arXiv subjects

Rodrigo C. de Lamare

Publications and source records attributed to Rodrigo C. de Lamare.

At least 19 recordsLinked to original sources

arXiv2026

Gaussian-trigonometric functional link artificial neural network: design and analysis

This paper proposes a Gaussian function-based trigonometric functional link artificial neural network (GTFLN) filter for linear-in-the-parameters nonlinear filtering. Compared with the adaptive exponential TFLN (AETFLN) filter, the GTFLN filter provides smooth and localized basis functions with reduced computational complexity, where modeling advantages are theoretically established through the smoothness, reproducing kernel Hilbert space, approximation error, and operator theory properties. To maximize the modeling performance, an optimized scaling parameter for the GTFLN filter is derived, yielding the optimized GTFLN (OGTFLN) filter. Least mean square (LMS) adaptation is applied to the GTFLN and OGTFLN filters for nonlinear system identification, resulting in the GTFLMS and OGTFLMS algorithms, respectively. Moreover, the theoretical steady-state excess mean-square error of the GTFLN filter is analyzed. Simulations validate the effectiveness of the theoretical analysis and demonstrate the improved performance of the GTFLN and OGTFLN filters over the linear-in-the-parameters benchmarks in nonlinear system identification and nonlinear acoustic echo cancellation. Based on the GTFLN filter, the filtered-g LMS (FgLMS) algorithm is proposed for nonlinear active noise control. Simulations demonstrate improved stability and noise reduction performance compared to the benchmarks.

eess.SP↗

arXiv2026

Study of Multiuser Scheduling Based on User Satisfaction for MU-MIMO Systems

Scheduling in multiuser multiple input multiple output (MU-MIMO) systems is essential for efficient resource allocation and overall performance enhancement. In this work, a multiuser scheduling problem is formulated to maximize the product of user equipments' (UEs) aggregate satisfactions, which maintains user fairness. Solving such a combinatorial problem using exhaustive search (EX), which requires evaluating all possible multiuser groups within a massive number of resource blocks (RBs), is prohibitive. Instead, we propose an efficient users' satisfaction based scheduling approach (US-SA). In our US-SA, a low dimension sub-grouping matrix is constructed {at each frame}, which is used to schedule the best multiuser group in each time slot; satisfied users are eliminated from the scheduling process. Our US-SA performs close to the optimal EX method in terms of satisfaction, transmitted data amount, spectral efficiency, latency, and fairness with lower computational cost. Moreover, our experiments demonstrate that the proposed scheme outperforms competing techniques.

cs.IT↗

arXiv2026

Study of Mixed-Integer Optimization Based on Graph-Based Decomposition for Cell-Free Networks

This letter develops a radio access network (RAN) framework for mixed discrete-continuous optimization problems that arise in user-centric cell=free massive multiple-antenna networks. The novel framework exploits the structural decomposition between discrete clustering decisions and continuous resource allocation variables by modeling the space of feasible serving states as a graph with Hamming-topology neighborhoods. A serving-state graph abstraction is introduced to enable topology-aware search-and-evaluate optimization procedures and a graph-based search-and-evaluate (GBSE) algorithm is devised along with their complexity analysis. Energy efficiency maximization at the RAN level is presented as an application of considered alongside the proposed framework and GBSE algorithm. Numerical results show that minimal Hamming neighborhoods offer an attractive trade-off between scalability and exploration capability in grap-based optimization and GBSE outperforms existing techniques.

cs.IT↗

arXiv2026

Study of Graph-Based Search for Energy-Efficient Clustering in Cell-Free Massive MIMO Networks

This paper investigates energy-efficient clustering in user-centric cell-free massive MIMO networks, addressing the access point clustering and power allocation problems via a mixed-integer fractional program. We propose a framework for energy-efficient clustering and power allocation with a graph-based structured search and describe its optimum solution via an exhaustive search. We also develop the Graph-Based Steepest Ascent (GBSA) algorithm, which combines a graph-based structured search along with continuous power allocation via fractional programming. The proposed GBSA algorithm achieves linear per-iteration complexity while reaching energy efficiency close to the global optimum, outperforming competing techniques and offering a scalable solution for future networks.

cs.IT↗

arXiv2026

Study of Code-Aided Channel Estimation for Metasurface-Based Holographic MIMO Systems

