pwc-archive/papers-with-abstracts huggingface dataset:
Task and Perception-aware Distributed Source Coding for Correlated Speech under Bandwidth-constrained Channels
Authors: Sagnik Bhattacharya, Muhammad Ahmed Mohsin, Ahsan Bilal, John M. Cioffi
Date: 2025-01-20 00:00:00

Emerging wireless AR/VR applications require real-time transmission of correlated high-fidelity speech from multiple resource-constrained devices over unreliable, bandwidth-limited channels. Existing autoencoder-based speech source coding methods fail to address the combination of the following - (1) dynamic bitrate adaptation without retraining the model, (2) leveraging correlations among multiple speech sources, and (3) balancing downstream task loss with realism of reconstructed speech. We propose a neural distributed principal component analysis (NDPCA)-aided distributed source coding algorithm for correlated speech sources transmitting to a central receiver. Our method includes a perception-aware downstream task loss function that balances perceptual realism with task-specific performance. Experiments show significant PSNR improvements under bandwidth constraints over naive autoencoder methods in task-agnostic (19%) and task-aware settings (52%). It also approaches the theoretical upper bound, where all correlated sources are sent to a single encoder, especially in low-bandwidth scenarios. Additionally, we present a rate-distortion-perception trade-off curve, enabling adaptive decisions based on application-specific realism needs.
Joint Power and Spectrum Orchestration for D2D Semantic Communication Underlying Energy-Efficient Cellular Networks
Authors: Le Xia, Yao Sun, Haijian Sun, Rose Qingyang Hu, Dusit Niyato, Muhammad Ali Imran
Date: 2025-01-30 00:00:00
Tasks: Management, Semantic Communication, Triplet

Semantic communication (SemCom) has been recently deemed a promising next-generation wireless technique to enable efficient spectrum savings and information exchanges, thus naturally introducing a novel and practical network paradigm where cellular and device-to-device (D2D) SemCom approaches coexist. Nevertheless, the involved wireless resource management becomes complicated and challenging due to the unique semantic performance measurements and energy-consuming semantic coding mechanism. To this end, this paper jointly investigates power control and spectrum reuse problems for energy-efficient D2D SemCom cellular networks. Concretely, we first model the user preference-aware semantic triplet transmission and leverage a novel metric of semantic value to identify the semantic information importance conveyed in SemCom. Then, we define the additional power consumption from semantic encoding in conjunction with basic power amplifier dissipation to derive the overall system energy efficiency (semantics/Joule). Next, we formulate an energy efficiency maximization problem for joint power and spectrum allocation subject to several SemCom-related and practical constraints. Afterward, we propose an optimal resource management solution by employing the fractional-to-subtractive problem transformation and decomposition while developing a three-stage method with theoretical analysis of its optimality guarantee and computational complexity. Numerical results demonstrate the adequate performance superiority of our proposed solution compared with different benchmarks.
Estimating unknown dynamics and cost as a bilinear system with Koopman-based Inverse Optimal Control
Authors: Victor Nan Fernandez-Ayala, Shankar A. Deka, Dimos V. Dimarogonas
Date: 2025-01-30 00:00:00

In this work, we address the challenge of approximating unknown system dynamics and costs by representing them as a bilinear system using Koopman-based Inverse Optimal Control (IOC). Using optimal trajectories, we construct a bilinear control system in transformed state variables through a modified Extended Dynamic Mode Decomposition with control (EDMDc) that maintains exact dynamical equivalence with the original nonlinear system. We derive Pontryagin's Maximum Principle (PMP) optimality conditions for this system, which closely resemble those of the inverse Linear Quadratic Regulator (LQR) problem due to the consistent control input and state independence from the control. This similarity allows us to apply modified inverse LQR theory, offering a more tractable and robust alternative to nonlinear Inverse Optimal Control methods, especially when dealing with unknown dynamics. Our approach also benefits from the extensive analytical properties of bilinear control systems, providing a solid foundation for further analysis and application. We demonstrate the effectiveness of the proposed method through theoretical analysis, simulation studies and a robotic experiment, highlighting its potential for broader applications in the approximation and design of control systems.
Tunable Multilayer Surface Plasmon Resonance Biosensor for Trace-Level Toxin Detection
Authors: Suripto Bhuiyan, Michael Geller
Date: 2025-01-30 00:00:00
Tasks: Sensitivity, Specificity

