pwc-archive/papers-with-abstracts huggingface dataset:
Millimeter-Wave Joint Radar and Communications With an RIS-Integrated Array
Authors: Steven Rivetti, Ozlem Tugfe Demir, Emil Bjornson, Mikael Skoglund
Date: 2025-04-13 00:00:00
Tasks: Position

In the context of the joint radar and communications (JRC) framework, reconfigurable intelligent surfaces (RISs) emerged as a promising technology for their ability to shape the propagation environment by adjusting their phase-shift coefficients. However, achieving perfect synchronization and effective collaboration between access points (APs) and RISs is crucial to successful operation. This paper investigates the performance of a bistatic JRC network operating in the millimeter-wave (mmWave) frequency band, where the receiving AP is equipped with an RIS-integrated array. This system simultaneously serves multiple UEs while estimating the position of a target with limited prior knowledge of its position. To achieve this, we optimize both the power allocation of the transmitted waveform and the RIS phase-shift matrix to minimize the position error bound (PEB) of the target. At the same time, we ensure that the UEs achieve an acceptable level of spectral efficiency. The numerical results show that an RIS-integrated array, even with a small number of receiving antennas, can achieve high localization accuracy. Additionally, optimized phase-shifts significantly improve the localization accuracy in comparison to a random phase-shift configuration.
A Hybrid Transmitting and Reflecting Beyond Diagonal Reconfigurable Intelligent Surface with Independent Beam Control and Power Splitting
Authors: Zhaoyang Ming, Shanpu Shen, Junhui Rao, Zan Li, Jichen Zhang, Chi Yuk Chiu, Ross Murch
Date: 2025-04-13 00:00:00
Tasks: Steering Control

A hybrid transmitting and reflecting beyond diagonal reconfigurable intelligent surface (BD-RIS) design is proposed. Operating in the same aperture, frequency band and polarization, the proposed BD-RIS features independent beam steering control of its reflected and transmitted waves. In addition it provides a hybrid mode with both reflected and transmitted waves using tunable power splitting between beams. The BD-RIS comprises two phase reconfigurable antenna arrays interconnected by an array of tunable two-port power splitters. The two-port power splitter in each BD-RIS cell is built upon a varactor in parallel with a bias inductor to exert tunable impedance variations on transmission lines. Provided with variable reverse DC voltages, the two-port power splitter can control the power ratio of S11 over S21 from -20 dB to 20 dB, thus allowing tunable power splitting. Each antenna is 2-bit phase reconfigurable with 200 MHz bandwidth at 2.4 GHz so that each cell of BD-RIS can also achieve independent reflection and transmission phase control. To characterize and optimize the electromagnetic response of the proposed BD-RIS design, a Th\'evenin equivalent model and corresponding analytical method is provided. A BD-RIS with 4 by 4 cells was also prototyped and tested. Experiments show that in reflection and transmission mode, the fabricated BD-RIS can realize beam steering in reflection and transmission space, respectively. It is also verified that when operating in hybrid mode, the BD-RIS enables independent beam steering of the reflected and transmitted waves. This work helps fill the gap between realizing practical hardware design and establishing an accurate physical model for the hybrid transmitting and reflecting BD-RIS, enabling hybrid transmitting and reflecting BD-RIS assisted wireless communications.
Deep Mismatch Channel Estimation in IRS based 6G Communication
Authors: Swapnil Saha, Md. Forkan Uddin
Date: 2025-04-13 00:00:00

We propose a channel estimation protocol to determine the uplink channel state information (CSI) at the base station for an intelligent reflecting surface (IRS) based wireless communication. More specifically, we develop a channel estimation scheme in a multi-user system with high estimation accuracy and low computational complexity. One of the state-of-the-art approaches to channel estimation is the deep learning-based approach. However, the data-driven model often experiences high computational complexity and, thus, is slow to channel estimation. Inspired by the success of utilizing domain knowledge to build effective data-driven models, the proposed scheme uses the high channel correlation property to train a shallow deep learning model. More specifically, utilizing the one coherent channel estimation, the model predicts the subsequent channel coherence CSI. We evaluate the performance of the proposed scheme in terms of normalized mean square error (NMSE) and spectral efficiency (SE) via simulation. The proposed scheme can estimate the CSI with reasonable success of lower NMSE, higher SE, and lower estimation time than existing schemes.
Wavefront Estimation From a Single Measurement: Uniqueness and Algorithms
Authors: Nicholas Chimitt, Ali Almuallem, Qi Guo, Stanley H. Chan
Date: 2025-04-13 00:00:00
Tasks: CPU

