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André L. F. de Almeida

Publications and source records attributed to André L. F. de Almeida.

At least 19 recordsLinked to original sources
arXiv2026

Time-Varying Parametric Channel Estimation With CP Decomposition Tensor Processing

Integrated sensing and communications (ISAC) is a key use case for sixth-generation (6G) wireless systems, where parametric channel estimation (PCE) plays a central role in enabling sensing, localization, and channel equalization in high-mobility scenarios. However, PCE is typically more computationally demanding than conventional channel estimation, which motivates the development of lower-complexity solutions. In this letter, we propose a fast PCE algorithm for time-varying and frequency-selective (TVFS) channels based on canonical polyadic (CP) decomposition and tensor processing, combined with ESPRIT-based initialization, component refinement, and exact line-search alternating coordinate descent. Two variants are presented: one for fully digital and another for hybrid receiver architectures. Numerical results show that the proposed method clearly outperforms a related CP-based baseline while achieving estimation performance close to a multiple-start SAGE benchmark at a substantially lower computational cost, with about one order of magnitude shorter execution time.

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arXiv2026

TRACE: A Trilinear Channel Estimation Framework for Tri-Hybrid Architectures with Reconfigurable Antennas

Tri-hybrid beamforming with reconfigurable antennas (RAs) adds an electromagnetic (EM)-domain degree of freedom to radio-frequency (RF) analog combining and baseband processing, but it also couples the EM angular response, the spatial steering matrix, and the path gains inside every received pilot. This letter develops TRACE, a trilinear channel estimation framework for the single-user uplink of a tri-hybrid receiver. Cycling the radiation pattern over the pilot dimension yields a third-order PARAFAC tensor of the received pilots, with factors corresponding to the EM-domain angular factor, the RF-projected steering factor, and the pilot-weighted fading factor. TRACE fits this tensor by alternating least squares and then recovers the physical channel parameters in closed form. Kruskal-based identifiability translates into design rules for the number of pilot slots, RF chains, and EM probing states. In a compressive configuration, TRACE attains the SNR slope of an oracle-support bound, staying within a factor of $1.4$ of it above $10$~dB, while outperforming a dictionary-based greedy baseline.

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arXiv2026

PARAFAC-Based Low-Latency Angular Tracking for RIS-Assisted Sensing

This paper proposes adaptive tensor tracking for angular trajectories (ATTRACT), a structure-aware alternating least squares algorithm for low-latency target tracking in reconfigurable intelligent surface (RIS)-assisted sensing. The received echo is represented as a dynamic third-order PARAFAC tensor that separates angular, delay, and Doppler information. Unlike batch tensor estimators, ATTRACT processes one data slice at a time and carries forward the preceding factor estimates, avoiding the need to collect an entire sensing window before updating the target state. Its distinguishing feature is the integration of delay and Doppler structure into each iterative update through the known transmitter-RIS channel, pilot signals, and RIS configurations. Numerical results show that, for a large enough RIS, ATTRACT achieves accuracy comparable to the offline tensor baseline at high signal-to-noise ratios while reducing computational overhead and providing slot-wise estimates.

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arXiv2026

TenSIM: Tensor-Based Channel Estimation for MIMO Systems with Stacked Intelligent Metasurfaces

Stacked intelligent metasurfaces (SIMs) are emerging as a promising architecture for sixth-generation (6G) and beyond wireless systems, enabling richer electromagnetic-wave manipulation than conventional single-layer metasurfaces. However, strong inter-layer coupling and multilinear parameter interactions make accurate, scalable channel estimation challenging. This paper proposes TenSIM, a tensor-based channel-estimation framework for SIM-assisted multiple-input multiple-output (MIMO) systems. By exploiting a structured SIM training protocol, TenSIM derives two parity-dependent observation models: a PARAllel FACtor (PARAFAC) model for odd-layer SIMs and a Tucker model for even-layer SIMs. These formulations decouple the transmitter-SIM and SIM-receiver channel factors while accounting for inter-layer wave coupling. Based on these tensor models, we develop alternating least squares estimators, establish rank-based identifiability conditions using the associated design matrices, and provide practical sufficient conditions for full-column-rank training designs, including scaling ambiguities. Numerical results reveal the main trade-offs. Both TenSIM-PARAFAC and TenSIM-Tucker improve with signal-to-noise ratio and training diversity, outperforming unstructured least-squares baselines by exploiting the tensor structure of the SIM cascade. TenSIM-PARAFAC offers better scalability, lower complexity, and stronger robustness to inter-layer spacing, whereas TenSIM-Tucker can achieve more accurate channel reconstruction when sufficient training and strong layer coupling are available. The framework also remains effective under imperfect or blind SIM training with additional pilot diversity. Overall, TenSIM offers a unified, physically interpretable approach to channel estimation in SIM-assisted MIMO systems, with explicit identifiability, complexity, and performance trade-offs.

