pwc-archive/papers-with-abstracts huggingface dataset:
Software demodulation of weak radio signals using convolutional neural network
Authors: Mykola Kozlenko, Ihor Lazarovych, Valerii Tkachuk, Vira Vialkova
Date: 2025-02-26 00:00:00

In this paper we proposed the use of JT65A radio communication protocol for data exchange in wide-area monitoring systems in electric power systems. We investigated the software demodulation of the multiple frequency shift keying weak signals transmitted with JT65A communication protocol using deep convolutional neural network. We presented the demodulation performance in form of symbol and bit error rates. We focused on the interference immunity of the protocol over an additive white Gaussian noise with average signal-to-noise ratios in the range from -30 dB to 0 dB, which was obtained for the first time. We proved that the interference immunity is about 1.5 dB less than the theoretical limit of non-coherent demodulation of orthogonal MFSK signals.
Circulant ADMM-Net for Fast High-resolution DoA Estimation
Authors: Youval Klioui
Date: 2025-02-26 00:00:00
Tasks: Direction of Arrival Estimation

This paper introduces CADMM-Net and CHADMM-Net, two deep neural networks for direction of arrival estimation within the least-absolute shrinkage and selection operator (LASSO) framework. These two networks are based on a structured deep unfolding of the alternating direction method of multipliers (ADMM) algorithm through the use of circulant as well as Hermitian-circulant matrices. Along with a computational complexity of $\mathcal{O}(N\log(N))$ per layer for the inference, where $N$ is the length of the dictionary $\mathbf{A}$, they additionally exhibit a memory footprint of $N$ and approximately half of $N$ for CADMMNet and CHADMM-Net, respectively, compared with $N^{2}$ for ADMM-Net. Furthermore, these structured networks exhibit a competitive performance against ADMM-Net, LISTA, TLISTA, and THLISTA with respect to the detection rate, the angular root-mean square error, and the normalized mean squared error.
Robust Over-the-Air Computation with Type-Based Multiple Access
Authors: Marc Martinez-Gost, Ana Pérez-Neira, Miguel Ángel Lagunas
Date: 2025-02-26 00:00:00
Tasks: Federated Learning

This paper utilizes the properties of type-based multiple access (TBMA) to investigate its effectiveness as a robust approach for over-the-air computation (AirComp) in the presence of Byzantine attacks, this is, adversarial strategies where malicious nodes intentionally distort their transmissions to corrupt the aggregated result. Unlike classical direct aggregation (DA) AirComp, which aggregates data in the amplitude of the signals and are highly vulnerable to attacks, TBMA distributes data over multiple radio resources, enabling the receiver to construct a histogram representation of the transmitted data. This structure allows the integration of classical robust estimators and supports the computation of diverse functions beyond the arithmetic mean, which is not feasible with DA. Through extensive simulations, we demonstrate that robust TBMA significantly outperforms DA, maintaining high accuracy even under adversarial conditions, and showcases its applicability in federated learning (FEEL) scenarios. Additionally, TBMA reduces channel state information (CSI) requirements, lowers energy consumption, and enhances resiliency by leveraging the diversity of the transmitted data. These results establish TBMA as a scalable and robust solution for AirComp, paving the way for secure and efficient aggregation in next-generation networks.
KAN-powered large-target detection for automotive radar
Authors: Vinay Kulkarni, V. V. Reddy, Neha Maheshwari
Date: 2025-02-26 00:00:00

This paper presents a novel radar signal detection pipeline focused on detecting large targets such as cars and SUVs. Traditional methods, such as Ordered-Statistic Constant False Alarm Rate (OS-CFAR), commonly used in automotive radar, are designed for point or isotropic target models. These may not adequately capture the Range-Doppler (RD) scattering patterns of larger targets, especially in high-resolution radar systems. Additional modules such as association and tracking are necessary to refine and consolidate the detections over multiple dwells. To address these limitations, we propose a detection technique based on the probability density function (pdf) of RD segments, leveraging the Kolmogorov-Arnold neural network (KAN) to learn the data and generate interpretable symbolic expressions for binary hypotheses. Beside the Monte-Carlo study showing better performance for the proposed KAN expression over OS-CFAR, it is shown to exhibit a probability of detection (PD) of 96% when transfer learned with field data. The false alarm rate (PFA) is comparable with OS-CFAR designed with PFA = $10^{-6}$. Additionally, the study also examines impact of the number of pdf bins representing RD segment on performance of the KAN-based detection.
DFT-based Near-field Beam Alignment: Model-based and Data-Driven Hybrid Approach
Authors: Hongjun Heo, Wan Choi
Date: 2025-02-26 00:00:00