Wavefront estimation is an essential component of adaptive optics where the goal is to recover the underlying phase from its Fourier magnitude. While this may sound identical to classical phase retrieval, wavefront estimation faces more strict requirements regarding uniqueness as adaptive optics systems need a unique phase to compensate for the distorted wavefront. Existing real-time wavefront estimation methodologies are dominated by sensing via specialized optical hardware due to their high speed, but they often have a low spatial resolution. A computational method that can perform both fast and accurate wavefront estimation with a single measurement can improve resolution and bring new applications such as real-time passive wavefront estimation, opening the door to a new generation of medical and defense applications. In this paper, we tackle the wavefront estimation problem by observing that the non-uniqueness is related to the geometry of the pupil shape. By analyzing the source of ambiguities and breaking the symmetry, we present a joint optics-algorithm approach by co-designing the shape of the pupil and the reconstruction neural network. Using our proposed lightweight neural network, we demonstrate wavefront estimation of a phase of size $128\times 128$ at $5,200$ frames per second on a CPU computer, achieving an average Strehl ratio up to $0.98$ in the noiseless case. We additionally test our method on real measurements using a spatial light modulator. Code is available at https://pages.github.itap.purdue.edu/StanleyChanGroup/wavefront-estimation/.
Orthogonal Time-Frequency Space (OTFS) Aided Media-Based Modulation System For 6G and Beyond Wireless Communications Networks
Authors: Burak Ahmet Ozden, Murat Kaymaz, Erdogan Aydin, Emir Aslandogan, Haci Ilhan, Ertugrul Basar, Miaowen Wen, Marco Di Renzo
Date: 2025-04-12 00:00:00

This paper proposes a new orthogonal time frequency space (OTFS)-based index modulation system called OTFS-aided media-based modulation (MBM) scheme (OTFS-MBM), which is a promising technique for high-mobility wireless communication systems. The OTFS technique transforms information into the delay-Doppler domain, providing robustness against channel variations, while the MBM system utilizes controllable radio frequency (RF) mirrors to enhance spectral efficiency. The combination of these two techniques offers improved bit error rate (BER) performance compared to conventional OTFS and OTFS-based spatial modulation (OTFS-SM) systems. The proposed system is evaluated through Monte Carlo simulations over high-mobility Rayleigh channels for various system parameters. Comparative throughput, spectral efficiency, and energy efficiency analyses are presented, and it is shown that OTFS-MBM outperforms traditional OTFS and OTFS-SM techniques. The proposed OTFS-MBM scheme stands out as a viable solution for sixth generation (6G) and next-generation wireless networks, enabling reliable communication in dynamic wireless environments.
A New OTFS-Based Index Modulation System for 6G and Beyond: OTFS-Based Code Index Modulation
Authors: Burak Ahmet Ozden, Erdogan Aydin, Emir Aslandogan, Haci Ilhan, Ertugrul Basar, Miaowen Wen, Marco Di Renzo
Date: 2025-04-12 00:00:00

This paper proposes the orthogonal time frequency space-based code index modulation (OTFS-CIM) scheme, a novel wireless communication system that combines OTFS modulation, which enhances error performance in high-mobility Rayleigh channels, with CIM technique, which improves spectral and energy efficiency, within a single-input multiple-output (SIMO) architecture. The proposed system is evaluated through Monte Carlo simulations for various system parameters. Results show that increasing the modulation order degrades performance, while more receive antennas enhance it. Comparative analyses of error performance, throughput, spectral efficiency, and energy saving demonstrate that OTFS-CIM outperforms traditional OTFS and OTFS-based spatial modulation (OTFS-SM) systems. Also, the proposed OTFS-CIM system outperforms benchmark systems in many performance metrics under high-mobility scenarios, making it a strong candidate for sixth generation (6G) and beyond.
Graph-Based Prediction Models for Data Debiasing
Authors: Dongze Wu, Hanyang Jiang, Yao Xie
Date: 2025-04-12 00:00:00
Tasks: Decision Making, Prediction