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arXiv2026

Channel Estimation for Beyond Diagonal RIS Exploiting Core Tensor Sparsity

Beyond diagonal reconfigurable intelligent surfaces (BD-RISs) enhance wave manipulation through inter-element couplings but pose significant channel estimation challenges due to cascaded channels and block-Kronecker structures. This paper proposes a compressive sensing framework exploiting the sparse Tucker decomposition of the measurement tensor and the Kronecker rank-one structure of channel components. Two algorithms are developed: Sparse Tensor Orthogonal Recovery Method (STORM), which uses orthogonal matching pursuit (OMP) for greedy support recovery, and Sparse Tensor subspace- Aided Recovery (STAR), which leverages subspace-based projection to enhance robustness to noise. Both perform joint sparse support identification, followed by a Kronecker rank-one factorization via singular value decomposition (SVD) to recover the channel parameters. Simulations show that STAR achieves oracle-assisted least squares (LS) performance at moderate-to-high signal-to-noise ratio (SNR) with significantly fewer measurements than baseline methods, enabling practical BD-RIS deployment in next-generation millimeter wave (mmWave)/sub-terahertz (sub-THz) networks.

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arXiv2026

Semi-Blind Joint Channel and Symbol Estimation for Beyond Diagonal Reconfigurable Surfaces

The beyond-diagonal reconfigurable intelligent surface (BD-RIS) is a recent architecture in which scattering elements are interconnected to enhance the degrees of freedom for wave control, yielding performance gains over traditional single-connected RISs. For BD-RIS, channel estimation, which is well studied for conventional RIS, becomes more challenging due to complex connections and a larger number of coefficients. Prior works have relied on pilot-assisted estimation followed by data decoding. This paper introduces a semi-blind tensor-based approach to joint channel and symbol estimation that reduces the need for dedicated training sequences by directly leveraging data symbols. We consider a practical scenario with time-varying user terminal-RIS channels under mobility. By reformulating the received signal from a tensor decomposition perspective, we develop two semi-blind receivers: a two-stage method that transforms the fourth-order PARATUCK model into a third-order PARAFAC model, and a single-stage iterative process based on the fourth-order TUCKER decomposition. Identifiability conditions for reliable joint recovery are derived, and numerical results demonstrate the performance advantages and trade-offs of the proposed schemes over existing solutions.

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arXiv2026

Deconstructing the Composite Channel for Beyond Diagonal RIS: Channel Estimation and Beamforming Design

As beyond-diagonal reconfigurable intelligent surfaces (BD-RISs) gain increasing attention in high-frequency wireless communications, accurate and scalable channel-estimation methods become essential. This paper develops a parametric channel-estimation and beamforming framework that deconstructs the composite BD-RIS channel into its generating directional factors, revealing the tensor structure induced jointly by propagation geometry and beyond-diagonal scattering. We propose two tensor-based estimators: Fourth-Order Tucker Channel Estimation (FORTE), which models the partially structured channel as a fourth-order Tucker tensor, and Fourth-Order PARAFAC Channel Estimation (FORPE), which captures the fully structured channel through a fourth-order PARAFAC model. By exploiting partial and full channel geometry, the proposed methods achieve higher estimation accuracy than Least Squares and Block Tucker Kronecker Factorization benchmarks. In particular, FORTE outperforms FORPE due to its more compact representation, attaining an NMSE of about 10^{-4} at 5 dB SNR. In contrast, FORPE provides essentially unique estimates of the composite-channel factor matrices, whereas FORTE identifies their subspaces. The proposed deconstruction also provides a structured representation useful for sensing-oriented parameter extraction and tensor-structured system optimization. Finally, the Tensor Optimization Framework for Beamforming, Combining, and Scattering (TenFormer) achieves spectral efficiency comparable to the benchmark design while significantly reducing computational complexity through parallel tensor-structured optimization.

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arXiv2026

Dimming Space-Time Code (DSTC) for Visible Light Communication with Semi-Blind Detection

Visible light communication (VLC) provides a unified framework for wireless data transmission and illumination, but its practical deployment requires transmission schemes that jointly satisfy communication and lighting constraints. In color-shift keying (CSK) systems, dimming remains a challenging and underexplored problem because the average optical power must be controlled without altering the perceived chromaticity. This paper proposes a dimming space-time code (DSTC) for CSK-based VLC systems, where a structured dimming matrix introduces controlled temporal power variations while satisfying physical feasibility, color preservation, and identifiability conditions. Two receiver architectures are developed: a pilot-assisted zero-forcing (ZF) receiver and a tensor-based semi-blind PARAFAC receiver that jointly estimates the channel and transmitted symbols using only one training time slot. Simulation results show that the proposed DSTC provides diversity gains and substantial BER reductions with respect to conventional CSK, while the tensor-based receiver improves spectral efficiency by reducing training overhead, with particular benefits in large-scale MIMO configurations.