Accurate beam alignment is a critical challenge in XL-MIMO systems, especially in the near-field regime, where conventional far-field assumptions no longer hold. Although 2D grid-based codebooks in the polar domain are widely accepted for capturing near-field effects, they often suffer from high complexity and inefficiency in both time and computational resources. To address this issue, we propose a novel line-of-sight (LoS) near-field beam alignment scheme that leverages the discrete Fourier transform (DFT) matrix, which is commonly used in far-field environments. This approach ensures backward compatibility with the legacy DFT codebook for far-field signals by allowing its reuse. By introducing a new method to analyze the energy spread effect, we define the concept of an $\epsilon$-approximated signal subspace, spanned by DFT vectors that exhibit significant correlation with the near-field channel vector. Building on this analysis, the proposed hybrid scheme integrates model-based principles with data-driven techniques. Specifically, it utilizes the properties of the DFT matrix for efficient coarse alignment while employing a deep neural network (DNN)-aided fine alignment process. The fine alignment operates within the reduced search space defined by the coarse alignment stage, significantly enhancing accuracy while reducing complexity. Simulation results demonstrate that the proposed scheme achieves superior alignment performance while reducing both computational and model complexity compared to existing methods.
Music Therapy based Stress Prediction using Homological Feature Analysis on EEG Signals
Authors: Srikireddy Dhanunjay Reddy, Tharun Kumar Reddy Bollu
Date: 2025-02-26 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Topological Data Analysis

Stress became a common factor in the busy daily routines of all academic and corporate working environments. Everyone checks for efficient stress-buster alternatives to calm down from work pressure. Instead of investing time in unnecessary efforts, this work shows the stress relief scenario of subjects by listening to Raag Darbari music notes as a simple add-on to their schedule. An innovative approach has been implemented on the MUSEI-EEG dataset using Topological Data Analysis (TDA) to analyze this stress relief study. This study reveals that persistent homological features can be robust biomarkers for classifying closely distributed subject data. The proposed TDA approach framework revealed homological features like birth-death rate and entropy efficacy in stress prediction using Electroencephalogram (EEG) signals with 86% average accuracy and 0.2 standard deviation.
Towards ISAC RIS-Enabled Passive Radar Target Localization
Authors: Ahmad Bazzi, Marwa Chafii
Date: 2025-02-26 00:00:00
Tasks: Integrated sensing and communication, ISAC

Incorporating integrated sensing and communication capabilities into forthcoming 6G wireless networks is crucial for achieving seamless synchronization between the digital and physical worlds. The following paper focuses on a scenario where a passive radar (PR) is subject to weak line-of-sight signals of opportunity, emanating from an access point and subsequently reflecting off targets, ultimately reaching the PR. Furthermore, a normalized least mean squares method is presented for jointly detecting the number of targets and estimating target angles of arrival (AoAs). The algorithm iteratively adjusts the steering vector estimates to minimize a suitable error cost function, while the target AoAs are identified via a peak-finding search conducted on the resulted power spectrum. Simulation results show the capabilities of the proposed localization method, as well as a 14 dB dynamic range reduction that can be achieved at the PR.
MTCA: Multi-Task Channel Analysis for Wireless Communication
Authors: Jun Jiang, Wenjun Yu, Yuan Gao, Shugong Xu
Date: 2025-02-26 00:00:00
Tasks: Prediction

