pwc-archive/papers-with-abstracts huggingface dataset:
Active Reconfigurable Intelligent Surfaces: Circuit Modeling and Reflection Amplification Optimization
Authors: Panagiotis Gavriilidis, Deepak Mishra, Besma Smida, Ertugrul Basar, Chau Yuen, George C. Alexandropoulos
Date: 2025-03-31 00:00:00

Reconfigurable Intelligent Surfaces (RISs) constitute a promising emerging technology that enables wireless systems to control the propagation environment to enhance diverse communication objectives. To mitigate double-fading attenuation in RIS-aided links, the paradigm of active metamaterials capable of amplifying their incident wave has emerged. In this paper, capitalizing on the inherent negative-resistance region of tunnel diodes, we propose their integration into each RIS unit element to enable RISs with reflection amplification entirely in the analog domain. We derive novel realistic phase-amplitude relationships and power constraints specific to this model, addressing gaps in the existing literature where amplitude limits are often chosen arbitrarily. This characterization of our active RIS unit elements is incorporated into two novel optimization frameworks targeting the spectral efficiency maximization of RIS-assisted Multiple-Input-Multiple-Output (MIMO) systems, which are solved via an one-step approach and an iterative Alternating Optimization (AO) method. The former approach is used to initialize the AO framework, enhancing both its performance and convergence. Our numerical investigations emphasize the importance of accurately modeling phase-amplitude dependencies, and provide key insights into the impact of RIS-induced noise as well as the trade-off between available power and the number of active elements.
A Simple BER Expression for FSO Systems with Weak Turbulence and Pointing Errors
Authors: C. Álvarez Roa, Y. C. Gültekin, K. Wu, C. W. Korevaar, A. Alvarado
Date: 2025-03-31 00:00:00

We develop a simple approximation for the average BER for an FSO system impacted by weak turbulence and pointing errors. Numerical results show that the proposed expression accurately predicts the true BER.
Reliable Traffic Monitoring Using Low-Cost Doppler Radar Units
Authors: Mishay Naidoo, Stephen Paine, Amit Kumar Mishra, Mohammed Yunus Abdul Gaffar
Date: 2025-03-31 00:00:00
Tasks: vehicle detection

Road traffic monitoring typically involves the counting and recording of vehicles on public roads over extended periods. The data gathered from such monitoring provides useful information to municipal authorities in urban areas. This paper presents a low-cost, widely deployable sensing subsystem based on Continuous Wave Doppler radar. The proposed system can perform vehicle detection and speed estimation with a total cost of less than 100 USD. The sensing system (including the hardware subsystem and the algorithms) is designed to be placed on the side of the road, allowing for easy deployment and serviceability.
Algorithm Design and Prototype Validation for Reconfigurable Intelligent Sensing Surface: Forward-Only Transmission
Authors: Cheng Luo, Luping Xiang, Jie Hu, Kun Yang
Date: 2025-03-31 00:00:00

Sensing-assisted communication schemes have recently garnered significant research attention. In this work, we design a dual-function reconfigurable intelligent surface (RIS), integrating both active and passive elements, referred to as the reconfigurable intelligent sensing surface (RISS), to enhance communication. By leveraging sensing results from the active elements, we propose communication enhancement and robust interference suppression schemes for both near-field and far-field models, implemented through the passive elements. These schemes remove the need for base station (BS) feedback for RISS control, simplifying the communication process by replacing traditional channel state information (CSI) feedback with real-time sensing from the active elements. The proposed schemes are theoretically analyzed and then validated using software-defined radio (SDR). Experimental results demonstrate the effectiveness of the sensing algorithms in real-world scenarios, such as direction of arrival (DOA) estimation and radio frequency (RF) identification recognition. Moreover, the RISS-assisted communication system shows strong performance in communication enhancement and interference suppression, particularly in near-field models.
ANNs-SaDE: A Machine-Learning-Based Design Automation Framework for Microwave Branch-Line Couplers
Authors: Tianqi Chen, Wei Huang, Qiang Wu, Li Yang, Roberto Gómez-García, Xi Zhu
Date: 2025-03-31 00:00:00

