by Maxwell's equations. This paper provides a tutorial overview of CAPA systems from the perspective of electromagnetic signal and information theory (ESIT), with an emphasis on the transition from discrete array models to physics-consistent continuous-aperture formulations. We review the electromagnetic foundations of CAPAs, practical hardware implementations, line-of-sight and multipath channel modeling, continuous-space beamforming and channel estimation, and the fundamental degrees of freedom and capacity limits of CAPA systems. We also highlight how tools such as wavenumber-domain methods, functional analysis, and compressive sensing can transform challenging infinite-dimensional problems into tractable finite-dimensional ones while preserving the essential physical structure of the channel. Overall, this tutorial aims to clarify the key principles, analytical tools, and open challenges that shape CAPA-enabled wireless communications.

cs.IT↗

arXiv2026

Quaternion-Domain Super MDS for Robust 3D Localization

This paper proposes a novel low-complexity three-dimensional (3D) localization algorithm for wireless sensor networks, termed quanternion-domain super multi-dimensional scaling (QD-SMDS). The algorithm is based on a reformulation of the SMDS, originally developed in the real domain, using quaternion algebra. By representing 3D coordinates as quaternions, the method constructs a rank-1 Gram edge kernel (GEK) matrix that integrates both relative distance and angular information between nodes, which enhances the noise reduction effect achieved through low-rank truncation employing singular value decomposition (SVD), thereby improving robustness against information loss. To further reduce computational complexity, we also propose a variant of QD-SMDS that eliminates the need for the computationally expensive SVD by leveraging the inherent structure of the quaternion-domain GEK matrix. This alternative directly estimates node coordinates using only matrix multiplications within the quaternion domain. Simulation results demonstrate that the proposed method significantly improves localization accuracy compared to the original SMDS algorithm, especially in scenarios with substantial measurement errors. The proposed method also achieves comparable localization accuracy without requiring SVD.

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arXiv2026

A Novel Framework for the Characterization of Continuous Electromagnetic Manifolds

A unified framework for the characterization of continuous electromagnetic (EM) manifolds for arbitrary multipleinput multiple-output (MIMO) system geometries is presented. The EM manifold refers to the set of all physically realizable radiated field vectors, parameterized by the array excitation, that encodes the full spatial structure of the antenna system including near-field phase variations, polarization, and mutual coupling. Building upon the discrete moment-matrix formulation, the proposed framework addresses three fundamental limitations simultaneously: (i) point-source near-field modeling errors in the radiation operator; (ii) confinement of the beamforming space to the $N$-dimensional subspace dictated by hardware port count; and (iii) restriction to linear (1D) array geometries. Each mesh element is modeled as a two-dimensional (2D) planar patch, whose spatially averaged Green's function is evaluated via Gauss-Legendre (GL) quadrature, yielding superior nearfield accuracy at negligible additional cost. A continuous feeding function $w(\mathbf{p})\in L^2(\mathcal{S}_\mathrm{T})$ is introduced as the infinite-dimensional limit of the $N$-port network, enabling optimization over a higher dimensional current subspace, decoupled from hardware constraints. Full-wave MATLAB Antenna Toolbox validation confirms near-field accuracy improvements over the state-of-the-art (SotA) baseline for both linear and planar array geometries, while maintaining reasonable computational complexity.

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arXiv2026

Quantum Clock Synchronization Networks: A Survey

Quantum clock synchronization (QCS) aims to establish a shared temporal reference between distant nodes by exploiting uniquely quantum phenomena such as entanglement, single-photon interference, and quantum correlations. In contrast to classical synchronization and time-transfer techniques, which are limited by signal propagation delays, atmospheric disturbances, and oscillator drift, QCS protocols offer the potential to surpass classical precision bounds and enhance resilience against adversarial manipulations. As precise and secure time synchronization underpins distributed quantum networks, navigation systems, and emerging quantum Internet infrastructures, understanding QCS principles, capabilities, and implementation challenges has become increasingly important. This survey provides a unified and critical overview of the rapidly growing QCS research landscape, highlighting fundamentals, protocol types, enabling resources, performance constraints, security considerations, and practical implementations of QCS. We first introduce the theoretical underpinnings of QCS, including entanglement-assisted time transfer, Hong-Ou-Mandel interference-based synchronization, and quantum slow-clock transport. We then categorize the main QCS protocols, ranging from ticking-qubit and entanglement-based schemes to time-of-arrival correlation methods, conveyor-belt synchronization, and quantum-enhanced two-way time transfer. This organization clarifies the relationships between protocol families and their achievable precision advantages over classical methods. Key quantum resources such as spontaneous parametric down-conversion-based entangled photon pairs, Greenberger-Horne-Zeilinger and W multipartite states, squeezed and frequency-entangled light, quantum frequency combs, and quantum memories are reviewed in the context of scalability and robustness.

