pwc-archive/papers-with-abstracts huggingface dataset:
Optimizing IoT-Based Asset and Utilization Tracking: Efficient Activity Classification with MiniRocket on Resource-Constrained Devices
Authors: Marco Giordano, Silvano Cortesi, Michele Crabolu, Lavinia Pedrollo, Giovanni Bellusci, Tommaso Bendinelli, Engin Türetken, Andrea Dunbar, Michele Magno
Date: 2023-10-23 00:00:00
Tasks: Time Series Classification

This paper introduces an effective solution for retrofitting construction power tools with low-power IoT to enable accurate activity classification. We address the challenge of distinguishing between when a power tool is being moved and when it is actually being used. To achieve classification accuracy and power consumption preservation a newly released algorithm called MiniRocket was employed. Known for its accuracy, scalability, and fast training for time-series classification, in this paper, it is proposed as a TinyML algorithm for inference on resource-constrained IoT devices. The paper demonstrates the portability and performance of MiniRocket on a resource-constrained, ultra-low power sensor node for floating-point and fixed-point arithmetic, matching up to 1% of the floating-point accuracy. The hyperparameters of the algorithm have been optimized for the task at hand to find a Pareto point that balances memory usage, accuracy and energy consumption. For the classification problem, we rely on an accelerometer as the sole sensor source, and BLE for data transmission. Extensive real-world construction data, using 16 different power tools, were collected, labeled, and used to validate the algorithm's performance directly embedded in the IoT device. Experimental results demonstrate that the proposed solution achieves an accuracy of 96.9% in distinguishing between real usage status and other motion statuses while consuming only 7kB of flash and 3kB of RAM. The final application exhibits an average current consumption of less than 15{\mu}W for the whole system, resulting in battery life performance ranging from 3 to 9 years depending on the battery capacity (250-500mAh) and the number of power tool usage hours (100-1500h).
A sampling construction of graphon 1-norm convergence
Authors: Xingchao Jian, Feng Ji, Wee Peng Tay
Date: 2023-10-23 00:00:00
Tasks: LEMMA, valid

In the short note, we describe a sampling construction that yields a sequence of graphons converging to a prescribed limit graphon in 1-norm. This convergence is stronger than the convergence in the cut norm, usually used to study graphon sequences. The note also contains errata of the previous version of the note.
Distributed MIMO for 6G sub-Networks in the Unlicensed Spectrum
Authors: Mohamed Elwekeil, Lorenzo Galati Giordano, Paolo Baracca, Stefano Buzzi
Date: 2023-10-23 00:00:00

In this paper, we consider the sixth generation (6G) sub-networks, where hyper reliable low latency communications (HRLLC) requirements are expected to be met. We focus on a scenario where multiple sub-networks are active in the service area and assess the feasibility of using the 6 GHz unlicensed spectrum to operate such deployment, evaluating the impact of listen before talk (LBT). Then, we explore the benefits of using distributed multiple input multiple output (MIMO), where the available antennas in every sub-network are distributed over a number of access points (APs). Specifically, we compare different configurations of distributed MIMO with respect to centralized MIMO, where a single AP with all antennas is located at the center of every sub-network.
Device Detection and Channel Estimation in MTC with Correlated Activity Pattern
Authors: Hamza Djelouat, Mikko J. Sillanpää, Markku Juntti
Date: 2023-10-23 00:00:00
Tasks: Action Detection, Activity Detection, Bayesian Inference, User Identification

This paper provides a solution for the activity detection and channel estimation problem in grant-free access with correlated device activity patterns. In particular, we consider a machine-type communications (MTC) network operating in event-triggered traffic mode, where the devices are distributed over clusters with an activity behaviour that exhibits both intra-cluster and inner-cluster sparsity patterns. Furthermore, to model the network's intra-cluster and inner-cluster sparsity, we propose a structured sparsity-inducing spike-and-slab prior which provides a flexible approach to encode the prior information about the correlated sparse activity pattern. Furthermore, we drive a Bayesian inference scheme based on the expectation propagation (EP) framework to solve the JUICE problem. Numerical results highlight the significant gains obtained by the proposed structured sparsity-inducing spike-and-slab prior in terms of both user identification accuracy and channel estimation performance.
Terahertz Induced Protein Interactions in a Random Medium
Authors: Hadeel Elayan, Andrew W. Eckford, Raviraj Adve
Date: 2023-10-23 00:00:00
Tasks: Protein Folding