The traditional method for designing branch-line couplers involves a trial-and-error optimization process that requires multiple design iterations through electromagnetic (EM) simulations. Thus, it is extremely time consuming and labor intensive. In this paper, a novel machine-learning-based framework is proposed to tackle this issue. It integrates artificial neural networks with a self-adaptive differential evolution algorithm (ANNs-SaDE). This framework enables the self-adaptive design of various types of microwave branch-line couplers by precisely optimizing essential electrical properties, such as coupling factor, isolation, and phase difference between output ports. The effectiveness of the ANNs-SaDE framework is demonstrated by the designs of folded single-stage branch-line couplers and multi-stage wideband branch-line couplers.
Semantic Packet Aggregation and Repeated Transmission for Text-to-Image Generation
Authors: Seunghun Lee, Jihong Park, Jinho Choi, HyunCheol Park
Date: 2025-03-31 00:00:00
Tasks: Image Generation, Semantic Segmentation, Text to Image Generation, Text-to-Image Generation

Text-based communication is expected to be prevalent in 6G applications such as wireless AI-generated content (AIGC). Motivated by this, this paper addresses the challenges of transmitting text prompts over erasure channels for a text-to-image AIGC task by developing the semantic segmentation and repeated transmission (SMART) algorithm. SMART groups words in text prompts into packets, prioritizing the task-specific significance of semantics within these packets, and optimizes the number of repeated transmissions. Simulation results show that SMART achieves higher similarities in received texts and generated images compared to a character-level packetization baseline, while reducing computing latency by orders of magnitude compared to an exhaustive search baseline.
Zak-OTFS for Mutually Unbiased Sensing and Communication
Authors: Nishant Mehrotra, Sandesh Rao Mattu, Robert Calderbank
Date: 2025-03-30 00:00:00
Tasks: Integrated sensing and communication

Waveforms with ideal ambiguity functions are fundamental to integrated sensing and communication, to active sensing (radar), and to uplink multiple access. We describe a general method of constructing waveforms using the discrete Zak transform (DZT) to convert sequences of length $MN$ in the time domain to waveforms in the delay-Doppler (DD) domain, each of which is defined by an $M\times N$ quasi-periodic array. The DZT preserves inner products, and we show that phase coded waveforms used in radar (CAZAC sequences) determine noise-like waveforms in the DD domain, each with low Peak to Average Power Ratio. In a Zak-OTFS communication system, we show that these waveforms are mutually unbiased with respect to every carrier and use them to integrate sensing and communication as spread pilots. We view each waveform as a linear combination of Zak-OTFS carriers and show that the self-ambiguity function is supported on a discrete line in the integers modulo $MN$. The sidelobes are significantly lower than the original CAZAC sequence, and the advantage of discrete support is better localization/resolution in delay and Doppler compared with standard methods based on chirps or tones. We show that the absolute value of the cross-ambiguity function for pairs of waveforms in the same family is small and constant. This property makes the waveforms ideal preambles in the 2-step RACH protocol introduced in Release 15, 3GPP to enable grant-free multiple access. The characteristics of the cross-ambiguity function make it possible to simultaneously detect multiple preambles in the presence of mobility and delay spread.
STAR-RIS-aided NOMA for Secured xURLLC
Authors: Song Lulu, Zhang Di, Jia Shaobo, Zhu Pengcheng, Li Yonghui
Date: 2025-03-30 00:00:00

Short packet-based advanced Internet of things (A-IoT) calls for not only the next generation of ultra-reliable low-latency communications (xURLLC) but also highly secured communications. In this paper, we aim to address this objective by developing a non-orthogonal multiple access (NOMA) system with untrusted user. There exist two key problems: The confidential/private message for the far user will be exposed to the untrusted near user with successful SIC; The restrictive trade-off among reliability, security and latency poses a great challenge in achieving secured xURLLC. In order to solve these issues, we introduce simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS), which provides additional degree of freedom to enable a secure and fair decoding order and achieve a desired trade-off among reliability, security and latency. To fully reveal the trade-off among reliability, security and latency, we characterize the reliability and security via decoding error probabilities. A leakage probability minimization problem is modeled to optimize the passive beamforming, power allocation and blocklength subject to secure successive interference cancellation (SIC) order, reliability and latency constraints. To solve this complex problem, we explore its intrinsic properties and propose an algorithm based on majorization minimization (MM) and alternative optimization (AO). Simulation results demonstrate the validness of our study in this paper.
Improving Neonatal Care: An Active Dry-Contact Electrode-based Continuous EEG Monitoring System with Seizure Detection
Authors: Nima L. Wickramasinghe, Dinuka Sandun Udayantha, Akila Abeyratne, Kavindu Weerasinghe, Kithmin Wickremasinghe, Jithangi Wanigasinghe, Anjula De Silva, Chamira U. S. Edussooriya
Date: 2025-03-30 00:00:00
Tasks: Deep Learning, EEG, Multimodal Deep Learning, Seizure Detection