In modern wireless communication systems, the effective processing of Channel State Information (CSI) is crucial for enhancing communication quality and reliability. However, current methods often handle different tasks in isolation, thereby neglecting the synergies among various tasks and leading to extract CSI features inadequately for subsequent analysis. To address these limitations, this paper introduces a novel Multi-Task Channel Analysis framework named MTCA, aimed at improving the performance of wireless communication even sensing. MTCA is designed to handle four critical tasks, including channel prediction, antenna-domain channel extrapolation, channel identification, and scenario classification. Experiments conducted on a multi-scenario, multi-antenna dataset tailored for UAV-based communications demonstrate that the proposed MTCA exhibits superior comprehension of CSI, achieving enhanced performance across all evaluated tasks. Notably, MTCA reached 100% prediction accuracy in channel identification and scenario classification. Compared to the previous state-of-the-art methods, MTCA improved channel prediction performance by 20.1% and antenna-domain extrapolation performance by 54.5%.
DRL-Based Secure Spectrum-Reuse D2D Communications with RIS Assistance
Authors: Maryam Asadi Ahmadabadi, Farimehr Zohari, S. Mohammad Razavizadeh
Date: 2025-02-26 00:00:00
Tasks: Deep Reinforcement Learning

This study examines the secrecy performance of an uplink device-to-device (D2D) communication system enhanced by reconfigurable intelligent surfaces (RIS) while considering the presence of multiple eavesdroppers. RIS technology is employed to improve wireless communication environment by intelligently reflecting signals, thereby improving both capacity and security. We employ deep reinforcement learning (DRL) to optimize resource allocation dynamically, addressing challenges in D2D pairs and optimizing RIS positioning and phase shifts in a changing wireless environment. Our simulations demonstrate that the developed DRL-based framework significantly maximizes the sum secrecy capacity of both D2D and cellular communications, achieving higher transmission secrecy rates compared to existing benchmarks. The results highlight the effectiveness of integrating RIS with D2D communications for improved security performance.
Transfer Learning Assisted Fast Design Migration Over Technology Nodes: A Study on Transformer Matching Network
Authors: Chenhao Chu, Yuhao Mao, Hua Wang
Date: 2025-02-25 00:00:00
Tasks: Transfer Learning

In this study, we introduce an innovative methodology for the design of mm-Wave passive networks that leverages knowledge transfer from a pre-trained synthesis neural network (NN) model in one technology node and achieves swift and reliable design adaptation across different integrated circuit (IC) technologies, operating frequencies, and metal options. We prove this concept through simulation-based demonstrations focusing on the training and comparison of the coefficient of determination (R2) of synthesis NNs for 1:1 on-chip transformers in GlobalFoundries(GF) 22nm FDX+ (target domain), with and without transfer learning from a model trained in GF 45nm SOI (source domain). In the experiments, we explore varying target data densities of 0.5%, 1%, 5%, and 100% with a complete dataset of 0.33 million in GF 22FDX+, and for comparative analysis, apply source data densities of 25%, 50%, 75%, and 100% with a complete dataset of 2.5 million in GF 45SOI. With the source data only at 30GHz, the experiments span target data from two metal options in GF 22FDX+ at frequencies of 30 and 39 GHz. The results prove that the transfer learning with the source domain knowledge (GF 45SOI) can both accelerate the training process in the target domain (GF 22FDX+) and improve the R2 values compared to models without knowledge transfer. Furthermore, it is observed that a model trained with just 5% of target data and augmented by transfer learning achieves R2 values superior to a model trained with 20% of the data without transfer, validating the advantage seen from 1% to 5% data density. This demonstrates a notable reduction of 4X in the necessary dataset size highlighting the efficacy of utilizing transfer learning to mm-Wave passive network design. The PyTorch learning and testing code is publicly available at https://github.com/ChenhaoChu/RFIC-TL.
A predator-prey model with age-structured role reversal
Authors: Luis Suarez, Maria K. Cameron, William F. Fagan, Doron Levy
Date: 2025-02-27 00:00:00

