pwc-archive/papers-with-abstracts huggingface dataset:
Trellis Waveform Shaping for Sidelobe Reduction in Integrated Sensing and Communications: A Duality with PAPR Mitigation
Authors: Henglin Pu, Husheng Li, Zhu Han, H. Vincent Poor
Date: 2025-05-14 00:00:00
Tasks: ISAC

A key challenge in integrated sensing and communications (ISAC) is the synthesis of waveforms that can modulate communication messages and achieve good sensing performance simultaneously. In ISAC systems, standard communication waveforms can be adapted for sensing, as the sensing receiver (co-located with the transmitter) has knowledge of the communication message and consequently the waveform. However, the randomness of communications may result in waveforms that have high sidelobes masking weak targets. Thus, it is desirable to refine communication waveforms to improve the sensing performance by reducing the integrated sidelobe levels (ISL). This is similar to the peak-to-average power ratio (PAPR) mitigation in orthogonal frequency division multiplexing (OFDM), in which the OFDM-modulated waveform needs to be refined to reduce the PAPR. In this paper, inspired by PAPR reduction algorithms in OFDM, we employ trellis shaping in OFDM-based ISAC systems to refine waveforms for specific sensing metrics using convolutional codes and Viterbi decoding. In such a scheme, the communication data is encoded and then mapped to the signaling constellation in different subcarriers, such that the time-domain sidelobes are reduced. An interesting observation is that sidelobe reduction in OFDM-based ISAC is dual to PAPR reduction in OFDM, thereby sharing a similar signaling structure. Numerical simulations and hardware software defined radio USRP experiments are carried out to demonstrate the effectiveness of the proposed trellis shaping approach.
Sensing-Assisted Channel Prediction in Complex Wireless Environments: An LLM-Based Approach
Authors: Junjie He, Zixiang Ren, Jianping Yao, Han Hu, Tony Xiao Han, Jie Xu
Date: 2025-05-14 00:00:00
Tasks: Integrated sensing and communication, ISAC, Language Modeling, Language Modelling, Large Language Model, Prediction

This letter studies the sensing-assisted channel prediction for a multi-antenna orthogonal frequency division multiplexing (OFDM) system operating in realistic and complex wireless environments. In this system,an integrated sensing and communication (ISAC) transmitter leverages the mono-static sensing capability to facilitate the prediction of its bi-static communication channel, by exploiting the fact that the sensing and communication channels share the same physical environment involving shared scatterers. Specifically, we propose a novel large language model (LLM)-based channel prediction approach,which adapts pre-trained text-based LLM to handle the complex-matrix-form channel state information (CSI) data. This approach utilizes the LLM's strong ability to capture the intricate spatiotemporal relationships between the multi-path sensing and communication channels, and thus efficiently predicts upcoming communication CSI based on historical communication and sensing CSI data. Experimental results show that the proposed LLM-based approach significantly outperforms conventional deep learning-based methods and the benchmark scheme without sensing assistance.
Mainlobe Jamming Suppression Using MIMO-STCA Radar
Authors: Huake Wang, Bairui Cai, Guisheng Liao
Date: 2025-05-14 00:00:00

Radar jamming suppression, particularly against mainlobe jamming, has become a critical focus in modern radar systems. This article investigates advanced mainlobe jamming suppression techniques utilizing a novel multiple-input multiple-output space-time coding array (MIMO-STCA) radar. Extending the capabilities of traditional MIMO radar, the MIMO-STCA framework introduces additional degrees of freedom (DoFs) in the range domain through the utilization of transmit time delays, offering enhanced resilience against interference. One of the key challenges in mainlobe jamming scenarios is the difficulty in obtaining interference-plus-noise samples that are free from target signal contamination. To address this, the study introduces a cumulative sampling-based non-homogeneous sample selection (CS-NHSS) algorithm to remove target-contaminated samples, ensuring accurate interference-plus-noise covariance matrix estimation and effective noise subspace separation. Building on this, the subsequent step is to apply the proposed noise subspace-based jamming mitigation (NSJM) algorithm, which leverages the orthogonality between noise and jamming subspace for effective jamming mitigation. However, NSJM performance can degrade due to spatial frequency mismatches caused by DoA or range quantization errors. To overcome this limitation, the study further proposes the robust jamming mitigation via noise subspace (RJNS) algorithm, incorporating adaptive beampattern control to achieve a flat-top mainlobe and broadened nulls, enhancing both anti-jamming effectiveness and robustness under non-ideal conditions. Simulation results verify the effectiveness of the proposed algorithms. Significant improvements in mainlobe jamming suppression are demonstrated through transmit-receive beampattern analysis and enhanced signal-to-interference-plus-noise ratio (SINR) curve.
Performance Analysis of Fluid Antenna System Aided OTFS Satellite Communications
Authors: Halvin Yang, Mahsa Derakhshani, Sangarapillai Lambotharan, Lajos Hanzo
Date: 2025-05-13 00:00:00