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arXiv2026

Channel Estimation for Movable Intelligent Surface

This paper proposes a tensor-based channel estimation framework for an uplink MIMO system assisted by a movable intelligent surface. The considered architecture combines a fixed transmissive metasurface with a smaller movable layer, whose discrete positions create an additional structured training dimension. By jointly exploiting fixed-layer phase patterns and movable-layer positions, the received pilots are modeled as a fourth-order PARAFAC tensor. A trilinear alternating least-squares receiver is then derived to estimate the individual channels and the position-dependent response. Importantly, the proposed method does not require prior knowledge of the movable-layer phase response at the receiver, since this unknown factor is estimated from the tensor structure of the received signal. Simulation results show that increasing the training length improves the NMSE of the estimated factors and the reconstructed cascaded channel.

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arXiv2026

Joint Channel and Symbol Estimation for RIS-Assisted Fluid Antenna Systems

This paper addresses joint channel and symbol estimation in reconfigurable intelligent surface (RIS)-aided multiuser uplink systems with fluid antennas (FAs) at the base station. We propose the Nested Tucker for Fluid Antenna Systems (NTFAS) protocol, in which FA port selection and user-dependent coding vary across blocks while the transmitted symbol matrix is shared across observations. This structure yields coupled Tucker models with common channel and data factors. A two-stage semi-blind bilinear alternating least squares (BALS) receiver is then developed to estimate the cascaded channel and symbols, and to separate the user-to-RIS and RIS-to-BS channels through the embedded PARAFAC structure. Simulations show that NTFAS improves cascaded-channel NMSE and spectral efficiency (SE) with respect to a competing semi-blind benchmark, while maintaining comparable BER performance.

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arXiv2026

Decoupled Delay-Doppler and Angle Estimation in BD-RIS Sensing via Nested Tucker Decomposition

We study single-target localization in a group-connected beyond-diagonal reconfigurable intelligent surface (BD-RIS)-assisted monostatic network with K element groups. We propose a Nested Tensor Factorization and Estimation (NTFE) algorithm that models the received signal as a 3rd-order nested Tucker tensor, decoupling the delay-Doppler and angle domains. The resulting two-stage procedure estimates the target-bearing tensor factors and then extracts the other physical parameters using subspace and closed-form steps. We also analyze identifiability and uniqueness conditions. Simulations show that NTFE exploits the group-connected BD-RIS structure and outperforms state-of-the-art sensing benchmarks.

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arXiv2026

Channel Estimation for Flexible Intelligent Metasurface Aided MIMO Communications

Flexible Intelligent Metasurfaces (FIMs) enable wireless systems to adapt their three-dimensional geometry through morphing, thereby providing new spatial degrees of freedom. However, continuous deformation complicates the accurate acquisition of Channel State Information (CSI). This work proposes a multidimensional framework for MIMO systems with active FIM arrays at both the transmitter and receiver. A split single-time-scale training protocol sequentially introduces spatial variation by morphing the receiver, then the transmitter. The resulting signal model is formulated as a PARAFAC decomposition, and an alternating least squares (ALS) algorithm is employed to estimate steering matrices and path gains. Our numerical results show that the proposed channel estimation method yields accurate CSI recovery for different system setups.

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arXiv2026

Low-Complexity Tensor-Based Monostatic Sensing for IRS-Assisted Communication Systems

This paper proposes a tensor-based parameter estimation algorithm for sensing in an intelligent reflecting surface-assisted system. We present a higher-order singular value decomposition-based solution that exploits the tensor structure of the received echo signal to jointly estimate the target's delay, Doppler, and angular information. Our tensor-based solution can estimate the parameters individually at low complexity, benefiting from parallel computation. Complexity analysis is carried out in comparison with a baseline scheme that does not exploit the intrinsic multilinear structure of the sensed signal. Simulation results show that our proposed tensor-based method can achieve the same performance as the reference method while drastically reducing the computational complexity.

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arXiv2026

PARAFAC-Based Time-Varying Channel Estimation for IRS-Aided Communications

This paper proposes a tensor-based parametric channel estimation technique for IRS-assisted communication systems with time-varying channel parameters. We exploit the multidimensional structure of the received signal by developing a $3$rd-order PARAFAC tensor model that is solved by employing the iteratively ALS algorithm. Our simulation results show that the proposed approach provides enhanced performance in terms of NMSE of the concatenated channel compared to the competing solutions by capitalizing on the intrinsic tensor structure of the received signal without increasing the computational complexity of the channel estimation.