Bias in data collection, arising from both under-reporting and over-reporting, poses significant challenges in critical applications such as healthcare and public safety. In this work, we introduce Graph-based Over- and Under-reporting Debiasing (GROUD), a novel graph-based optimization framework that debiases reported data by jointly estimating the true incident counts and the associated reporting bias probabilities. By modeling the bias as a smooth signal over a graph constructed from geophysical or feature-based similarities, our convex formulation not only ensures a unique solution but also comes with theoretical recovery guarantees under certain assumptions. We validate GROUD on both challenging simulated experiments and real-world datasets -- including Atlanta emergency calls and COVID-19 vaccine adverse event reports -- demonstrating its robustness and superior performance in accurately recovering debiased counts. This approach paves the way for more reliable downstream decision-making in systems affected by reporting irregularities.
Macroscale Molecular Communication in IoT-based Pipeline Inspection and Monitoring Applications: Preliminary Experiment and Mathematical Model
Authors: Muneer M. Al-Zubi, Mohamed-Slim Alouini
Date: 2025-04-12 00:00:00

Today, pipeline networks serve as critical infrastructure for transporting materials such as water, gas, and oil. Modern technologies such as the Internet of Things (IoT), sensor nodes, and inspection robots enable efficient pipeline monitoring and inspection. They can help detect and monitor various conditions and defects in pipelines such as cracks, corrosion, leakage, pressure, flow, and temperature. Since most pipelines are buried underground, wireless communication links suffer from significant attenuation and noise due to harsh environmental conditions. In such systems, communication links are required between the sensor nodes as well as between the external control/monitoring unit or sensor node and the inspection robot inside the pipeline. In this paper, we propose a macroscale molecular communication (MC) system in the IoT-based pipeline inspection and monitoring networks to address this challenge. We develop a mathematical model and implement a preliminary experimental testbed to validate the system and demonstrate its feasibility by transmitting and reconstructing binary sequences using volatile organic compound (VOC) as an information signal. We examined the impact of various system parameters including airflow carrier velocity, released VOC velocity, emission duration, and bit duration. Results indicate that these parameters significantly influence the received molecular signal, emphasizing the need for optimal configuration. This work serves as a preliminary step for further research on the application of MC in IoT-based pipeline inspection and monitoring systems.
Channel Estimation for mmWave Pinching-Antenna Systems
Authors: Gui Zhou, Vasilis Papanikolaou, Zhiguo Ding, Robert Schober
Date: 2025-04-12 00:00:00

The full potential of pinching-antenna systems (PAS) can be unblocked if pinching antennas can be accurately activated at positions tailored for the serving users', which means that acquiring accurate channel state information (CSI) at arbitrary positions along the waveguide is essential for the precise placement of antennas. In this work, we propose an innovative channel estimation scheme for millimeter-wave (mmWave) PAS. The proposed approach requires activating only a small number of pinching antennas, thereby limiting antenna switching and pilot overhead. Specifically, a base station (BS) equipped with a waveguide selectively activates subarrays located near and far from the feed point, each comprising a small number of pinching antennas. This configuration effectively emulates a large-aperture array, enabling high-accuracy estimation of multipath propagation parameters, including angles, delays, and path gains. Simulation results demonstrate that the proposed method achieves accurate CSI estimation and data rates while effectively reducing hardware switching and pilot overhead.
Complexity-Scalable Near-Optimal Transceiver Design for Massive MIMO-BICM Systems
Authors: Jie Yang, Wanchen Hu, Yi Jiang, Shuangyang Li, Xin Wang, Derrick Wing Kwan Ng, Giuseppe Caire
Date: 2025-04-12 00:00:00