Internet-of-Things (IoT) networks typically rely on satellite communications to provide coverage in rural areas. However, high-mobility satellite links introduce severe Doppler and delay spreads, which necessitate the use of orthogonal time frequency space (OTFS) modulation for reliable data transmission. Furthermore, the space and energy constraints on IoT devices make the perfect use case for fluid antenna systems (FAS) due to their mechanical simplicity. Hence, we propose a sophisticated FAS aided OTFS (FAS-OTFS) framework for satellite-based IoT networks. We derive analytical expressions for both the outage probability and ergodic capacity of FAS-OTFS under a general channel model, where the expressions derived are presented in integral form or as analytical bounds for efficient numerical evaluation. Additionally, we investigate a single-path fading scenario, where closed-form expressions are obtained. Our numerical results demonstrate significant performance gains in terms of both the outage probability and capacity compared to conventional OTFS systems, confirming the efficacy of FAS-OTFS in energy-constrained high-mobility environments. Our findings establish FAS-OTFS as a promising candidate for next-generation IoT communications over satellite links.
Bandlimited signal reconstruction from orthogonally-projected data
Authors: Nguyen T. Thao, Marek Miskowicz
Date: 2025-05-13 00:00:00

We show that a broad class of signal acquisition schemes can be interpreted as recording data from a signal $x$ in a space $\cal U$ (typically, though not exclusively, a space of bandlimited functions) via an orthogonal projection $w = P_{\cal V} x$ onto another space $\cal V$. A basic reconstruction method in this case consists in alternating projections between the input space $\cal U$ and the affine space $\cal W$ of signals $u$ satisfying $P_{\cal V} u = w$ (POCS method). Although this method is classically known to be slow, our work reveals new insights and contributions: (i) it applies to new complex encoders emerging from event-based sampling, for which no faster reconstruction method is currently available; (ii) beyond perfect reconstruction, it converges robustly under insufficient (e.g., sub-Nyquist) or inconsistent data (due to noise or errors); (iii) the limit of convergence achieves optimal least-squares approximations under such conditions; (iv) semi-convergence inherently results in regularized reconstructions under ill-posed data acquisition conditions; (v) when $w$ is produced by discrete sampling, the iterative method can be rigorously discretized for DSP implementation -- even with non-separable input spaces $\cal U$. While moving beyond the traditional focus on perfect reconstruction in harmonic analysis, our analysis preserves the deterministic framework of infinite-dimensional Hilbert spaces, consistent with Shannon's sampling theory. For illustration, we apply our proposed theory to two contrasting sampling situations: multi-channel time encoding and nonuniform point sampling in a Sobolev space.
Tri-Hybrid Multi-User Precoding Using Pattern-Reconfigurable Antennas: Fundamental Models and Practical Algorithms
Authors: Pinjun Zheng, Yuchen Zhang, Tareq Y. Al-Naffouri, Md. Jahangir Hossain, Anas Chaaban
Date: 2025-05-13 00:00:00