Folding of proteins into their correct native structure is key to their function. Simultaneously, the intricate interplay between cell movement and protein conformation highlights the complex nature of cellular processes. In this work, we demonstrate the impact of Terahertz (THz) signaling on controlling protein conformational changes in a random medium. Our system of interest consists of a communication link that involves a nanoantenna transmitter, a protein receiver, and a channel composed of moving red blood cells. Due to the system dynamics, we investigate the influence of both the fast and slow channel variations on protein folding. Specifically, we analyze the system's selectivity to asses the effectiveness of the induced THz interaction in targeting a specific group of proteins under fading conditions. By optimizing the selectivity metric with respect to the nanoantenna power and frequency, it is possible to enhance the controllability of protein interactions. Our probabilistic analysis provides a new perspective regarding electromagnetically triggered protein molecules, their microenvironment and their interaction with surrounding particles. It helps elucidate how external conditions impact the protein folding kinetics and pathways. This results in not only understanding the mechanisms underlying THz-induced protein interactions but also engineering these still-emerging tools.
Channel State Information-Free Location-Privacy Enhancement: Delay-Angle Information Spoofing
Authors: Jianxiu Li, Urbashi Mitra
Date: 2023-10-23 00:00:00

In this paper, a delay-angle information spoofing (DAIS) strategy is proposed for location-privacy enhancement. By shifting the location-relevant delays and angles without the aid of channel state information (CSI) at the transmitter, the eavesdropper is obfuscated by a physical location that is distinct from the true one. A precoder is designed to preserve location-privacy while the legitimate localizer can remove the obfuscation with the securely shared information. Then, a lower bound on the localization error is derived via the analysis of the geometric mismatch caused by DAIS, validating the enhanced location-privacy. The statistical hardness for the estimation of the shared information is also investigated to assess the robustness to the potential leakage of the designed precoder structure. Numerical comparisons show that the proposed DAIS scheme results in more than 15 dB performance degradation for the illegitimate localizer at high signal-to-noise ratios, which is comparable to a recently proposed CSI-free location-privacy enhancement strategy and is less sensitive to the precoder structure leakage than the prior approach.
Submodular Optimization for Placement of Intelligent Reflecting Surfaces in Sensing Systems
Authors: Zahra Esmaeilbeig, Kumar Vijay Mishra, Arian Eamaz, Mojtaba Soltanalian
Date: 2023-10-22 00:00:00

Intelligent reflecting surfaces (IRS) and their optimal deployment are the new technological frontier in sensing applications. Recently, IRS have demonstrated potential in advancing target estimation and detection. While the optimal phase-shift of IRS for different tasks has been studied extensively in the literature, the optimal placement of multiple IRS platforms for sensing applications is less explored. In this paper, we design the placement of IRS platforms for sensing by maximizing the mutual information. In particular, we use this criterion to determine an approximately optimal placement of IRS platforms to illuminate an area where the target has a hypothetical presence. After demonstrating the submodularity of the mutual information criteria, we tackle the design problem by means of a constant-factor approximation algorithm for submodular optimization. Numerical results are presented to validate the proposed submodular optimization framework for optimal IRS placement with worst case performance bounded to $1-1/e\approx 63 \%$.
A Coordinate Descent Approach to Atomic Norm Denoising
Authors: Ruifu Li, Danijela Cabric
Date: 2023-10-22 00:00:00
Tasks: Denoising, Super-Resolution