This work proposes an iterative code-aided detection, decoding, and channel estimation scheme for metasurface-based holographic MIMO systems employing stacked intelligent metasurfaces (SIM-RIS) and their fully connected counterparts (BD-SIM-RIS). A novel channel estimation strategy is developed by exploiting low-density parity-check (LDPC) coding in the uplink, enabling both pilot and parity bits of the encoded packet to contribute to the iterative refinement of the channel. In addition, closed-form expressions for the metasurface parameter design are derived and incorporated into an alternating-optimization (AO) procedure. Numerical results demonstrate substantial gains in normalized mean square error (NMSE) and bit error rate (BER), with particularly strong improvements observed for BD-SIM-RIS architectures.

cs.IT↗

arXiv2026

Channel Chart Location Privacy Based on Geo-Indistinguishability

Channel charting enables location-based services (LBSs) without requiring explicit position information by using pseudo-locations from the channel chart. While this property implies inherent privacy advantages, it does not provide formal privacy guarantees. In this work, we address location privacy in channel charting referred to as chart location indistinguishability (CLI), which extends geo-indistinguishability (GI) to channel charting representations. In order to achieve CLI, a standard planar Laplace mechanism is investigated and a geometry-aware Mahalanobis norm planar Laplace (MNPL) mechanism is devised. The proposed MNPL mechanism perturbs the channel chart by injecting noise aligned with the local structure of the chart. In the CLI framework with MNPL, privacy is defined in latent channel chart manifolds using locally adaptive covariance derived from chart neighborhoods, while preserving manifold topology under privacy constraints. In addition, differential privacy is considered as a privacy baseline. The proposed approach is evaluated across multiple channel charting schemes. The performance is assessed using utility metrics such as quality loss (QL) and range query error (RQE), as well as geometry-aware metrics including trustworthiness (TW) and continuity (CT). Numerical results demonstrate that the proposed privacy mechanism provides strong privacy guarantees while preserving the channel chart for LBSs tasks.

cs.CR↗

arXiv2026

Benchmarking Wireless Representations: High-Dimensional vs. Compressed Embeddings for Efficiency and Robustness

Building on recent advances in representation learning for wireless channels, this work investigates the cost-benefit trade-offs of high-dimensional channel embeddings in practical systems. We benchmark multiple wireless representations: high-dimensional learned embeddings from a wireless foundation model, compact autoencoder-based representations with significantly lower dimensionality, and raw data baselines, evaluating their performance across diverse downstream tasks. We then systematically analyze data efficiency, noise robustness, and computational complexity, explicitly characterizing the resource overhead associated with high-dimensional embeddings. Beyond standard tasks such as line-of-sight/non-line-of-sight (LoS/NLoS) classification and beam selection, we introduce power allocation as a new downstream task. Our results reveal clear trade-offs: while high-dimensional embeddings can perform well in few-shot regimes for certain tasks, they incur substantial latency and parameter overhead. In contrast, compressed latent representations learned by autoencoders demonstrate improved noise robustness and more stable performance across tasks, while significantly reducing computational and transmission costs.

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arXiv2026

Robust MMSE Precoding for Out-of-Cluster Interference Mitigation in Cell-Free MIMO Networks

In this work, we develop a linear robust minimum mean-square error (RMMSE) precoder to mitigate the effects of imperfect channel state information (CSI) and the intra-cluster (ICL) and out-of-cluster (OCL) interference in cell-free (CF) multiple-antenna systems. The proposed precoder includes statistical information of the OCL interference in its derivation, allowing a more effective interference mitigation. An analysis of the sum-rate that can be obtained by the CF system is carried out and an expression quantifying the theoretical gains of mitigating OCL interference are derived. Simulation results corroborate that the proposed RMMSE precoder effectively mitigates ICL and OCL interference.

cs.IT↗

arXiv2026

RHOSI: Efficient Anti-Jamming Resource Allocation with Holographic Surfaces in UAV-enabled ISAC

This paper investigates the susceptibility of Integrated Sensing and Communication (ISAC) systems to hostile jamming, focusing on an aerial Reconfigurable Holographic Surface (RHS)-aided unmanned aerial vehicle (UAV). The proposed framework, termed RHOSI, enhances ISAC's resilience by dynamically shaping the wireless propagation environment. Specifically, RHOSI introduces a strategy to improve jamming resistance by jointly optimizing transmit beamforming at the hybrid base station, RHS phase shift configuration, and UAV spatial deployment, while ensuring the required echo signal-to-interference-plus-noise ratios for reliable sensing. The resulting non-linear optimization problem features highly coupled variables, which are decomposed into sub-problems and solved using an alternating optimization (AO) approach. Simulation results confirm the practicality and effectiveness of RHOSI in significantly improving the throughput and robustness of ISAC under adversarial jamming.