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arXiv2026

Low-Complexity Tensor Beamforming for RIS-Aided Multiuser Multistream MIMO Systems

We address joint active and passive beamforming for uplink RIS-assisted multi-user multi-stream MIMO systems with joint detection. The coupled design of the receive combiner, block-diagonal user precoders, and RIS phase vector is formulated through a third-order composite channel tensor. Exploiting this multilinear structure, we propose a multi-stream tensor alternating optimization method that updates the combiner, user precoders, and RIS coefficients via low-dimensional tensor projections. Simulations show that the proposed method approaches a multi-start alternating-optimization benchmark while reducing computational complexity and improving large-RIS scaling.

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arXiv2026

Uplink Channel Estimation for Multi-User MISO Systems Assisted by a Fluid Reconfigurable Intelligent Surface

Fluid reconfigurable intelligent surfaces (FRISs) have recently emerged as a promising paradigm for wireless communications, wherein the reflecting elements can dynamically select their effective radiating positions from a dense preset grid, thereby introducing an additional degree of freedom. In contrast to conventional RIS architectures, FRISs can achieve spatial diversity with fewer physical elements. However, beyond the cascaded channel structure, FRIS-assisted systems are also affected by uncertainties arising from element-position mismatches caused by calibration inaccuracies or motion errors, which may degrade channel state information. To the best of our knowledge, channel estimation (CE) for FRIS-assisted systems under position uncertainty remains unexplored. To fill this gap, we propose a CE framework for a multi-user FRIS-assisted uplink system based on a two-time-scale FRIS configuration protocol that captures both reflection phase-shift and element-motion dynamics. By capitalizing on orthogonal pilot sequences and tensor modeling, we derive a closed-form solution that jointly estimates the individual channels and the motion-induced phase coefficients. Numerical results demonstrate notable performance in the presence of unknown position deviations.

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arXiv2026

Semi-Blind Receivers for RIS-Aided Fluid Antenna Systems

Reconfigurable intelligent surfaces (RISs) and fluid antennas (FAs) are key technologies for enhancing spatial degrees of freedom in future wireless networks. However, channel acquisition in RIS-aided FA systems is challenging as cascaded links depend on time-varying antenna-port selections and RIS configurations, leading to high training overhead in conventional pilot-based methods. We propose a semi-blind estimation framework for this joint architecture to estimate channels and symbols concurrently. Two hierarchical transmission protocols are introduced, resulting in distinct tensor models. Protocol 1 uses a two-time-scale structure yielding a PARAFAC (PF) model, while Protocol 2 employs a single-time-scale structure with blockwise spatial variations, leading to a Nested PARAFAC2 (NPF) model. For both, we develop semi-blind receivers based on trilinear alternating least squares to jointly estimate user-to-RIS channels, RIS-to-BS channels, and transmitted symbols by exploiting spatio-temporal diversity from FA and RIS reconfiguration. We derive identifiability conditions and computational complexity, revealing a fundamental trade-off: the PF receiver (Protocol 1) more aggressively exploits joint RIS/FA reconfiguration for stronger robustness, whereas the NPF receiver (Protocol 2) offers a flexible, lower-complexity alternative. Simulations show the proposed receivers achieve accurate recovery with significantly reduced training overhead, demonstrating the effectiveness of tensor-based semi-blind processing for RIS-aided fluid antenna communications.

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arXiv2026

Multi-Target Estimation via Tensor Decomposition for Beyond Diagonal RIS-Aided Bistatic Sensing

We investigate the performance of beyond-diagonal reconfigurable intelligent surfaces (BD-RIS) for bistatic MIMO multi-target sensing using a two-stage tensor Doppler-delay-angle estimation (TenDAE). The first stage solves a Kronecker sum approximation (KSA) with a rank equal to the number of targets. The second stage employs a nested tensor factorization estimation (NTFE) that exploits the inherent multidimensional structure via two tensor decompositions that are solved in parallel. The first employs a PARAFAC decomposition to extract the targets' angles, and the second uses a nested PARAFAC decomposition to find the targets' delay and Doppler parameters. This two-stage approach decouples acquisition of the angles and delays/Dopplers using either alternating least squares or a higher-order singular value decomposition, followed by a high-resolution subspace technique, such as ESPRIT. We further compare the performance of a BD-RIS with a classical diagonal RIS. For the latter, we solve a Khatri-Rao sum approximation problem rather than the KSA due to the specific structure of the received signal. Notably, our NTFE framework remains blind to the underlying RIS architecture while simultaneously estimating all targets with minimal sensing resources. Additionally, we show that employing a nested-PARAFAC decomposition enables the decoupling of the delay-Doppler and angle domains. We also derive the Cramér-Rao lower bound to further assess the performance of the TenDAE framework. Finally, we numerically evaluate the solutions presented in this paper and demonstrate their efficiency in terms of RMSE compared with state-of-the-art approaches.

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