arXiv subjects

Felipe A. P. de Figueiredo

Publications and source records attributed to Felipe A. P. de Figueiredo.

17 recordsLinked to original sources

arXiv2026

Trellis-Based Noise Modulation with Soft-Decision Viterbi Detection

Noise modulation conveys information through the statistical properties of noise rather than conventional deterministic signal parameters. This paper investigates a trellis-based noise modulation framework that exploits temporal dependencies between successive noise-power symbols. A binary filtering-based configuration is first developed as an illustrative finite-state model for comparing hard- and soft-decision sequence detection. A joint soft Viterbi receiver is then proposed, which directly incorporates the received noise energy into a likelihood-based branch metric, avoiding the information loss associated with intermediate hard decisions. The framework is further extended to an N-ary state-dependent trellis-based noise modulation scheme, where the transmitted noise-power level depends jointly on the current input symbol and trellis state. A Bhattacharyya-distance-based method is employed for systematic power-level design. Simulation results demonstrate the advantage of soft-decision sequence detection over hard-decision processing and investigate the effects of power-level design, traceback depth, energy-per-bit-to-noise ratio, symbol duration, and modulation order. The results also reveal the trade-off between spectral efficiency, detection reliability, and trellis complexity in higher-order noise modulation.

eess.SP↗

arXiv2026

Fluid Antenna Multiple Access for Noise Modulation

Noise modulation (NoiseMod) encodes information in the variance of a noise-like waveform. Its noncoherent structure enables low-complexity links, but co-channel users are challenging because interference affects the same variance statistic used for detection. We study fluid antenna multiple access (FAMA) for two-level NoiseMod with independent random bits from each interferer, producing two possible nonzero interference variances. We adopt exact finite-sample maximum-likelihood energy detection from prior NoiseMod/TNM work. We derive the exact single-port moment-generating function and moments of the random-bit aggregate interference, including heterogeneous user powers, and prove that the port minimizing conditional BEP is $\arg\max_k H_k/(σ_w^2+J_k)$, where $H_k$ is desired-link power and $J_k$ is instantaneous bit-state-aware interference variance. For independent fading ports, the common interferer-bit vector couples per-port decision metrics; conditioning on the number of high-state interferers yields an exact binomial-mixture order-statistic representation and avoids the generally invalid rule $F_{Z_{\max}}=F_Z^{N_p}$. For spatially correlated operation, an integer-$μ$ $κ$-$μ$ benchmark uses full pairwise Jakes correlation in scattered fields, with channel-averaged BEP evaluated by conditional Monte Carlo. A standard-error-aware load study shows increased admissible co-channel users as spatial degrees of freedom grow. Finally, a finite-sample variance-domain sensing diagnostic quantifies the oracle gap under frozen channels and interferer states, providing a lower-bound warning on fast-FAMA sensing difficulty rather than a deployable acquisition protocol.

cs.IT↗

arXiv2026

Fluid Antenna-Aided Noise Modulation: Spatial Diversity for Variance-Based Wireless Communication

Noise modulation (NoiseMod) encodes information in the \emph{variance} of a transmitted noise-like waveform rather than in its amplitude, phase, or frequency, and is attractive for ultra-low-power and covert links. Its main weakness is that, unlike classical modulation, it exhibits \emph{no} diversity under Rayleigh fading: its bit error probability (BEP) decays only as $1/(N_sδ)$, where $N_s$ is the number of noise samples per bit and $δ$ the useful-to-thermal noise variance ratio. Independently, fluid antenna systems (FAS) have been shown to recover substantial selection diversity from a single radiating element that switches among $N_p$ closely spaced ports, without extra radio-frequency chains. This paper combines the two: we equip a NoiseMod receiver with a fluid antenna and derive its average BEP. For idealized, mutually independent ports, we obtain an exact closed-form BEP via order statistics of the port envelopes. For the physically accurate, spatially correlated case governed by Jake's model, we build a semi-analytical BEP using the two-stage channel approximation of Khammassi \emph{et al.} We validate both regimes against full signal-level Monte Carlo simulation and show that (i) FAS restores a diversity order that grows with the number of ports $N_p$ when ports are weakly correlated, (ii) this gain saturates once the fluid-antenna aperture $Wλ$ is fixed and $N_p$ grows, mirroring the outage-probability saturation reported for FAS, now observed for BEP, and (iii) an intrinsic, correlation-independent (and $δ$-independent) BEP floor set only by $N_s$ and the variance ratio $α$ persists regardless of the antenna diversity order.