Atomic norm minimization is of great interest in various applications of sparse signal processing including super-resolution line-spectral estimation and signal denoising. In practice, atomic norm minimization (ANM) is formulated as semi-definite programming (SDP) that is generally hard to solve. This work introduces a low-complexity solver for a type of ANM known as atomic norm soft thresholding (AST). The proposed method uses the framework of coordinate descent and exploits the sparsity-inducing nature of atomic-norm regularization. Specifically, this work first provides an equivalent, non-convex formulation of AST. It is then proved that applying a coordinate descent algorithm on the non-convex formulation leads to convergence to the global solution. For the case of a single measurement vector of length N and complex exponential basis, the complexity of each step in the coordinate descent procedure is O(N log N ), rendering the method efficient for large-scale problems. Through simulations, the proposed solver is shown to be faster than alternating direction method of multiplier (ADMM) or customized interior point SDP solver if the problems are sparse. It is demonstrated that the coordinate descent solver can be modified for AST with multiple dimensions and multiple measurement vectors as well as a variety of general basis.
Spatial Sigma-Delta Modulation for Coarsely Quantized Massive MIMO Downlink: Flexible Designs by Convex Optimization
Authors: Wai-Yiu Keung, Wing-Kin Ma
Date: 2023-10-22 00:00:00
Tasks: Quantization

This paper considers the context of multiuser massive MIMO downlink precoding with low-resolution digital-to-analog converters (DACs) at the transmitter. This subject is motivated by the consideration that it is expensive to employ high-resolution DACs for practical massive MIMO implementations. The challenge with using low-resolution DACs is to overcome the detrimental quantization error effects. Recently, spatial Sigma-Delta modulation has arisen as a viable way to put quantization errors under control. This approach takes insight from temporal Sigma-Delta modulation in classical DAC studies. Assuming a 1D uniform linear transmit antenna array, the principle is to shape the quantization errors in space such that the shaped quantization errors are pushed away from the user-serving angle sector. In the previous studies, spatial Sigma-Delta modulation was performed by direct application of the basic first- and second-order modulators from the Sigma-Delta literature. In this paper, we develop a general Sigma-Delta modulator design framework for any given order, for any given number of quantization levels, and for any given angle sector. We formulate our design as a problem of maximizing the signal-to-quantization-and-noise ratios experienced by the users. The formulated problem is convex and can be efficiently solved by available solvers. Our proposed framework offers the alternative option of focused quantization error suppression in accordance with channel state information. Our framework can also be extended to 2D planar transmit antenna arrays. We perform numerical study under different operating conditions, and the numerical results suggest that, given a moderate number of quantization levels, say, 5 to 7 levels, our optimization-based Sigma-Delta modulation schemes can lead to bit error rate performance close to that of the unquantized counterpart.
Photoplethysmography based atrial fibrillation detection: an updated review from July 2019
Authors: Cheng Ding, Ran Xiao, Weijia Wang, Elizabeth Holdsworth, Xiao Hu
Date: 2023-10-22 00:00:00
Tasks: Atrial Fibrillation Detection, Photoplethysmography (PPG)

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with significant health ramifications, including an elevated susceptibility to ischemic stroke, heart disease, and heightened mortality. Photoplethysmography (PPG) has emerged as a promising technology for continuous AF monitoring for its cost-effectiveness and widespread integration into wearable devices. Our team previously conducted an exhaustive review on PPG-based AF detection before June 2019. However, since then, more advanced technologies have emerged in this field. This paper offers a comprehensive review of the latest advancements in PPG-based AF detection, utilizing digital health and artificial intelligence (AI) solutions, within the timeframe spanning from July 2019 to December 2022. Through extensive exploration of scientific databases, we have identified 59 pertinent studies. Our comprehensive review encompasses an in-depth assessment of the statistical methodologies, traditional machine learning techniques, and deep learning approaches employed in these studies. In addition, we address the challenges encountered in the domain of PPG-based AF detection. Furthermore, we maintain a dedicated website to curate the latest research in this area, with regular updates on a regular basis.
Green Beamforming Design for Integrated Sensing and Communication Systems: A Practical Approach Using Beam-Matching Error Metrics
Authors: Luping Xiang, Ke Xu, Jie Hu, Kun Yang
Date: 2023-10-21 00:00:00
Tasks: Integrated sensing and communication, ISAC