Future wireless networks are envisioned to employ multiple-input multiple-output (MIMO) transmissions with large array sizes, and therefore, the adoption of complexity-scalable transceiver becomes important. In this paper, we propose a novel complexity-scalable transceiver design for MIMO systems exploiting bit-interleaved coded modulation (termed MIMO-BICM systems). The proposed scheme leverages the channel bidiagonalization decomposition (CBD), based on which an optimization framework for the precoder and post-processor is developed for maximizing the mutual information (MI) with finite-alphabet inputs. Particularly, we unveil that the desired precoder and post-processor behave distinctively with respect to the operating signal-to-noise ratio (SNR), where the equivalent channel condition number (ECCN) serves as an effective indicator for the overall achievable rate performance. Specifically, at low SNRs, diagonal transmission with a large ECCN is advantageous, while at high SNRs, uniform subchannel gains with a small ECCN are preferred. This allows us to further propose a low-complexity generalized parallel CBD design (GP-CBD) based on Givens rotation according to a well-approximated closed-form performance metric on the achievable rates that takes into account the insights from the ECCN. Numerical results validate the superior performance of the proposed scheme in terms of achievable rate and bit error rate (BER), compared to state-of-the-art designs across various modulation and coding schemes (MCSs).
Accurate Diagnosis of Respiratory Viruses Using an Explainable Machine Learning with Mid-Infrared Biomolecular Fingerprinting of Nasopharyngeal Secretions
Authors: Wenwen Zhang, Zhouzhuo Tang, Yingmei Feng, Xia Yu, Qi Jie Wang, Zhiping Lin
Date: 2025-04-12 00:00:00
Tasks: Diagnostic, Specificity

Accurate identification of respiratory viruses (RVs) is critical for outbreak control and public health. This study presents a diagnostic system that combines Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR) from nasopharyngeal secretions with an explainable Rotary Position Embedding-Sparse Attention Transformer (RoPE-SAT) model to accurately identify multiple RVs within 10 minutes. Spectral data (4000-00 cm-1) were collected, and the bio-fingerprint region (1800-900 cm-1) was employed for analysis. Standard normal variate (SNV) normalization and second-order derivation were applied to reduce scattering and baseline drift. Gradient-weighted class activation mapping (Grad-CAM) was employed to generate saliency maps, highlighting spectral regions most relevant to classification and enhancing the interpretability of model outputs. Two independent cohorts from Beijing Youan Hospital, processed with different viral transport media (VTMs) and drying methods, were evaluated, with one including influenza B, SARS-CoV-2, and healthy controls, and the other including mycoplasma, SARS-CoV-2, and healthy controls. The model achieved sensitivity and specificity above 94.40% across both cohorts. By correlating model-selected infrared regions with known biomolecular signatures, we verified that the system effectively recognizes virus-specific spectral fingerprints, including lipids, Amide I, Amide II, Amide III, nucleic acids, and carbohydrates, and leverages their weighted contributions for accurate classification.
Hybrid Beamforming for RIS-Assisted Multiuser Fluid Antenna Systems
Authors: Jiangong Chen, Yue Xiao, Zhendong Peng, Jing Zhu, Xia Lei, Christos Masouros, Kai-Kit Wong
Date: 2025-04-12 00:00:00
Tasks: Position

Recent advances in reconfigurable antennas have led to the new concept of the fluid antenna system (FAS) for shape and position flexibility, as another degree of freedom for wireless communication enhancement. This paper explores the integration of a transmit FAS array for hybrid beamforming (HBF) into a reconfigurable intelligent surface (RIS)-assisted communication architecture for multiuser communications in the downlink, corresponding to the downlink RIS-assisted multiuser multiple-input single-output (MISO) FAS model (Tx RIS-assisted-MISO-FAS). By considering Rician channel fading, we formulate a sum-rate maximization optimization problem to alternately optimize the HBF matrix, the RIS phase-shift matrix, and the FAS position. Due to the strong coupling of multiple optimization variables, the multi-fractional summation in the sum-rate expression, the modulus-1 limitation of analog phase shifters and RIS, and the antenna position variables appearing in the exponent, this problem is highly non-convex, which is addressed through the block coordinate descent (BCD) framework in conjunction with semidefinite relaxation (SDR) and majorization-minimization (MM) methods. To reduce the computational complexity, we then propose a low-complexity grating-lobe (GL)-based telescopic-FAS (TFA) with multiple delicately deployed RISs under the sub-connected HBF architecture and the line-of-sight (LoS)-dominant channel condition, to allow closed-form solutions for the HBF and TFA position. Our simulation results illustrate that the former optimization scheme significantly enhances the achievable rate of the proposed system, while the GL-based TFA scheme also provides a considerable gain over conventional fixed-position antenna (FPA) systems, requiring statistical channel state information (CSI) only and with low computational complexity.
Self-Supervised Autoencoder Network for Robust Heart Rate Extraction from Noisy Photoplethysmogram: Applying Blind Source Separation to Biosignal Analysis
Authors: Matthew B. Webster, Dongheon Lee, Joonnyong Lee
Date: 2025-04-12 00:00:00
Tasks: blind source separation