Objective: Neonates are highly susceptible to seizures, which can have severe long-term consequences if undetected and left untreated. Early detection is crucial and typically requires continuous electroencephalography (EEG) monitoring in a hospital setting, which is costly, inconvenient, and requires specialized experts for diagnosis. In this work, we propose a new low-cost active dry-contact electrode-based adjustable EEG headset, a new explainable deep learning model to detect neonatal seizures, and an advanced signal processing algorithm to remove artifacts to address the key aspects that lead to the underdiagnosis of neonatal seizures. Methods: EEG signals are acquired through active electrodes and processed using a custom-designed analog front end (AFE) that filters and digitizes the captured EEG signals. The adjustable headset is designed using three-dimensional (3D) printing and laser cutting to fit a wide range of head sizes. A deep learning model is developed to classify seizure and non-seizure epochs in real-time. Furthermore, a separate multimodal deep learning model is designed to remove noise artifacts. The device is tested on a pediatric patient with absence seizures in a hospital setting. Simultaneous recordings are captured using both the custom device and the commercial wet electrode device available in the hospital for comparison. Results: The signals obtained using our custom design and a commercial device show a high correlation (>0.8). Further analysis using signal-to-noise ratio values shows that our device can mitigate noise similar to the commercial device. The proposed deep learning model has improvements in accuracy and recall by 2.76% and 16.33%, respectively, compared to the state-of-the-art. Furthermore, the developed artifact removal algorithm can identify and remove artifacts while keeping seizure patterns intact.
A Method for Localization of Cellular Users from Call Detail Records
Authors: Steven W. Ellingson
Date: 2025-03-30 00:00:00

A common problem in justice applications is localization of a user of a cellular network using a call detail record (CDR), which typically reveals only the base station and sector to which the user was connected. This precludes precise estimation of location. Instead, one is limited to estimating a region of plausible locations (RPL) using static information such as sector antenna orientation, beamwidth, and locations of nearby base stations. In this paper, we propose a method for RPL estimation in which the shape bounding the RPL is derived from a model of the antenna pattern via the Friis Transmission Equation, and the size of the RPL is determined by mean distance to nearby base stations. The performance of the proposed method is evaluated by "best server" analysis of measurements acquired from drive testing in the vicinity of Winter Garden, Florida, observing three 700 MHz-band LTE cellular networks serving this area. Of the 16 sectors evaluated, the aggregate error rate (i.e., fraction of users located outside the RPL estimated for the associated sector) is found to be 1.3%, with worst per-sector error rate of about 13.3% and error rates below 1.8% for 13 of the 16 sectors. The principal difficulty is shown to be estimation of RPL size, which entails a tradeoff between minimizing RPL area (yielding the "tightest" localization) and minimizing error rate.
Multi-Agent Reinforcement Learning for Graph Discovery in D2D-Enabled Federated Learning
Authors: Satyavrat Wagle, Anindya Bijoy Das, David J. Love, Christopher G. Brinton
Date: 2025-03-29 00:00:00
Tasks: Diversity, Federated Learning, Multi-agent Reinforcement Learning, Reinforcement Learning (RL)