In this paper, we propose a green beamforming design for the integrated sensing and communication (ISAC) system, using beam-matching error to assess radar performance. The beam-matching error metric, which considers the mean square error between the desired and designed beam patterns, provides a more practical evaluation approach. To tackle the non-convex challenge inherent in beamforming design, we apply semidefinite relaxation (SDR) to address the rank-one relaxation issue, followed by the iterative rank minimization algorithm (IRM) for rank-one recovery. The simulation results showcase the effectiveness of our proposed optimal beamforming design, emphasizing the exceptional performance of the radar component in sensing tasks.
Robust NOMA-assisted OTFS-ISAC Network Design with 3D Motion Prediction Topology
Authors: Luping Xiang, Ke Xu, Jie Hu, Christos Masouros, Kun Yang
Date: 2023-10-21 00:00:00
Tasks: Fairness, Integrated sensing and communication, ISAC, motion prediction, Position

This paper proposes a novel non-orthogonal multiple access (NOMA)-assisted orthogonal time-frequency space (OTFS)-integrated sensing and communication (ISAC) network, which uses unmanned aerial vehicles (UAVs) as air base stations to support multiple users. By employing ISAC, the UAV extracts position and velocity information from the user's echo signals, and non-orthogonal power allocation is conducted to achieve a superior achievable rate. A 3D motion prediction topology is used to guide the NOMA transmission for multiple users, and a robust power allocation solution is proposed under perfect and imperfect channel estimation for Maxi-min Fairness (MMF) and Maximum sum-Rate (SR) problems. Simulation results demonstrate the superiority of the proposed NOMA-assisted OTFS-ISAC system over other systems in terms of achievable rate under both perfect and imperfect channel conditions with the aid of 3D motion prediction topology.
Goal-oriented Communications for the IoT: System Design and Adaptive Resource Optimization
Authors: Paolo Di Lorenzo, Mattia Merluzzi, Francesco Binucci, Claudio Battiloro, Paolo Banelli, Emilio Calvanese Strinati, Sergio Barbarossa
Date: 2023-10-21 00:00:00
Tasks: Federated Learning

Internet of Things (IoT) applications combine sensing, wireless communication, intelligence, and actuation, enabling the interaction among heterogeneous devices that collect and process considerable amounts of data. However, the effectiveness of IoT applications needs to face the limitation of available resources, including spectrum, energy, computing, learning and inference capabilities. This paper challenges the prevailing approach to IoT communication, which prioritizes the usage of resources in order to guarantee perfect recovery, at the bit level, of the data transmitted by the sensors to the central unit. We propose a novel approach, called goal-oriented (GO) IoT system design, that transcends traditional bit-related metrics and focuses directly on the fulfillment of the goal motivating the exchange of data. The improvement is then achieved through a comprehensive system optimization, integrating sensing, communication, computation, learning, and control. We provide numerical results demonstrating the practical applications of our methodology in compelling use cases such as edge inference, cooperative sensing, and federated learning. These examples highlight the effectiveness and real-world implications of our proposed approach, with the potential to revolutionize IoT systems.
NMR Spectra Denoising with Vandermonde Constraints
Authors: Di Guo, Runmin Xu, Jinyu Wu, Meijin Lin, Xiaofeng Du, Xiaobo Qu
Date: 2023-10-21 00:00:00
Tasks: Denoising

Nuclear magnetic resonance (NMR) spectroscopy serves as an important tool to analyze chemicals and proteins in bioengineering. However, NMR signals are easily contaminated by noise during the data acquisition, which can affect subsequent quantitative analysis. Therefore, denoising NMR signals has been a long-time concern. In this work, we propose an optimization model-based iterative denoising method, CHORD-V, by treating the time-domain NMR signal as damped exponentials and maintaining the exponential signal form with a Vandermonde factorization. Results on both synthetic and realistic NMR data show that CHORD-V has a superior denoising performance over typical Cadzow and rQRd methods, and the state-of-the-art CHORD method. CHORD-V restores low-intensity spectral peaks more accurately, especially when the noise is relatively high.
ML-Based Feedback-Free Adaptive MCS Selection for Massive Multi-User MIMO
Authors: Qing An, Mehdi Zafari, Chris Dick, Santiago Segarra, Ashutosh Sabharwal, Rahman Doost-Mohammady
Date: 2023-10-20 00:00:00