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arXiv2026

Study of Robust Power Allocation for User-Centric Cell-Free Massive MIMO Networks

In cell-free massive multiple-input multiple-output (MIMO) networks, robust resource allocation is critical to ensure reliable system performance in the presence of channel uncertainties resulting from imperfect channel state information (CSI). In this work, we propose a robust power allocation method that formulates the power optimization problem into a least-squares framework, enhanced by Tikhonov regularization to mitigate the adverse effects of channel estimation errors. We integrate our approach with zero-forcing precoding, enabling a design that is both computationally efficient and resilient to CSI imperfections. Numerical results indicate that the proposed method outperforms existing non-robust techniques while benefiting from low computational overhead, making it well-suited for large-scale deployments under CSI uncertainty.

cs.IT↗

arXiv2026

Study of Switched Step-size Based Filtered-x NLMS Algorithm for Active Noise Cancellation

While the filtered-x normalized least mean square (FxNLMS) algorithm is widely applied due to its simple structure and easy implementation for active noise control system, it faces two critical limitations: the fixed step-size causes a trade-off between convergence rate and steady-state residual error, and its performance deteriorates significantly in impulsive noise environments. To address the step-size constraint issue, we propose the switched \mbox{step-size} FxNLMS (SSS-FxNLMS) algorithm. Specifically, we derive the \mbox{mean-square} deviation (MSD) trend of the FxNLMS algorithm, and then by comparing the MSD trends corresponding to different \mbox{step-sizes}, the optimal step-size for each iteration is selected. Furthermore, to enhance the algorithm's robustness in impulsive noise scenarios, we integrate a robust strategy into the SSS-FxNLMS algorithm, resulting in a robust variant of it. The effectiveness and superiority of the proposed algorithms has been confirmed through computer simulations in different noise scenarios.

cs.IT↗

arXiv2026

Study of Adaptive Reliability-Driven Conditional Innovation Decoding for LDPC Codes

In this work, we present an adaptive reliability-driven conditional innovation (AR-CID) decoding algorithm for low-density parity check (LDPC) codes. The proposed AR-CID decoding algorithm consists of one stage of message quality checking and another stage of message passing refinement, which are incorporated into a residual belief propagation decoding strategy. An analysis of the AR-CID decoding algorithm is carried out along with a study of its computational complexity and latency characteristics. Simulation results for several examples of LDPC codes, including short and medium-length codes over an extended range of channel conditions, indicate that the proposed AR-CID decoding algorithm outperforms competing decoding techniques and has an extremely fast convergence, making it particularly suitable for low-delay applications.

cs.IT↗

arXiv2025

Iterative Channel Estimation, Detection and Decoding for Multi-Antenna Systems with RIS

This work proposes an iterative channel estimation, detection and decoding (ICEDD) scheme for the uplink of multi-user multi-antenna systems assisted by multiple reconfigurable intelligent surfaces (RIS)}. A novel iterative code-aided channel estimation (ICCE) technique is developed that uses low-density parity-check (LDPC) codes and iterative processing to enhance estimation accuracy while reducing pilot overhead. The core idea is to exploit encoded pilots (EP), enabling the use of both pilot and parity bits to iteratively refine channel estimates. To further improve performance, an iterative channel tracking (ICT) method is proposed that takes advantage of the temporal correlation of the channel. An analytical evaluation of the proposed estimator is provided in terms of normalized mean-squared error (NMSE), along with a study of its computational complexity and the impact of the code rate. Numerical results validate the performance of the proposed scheme in a sub-6 GHz multi-RIS scenario with non-sparse propagation, under both LOS and NLOS conditions, and different RIS architectures.

cs.IT↗

arXiv2025

Direction Finding with Sparse Arrays Based on Variable Window Size Spatial Smoothing