We propose a predator-prey model with an age-structured predator population that exhibits a functional role reversal. The structure of the predator population in our model embodies the ecological concept of an "ontogenetic niche shift", in which a species' functional role changes as it grows. This structure adds complexity to our model but increases its biological relevance. The time evolution of the age-structured predator population is motivated by the Kermack-McKendrick Renewal Equation (KMRE). Unlike KMRE, the predator population's birth and death rate functions depend on the prey population's size. We establish the existence, uniqueness, and positivity of the solutions to the proposed model's initial value problem. The dynamical properties of the proposed model are investigated via Latin Hypercube Sampling in the 15-dimensional space of its parameters. Our Linear Discriminant Analysis suggests that the most influential parameters are the maturation age of the predator and the rate of consumption of juvenile predators by the prey. We carry out a detailed study of the long-term behavior of the proposed model as a function of these two parameters. In addition, we reduce the proposed age-structured model to ordinary and delayed differential equation (ODE and DDE) models. The comparison of the long-term behavior of the ODE, DDE, and the age-structured models with matching parameter settings shows that the age structure promotes the instability of the Coexistence Equilibrium and the emergence of the Coexistence Periodic Attractor.
Age Group Sensitivity Analysis of Epidemic Models: Investigating the Impact of Contact Matrix Structure
Authors: Zsolt Vizi, Evans Kiptoo Korir, Norbert Bogya, Csaba Rosztóczy, Géza Makay, Péter Boldog
Date: 2025-02-26 00:00:00
Tasks: Sensitivity

Understanding the role of different age groups in disease transmission is crucial for designing effective intervention strategies. A key parameter in age-structured epidemic models is the contact matrix, which defines the interaction structure between age groups. However, accurately estimating contact matrices is challenging, as different age groups respond differently to surveys and are accessible through different channels. This variability introduces significant epistemic uncertainty in epidemic models. In this study, we introduce the Age Group Sensitivity Analysis (AGSA) method, a novel framework for assessing the impact of age-structured contact patterns on epidemic outcomes. Our approach integrates age-stratified epidemic models with Latin Hypercube Sampling (LHS) and the Partial Rank Correlation Coefficient (PRCC) method, enabling a systematic sensitivity analysis of age-specific interactions. Additionally, we propose a new sensitivity aggregation technique that quantifies the contribution of each age group to key epidemic parameters. By identifying the age groups to which the model is most sensitive, AGSA helps pinpoint those that introduce the greatest epistemic uncertainty. This allows for targeted data collection efforts, focusing surveys and empirical studies on the most influential age groups to improve model accuracy. As a result, AGSA can enhance epidemic forecasting and inform the design of more effective and efficient public health interventions.
A Mathematical Model of the Cell Cycle: Exploring the Impact of Zingerone on Cancer Cell Proliferation
Authors: Roumen Anguelov, Micaela Goddard, Yvette Hlophe, Kganya Letsoalo, June Serem
Date: 2025-02-25 00:00:00

This paper presents a mathematical model that explores the interactions between Cyclin-Dependent Kinase 1 (CDK1) and the Anaphase-Promoting Complex (APC) in cancer cells. Through the analysis of a dynamical system simulating the CDK1-APC network, we investigate the system's behavior and its implications for cancer progression and potential therapeutic interventions. Our findings highlight the critical role of CDK1-APC interactions in regulating the cell cycle and examine the impact of Zingerone, a compound derived from ginger, on modulating the period of the oscillatory dynamics. These results provide new insights into the potential of Zingerone to influence cell proliferation and offer avenues for less harmful cancer treatments. The quantitative analysis is conducted by first theoretically deriving the cell viability as a function of time and Zingerone concentration, and then validating this function by using experimental data.
Modified FOX Optimizer for Solving optimization problems
Authors: Dler O. Hasan, Hardi M. Mohammed, Zrar Khalid Abdul
Date: 2025-02-27 00:00:00
Tasks: Position

The FOX optimizer, inspired by red fox hunting behavior, is a powerful algorithm for solving real-world and engineering problems. However, despite balancing exploration and exploitation, it can prematurely converge to local optima, as agent positions are updated solely based on the current best-known position, causing all agents to converge on one location. This study proposes the modified FOX optimizer (mFOX) to enhance exploration and balance exploration and exploitation in three steps. First, the Oppositional-Based Learning (OBL) strategy is used to improve the initial population. Second, control parameters are refined to achieve a better balance between exploration and exploitation. Third, a new update equation is introduced, allowing agents to adjust their positions relative to one another rather than relying solely on the best-known position. This approach improves exploration efficiency without adding complexity. The mFOX algorithm's performance is evaluated against 12 well-known algorithms on 23 classical benchmark functions, 10 CEC2019 functions, and 12 CEC2022 functions. It outperforms competitors in 74% of the classical benchmarks, 60% of the CEC2019 benchmarks, and 58% of the CEC2022 benchmarks. Additionally, mFOX effectively addresses four engineering problems. These results demonstrate mFOX's strong competitiveness in solving complex optimization tasks, including unimodal, constrained, and high-dimensional problems.
Physics-Informed Neural Networks for Optimal Vaccination Plan in SIR Epidemic Models
Authors: Minseok Kim, Yeongjong Kim, Yeoneung Kim
Date: 2025-02-27 00:00:00