Biosignals can be viewed as mixtures measuring particular physiological events, and blind source separation (BSS) aims to extract underlying source signals from mixtures. This paper proposes a self-supervised multi-encoder autoencoder (MEAE) to separate heartbeat-related source signals from photoplethysmogram (PPG), enhancing heart rate (HR) detection in noisy PPG data. The MEAE is trained on PPG signals from a large open polysomnography database without any pre-processing or data selection. The trained network is then applied to a noisy PPG dataset collected during the daily activities of nine subjects. The extracted heartbeat-related source signal significantly improves HR detection as compared to the original PPG. The absence of pre-processing and the self-supervised nature of the proposed method, combined with its strong performance, highlight the potential of MEAE for BSS in biosignal analysis.
Ray-Based Characterization of the AMPLE Model from 0.85 to 5 GHz
Authors: Lingyou Zhou, Xin Dong, Kehai Qiu, Gang Yu, Jie Zhang, Jiliang Zhang
Date: 2025-04-12 00:00:00
Tasks: Prediction

In this paper, we characterize the adaptive multiple path loss exponent (AMPLE) radio propagation model under urban macrocell (UMa) and urban microcell (UMi) scenarios from 0.85-5 GHz using Ranplan Professional. We first enhance the original AMPLE model by introducing an additional frequency coefficient to support path loss prediction across multiple carrier frequencies. By using measurement-validated Ranplan Professional simulator, we simulate four cities and validate the simulations for further path loss model characterization. Specifically, we extract the close-in (CI) model parameters from the simulations and compare them with parameters extracted from measurements in other works. Under the ray-based model characterization, we compare the AMPLE model with the 3rd Generation Partnership Project (3GPP) path loss model, the CI model, the alpha-beta-gamma (ABG) model, and those with simulation calibrations. In addition to standard performance metrics, we introduce the prediction-measurement difference error (PMDE) to assess overall prediction alignment with measurement, and mean simulation time per data point to evaluate model complexity. The results show that the AMPLE model outperforms existing models while maintaining similar model complexity.
Leveraging Large Self-Supervised Time-Series Models for Transferable Diagnosis in Cross-Aircraft Type Bleed Air System
Authors: Yilin Wang, Peixuan Lei, Xuyang Wang, Liangliang Jiang, Liming Xuan, Wei Cheng, Honghua Zhao, Yuanxiang Li
Date: 2025-04-12 00:00:00
Tasks: Anomaly Detection, Diagnostic, Self-Supervised Learning, Time Series

Bleed Air System (BAS) is critical for maintaining flight safety and operational efficiency, supporting functions such as cabin pressurization, air conditioning, and engine anti-icing. However, BAS malfunctions, including overpressure, low pressure, and overheating, pose significant risks such as cabin depressurization, equipment failure, or engine damage. Current diagnostic approaches face notable limitations when applied across different aircraft types, particularly for newer models that lack sufficient operational data. To address these challenges, this paper presents a self-supervised learning-based foundation model that enables the transfer of diagnostic knowledge from mature aircraft (e.g., A320, A330) to newer ones (e.g., C919). Leveraging self-supervised pretraining, the model learns universal feature representations from flight signals without requiring labeled data, making it effective in data-scarce scenarios. This model enhances both anomaly detection and baseline signal prediction, thereby improving system reliability. The paper introduces a cross-model dataset, a self-supervised learning framework for BAS diagnostics, and a novel Joint Baseline and Anomaly Detection Loss Function tailored to real-world flight data. These innovations facilitate efficient transfer of diagnostic knowledge across aircraft types, ensuring robust support for early operational stages of new models. Additionally, the paper explores the relationship between model capacity and transferability, providing a foundation for future research on large-scale flight signal models.