In this work, we introduce a variable window size (VWS) spatial smoothing framework that enhances coarray-based direction of arrival (DOA) estimation for sparse linear arrays. By compressing the smoothing aperture, the proposed VWS Coarray MUSIC (VWS-CA-MUSIC) and VWS Coarray root-MUSIC (VWS-CA-rMUSIC) algorithms replace part of the perturbed rank-one outer products in the smoothed coarray data with unperturbed low-rank additional terms, increasing the separation between signal and noise subspaces, while preserving the signal subspace span. We also derive the bounds that guarantees identifiability, by limiting the values that can be assumed by the compression parameter. Simulations with sparse geometries reveal significant performance improvements and complexity savings relative to the fixed-window coarray MUSIC method.

cs.LG↗

arXiv2025

Channel State Information Preprocessing for CSI-based Physical-Layer Authentication Using Reconciliation

This paper introduces an adaptive preprocessing technique to enhance the accuracy of channel state information-based physical layer authentication (CSI-PLA) alleviating CSI variations and inconsistencies in the time domain. To this end, we develop an adaptive robust principal component analysis (A-RPCA) preprocessing method based on robust principal component analysis (RPCA). The performance evaluation is then conducted using a PLA framework based on information reconciliation, in which Gaussian approximation (GA) for Polar codes is leveraged for the design of short codelength Slepian Wolf decoders. Furthermore, an analysis of the proposed A-RPCA methods is carried out. Simulation results show that compared to a baseline scheme without preprocessing and without reconciliation, the proposed A-RPCA method substantially reduces the error probability after reconciliation and also substantially increases the detection probabilities that is also 1 in both line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. We have compared against state-of the-art preprocessing schemes in both synthetic and real datasets, including principal component analysis (PCA) and robust PCA, autoencoders and the recursive projected compressive sensing (ReProCS) framework and we have validated the superior performance of the proposed approach.

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arXiv2025

Robust Precoding for Resilient Cell-Free Networks

This paper presents a robust precoder design for resilient cell-free massive MIMO (CF-mMIMO) systems that minimizes the weighted sum of desired signal mean square error (MSE) and residual interference leakage power under a total transmit power constraint. The proposed robust precoder incorporates channel state information (CSI) error statistics to enhance resilience against CSI imperfections. We employ an alternating optimization algorithm initialized with a minimum MSE-type solution, which iteratively refines the precoder while maintaining low computational complexity and ensuring fast convergence. Numerical results show that the proposed method significantly outperforms conventional linear precoders, providing an effective balance between performance and computational efficiency.

cs.IT↗

arXiv2025

Study of Iterative Dynamic Channel Tracking for Multiple RIS-Assisted MIMO Systems

The use of multiple Reconfigurable Intelligent Sur- faces (RIS) has gained attention in 6G networks to enhance coverage. However, the feasibility of deploying multiple RIS relies on efficient channel estimation and reduced pilot overhead. To address these challenges, this work proposes an iterative channel estimation scheme that exploits low-density parity-check (LDPC) codes, channel coherence time, and iterative processing to improve estimation accuracy while minimizing pilot length. Encoded pilots are used to strengthen the iterative processing, leveraging both pilot and parity bits, while previous estimates are incorporated to further reduce overhead. Simulations consider a sub-6 GHz scenario with non-sparse channels and multiple RIS under both LOS and NLOS conditions. The results show that the proposed method outperforms existing approaches, achieving significant gains with substantially lower pilot overhead.

cs.IT↗

arXiv2025

Study of Cluster-Based Routing Based on Machine Learning for UAV Networks in 6G

The sixth generation (6G) wireless networks are envisioned to deliver ultra-low latency, massive connectivity, and high data rates, enabling advanced applications such as autonomous {unmaned aerial vehicles (UAV)} swarms and aerial edge computing. However, realizing this vision in Flying Ad Hoc Networks (FANETs) requires intelligent and adaptive clustering mechanisms to ensure efficient routing and resource utilization. This paper proposes a novel machine learning-driven framework for dynamic cluster formation and cluster head selection in 6G-enabled FANETs. The system leverages mobility prediction using {Extreme Gradient Boosting (XGBoost)} and a composite optimization strategy based on signal strength and spatial proximity to identify optimal cluster heads. To evaluate the proposed method, comprehensive simulations were conducted in both centralized (5G) and decentralized (6G) topologies using realistic video traffic patterns. Results show that the proposed model achieves significant improvements in delay, jitter, and throughput in decentralized scenarios. These findings demonstrate the potential of combining machine learning with clustering techniques to enhance scalability, stability, and performance in next-generation aerial networks.

cs.NI↗