In this paper, we investigate the performance of wireless-powered communication (WPC) systems operating under the harvest-then-transmit protocol with a three-piecewise nonlinear energy harvesting (EH) model, which explicitly captures the sensitivity and saturation behavior of practical EH circuits. In parallel, we adopt a versatile fading characterization by assuming Nakagami-mchannels in the EH phase and generalized Gamma channels in the information transfer phase, the latter being of practical interest as it unifies several standard fading distributions and accurately approximates fading environments in next-generation networks. Based on this setting, we develop a novel analytical framework that yields, for the first time, exact analytical expressions for the average throughput in the delay-limited, delay-tolerant, and Quality of Service (QoS) delay-constrained transmission modes. We then apply the developed framework to study the throughput of WPC-enabled reconfigurable intelligent surface-assisted systems as a direct application example. We conduct extensive Monte-Carlo simulations under various system configurations to confirm the accuracy of the analytical results. Our results show that nonlinear EH effects yield a more realistic benchmark for the average throughput compared to the conventional linear EH model, particularly under stringent sensitivity and saturation constraints.

Thermal noise modulation (TNM) encodes information in thevarianceof Johnson noise, enabling ultra-low/zeropower operation with intrinsic physical-layer security. While recent demonstrations have focused on binary TNM, its twolevel alphabet fundamentally limits spectral efficiency. This paper develops anM-ary TNM (M-TNM) framework that maps symbols to multiple variance levels, thereby conveying (log2M) bits per channel use. We first establish a structural property of the optimal detector: for zero-mean Gaussian classes with ordered variances, the maximum-likelihood (ML) decision regions are contiguous in the energy statistic and each symbol’s boundaries depend only on its two adjacent variance levels. Leveraging this result, we derive closed-form expressions for theoptimalML detection thresholds and the associatedexactaverage symbol error rate (SER) forM-TNM. We then formulatevariance-constellation optimization—the selection and spacing of symbol variances—as a SER-minimization problem under practical constraints. Analytical results are validated via extensive Monte Carlo simulations, which show near-perfect agreement with theory and demonstrate substantial BES reductions for optimized constellations compared with naïve (e.g., uniformly spaced) variance levels. The proposedM-TNM framework thus improves spectral efficiency while preserving TNM’s energy and security promise, positioning variance-domain modulation as a viable physical-layer technique for bandwidth-limited, large-scale Internet of Things (IoT) deployments.

This work investigates the bit error probability (BEP) of thermal noise modulation (TNM) in Rayleigh fading channels. TNM has emerged as a promising ultra-low-power modulation scheme, where information is conveyed through variations in thermal noise variance. Existing studies provide only approximate BEP expressions and assume perfect channel state information (CSI) at the receiver. Furthermore, current detection strategies require per-symbol threshold adjustments, which, along with CSI estimation, introduce significant computational overhead for power-constrained devices. To address these limitations, we propose a fixed-threshold detection strategy that eliminates the need for channel estimation and threshold adaptation. Additionally, we derive an exact closed-form BEP expression for TNM under Rayleigh fading and formulate the problem of optimal threshold selection as an optimization task, solvable using efficient line-search techniques such as gradient descent. The accuracy of our analytical results is validated through Monte Carlo simulations, demonstrating strong agreement with theoretical predictions. These contributions provide a more precise characterization of TNM performance in fading environments, paving the way for its practical implementation in energy-efficient wireless systems.

This article presents a complete system model implementation of a digital communication system using Universal software radio peripheral (USRP) and Matlab. In particular, a software defined radio (SDR)-transceiver is implemented in Matlab and configured on the USRP, where several tests and measurements are performed. The deployed system allows investigating the impact of wireless channel impairments including amplitude attenuation, phase shift, time delay, and frequency offset on the overall system performance. As well, synchronization algorithms for symbol synchronization, carrier synchronization, and frame synchronization are presented and evaluated. Reported results reveal accurate implementation and robust design of variant digital communication systems. Results for 16, 64, and 256-quadrature amplitude modulation (QAM) are presented and discussed. As well, reported measurement for the bit error rate (BER) reveal accurate matching with theoretical results over Rician fading channel with a Rician K-factor of 10. Besides, the impact of the pulse shaping parameters and over sampling ratio on the overall system performance is illustrated and discussed.

This article proposes two low complexity decoders for non coherent differential space shift keying (DSSK) multiple input multiple output (MIMO) system by representing the detection problem as a hierarchical tree structure. Such presentation enables yielding the optimal maximum likelihood (ML) performance but with a massive reduction in computational complexity. Attained reduction is accomplished through 1) utilizing the error-repetitive property of DSSK detection to reduce the number of computed error terms and 2) developing two efficient searching strategies that avoid searching the entire alphabet as in ML detectors. These searching strategies enable a trade–off between performance and complexity. Unlike existing sparse recovery (SR) decoders, which require a projection phase to ensure the closure property (i.e., only single transmit antenna is active at each particular time instant and the multiplication of a constellation matrix with another one will produce a matrix from within the set), the new algorithms, inherently, maintain the closure property of DSSK system without additional procedures. Reported results reveal that optimal ML performance is obtained with a huge reduction in complexity by proper adjustment of parameters. It is also shown that the proposed algorithm performs better than state of the art SR algorithms, and the feasibility of massive MIMO configurations with low complexity is demonstrated.