quant-ph↗

arXiv2026

Low Rank Tensor Completion via Adaptive ADMM

We consider a novel algorithm, for the completion of partially observed low-rank tensors, as a generalization of matrix completion. The proposed low-rank tensor completion (TC) method builds on the conventional nuclear norm (NN) minimization-based low-rank TC paradigm, by leveraging the alternating direction method of multipliers (ADMM) optimization framework. To that extend the original NN minimization problem is reformulated into multiple subproblems, which are then solved iteratively via closed-form proximal operators, making use of over-relaxation and an adaptive penalty parameter update scheme, to further speed up convergence and improve the overall performance of the method. Simulation results demonstrate the superior performance of the new method in terms of normalized mean square error (NMSE), compared to the conventional state-of-the-art (SotA) techniques, including NN minimization approaches, as well as a mixture of the latter with a matrix factorization approach, while its convergence can be significantly improved by initializing the algorithm with the solution of the SotA.

stat.ML↗

arXiv2026

Regularized Approximate Message Passing for Overloaded Discrete Linear Inversion

We propose regularized approximate message passing (RAMP), a low-complexity algorithm for discrete signal detection in overloaded multiple-input multiple-output (MIMO) systems where the number of transmit antennas exceeds the number of receive antennas. While the state-of-the-art (SotA) iterative discrete least squares (IDLS) framework achieves near-optimal discrete-aware performance, its iterative matrix inversions impose a prohibitive $\mathcal{O}(M^3)$ complexity. RAMP resolves this by deriving an adaptive, state-dependent scalar denoiser that enforces arbitrary discrete constellation constraints within the approximate message passing (AMP) framework, reducing per-iteration complexity to $\mathcal{O}(NM)$. A robust variant is further proposed by incorporating an $\ell_2$-norm penalty, analogous to a linear minimum mean squared error (LMMSE) estimator, to enhance noise resilience. Simulation results under uncorrelated Rayleigh fading demonstrate that both proposed algorithms closely track their exact IDLS counterparts while avoiding the catastrophic failure of standard AMP in the overloaded regime, achieving steep bit error rate (BER) waterfall curves at a fraction of the computational cost.

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arXiv2026

1-bit Quantized Continuous Aperture Arrays

Continuous aperture arrays (CAPAs) have emerged as a promising physical-layer paradigm for sixth generation (6G) systems, offering spatial degrees of freedom beyond those of conventional discrete antenna arrays. This paper investigates the interaction between the CAPA receive architecture and low-cost 1-bit analog-to-digital converters (ADCs), which impose a severe nonlinear distortion penalty in conventional discrete systems. For Rayleigh fading, we derive a moment matching approximation (MMA)-based closed-form symbol error probability (SEP) approximation based on Gamma moment-matching of the spatial eigenvalue distribution, and show that CAPAs incur a diversity-order penalty governed by Jensen's inequality on the mode eigenvalues. For line-of-sight (LoS) propagation, we prove that CAPA achieves exactly the unquantized additive white Gaussian noise (AWGN) performance bound under perfect spatial and phase alignment, completely eliminating the 1-bit penalty that forces discrete systems to double their antenna count. Monte Carlo simulations under Rayleigh, Rician, and LoS conditions validate all analytical results.

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arXiv2026

Dual-Chirp AFDM for Joint Delay-Doppler Estimation with Rydberg Atomic Quantum Receivers

In this paper, we propose a joint delay-Doppler estimation framework for Rydberg atomic quantum receivers (RAQRs) leveraging affine frequency division multiplexing (AFDM), as a future enabler of hyper integrated sensing and communication (ISAC) in 6G and beyond. The proposed approach preserves the extreme sensitivity of RAQRs, while offering a pioneering solution to the joint estimation of delay-Doppler parameters of mobile targets, which has yet to be addressed in the literature due to the inherent coupling of time-frequency parameters in the optical readout of RAQRs to the best of our knowledge. To overcome this unavoidable ambiguity, we propose a dual-chirp AFDM framework where the utilization of distinct chirp parameters effectively converts the otherwise ambiguous estimation problem into a full-rank system, enabling unique delay-Doppler parameter extraction from RAQRs. Numerical simulations verify that the proposed dual-chirp AFDM shows superior delay-Doppler estimation performance compared to the classical single-chirp AFDM over RAQRs.