Augmenting federated learning (FL) with device-to-device (D2D) communications can help improve convergence speed and reduce model bias through local information exchange. However, data privacy concerns, trust constraints between devices, and unreliable wireless channels each pose challenges in finding an effective yet resource efficient D2D graph structure. In this paper, we develop a decentralized reinforcement learning (RL) method for D2D graph discovery that promotes communication of impactful datapoints over reliable links for multiple learning paradigms, while following both data and device-specific trust constraints. An independent RL agent at each device trains a policy to predict the impact of incoming links in a decentralized manner without exposure of local data or significant communication overhead. For supervised settings, the D2D graph aims to improve device-specific label diversity without compromising system-level performance. For semi-supervised settings, we enable this by incorporating distributed label propagation. For unsupervised settings, we develop a variation-based diversity metric which estimates data diversity in terms of occupied latent space. Numerical experiments on five widely used datasets confirm that the data diversity improvements induced by our method increase convergence speed by up to 3 times while reducing energy consumption by up to 5 times. They also show that our method is resilient to stragglers and changes in the aggregation interval. Finally, we show that our method offers scalability benefits for larger system sizes without increases in relative overhead, and adaptability to various downstream FL architectures and to dynamic wireless environments.
Least-Squares Khatri-Rao Factorization of a Polynomial Matrix
Authors: Faizan A. Khattak, Fazal-E-Asim, Stephan Weiss, Andre L. F. de Almeida
Date: 2025-03-29 00:00:00
Tasks: Tensor Decomposition

The Khatri-Rao product is extensively used in array processing, tensor decomposition, and multi-way data analysis. Many applications require a least-squares (LS) Khatri-Rao factorization. In broadband sensor array problems, polynomial matrices effectively model frequency-dependent behaviors, necessitating extensions of conventional linear algebra techniques. This paper generalizes LS Khatri-Rao factorization from ordinary to polynomial matrices by applying it to the discrete Fourier transform (DFT) samples of polynomial matrices. Phase coherence across bin-wise Khatri-Rao factors is ensured via a phasesmoothing algorithm. The proposed method is validated through broadband angle-of-arrival (AoA) estimation for uniform planar arrays (UPAs), where the steering matrix is a polynomial matrix, which can be represented as a Khatri-Rao product between steering matrix in azimuth and elevation directions.
Advancing THz Radio Map Construction and Obstacle Sensing: An Integrated Generative Framework in ISAC
Authors: Tianyu Hu, Shuai Wang, Yunhang Xie, Lingxiang Li, Zhi Chen, Boyu Ning, Wassim Hamidouche, Lina Bariah, Samson Lasaulce, Merouane Debbah
Date: 2025-03-29 00:00:00
Tasks: Generative Adversarial Network, Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) in the terahertz (THz) band enables obstacle detection, which in turn facilitates efficient beam management to mitigate THz signal blockage. Simultaneously, a THz radio map, which captures signal propagation characteristics through the distribution of received signal strength (RSS), is well-suited for sensing, as it inherently contains obstacle-related information and reflects the unique properties of the THz channel. This means that communication-assisted sensing in ISAC can be effectively achieved using a THz radio map. However, constructing a radio map presents significant challenges due to the sparse deployment of THz sensors and their limited ability to accurately measure the RSS distribution, which directly affects obstacle sensing. In this paper, we formulate an integrated problem for the first time, leveraging the mutual enhancement between sensed obstacles and the constructed THz radio maps. To address this challenge while improving generalization, we propose an integration framework based on a conditional generative adversarial network (CGAN), which uncovers the manifold structure of THz radio maps embedded with obstacle information. Furthermore, recognizing the shared environmental semantics across THz radio maps from different beam directions, we introduce a novel voting-based sensing scheme, where obstacles are detected by aggregating votes from THz radio maps generated by the CGAN. Simulation results demonstrate that the proposed framework outperforms non-integrated baselines in both radio map construction and obstacle sensing, achieving up to 44.3% and 90.6% reductions in mean squared error (MSE), respectively, in a real-world scenario. These results validate the effectiveness of the proposed voting-based scheme.
A Comprehensive Comparison between Terahertz and Optical Wireless Communications
Authors: Mingqing Liu, Hossein Kazemi, Majid Safari, Iman Tavakkolnia, Harald Haas
Date: 2025-03-29 00:00:00