This paper presents a comprehensive study on a novel multilayer surface plasmon resonance (SPR) biosensor designed for detecting trace-level toxins in liquid samples with exceptional precision and efficiency. Leveraging the Kretschmann configuration, the proposed design integrates advanced two-dimensional materials, including black phosphorus (BP) and transition metal dichalcogenides (TMDs), to significantly enhance the performance metrics of the sensor. Key innovations include the optimization of sensitivity through precise material layering, minimization of full-width at half-maximum (FWHM) to improve signal resolution, and maximization of the figure of merit (FoM) for superior detection accuracy. Numerical simulations are employed to validate the structural and functional enhancements of the biosensor. The results demonstrate improved interaction between the evanescent field and the analyte, enabling detection at trace concentrations with higher specificity. This biosensor is poised to contribute to advancements in biochemical sensing, environmental monitoring, and other critical applications requiring high-sensitivity toxin detection.
Waste Animal Bone-derived Calcium Phosphate Particles with High Solar Reflectance
Authors: Nathaniel LeCompte, Andrew Caratenuto, Yi Zheng
Date: 2025-01-30 00:00:00

Highly reflective Calcium Phosphate (CAP) nanoparticles have been obtained from waste chicken and porcine bones. Chicken and pork bones have been processed and calcined at temperatures between 600{\deg}C and 1200{\deg}C to remove organic material and resulting in CAP bio-ceramic compounds with high reflectance. The reflectivity of the materials in the solar wavelength region is on par with chemically synthesized CAP. The high reflectivity, consistently over 90%, as well as the size distribution and packing density of the nanoparticles obtained in these early bone studies make a strong case for pursuing this avenue to obtain pigment for high solar reflectivity applications, such as passive daytime radiative cooling. The results presented indicate a viable path toward a cost-effective and eco-friendly source of highly reflective cooling pigments. By sourcing calcium phosphates from animal bones, there is also the potential to divert large quantities of bone waste generated by the meat industry from landfills, further contributing toward sustainability and energy reduction efforts in the construction industry and beyond.
Impedance Trajectory Analysis during Power Swing for Grid-Forming Inverter with Different Current Limiters
Authors: Yanshu Niu, Zhe Yang, Bikash C. Pal
Date: 2025-01-30 00:00:00

Grid-forming (GFM) inverter-based resources (IBRs) are capable of emulating the external characteristics of synchronous generators (SGs) through the careful design of the control loops. However, the current limiter in the control loops of the GFM IBR poses challenges to the effectiveness of power swing detection functions designed for SG-based systems. Among various current limiting strategies, current saturation algorithms (CSAs), widely employed for their strict current limiting capability, are the focus of this paper. The paper presents a theoretical analysis of the conditions for entering and exiting the current saturation mode of the GFM IBR under three CSAs. Furthermore, the corresponding impedance trajectories observed by the distance relay on the GFM IBR side are investigated. The analysis results reveal that the unique impedance trajectories under these CSAs markedly differ from those associated with SGs. Moreover, it is demonstrated that the conventional power swing detection scheme may lose functionality due to the rapid movement of the trajectory or its failure to pass through the detection zones. Conclusions are validated through simulations in MATLAB/Simulink.
A machine-learning optimized vertical-axis wind turbine
Authors: Huan Liu, Richard D. James
Date: 2025-01-27 00:00:00

Vertical-axis wind turbines (VAWTs) have garnered increasing attention in the field of renewable energy due to their unique advantages over traditional horizontal-axis wind turbines (HAWTs). However, traditional VAWTs including Darrieus and Savonius types suffer from significant drawbacks -- negative torque regions exist during rotation. In this work, we propose a new design of VAWT, which combines design principles from both Darrieus and Savonius but addresses their inherent defects. The performance of the proposed VAWT is evaluated through numerical simulations and validated by experimental testing. The results demonstrate that its power output is approximately three times greater than that of traditional Savonius VAWTs of comparable size. The performance of the proposed VAWT is further optimized using machine learning techniques, including Gaussian process regression and neural networks, based on extensive supercomputer simulations. This optimization leads to a 30% increase in power output.
IV Estimation of Heterogeneous Spatial Dynamic Panel Models with Interactive Effects
Authors: Jia Chen, Guowei Cui, Vasilis Sarafidis, Takashi Yamagata
Date: 2025-01-30 00:00:00
Tasks: valid