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arXiv2026

Environment-aware Near-field UE Tracking under Partial Blockage and Reflection

This paper proposes an environment-aware near-field (NF) user equipment (UE) tracking method for extremely large aperture arrays. By integrating known surface geometries and tracking the line-of-sight (LOS) and non-line-of-sight (NLOS) indicators per antenna element, the method captures partial blockages and reflections specific to the NF spherical-wavefront regime, which are unavailable under the conventional far-field (FF) assumption. The UE positions are tracked by maximizing the cosine similarity between the predicted and received channels, enabling tracking even under complete LOS obstruction. Simulation results confirm that increasing environment-awareness improves accuracy, and that NF consistently outperforms FF baselines, achieving a $0.22\,\mathrm{m}$ root-mean-square error with full environment-awareness.

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arXiv2026

Reciprocal Beyond-Diagonal Reconfigurable Intelligent Surface (BD-RIS): Scattering Matrix Design via Manifold Optimization

Beyond-diagonal reconfigurable intelligent surfaces (BD-RISs) are emerging as a transformative technology in wireless communications, enabling enhanced performance and quality of service (QoS) of wireless systems in harsh urban environments due to their relatively low cost and advanced signal processing capabilities. Generally, BD-RIS systems are employed to improve robustness, increase achievable rates, and enhance energy efficiency of wireless systems in both direct and indirect ways. The direct way is to produce a favorable propagation environment via the design of optimized scattering matrices, while the indirect way is to reap additional improvements via the design of multiple-input multiple-output (MIMO) beamformers that further exploit the latter "engineered" medium. In this article, the problem of sum-rate maximization via BD-RIS is examined, with a focus on feasibility, namely low-complexity physical implementation, by enforcing reciprocity in the BD-RIS design in a manner that adheres to the geometry of the manifold of symmetric matrices. To that end, the sum-rate objective is transformed into a quadratic function via fractional programming (FP), augmented via the also quadratic reciprocity constraint in the form of a regularization term, while the unitary constraint is dealt with via a manifold optimization framework. Simulation results demonstrate the effectiveness of the proposed method in outperforming current state-of-the-art (SotA) approaches in terms of sum-rate maximization.

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arXiv2026

Fundamental Limits of Rigid Body Localization

We consider a novel and general approach to easily compute the Cramér-Rao Lower Bounds (CRLBs) of rigid body localization (RBL) problem using arbitrary types of information. To that end, we adopt an information-centric construction of the Fisher information matrix (FIM), which allows capturing the contribution of each measurement towards the FIM explicitly, both in terms of input measurement types, as well as of their error distributions. Taking advantage of this approach, we derive a generic framework for evaluating the CRLB, which is applicable to arbitrary rigid body localization scenarios, and which, unlike the formulation of FIM commonly used in point-target localization, is better suited to RBL problems as it explicitly allows capturing the precision in both the translation vector and the rotation matrix (or alternative the rotation angles) of the rigid body, with respect to a reference. Examples of CRLBs obtained via the proposed approach are given in closed form, including the bound incorporating an orthonormality constraint onto the rotation matrix, which enables a straightforward adjustment of the derived bound when new measurements are added or removed. Numerical results illustrate that the derived expression correctly lower-bounds the errors of estimated localization parameters obtained via various related state-of-the-art (SotA) estimators, revealing their accuracies and suggesting that SotA RBL algorithms can still be improved.

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arXiv2026

Continuous-Time Analysis of AFDM: Pulse-Shaping, Fundamental Bounds and Impact of Hardware Impairments

Affine frequency division multiplexing (AFDM) has recently emerged as a resilient waveform candidate for high-mobility next-generation wireless systems. However, current literature mostly focuses on discrete time (DT) models, often overlooking effects and hardware non-idealities of actual continuous time (CT) signal generation. In this paper, we bridge this gap by developing a CT-analytical framework based on the affine Fourier series (AFS) representation, which allows us to demonstrate that strictly bandlimited pulses and subcarrier suppression strategies are essential to maintain the multicarrier structure of the transmitted signal. In addition, we derive the analytical power spectral density of AFDM and compare its spectral characteristics in comparison with those of other multicarrier schemes, taking into account the impact of realistic truncated pulse-shaping. Furthermore, we analyze the sensitivity of the CT model to phase noise, carrier frequency offset, and sampling jitter, providing a theoretical analysis of communication performance. Finally, we derive closed-form Cramér-Rao bounds for channel parameter estimation, showing that the chirped modulation peculiar of AFDM increases estimation variance but enables the resolution of Doppler ambiguities. Our findings lay the necessary theoretical and practical foundations for the implementation of AFDM in realistic wireless transceivers.

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