This work focuses on understanding the minimum eradication time for the controlled Susceptible-Infectious-Recovered (SIR) model in the time-homogeneous setting, where the infection and recovery rates are constant. The eradication time is defined as the earliest time the infectious population drops below a given threshold and remains below it. For time-homogeneous models, the eradication time is well-defined due to the predictable dynamics of the infectious population, and optimal control strategies can be systematically studied. We utilize Physics-Informed Neural Networks (PINNs) to solve the partial differential equation (PDE) governing the eradication time and derive the corresponding optimal vaccination control. The PINN framework enables a mesh-free solution to the PDE by embedding the dynamics directly into the loss function of a deep neural network. We use a variable scaling method to ensure stable training of PINN and mathematically analyze that this method is effective in our setting. This approach provides an efficient computational alternative to traditional numerical methods, allowing for an approximation of the eradication time and the optimal control strategy. Through numerical experiments, we validate the effectiveness of the proposed method in computing the minimum eradication time and achieving optimal control. This work offers a novel application of PINNs to epidemic modeling, bridging mathematical theory and computational practice for time-homogeneous SIR models.
Inexact Moreau Envelope Lagrangian Method for Non-Convex Constrained Optimization under Local Error Bound Conditions on Constraint Functions
Authors: Yankun Huang, Qihang Lin, Yangyang Xu
Date: 2025-02-27 00:00:00

In this paper, we study the inexact Moreau envelope Lagrangian (iMELa) method for solving smooth non-convex optimization problems over a simple polytope with additional convex inequality constraints. By incorporating a proximal term into the traditional Lagrangian function, the iMELa method approximately solves a convex optimization subproblem over the polyhedral set at each main iteration. Under the assumption of a local error bound condition for subsets of the feasible set defined by subsets of the constraints, we establish that the iMELa method can find an $\epsilon$-Karush-Kuhn-Tucker point with $\tilde O(\epsilon^{-2})$ gradient oracle complexity.
LORENZA: Enhancing Generalization in Low-Rank Gradient LLM Training via Efficient Zeroth-Order Adaptive SAM
Authors: Yehonathan Refael, Iftach Arbel, Ofir Lindenbaum, Tom Tirer
Date: 2025-02-26 00:00:00
Tasks: parameter-efficient fine-tuning

We study robust parameter-efficient fine-tuning (PEFT) techniques designed to improve accuracy and generalization while operating within strict computational and memory hardware constraints, specifically focusing on large-language models (LLMs). Existing PEFT methods often lack robustness and fail to generalize effectively across diverse tasks, leading to suboptimal performance in real-world scenarios. To address this, we present a new highly computationally efficient framework called AdaZo-SAM, combining Adam and Sharpness-Aware Minimization (SAM) while requiring only a single-gradient computation in every iteration. This is achieved using a stochastic zeroth-order estimation to find SAM's ascent perturbation. We provide a convergence guarantee for AdaZo-SAM and show that it improves the generalization ability of state-of-the-art PEFT methods. Additionally, we design a low-rank gradient optimization method named LORENZA, which is a memory-efficient version of AdaZo-SAM. LORENZA utilizes a randomized SVD scheme to efficiently compute the subspace projection matrix and apply optimization steps onto the selected subspace. This technique enables full-parameter fine-tuning with adaptive low-rank gradient updates, achieving the same reduced memory consumption as gradient-low-rank-projection methods. We provide a convergence analysis of LORENZA and demonstrate its merits for pre-training and fine-tuning LLMs.