cs.IT↗

arXiv2026

On the Practical Performance of Noise Modulation for Ultra-Low-Power IoT: Limitations, Capacity, and Energy Trade-offs

Ultra-low-power (ULP) Internet of Things (IoT) applications demand communication architectures with minimal energy consumption. Noise Modulation (NoiseMod) addresses this by encoding data through the statistical variance of a noise-like signal, eliminating the need for a coherent carrier. To bridge the gap between theoretical potential and practical deployment, this paper benchmarks NoiseMod against standard modulations like BPSK and NC-FSK. We analytically derive the optimal detection threshold and Bit Error Rate (BER) for AWGN and Rayleigh fading channels. Our results show that non-coherent NoiseMod suffers a catastrophic error floor in fading environments, making architectural additions like channel state information (CSI) estimation and 2-antenna selection diversity desirable. Using an ADC-aware energy model, we reveal that NoiseMod's oversampling severely bottlenecks capacity and imposes an 8 dB SNR penalty compared to NC-FSK for a $10^{-3}$ BER in AWGN. Despite its oscillator-free design drastically reducing baseline circuit power, these limitations establish a critical energy crossover distance, which decreases with frequency. Below this distance, NoiseMod offers superior energy efficiency; beyond it, the radiated power needed to overcome its SNR penalty makes coherent schemes like BPSK vastly superior.

cs.IT↗

arXiv2026

Performance Limits of FRIS Systems in Nakagami-$m$ Fading

Fluid reconfigurable intelligent surfaces (FRIS) have recently emerged as a promising technology for enhancing wireless link reliability through spatial decorrelation. However, their performance analysis remains challenging due to the sum-product structure of the cascaded channel. This letter develops a rigorous analytical framework for FRIS-assisted wireless systems over arbitrarily correlated Nakagami-$m$ fading channels. Specifically, we introduce a physically consistent correlation model for Nakagami-$m$ fading and derive tractable statistical characterizations for the cascaded channel. These results lead to rigorous lower bounds for the outage probability (OP), with a simplified expression also obtained for the independent and identically distributed case. To the best of our knowledge, these are the first strict OP lower bounds reported for an FRIS-aided wireless system under arbitrarily correlated Nakagami-$m$ fading. CLT- and Gamma-based approximations are included as benchmark methods. Notably, the numerical results show that the proposed OP bound not only provides rigorous performance guarantees but also yields a noticeably tighter OP characterization than the CLT approximation in the high-SNR regime.

eess.SP↗

arXiv2026

Fast-TurboQuant: A Multiplier-Free Online Vector Quantization Approach

As large language models scale, memory bandwidth for key-value caches and retrieval-augmented generation systems becomes a critical bottleneck. While 1-bit quantization addresses this constraint, recent TurboQuant relies on dense random rotation matrices to condition the vector distribution before quantization. This projection demands millions of floating-point multiplications per embedding, making it difficult to deploy on constrained edge silicon. We introduce Fast-TurboQuant, a multiplier-free projection architecture that replaces the dense matrix with a structured fast Johnson-Lindenstrauss transform. By applying a Rademacher phase inversion followed by a fast Walsh-Hadamard transform (FWHT), the method leverages sub-Gaussian concentration to satisfy the prerequisites of scalar Lloyd-Max quantization without Gaussian projections. This substitution reduces the arithmetic complexity to only additions, eliminating hardware multipliers. Evaluation on DBpedia OpenAI-3 Large embeddings demonstrates a 19.7 times algorithmic speedup under sequential execution. Furthermore, the dimension expansion due to the FWHT zero-padding reduces the mean squared error and improves Recall@10.

cs.LG↗

arXiv2026

Evaluation of AutoML Frameworks for IDS under Imbalanced Data Conditions of the NSL-KDD Dataset