The integration of pattern-reconfigurable antennas into hybrid multiple-input multiple-output (MIMO) architectures presents a promising path toward high-efficiency and low-cost transceiver solutions. Pattern-reconfigurable antennas can dynamically steer per-antenna radiation patterns, enabling more efficient power utilization and interference suppression. In this work, we study a tri-hybrid MIMO architecture for multi-user communication that integrates digital, analog, and antenna-domain precoding using pattern-reconfigurable antennas. For characterizing the reconfigurability of antenna radiation patterns, we develop two models -- Model~I and Model~II. Model~I captures realistic hardware constraints through limited pattern selection, while Model~II explores the performance upper bound by assuming arbitrary pattern generation. Based on these models, we develop two corresponding tri-hybrid precoding algorithms grounded in the weighted minimum mean square error (WMMSE) framework, which alternately optimize the digital, analog, and antenna precoders under practical per-antenna power constraints. Realistic simulations conducted in ray-tracing generated environments are utilized to evaluate the proposed system and algorithms. The results demonstrate the significant potential of the considered tri-hybrid architecture in enhancing communication performance and hardware efficiency. However, they also reveal that the existing hardware is not yet capable of fully realizing these performance gains, underscoring the need for joint progress in antenna design and communication theory development.
DeepCPD: Deep Learning Based In-Car Child Presence Detection Using WiFi
Authors: Sakila S. Jayaweera, Beibei Wang, Wei-Hsiang Wang, K. J. Ray Liu
Date: 2025-05-13 00:00:00

Child presence detection (CPD) is a vital technology for vehicles to prevent heat-related fatalities or injuries by detecting the presence of a child left unattended. Regulatory agencies around the world are planning to mandate CPD systems in the near future. However, existing solutions have limitations in terms of accuracy, coverage, and additional device requirements. While WiFi-based solutions can overcome the limitations, existing approaches struggle to reliably distinguish between adult and child presence, leading to frequent false alarms, and are often sensitive to environmental variations. In this paper, we present DeepCPD, a novel deep learning framework designed for accurate child presence detection in smart vehicles. DeepCPD utilizes an environment-independent feature-the auto-correlation function (ACF) derived from WiFi channel state information (CSI)-to capture human-related signatures while mitigating environmental distortions. A Transformer-based architecture, followed by a multilayer perceptron (MLP), is employed to differentiate adults from children by modeling motion patterns and subtle body size differences. To address the limited availability of in-vehicle child and adult data, we introduce a two-stage learning strategy that significantly enhances model generalization. Extensive experiments conducted across more than 25 car models and over 500 hours of data collection demonstrate that DeepCPD achieves an overall accuracy of 92.86%, outperforming a CNN baseline by a substantial margin (79.55%). Additionally, the model attains a 91.45% detection rate for children while maintaining a low false alarm rate of 6.14%.
RDA-PSO: A computational method to quantify the diffusive dispersal of insects
Authors: Lidia Mrad, Joceline Lega
Date: 2025-05-13 00:00:00

This article introduces a computational method, called "Recapture of Diffusive Agents & Particle Swarm Optimization" (RDA-PSO), designed to estimate the dispersal parameter of diffusive insects in mark-release-recapture (MRR) field experiments. In addition to describing the method, its properties are discussed, with particular focus on robustness in estimating the observed diffusion coefficient. It is shown that RDA-PSO provides a simple and reliable approach to quantify mosquito dispersal that outperforms other techniques based on the solution of the diffusion equation, three of which are also introduced in this work. Examples of application to both synthetic and real field data for the yellow fever mosquito are provided.
Gatheral double stochastic volatility model with Skorokhod reflection
Authors: Yuliya Mishura, Andrey Pilipenko, Kostiantyn Ralchenko
Date: 2025-05-14 00:00:00

We investigate the Gatheral model of double mean-reverting stochastic volatility, in which the drift term itself follows a mean-reverting process, and the overall model exhibits mean-reverting behavior. We demonstrate that such processes can attain values arbitrarily close to zero and remain near zero for extended periods, making them practically and statistically indistinguishable from zero. To address this issue, we propose a modified model incorporating Skorokhod reflection, which preserves the model's flexibility while preventing volatility from approaching zero.
DeepPlantCRE: A Transformer-CNN Hybrid Framework for Plant Gene Expression Modeling and Cross-Species Generalization
Authors: Yingjun Wu, Jingyun Huang, Liang Ming, Pengcheng Deng, Maojun Wang, Zeyu Zhang
Date: 2025-05-15 00:00:00