This paper develops a Mean Group Instrumental Variables (MGIV) estimator for spatial dynamic panel data models with interactive effects, under large N and T asymptotics. Unlike existing approaches that typically impose slope-parameter homogeneity, MGIV accommodates cross-sectional heterogeneity in slope coefficients. The proposed estimator is linear, making it computationally efficient and robust. Furthermore, it avoids the incidental parameters problem, enabling asymptotically valid inferences without requiring bias correction. The Monte Carlo experiments indicate strong finite-sample performance of the MGIV estimator across various sample sizes and parameter configurations. The practical utility of the estimator is illustrated through an application to regional economic growth in Europe. By explicitly incorporating heterogeneity, our approach provides fresh insights into the determinants of regional growth, underscoring the critical roles of spatial and temporal dependencies.
Universal Inference for Incomplete Discrete Choice Models
Authors: Hiroaki Kaido, Yi Zhang
Date: 2025-01-29 00:00:00
Tasks: counterfactual, Discrete Choice Models

A growing number of empirical models exhibit set-valued predictions. This paper develops a tractable inference method with finite-sample validity for such models. The proposed procedure uses a robust version of the universal inference framework by Wasserman et al. (2020) and avoids using moment selection tuning parameters, resampling, or simulations. The method is designed for constructing confidence intervals for counterfactual objects and other functionals of the underlying parameter. It can be used in applications that involve model incompleteness, discrete and continuous covariates, and parameters containing nuisance components.
What Proportion of Knowledge is Patented?
Authors: Gaetan de Rassenfosse
Date: 2025-01-29 00:00:00

This article examines the proportion of technical knowledge that is eventually patented and discusses implications for innovation. Drawing from economic theory and information science, the study considers a continuum from raw data to applied understanding, while distinguishing between patentable inventions and other types of knowledge. The article explains that although the patent system is designed to incentivize the creation of economically valuable technology, only a small fraction of patentable inventions receive patent protection. Empirical evidence from surveys and historical data reveal that patenting remains an exception rather than a rule in many, but not all, industries. Furthermore, the article explores the trade-offs inherent in public disclosure requirements in patent law, which facilitate knowledge dissemination and may aid competitors. In doing so, it highlights the critical balance between rewarding inventors and preserving the free flow of knowledge, ultimately underscoring the complex role patents play in modern innovation ecosystems.
Distilling Knowledge for Designing Computational Imaging Systems
Authors: Leon Suarez-Rodriguez, Roman Jacome, Henry Arguello
Date: 2025-01-29 00:00:00
Tasks: Decoder, Image Reconstruction, Knowledge Distillation, Transfer Learning

Designing the physical encoder is crucial for accurate image reconstruction in computational imaging (CI) systems. Currently, these systems are designed via end-to-end (E2E) optimization, where the encoder is modeled as a neural network layer and is jointly optimized with the decoder. However, the performance of E2E optimization is significantly reduced by the physical constraints imposed on the encoder. Also, since the E2E learns the parameters of the encoder by backpropagating the reconstruction error, it does not promote optimal intermediate outputs and suffers from gradient vanishing. To address these limitations, we reinterpret the concept of knowledge distillation (KD) for designing a physically constrained CI system by transferring the knowledge of a pretrained, less-constrained CI system. Our approach involves three steps: (1) Given the original CI system (student), a teacher system is created by relaxing the constraints on the student's encoder. (2) The teacher is optimized to solve a less-constrained version of the student's problem. (3) The teacher guides the training of the student through two proposed knowledge transfer functions, targeting both the encoder and the decoder feature space. The proposed method can be employed to any imaging modality since the relaxation scheme and the loss functions can be adapted according to the physical acquisition and the employed decoder. This approach was validated on three representative CI modalities: magnetic resonance, single-pixel, and compressive spectral imaging. Simulations show that a teacher system with an encoder that has a structure similar to that of the student encoder provides effective guidance. Our approach achieves significantly improved reconstruction performance and encoder design, outperforming both E2E optimization and traditional non-data-driven encoder designs.
Near-Field SAR Imaging of Moving Targets on Roads
Authors: Raz Rajwan, Amir Boag
Date: 2025-01-30 00:00:00