As wireless communication systems strive to improve spectral efficiency, there has been a growing interest in employing machine learning (ML)-based approaches for adaptive modulation and coding scheme (MCS) selection. In this paper, we introduce a new adaptive MCS selection framework for massive MIMO systems that operates without any feedback from users by solely relying on instantaneous uplink channel estimates. Our proposed method can effectively operate in multi-user scenarios where user feedback imposes excessive delay and bandwidth overhead. To learn the mapping between the user channel matrices and the optimal MCS level of each user, we develop a Convolutional Neural Network (CNN)-Long Short-Term Memory Network (LSTM)-based model and compare the performance with the state-of-the-art methods. Finally, we validate the effectiveness of our algorithm by evaluating it experimentally using real-world datasets collected from the RENEW massive MIMO platform.
V2X Sidelink Positioning in FR1: Scenarios, Algorithms, and Performance Evaluation
Authors: Yu Ge, Maximilian Stark, Musa Furkan Keskin, Frank Hofmann, Thomas Hansen, Henk Wymeersch
Date: 2023-10-20 00:00:00
Tasks: parameter estimation, Tensor Decomposition

In this paper, we investigate sub-6 GHz V2X sidelink positioning scenarios in 5G vehicular networks through a comprehensive end-to-end methodology encompassing ray-tracing-based channel modeling, novel theoretical performance bounds, high-resolution channel parameter estimation, and geometric positioning using a round-trip-time (RTT) protocol. We first derive a novel, approximate Cram\'er-Rao bound (CRB) on the connected road user (CRU) position, explicitly taking into account multipath interference, path merging, and the RTT protocol. Capitalizing on tensor decomposition and ESPRIT methods, we propose high-resolution channel parameter estimation algorithms specifically tailored to dense multipath V2X sidelink environments, designed to detect multipath components (MPCs) and extract line-of-sight (LoS) parameters. Finally, using realistic ray-tracing data and antenna patterns, comprehensive simulations are conducted to evaluate channel estimation and positioning performance, indicating that sub-meter accuracy can be achieved in sub-6 GHz V2X with the proposed algorithms.
Equivalence of Mass Action and Poisson Network SIR Epidemic Models
Authors: Grzegorz A. Rempala
Date: 2023-10-21 00:00:00

This brief note highlights a largely overlooked similarity between the SIR ordinary differential equations used for epidemics on the configuration model of a Poisson network and the classical mass-action SIR equations introduced nearly a century ago by Kermack and McKendrick. We demonstrate that the decline pattern in susceptibles is identical for both models. This equivalence carries practical implications: the susceptibles decay curve, often referred to as the epidemic or incidence curve, is frequently used in empirical studies to forecast epidemic dynamics. Although the curves for susceptibles align perfectly, those for infections do differ. Yet, the infection curves tend to converge and become almost indistinguishable in high-degree networks. In summary, our analysis suggests that under many practical scenarios, it is acceptable to use the classical SIR model as a close approximation to the Poisson SIR network model.
The Measure Preserving Martingale Sinkhorn Algorithm
Authors: Benjamin Joseph, Gregoire Loeper, Jan Obloj
Date: 2023-10-20 00:00:00

We contribute to the recent studies of the so-called Bass martingale. Backhoff-Veraguas et al. (2020) showed it is the solution to the martingale Benamou-Brenier (mBB) problem, i.e., among all martingales with prescribed initial and terminal distributions it is the one closest to the Brownian motion. We link it with semimartingale optimal transport and deduce an alternative way to derive the dual formulation recently obtained in Backhoff-Veraguas et al. (2023). We then consider computational methods to compute the Bass martingale. The dual formulation of the transport problem leads to an iterative scheme that mirrors to the celebrated Sinkhorn algorithm for entropic optimal transport. We call it the measure preserving martingale Sinkhorn (MPMS) algorithm. We prove that in any dimension, each step of the algorithm improves the value of the dual problem, which implies its convergence. Our MPMS algorithm is equivalent to the fixed-point method of Conze and Henry-Labordere (2021), studied in Acciaio et al. (2023), and performs very well on a range of examples, including real market data.