The investigation of plant transcriptional regulation constitutes a fundamental basis for crop breeding, where cis-regulatory elements (CREs), as the key factor determining gene expression, have become the focus of crop genetic improvement research. Deep learning techniques, leveraging their exceptional capacity for high-dimensional feature extraction and nonlinear regulatory relationship modeling, have been extensively employed in this field. However, current methodologies present notable limitations: single CNN-based architectures struggle to capture long-range regulatory interactions, while existing CNN-Transformer hybrid models demonstrate proneness to overfitting and inadequate generalization in cross-species prediction contexts. To address these challenges, this study proposes DeepPlantCRE, a deep-learning framework for plant gene expression prediction and CRE Extraction. The model employs a Transformer-CNN hybrid architecture that achieves enhanced Accuracy, AUC-ROC, and F1-score metrics over existing baselines (DeepCRE and PhytoExpr), with improved generalization performance and overfitting inhibiting. Cross-species validation experiments conducted on gene expression datasets from \textit{Gossypium}, \textit{Arabidopsis thaliana}, \textit{Solanum lycopersicum}, \textit{Sorghum bicolor}, and \textit{Arabidopsis thaliana} reveal that the model achieves peak prediction accuracy of 92.3\%, particularly excelling in complex genomic data analysis. Furthermore, interpretability investigations using DeepLIFT and Transcription Factor Motif Discovery from the importance scores algorithm (TF-MoDISco) demonstrate that the derived motifs from our model exhibit high concordance with known transcription factor binding sites (TFBSs) such as MYR2, TSO1 in JASPAR plant database, substantiating the potential of biological interpretability and practical agricultural application of DeepPlantCRE.
Deep Learning and Explainable AI: New Pathways to Genetic Insights
Authors: Chenyu Wang, Chaoying Zuo, Zihan Su, Yuhang Xing, Lu Li, Maojun Wang, Zeyu Zhang
Date: 2025-05-15 00:00:00
Tasks: Deep Learning

Deep learning-based AI models have been extensively applied in genomics, achieving remarkable success across diverse applications. As these models gain prominence, there exists an urgent need for interpretability methods to establish trustworthiness in model-driven decisions. For genetic researchers, interpretable insights derived from these models hold significant value in providing novel perspectives for understanding biological processes. Current interpretability analyses in genomics predominantly rely on intuition and experience rather than rigorous theoretical foundations. In this review, we systematically categorize interpretability methods into input-based and model-based approaches, while critically evaluating their limitations through concrete biological application scenarios. Furthermore, we establish theoretical underpinnings to elucidate the origins of these constraints through formal mathematical demonstrations, aiming to assist genetic researchers in better understanding and designing models in the future. Finally, we provide feasible suggestions for future research on interpretability in the field of genetics.
KINDLE: Knowledge-Guided Distillation for Prior-Free Gene Regulatory Network Inference
Authors: Rui Peng, Yuchen Lu, Qichen Sun, Yuxing Lu, Chi Zhang, Ziru Liu, Jinzhuo Wang
Date: 2025-05-14 00:00:00

Gene regulatory network (GRN) inference serves as a cornerstone for deciphering cellular decision-making processes. Early approaches rely exclusively on gene expression data, thus their predictive power remain fundamentally constrained by the vast combinatorial space of potential gene-gene interactions. Subsequent methods integrate prior knowledge to mitigate this challenge by restricting the solution space to biologically plausible interactions. However, we argue that the effectiveness of these approaches is contingent upon the precision of prior information and the reduction in the search space will circumscribe the models' potential for novel biological discoveries. To address these limitations, we introduce KINDLE, a three-stage framework that decouples GRN inference from prior knowledge dependencies. KINDLE trains a teacher model that integrates prior knowledge with temporal gene expression dynamics and subsequently distills this encoded knowledge to a student model, enabling accurate GRN inference solely from expression data without access to any prior. KINDLE achieves state-of-the-art performance across four benchmark datasets. Notably, it successfully identifies key transcription factors governing mouse embryonic development and precisely characterizes their functional roles. In mouse hematopoietic stem cell data, KINDLE accurately predicts fate transition outcomes following knockout of two critical regulators (Gata1 and Spi1). These biological validations demonstrate our framework's dual capability in maintaining topological inference precision while preserving discovery potential for novel biological mechanisms.
An Exact Moment-Based Approach for Chemical Reaction-Diffusion Networks: From Mass Action to Hill Functions
Authors: Manuel Eduardo Hernández-García, Eduardo Moreno-Barbosa, Jorge Velázquez-Castro
Date: 2025-05-14 00:00:00