This paper introduces a single-channel SAR algorithm designed to detect and produce high-fidelity images of moving targets in spotlight mode. The proposed fast backprojection algorithm utilizes multi-level interpolations and aggregation of coarse images produced from partial datasets. Specifically designed for near-field scenarios and assuming a circular radar trajectory, the algorithm demonstrates enhanced efficiency in detecting both moving and stationary vehicles on roads.
Pinching-Antenna Systems (PASS): Architecture Designs, Opportunities, and Outlook
Authors: Yuanwei Liu, Zhaolin Wang, Xidong Mu, Chongjun Ouyang, Xiaoxia Xu, Zhiguo Ding
Date: 2025-01-30 00:00:00

This article proposes a novel design for the Pinching Antenna SyStems (PASS) and advocates simple yet efficient wireless communications over the ``last meter''. First, the potential benefits of PASS are discussed in the paper by reviewing an existing prototype. Then, the fundamentals of PASS are introduced, including their physical principles, signal models, and communication designs. In contrast to existing multi-antenna systems, PASS brings a novel concept, termed \emph{Pinching Beamforming}, which is achieved by dynamically adjusting the positions of pinching antennas (PAs). Based on this concept, a couple of practical transmission architectures are proposed for realizing the full potentia of PASS, namely non-multiplexing and multiplexing architectures. More particularly, 1) the non-multiplexing architecture is featured by simple baseband signal processing and relies on the pinching beamforming only; while 2) the multiplexing architecture provides enhanced signal processing capabilities with joint baseband and pinching beamforming, which can be further divided into sub-connected, fully-connected, and phase-shifter-based fully-connected schemes. Furthermore, several emerging scenarios are put forward for integrating PASS into future wireless networks. As a further advance, by demonstrating a few numerical case studies, the significant performance gain of PASS is revealed compared to conventional multi-antenna systems. Finally, several research opportunities and open problems of PASS are highlighted.
A Hybrid Dynamic Subarray Architecture for Efficient DOA Estimation in THz Ultra-Massive Hybrid MIMO Systems
Authors: Ye Tian, Jiaji Ren, Tuo Wu, Wei Liu, Chau Yuen, Merouane Debbah, Naofal Al-Dhahir, Matthew C. Valenti, Hing Cheung So, Yonina C. Eldar
Date: 2025-01-30 00:00:00
Tasks: Computational Efficiency

Terahertz (THz) communication combined with ultra-massive multiple-input multiple-output (UM-MIMO) technology is promising for 6G wireless systems, where fast and precise direction-of-arrival (DOA) estimation is crucial for effective beamforming. However, finding DOAs in THz UM-MIMO systems faces significant challenges: while reducing hardware complexity, the hybrid analog-digital (HAD) architecture introduces inherent difficulties in spatial information acquisition the large-scale antenna array causes significant deviations in eigenvalue decomposition results; and conventional two-dimensional DOA estimation methods incur prohibitively high computational overhead, hindering fast and accurate realization. To address these challenges, we propose a hybrid dynamic subarray (HDS) architecture that strategically divides antenna elements into subarrays, ensuring phase differences between subarrays correlate exclusively with single-dimensional DOAs. Leveraging this architectural innovation, we develop two efficient algorithms for DOA estimation: a reduced-dimension MUSIC (RD-MUSIC) algorithm that enables fast processing by correcting large-scale array estimation bias, and an improved version that further accelerates estimation by exploiting THz channel sparsity to obtain initial closed-form solutions through specialized two-RF-chain configuration. Furthermore, we develop a theoretical framework through Cram\'{e}r-Rao lower bound analysis, providing fundamental insights for different HDS configurations. Extensive simulations demonstrate that our solution achieves both superior estimation accuracy and computational efficiency, making it particularly suitable for practical THz UM-MIMO systems.
Time Frequency Localized Pulse for Delay Doppler Domain Data Transmission
Authors: Sanoopkumar P. S, Muyiwa Balogun, Liam Barry, Arman Farhang
Date: 2025-01-30 00:00:00