This work investigates the impact of severe class imbalance on the performance of automated machine learning (AutoML) frameworks for multiclass network intrusion detection using the NSL-KDD dataset. Unlike previous studies that simplify the problem through binary classification or minority-class removal, we preserve the original five-class distribution, including highly underrepresented attacks such as R2L and U2R, enabling a realistic evaluation of imbalance-sensitive learning behavior. Nine open-source AutoML frameworks were analyzed under a unified and reproducible experimental protocol, considering differences in architectural design, ensemble strategies, validation procedures, hyperparameter optimization, and imbalance-handling mechanisms. The results demonstrate that frameworks incorporating ensemble learning and imbalance-aware optimization achieve better minority-class discrimination. PyCaret obtained the best overall performance, reaching 66\% macro-F1, followed by AutoGluon with 55\%, whereas frameworks lacking native balancing support exhibited significant degradation in minority-class detection capability. The analysis further shows that accuracy-oriented optimization alone is insufficient for highly imbalanced IDS scenarios, since high-weighted metrics may coexist with poor generalization on rare attack categories. As a contribution, this work establishes a standardized benchmark for AutoML-based intrusion detection under severe multiclass imbalance, highlighting current architectural limitations and the need for native integration of imbalance-aware optimization, resampling, and stratified evaluation strategies into automated learning pipelines. The source code is publicly available.

cs.LG↗

arXiv2026

A Double Proportionate Sparse Adaptive Filter for Impulsive Noise Environments

Sparse adaptive filters and impulsive noise robust algorithms have largely been developed along separate tracks, leaving a gap when both properties are needed simultaneously. This letter proposes the double proportionate sparse adaptive filter (DP-SAF), which closes this gap within a single $\mathcal{O}(M)$ update. Two independent diagonal gain matrices are introduced; one scales the adaptation step proportionately to coefficient magnitudes, and the other applies a magnitude-dependent zero-attraction that is strongest for inactive taps. A sign-error update provides robustness against impulsive corruptions. Both gain matrices are derived from a minimum-norm optimization framework. Simulations under a Bernoulli impulsive noise model show that DP-SAF consistently achieves a better steady-state MSD than the competing algorithms while matching or exceeding their convergence speeds.

eess.SP↗

arXiv2026

Exact Statistical Characterization and Performance Analysis of Fluid Reconfigurable Intelligent Surfaces

Fluid reconfigurable intelligent surfaces (FRIS) extend conventional RIS architectures by enabling physical reconfiguration of element positions, thereby introducing a fundamentally new degree of freedom for controlling spatial correlation and improving link reliability. Despite this promise, rigorous performance analysis of FRIS-assisted wireless systems has remained challenging, as exact statistical analyses of the end-to-end cascaded channels have been unavailable. This paper addresses this gap by providing the first exact closed-form characterization of the end-to-end cascaded channel gain in FRIS-aided systems under general spatial correlation. By exploiting the spectral structure of the FRIS-induced correlation matrix, we show that the channel gain statistics can be represented as a finite linear combination of K-distributions. This unified formulation naturally captures fully correlated, effectively decorrelated, and intrinsically uncorrelated operating regimes as special cases. Building on the derived channel statistics, we further obtain exact closed-form expressions for the outage probability and ergodic capacity. We also conduct an outage-based asymptotic analysis, which reveals the true diversity order of the system. Numerical results corroborate the proposed analytical framework via Monte Carlo simulations, benchmark its accuracy against state-of-the-art approximation-based approaches, and demonstrate that fluidic reconfiguration can yield tangible reliability gains by reshaping the spatial correlation structure.

eess.SP↗

arXiv2026

Dual-Domain Sparse Adaptive Filtering: Exploiting Error Memory for Improved Performance