Biochemical systems are inherently stochastic, particularly those with small-molecule populations. The spatial distribution of molecules plays a critical role and requires the inclusion of spatial coordinates in their analysis. Stochastic models such as the chemical master equation are commonly used to study these systems. However, analytical solutions are limited to specific cases, and stochastic simulations require significant computational resources. To mitigate these challenges, approximation methods, such as the moment approach, reduce the system to a set of ordinary differential equations, thereby lowering the computational requirements. This study investigates the conditions under which the second-moment approach yields exact results during the dynamic evolution of chemical reaction-diffusion networks. The analysis encompasses second-order or higher-order reactions and Hill functions without relying on higher-order moment estimations or closure approximations. Starting with stationary states, we extended the analysis to a dynamic evolution. An enzymatic process and an antithetic feedback system were examined for purely reactive systems, demonstrating the approach's accuracy in capturing system behavior and quantifying errors. The study was further extended to genetic regulatory networks governed by Hill functions, including both purely reactive and reaction-diffusion systems, validating the method in spatially distributed contexts. This framework enables precise characterization of biochemical systems, avoiding information loss typically associated with approximations and allowing for stability analysis under fluctuations. These findings optimize computational strategies while providing insights into intracellular signaling and regulatory processes, paving the way for efficient and accurate stochastic modeling in biochemical systems.
Real-World fNIRS-Based Brain-Computer Interfaces: Benchmarking Deep Learning and Classical Models in Interactive Gaming
Authors: Mohammad Ghalavand, Javad Hatami, Seyed Kamaledin Setarehdan, Hananeh Ghalavand
Date: 2025-05-15 00:00:00
Tasks: Benchmarking, Data Augmentation, Feature Importance

Brain-Computer Interfaces enable direct communication between the brain and external systems, with functional Near-Infrared Spectroscopy emerging as a portable and non-invasive method for capturing cerebral hemodynamics. This study investigates the classification of rest and task states during a realistic, interactive tennis simulation using fNIRS signals and a range of machine learning approaches. We benchmarked traditional classifiers based on engineered features, Long Short-Term Memory networks on raw time-series data, and Convolutional Neural Networks applied to Gramian Angular Field-transformed images. Ensemble models like Extra Trees and Gradient Boosting achieved accuracies above 97 percent, while the ResNet-based CNN reached 95.0 percent accuracy with a near-perfect AUC of 99.2 percent, outperforming both LSTM and EfficientNet architectures. A novel data augmentation strategy was employed to equalize trial durations while preserving physiological integrity. Feature importance analyses revealed that both oxygenated and deoxygenated hemoglobin signals, particularly slope and RMS metrics, were key contributors to classification performance. These findings demonstrate the strong potential of fNIRS-based BCIs for deployment in dynamic, real-world environments and underscore the advantages of deep learning models in decoding complex neural signals.
Neural encoding of real world face perception
Authors: Arish Alreja, Michael J. Ward, Lisa S. Parker, R. Mark Richardson, Louis-Philippe Morency, Taylor J. Abel, Avniel Singh Ghuman
Date: 2025-05-13 00:00:00

Social perception unfolds as we freely interact with people around us. We investigated the neural basis of real world face perception using multi electrode intracranial recordings in humans during spontaneous interactions with friends, family, and others. Computational models reconstructed the faces participants looked at during natural interactions, including facial expressions and motion, from brain activity alone. The results highlighted a critical role for the social vision pathway, a network of areas spanning parietal, temporal, and occipital cortex. This network was more sharply tuned to subtle expressions compared to intense expressions, which was confirmed with controlled psychophysical experiments. These findings reveal that the human social vision pathway encodes facial expressions and motion as deviations from a neutral expression prototype during natural social interactions in real life.