This paper presents a comprehensive quantitative comparison between Terahertz (THz) communication (TeraCom) and optical wireless communication (OWC) technologies, focusing on both indoor and outdoor environments. We propose a comparison method for TeraCom and vertical-cavity surface-emitting laser (VCSEL)-based OWC in indoor scenarios, incorporating misalignment effects by modeling the THz antenna radiation pattern within a multi-ray THz channel model and using a Gaussian beam model for VCSEL-based OWC. Unified beamwidth parameters allow for a detailed analysis of misalignment impact on both systems. Furthermore, we develop power consumption models for each technology, integrating key parameters such as THz phase noise, VCSEL non-linearities, and photodetector bandwidth-area tradeoffs. These models enable an in-depth analysis of energy efficiency in indoor environments, including multi-transmitter coverage scenarios. For outdoor scenarios, we summarize existing stochastic channel models addressing path loss, pointing errors, and small-scale fading for free space optics (FSO) and THz links. We then apply these models to unmanned aerial vehicle (UAV) applications to assess performance in dynamic conditions. Our results provide critical insights into the suitability of each technology for various deployment scenarios.
MCRB for Parameter Estimation from One-Bit Quantized and Oversampled Measurements
Authors: Nadav E. Rosenthal, Joseph Tabrikian
Date: 2025-03-28 00:00:00
Tasks: Direction of Arrival Estimation, parameter estimation, Quantization

One-bit quantization has garnered significant attention in recent years for various signal processing and communication applications. Estimating model parameters from one bit quantized data can be challenging, particularly when the quantization process is explicitly accounted for in the estimator. In many cases, the estimator disregards quantization effects, leading to model misspecification. Consequently, estimation errors arise from both quantization and misspecification. Traditional performance bounds, such as the Cramer-Rao bound (CRB), fail to capture the impact of misspecification on estimation performance. To address this limitation, we derive the misspecified CRB (MCRB) for parameter estimation in a quantized data model consisting of a signal component in additive Gaussian noise. We apply this bound to direction-of-arrival estimation using quantized measurements from a sensor array and to frequency estimation with oversampled quantized data. The simulations show that the MCRB is asymptotically achieved by the mean-squared-error of the misspecified maximum-likelihood estimator. Our results demonstrate that, unlike in finely quantized scenarios, oversampling can significantly enhance the estimation performance in the presence of misspecified one-bit quantized measurements.
Optimized Vehicular Antenna Placement for Phase-Coherent Positioning
Authors: Victor Pettersson, Musa Furkan Keskin, Carina Marcus, Henk Wymeersch
Date: 2025-03-28 00:00:00

Distributed multi-antenna systems are an important enabling technology for future intelligent transportation systems (ITS), showing promising performance in vehicular communications and near-field (NF) localization applications. This work investigates optimal deployments of phase-coherent sub-arrays on a vehicle for NF localization in terms of a Cram\'er-Rao lower bound (CRLB)-based metric. Sub-array placements consider practical geometrical constraints on a three-dimensional vehicle model accounting for self-occlusions. Results show that, for coherent NF localization of the vehicle, the aperture spanned by the sub-arrays should be maximized and a larger number of sub-arrays results in more even coverage over the vehicle orientations under a fixed total number of antenna elements, contrasting with the outcomes of incoherent localization. Moreover, while coherent NF processing significantly enhances accuracy, it also leads to more intricate cost functions, necessitating computationally more complex algorithms than incoherent processing.
Fighting Fire with Fire: Channel-Independent RF Fingerprinting via the Ratio of Linear to Logarithmic Differential Spectrum
Authors: Tianshu Chen, Aiqun Hu, Shiqi Zhang
Date: 2025-03-28 00:00:00

Eliminating the influence of temporally varying channel components on the radio frequency fingerprint (RFF) extraction has been an enduring and challenging issue. To overcome this problem, we propose a channel-independent RFF extraction method inspired by the idea of 'fighting fire with fire'. Specifically, we derive the linear differential spectrum and the logarithmic differential spectrum of the channel frequency responses (CFRs) from the received signals at different times, and then calculate the ratio of the two spectrums. It is found that the division operation effectively counteracts the channel effects, while simultaneously preserving the integrity of the RFFs. Our experiments on LTE-V2X, LoRa and Wi-Fi devices show that the proposed method achieves an average identification accuracy exceeding 95% across various environments.