Many signal processing applications such as acoustic echo cancellation and wireless channel estimation require identifying systems where only a small fraction of coefficients are actually active, i.e. sparse systems. Zero-attracting adaptive filters tackle this by adding a penalty that pulls inactive coefficients toward zero, speeding up convergence. However, these algorithms determine which coefficients to penalize based solely on their current size. This creates a problem during early adaptation since active coefficients that should eventually grow large start out small, making them look identical to truly inactive coefficients. The algorithm ends up applying strong penalties to the very coefficients it needs to develop, slowing down the initial convergence. This paper provides a solution to this problem by introducing a dual-domain approach that looks at coefficients from two perspectives simultaneously. Beyond just tracking coefficient magnitude, we introduce an error-memory vector that monitors how persistently each coefficient contributes to the adaptation error over time. If a coefficient keeps showing up in the error signal, it is probably active even if it is still small. By combining both views, the proposed dual-domain sparse adaptive filter (DD-SAF) can identify active coefficients early and eliminate penalties accordingly. Moreover, complete theoretical analysis is derived. The analysis shows that DD-SAF maintains the same stability properties as standard least-mean-square (LMS) while achieves provably better steady-state performance than existing methods. Simulations demonstrate that the DD-SAF converges to the steady-state faster and/or convergences to a lower mean-square-deviation (MSD) than the standard LMS and the reweighted zero-attracting LMS (RZA-LMS) algorithms for sparse system identification settings.

eess.SP↗

arXiv2025

Optimal Bit Detection in Thermal Noise Communication Systems Under Rician Fading

Thermal noise communication (TNC) enables ultra-low-power wireless links for Internet of Things (IoT) devices by modulating the variance of thermal noise, rather than using active carriers. Existing analyses often rely on Gaussian approximations and overlook fading effects, which limits their accuracy. This paper presents an accurate analytical framework for optimal bit detection in TNC systems under Rician fading. Using chi-squared statistics, we derive the optimal maximum-likelihood detection threshold and an expression for the bit error probability (BEP) via Gauss-Laguerre quadrature. The proposed model eliminates approximation errors and accurately characterizes performance for finite sample sizes. Monte Carlo simulations confirm the analytical results and demonstrate significant improvements in BEP compared with suboptimal Gaussian-based detection. Furthermore, the influence of key parameters, sample size, resistance ratio, and Rician K-factor, is quantified. The proposed framework provides a solid foundation for designing energy-efficient TNC receivers in future B5G/6G and large-scale IoT systems.

eess.SP↗

arXiv2025

3D 8-Ary Noise Modulation Using Bayesian- and Kurtosis-based Detectors

This paper presents a novel three-dimensional (3D) 8-ary noise modulation scheme that introduces a new dimension: the mixture probability of a Mixture of Gaussian (MoG) distribution. This proposed approach utilizes the dimensions of mean and variance, in addition to the new probability dimension. Within this framework, each transmitted symbol carries three bits, each corresponding to a distinct sub-channel. For detection, a combination of specialized detectors is employed: a simple threshold based detector for the first sub-channel bit (modulated by the mean), a Maximum-Likelihood (ML) detector for the second sub-channel bit (modulated by the variance), a Kurtosis-based, Jarque-Bera (JB) test, and Bayesian Hypothesis (BHT)-based detectors for the third bit (modulated by the MoG probability). The Kurtosis- and JB-based detectors specifically distinguish between Gaussian (or near-Gaussian) and non-Gaussian MoG distributions by leveraging higher-order statistical measures. The Bit Error Probabilities (BEPs) are derived for the threshold-, Kurtosis-, and BHT-based detectors. The optimum threshold for the Kurtosis-based detector is also derived in a tractable manner. Simulation results demonstrate that a comparably low BEP is achieved for the third sub-channel bit relative to existing two-dimensional (2D) schemes. Simultaneously, the proposed scheme increases the data rate by a factor of 1.5 and 3 compared to the Generalized Quadratic noise modulator and the classical binary KLJN noise modulator, respectively. Furthermore, the Kurtosis-based detector offers a low-complexity solution, achieving an acceptable BEP of approximately 0.06.

eess.SP↗

arXiv2025

Composite Generalized Quadratic Noise Modulation via Signal Addition: Towards Higher Dimensional Noise Modulations