Orthogonal time frequency space (OTFS) is a strong candidate waveform for sixth generation wireless communication networks (6G), which can effectively handle time varying wireless channels. In this paper, we analyze the effect of fractional delay in delay Doppler (DD) domain multiplexing techniques. We develop a vector-matrix input-output relationship for the DD domain data transmission system by incorporating the effective pulse shaping filter between the transmitter and receiver along with the channel. Using this input-output relationship, we analyze the effect of the pulse shaping filter on the channel estimation and BER performance in the presence of fractional delay and uncompensated fractional timing offset (TO). For the first time, we propose the use of time-frequency localized (TFL) pulse shaping for the OTFS waveform to overcome the interference due to fractional delays. We show that our proposed TFL-OTFS outperforms the widely used raised cosine pulse-shaped OTFS (RC-OTFS) in the presence of fractional delays. Additionally, TFL-OTFS also shows very high robustness against uncompensated fractional TO, compared to RC-OTFS.
Signaling Design for Noncoherent Distributed Integrated Sensing and Communication Systems
Authors: Kawon Han, Kaitao Meng, Christos Masouros
Date: 2025-01-30 00:00:00
Tasks: Integrated sensing and communication, ISAC

The ultimate goal of enabling sensing through the cellular network is to obtain coordinated sensing of an unprecedented scale, through distributed integrated sensing and communication (D-ISAC). This, however, introduces challenges related to synchronization and demands new transmission methodologies. In this paper, we propose a transmit signal design framework for D-ISAC systems, where multiple ISAC nodes cooperatively perform sensing and communication without requiring phase-level synchronization. The proposed framework employing orthogonal frequency division multiplexing (OFDM) jointly designs downlink coordinated multi-point (CoMP) communication signals and multi-input multi-output (MIMO) radar signals, leveraging both collocated and distributed MIMO radars to estimate angle-of-arrival (AOA) and time-of-flight (TOF) from all possible multi-static measurements for target localization. To design the optimal D-ISAC transmit signal, we use the target localization Cram\'er-Rao bound (CRB) as the sensing performance metric and the signal-to-interference-plus-noise ratio (SINR) as the communication performance metric. Then, an optimization problem is formulated to minimize the localization CRB while maintaining a minimum SINR requirement for each communication user. Moreover, we present three distinct transmit signal design approaches, including optimal, orthogonal, and beamforming designs, which reveal trade-offs between ISAC performance and computational complexity. Unlike single-node ISAC systems, the proposed D-ISAC designs involve per-subcarrier sensing signal optimization to enable accurate TOF estimation, which contributes to the target localization performance. Numerical simulations demonstrate the effectiveness of the proposed designs in achieving flexible ISAC trade-offs and efficient D-ISAC signal transmission.
Harnessing Wavefront Curvature and Spatial Correlation in Noncoherent MIMO Communications
Authors: Aniol Martí, Luca Sanguinetti, Meritxell Lamarca, Jaume Riba
Date: 2025-01-29 00:00:00
Tasks: valid

Noncoherent communication systems have regained interest due to the growing demand for high-mobility and low-latency applications. Most existing studies using large antenna arrays rely on the far-field approximation, which assumes locally plane wavefronts. This assumption becomes inaccurate at higher frequencies and shorter ranges, where wavefront curvature plays a significant role and antenna arrays may operate in the radiative near field. In this letter, we adopt a model for the channel spatial correlation matrix that remains valid in both near and far field scenarios. Using this model, we demonstrate that energy-based noncoherent systems can leverage the benefits of wavefront spherical curvature, even beyond the Fraunhofer distance, revealing that the classical far-field approximation may significantly underestimate system performance. Moreover, we show that large antenna arrays enable the multiplexing of various users even with a noncoherent processing, as well as permitting near-optimal detection with low computational complexity.