Very recently, signed quadrature spatial modulation (sQSM) is developed as a competent technique that expands the spatial constellation diagram of QSM system by adding a bipolar dimension. Despite the enhanced spectral efficiency, the existing optimal maximum likelihood (ML) presents a serious computational challenge for large scale sQSM systems. Therefore, developing a reduced complexity detector for sQSM schemes is of significant importance to enable implementing and enjoying the inherent advantages of this promising system. Toward this end, a Tree Search (TS) optimal low complexity detector, calledTSopt, for sQSM Multiple Input Multiple Output (MIMO) system is proposed and analyzed in this paper. The proposed detector expands the computationally complex ML detector for sQSM into a tree-structure representation. The idea of the suggested algorithm is to employ an efficient searching strategy that can expeditiously find the branch corresponding to the minimum error without tracing the entire nodes as in the ML case. It is reported that the proposed TSopt algorithm achieves the exact error performance as ML detector but with substantial reduction in computational complexity. Besides, complexity analysis in terms of the number of visited nodes of the TSopt algorithm is analyzed and a closed-form expression for the expected complexity at high SNR values is derived. Reported results disclose agreement between simulation and expected analytical complexity with substantial gains of around 60–80% in complexity reduction for different system parameters.

The study of recurrent copy number variations (CNVs) plays an important role in understanding the onset and evolution of complex diseases such as cancer. Array-based comparative genomic hybridization (aCGH) is a widely used microarray based technology for identifying CNVs. However, due to high noise levels and inter-sample variability, detecting recurrent CNVs from aCGH data remains a challenging topic. This paper proposes a novel method for identification of the recurrent CNVs. In the proposed method, the noisy aCGH data is modeled as the superposition of three matrices: a full-rank matrix of weighted piece-wise generating signals accounting for the clean aCGH data, a Gaussian noise matrix to model the inherent experimentation errors and other sources of error, and a sparse matrix to capture the sparse inter-sample (sample-specific) variations. We demonstrated the ability of our method to separate accurately recurrent CNVs from sample-specific variations and noise in both simulated (artificial) data and real data. The proposed method produced more accurate results than current state-of-the-art methods used in recurrent CNV detection and exhibited robustness to noise and sample-specific variations.

Copy number variations (CNVs) play a role in the development of several diseases, including cancer. The detection or recurrent CNVs enables us to study the regions in which they occur and understand their contribution to the formation of disease. Microarray technologies, and Array-based comparative genomic hybridization (a C GH) in particular, have been widely used in the detection of CNVs. However, due to inter-sample variability and high noise levels, simple pattern detection methods experience significant challenges in recovering the recurrent CNV regions. In this work, we propose a new method for detecting recurrent CNV regions. To achieve this goal, we propose a matrix decomposition method in which the observed aCGH probe values are estimated using two elements: i) we use a full-rank matrix of weighted piece-wise generator signals to recover the recurrent CNVs. ii) We use a Gaussian matrix combined with a sparse matrix to capture the different types of noise and outlier values. We then evaluate the ability of our method to detect recurrent CNVs from several noisy simulated and real datasets. The results showed that our method is able to detect recurrent CNVs more accurately than current methods. Our method returned clean signals, exhibiting robustness to noise and outlier probe values.

BACKGROUND: Analyzing Variance heterogeneity in genome wide association studies (vGWAS) is an emerging approach for detecting genetic loci involved in gene-gene and gene-environment interactions. vGWAS analysis detects variability in phenotype values across genotypes, as opposed to typical GWAS analysis, which detects variations in the mean phenotype value. RESULTS: A handful of vGWAS analysis methods have been recently introduced in the literature. However, very little work has been done for evaluating these methods. To enable the development of better vGWAS analysis methods, this work presents the first quantitative vGWAS simulation procedure. To that end, we describe the mathematical framework and algorithm for generating quantitative vGWAS phenotype data from genotype profiles. Our simulation model accounts for both haploid and diploid genotypes under different modes of dominance. Our model is also able to simulate any number of genetic loci causing mean and variance heterogeneity. CONCLUSIONS: We demonstrate the utility of our simulation procedure through generating a variety of genetic loci types to evaluate common GWAS and vGWAS analysis methods. The results of this evaluation highlight the challenges current tools face in detecting GWAS and vGWAS loci.

BACKGROUND: Biomarker detection presents itself as a major means of translating biological data into clinical applications. Due to the recent advances in high throughput sequencing technologies, an increased number of metagenomics studies have suggested the dysbiosis in microbial communities as potential biomarker for certain diseases. The reproducibility of the results drawn from metagenomic data is crucial for clinical applications and to prevent incorrect biological conclusions. The variability in the sample size and the subjects participating in the experiments induce diversity, which may drastically change the outcome of biomarker detection algorithms. Therefore, a robust biomarker detection algorithm that ensures the consistency of the results irrespective of the natural diversity present in the samples is needed. RESULTS: Toward this end, this paper proposes a novel Regularized Low Rank-Sparse Decomposition (RegLRSD) algorithm. RegLRSD models the bacterial abundance data as a superposition between a sparse matrix and a low-rank matrix, which account for the differentially and non-differentially abundant microbes, respectively. Hence, the biomarker detection problem is cast as a matrix decomposition problem. In order to yield more consistent and solid biological conclusions, RegLRSD incorporates the prior knowledge that the irrelevant microbes do not exhibit significant variation between samples belonging to different phenotypes. Moreover, an efficient algorithm to extract the sparse matrix is proposed. Comprehensive comparisons of RegLRSD with the state-of-the-art algorithms on three realistic datasets are presented. The obtained results demonstrate that RegLRSD consistently outperforms the other algorithms in terms of reproducibility performance and provides a marker list with high classification accuracy. CONCLUSIONS: The proposed RegLRSD algorithm for biomarker detection provides high reproducibility and classification accuracy performance regardless of the dataset complexity and the number of selected biomarkers. This renders RegLRSD as a reliable and powerful tool for identifying potential metagenomic biomarkers.