This letter proposes superposing two Generalized Quadratic Noise Modulators (GQNM) by simply adding their outputs. It creates a 16-ary noise modulator that resembles QAM modulators in classical communication. It modulates the information bits on four different means and four different variances. It could also be applied to reach higher-order modulations than 16-ary schemes by adding the outputs of more than two modulators, which is not discussed in detail in this letter and left for future work. By selecting the parameters necessary for satisfying the theoretical distinguishability conditions provided in the paper, we can reach better performances in comparison to the Kirchhoff-Law Johnson Noise (KLJN) modulator and the GQNM modulator, which is verified by the simulations. The better result in terms of smaller Bit Error Probability (BEP) is achieved by increasing the complexity in the modulator, the transmitter, and the detectors in the receiver.

eess.SP↗

arXiv2025

Resistor Hopping KLJN Noise Communication Using Small Bias Voltages Supported by ML and Optimum Threshold-Based Detectors

In this paper, a Resistor Hopping (RH) scheme with the addition of biases is proposed for secure Kirchhoff Law Johnson-Noise (KLJN) communication. The RH approach enables us to increase the bit rate of secure communication between Alice and Bob, while also ensuring that the inherent unconditional security of KLJN is satisfied. The biases are added to the proposed scheme to better distinguish between Gaussian distributed noises in terms of their means, rather than just using variances. Throughout the paper, we strive to minimize biases to achieve a power-efficient scheme. For the detection part of the proposed algorithm, a Maximum-Likelihood (ML) detector is derived. The separability condition of Gaussian distributions is investigated, along with the provision of a threshold-based detector that offers both simple and optimal thresholds in terms of minimizing the error probability. Some analysis of the proposed RH-KLJN communication scheme is provided, including Physical Layer Security (PLS) equations. Simulation results demonstrate the advantages of the proposed scheme over the classical KLJN scheme, offering a higher data rate and lower bit error probability at the expense of increased complexity.

eess.SP↗

arXiv2025

A Generalized Framework for Quadratic Noise Modulation Using Non-Gaussian Distributions

This letter generalizes noise modulation by introducing two voltage biases and employing non-Gaussian noise distributions, such as Mixture of Gaussian (MoG) and Laplacian, in addition to traditional Gaussian noise. The proposed framework doubles the data rate by enabling discrimination in both the mean and variance of transmitted noise symbols. This novel modulation scheme is referred to as Generalized Quadratic Noise Modulation (GQNM). Closed-form expressions for the Bit Error Probability (BEP) are derived for the Generalized Gaussian (GG) and Gaussian Mixture of Two Gaussians (GMoTG) cases. Simulation results demonstrate the advantages of the generalized modulation scheme, particularly under non-Gaussian noise assumptions, highlighting its potential for enhanced performance in low-power and secure communication systems.

eess.SP↗

arXiv2020

Secure Vehicular Communications through Reconfigurable Intelligent Surfaces

Reconfigurable intelligent surfaces (RIS) is considered as a revolutionary technique to improve the wireless system performance by reconfiguring the radio wave propagation environment artificially. Motivated by the potential of RIS in vehicular networks, we analyze the secrecy outage performance of RIS-aided vehicular communications in this paper. More specifically, two vehicular communication scenarios are considered, i.e., a vehicular-to-vehicular (V2V) communication where the RIS acts as a relay and a vehicular-to-infrastructure (V2I) scenario where the RIS functions as the receiver. In both scenarios, a passive eavesdropper is present attempting to retrieve the transmitted information. Closed-form expressions for the secrecy outage probability (SOP) are derived and verified. The results demonstrate the potential of improving secrecy with the aid of RIS under both V2V and V2I communications.

eess.SP↗

arXiv2015

Uplink Performance Evaluation of Massive MU-MIMO Systems

The present paper deals with an OFDM-based uplink within a multi-user MIMO (MU-MIMO) system where a massive MIMO approach is employed. In this context, the linear detectors Minimum Mean-Squared Error (MMSE), Zero Forcing (ZF) and Maximum Ratio Combining (MRC) are considered and assessed. This papers includes Bit Error Rate (BER) results for uncoded QPSK/OFDM transmissions through a flat Rayleigh fading channel under the assumption of perfect power control and channel estimation. BER results are obtained through Monte Carlo simulations. Performance results are discussed in detail and we confirm the achievable "massive MIMO" effects, even for a reduced complexity detection technique, when the number of receive antennas at BS is much larger than the number of transmit antennas.

cs.IT↗