pwc-archive/papers-with-abstracts huggingface dataset:
Joint Channel, Data, and Radar Parameter Estimation for AFDM Systems in Doubly-Dispersive Channels
Authors: Kuranage Roche Rayan Ranasinghe, Hyeon Seok Rou, Giuseppe Thadeu Freitas de Abreu, Takumi Takahashi, Kenta Ito
Date: 2024-05-27 00:00:00
Tasks: Denoising, ISAC, parameter estimation

We propose new schemes for joint channel and data estimation (JCDE) and radar parameter estimation (RPE) in doubly-dispersive channels, such that integrated sensing and communications (ISAC) is enabled by user equipment (UE) independently performing JCDE, and base stations (BSs) performing RPE. The contributed JCDE and RPE schemes are designed for waveforms known to perform well in doubly-dispersive channels, under a unified model that captures the features of either legacy orthogonal frequency division multiplexing (OFDM), state-of-the-art (SotA) orthogonal time frequency space (OTFS), and next-generation affine frequency division multiplexing (AFDM) systems. The proposed JCDE algorithm is based on a Bayesian parametric bilinear Gaussian belief propagation (PBiGaBP) framework first proposed for OTFS and here shown to apply to all aforementioned waveforms, while the RPE scheme is based on a new probabilistic data association (PDA) approach incorporating a Bernoulli-Gaussian denoising, optimized via expectation maximization (EM). Simulation results demonstrate that JCDE in AFDM systems utilizing a single pilot per block significantly outperforms the SotA alternative even if the latter is granted a substantial power advantage. Similarly, the AFDM-based RPE scheme is found to outperform the OTFS-based approach, as well as the sparse Bayesian learning (SBL) technique, regardless of the waveform used.
CycloDSP: A cyclostationary signal analysis tool for GNU Radio
Authors: Donatella Darsena, Giacinto Gelli, Ivan Iudice
Date: 2024-05-27 00:00:00

In this paper, we present a first attempt to incorporate in the GNU Radio ecosystem a tool called CycloDSP, devoted to the analysis of complex-valued cyclostationary signals. Such signals are ubiquitous in communication and signal processing, exhibiting periodic or almost periodic statistics that are characterized by a countable set of cycle frequencies, which are related to the main signal periodicities. Common cycle frequencies for modulated signals are multiple of the baudrate and/or combination of the carrier frequency and baudrate. Conventional estimation strategies for cyclostationary signal analysis typically exhibit a high computational burden. Many approaches aimed at reducing complexity exploit fast Fourier transform (FFT) algorithms, which are very efficient for batch data but are not suited for continuously streaming data, typically encountered in software-defined radio (SDR) applications. The aim of this paper is to develop an out-of-tree (OOT) GNU Radio module containing a set of building block functions, aimed at estimating functions, such as the cyclic correlation functions, typically employed for cyclostationary signal analysis. The proposed implementation must be designed so as to ensure high efficiency in processing continuous data streams of complex samples, both in terms of computational load and data storage, in order to be executed on general purpose computers. We tested our implementation by estimating in real-time the second-order cyclic statistics of a Gaussian minimum-shift keying (GMSK) modulated signal, which belongs to a class of signals commonly employed in aeronautical telemetry datalinks.
Extraction of In-Phase and Quadrature Components by Time-Encoding Sampling
Authors: Y. H. Shao, S. Y. Chen, H. Z. Yang, F. Xi, H. Hong, Z. Liu
Date: 2024-05-27 00:00:00

Time encoding machine (TEM) is a biologically-inspired scheme to perform signal sampling using timing. In this paper, we study its application to the sampling of bandpass signals. We propose an integrate-and-fire TEM scheme by which the in-phase (I) and quadrature (Q) components are extracted through reconstruction. We design the TEM according to the signal bandwidth and amplitude instead of upper-edge frequency and amplitude as in the case of bandlimited/lowpass signals. We show that the I and Q components can be perfectly reconstructed from the TEM measurements if the minimum firing rate is equal to the Landau's rate of the signal. For the reconstruction of I and Q components, we develop an alternating projection onto convex sets (POCS) algorithm in which two POCS algorithms are alternately iterated. For the algorithm analysis, we define a solution space of vector-valued signals and prove that the proposed reconstruction algorithm converges to the correct unique solution in the noiseless case. The proposed TEM can operate regardless of the center frequencies of the bandpass signals. This is quite different from traditional bandpass sampling, where the center frequency should be carefully allocated for Landau's rate and its variations have the negative effect on the sampling performance. In addition, the proposed TEM achieves certain reconstructed signal-to-noise-plus-distortion ratios for small firing rates in thermal noise, which is unavoidably present and will be aliased to the Nyquist band in the traditional sampling such that high sampling rates are required. We demonstrate the reconstruction performance and substantiate our claims via simulation experiments.
Coverage Analysis of Downlink Transmission in Multi-Connectivity Cellular V2X Networks
Authors: Luofang Jiao, Tianqi Zhang, Jiwei Zhao, Yunting Xu, Haibo Zhou
Date: 2024-05-27 00:00:00

With the increasing of connected vehicles in the fifth-generation mobile communication networks (5G) and beyond 5G (B5G), ensuring the reliable and high-speed cellular vehicle-to-everything (C-V2X) communication has posed significant challenges due to the high mobility of vehicles. For improving the network performance and reliability, multi-connectivity technology has emerged as a crucial transmission mode for C-V2X in the 5G era. To this end, this paper proposes a framework for analyzing the performance of multi-connectivity in C-V2X downlink transmission, with a focus on the performance indicators of joint distance distribution and coverage probability. Specifically, we first derive the joint distance distribution of multi-connectivity. By leveraging the tools of stochastic geometry, we then obtain the analytical expressions of coverage probability based on the previous results for general multi-connectivity cases in C-V2X. Subsequently, we evaluate the effect of path loss exponent and downlink base station density on coverage probability based on the proposed analytical framework. Finally, extensive Monte Carlo simulations are conducted to validate the effectiveness of the proposed analytical framework and the simulation results reveal that multi-connectivity technology can significantly enhance the coverage probability in C-V2X.
Coil Reweighting to Suppress Motion Artifacts in Real-Time Exercise Cine Imaging
Authors: Chong Chen, Yingmin Liu, Yu Ding, Matthew Tong, Preethi Chandrasekaran, Christopher Crabtree, Syed M. Arshad, Yuchi Han, Rizwan Ahmad
Date: 2024-05-26 00:00:00

Background: Accelerated real-time cine (RT-Cine) imaging enables cardiac function assessment without the need for breath-holding. However, when performed during in-magnet exercise, RT-Cine images may exhibit significant motion artifacts. Methods: By projecting the time-averaged images to the subspace spanned by the coil sensitivity maps, we propose a coil reweighting (CR) method to automatically suppress a subset of receive coils that introduces a high level of artifacts in the reconstructed image. RT-Cine data collected at rest and during exercise from ten healthy volunteers and six patients were utilized to assess the performance of the proposed method. One short-axis and one two-chamber RT-Cine series reconstructed with and without CR from each subject were visually scored by two cardiologists in terms of the level of artifacts on a scale of 1 (worst) to 5 (best). Results: For healthy volunteers, applying CR to RT-Cine images collected at rest did not significantly change the image quality score (p=1). In contrast, for RT-Cine images collected during exercise, CR significantly improved the score from 3.9 to 4.68 (p<0.001). Similarly, in patients, CR did not significantly change the score for images collected at rest (p=0.031) but markedly improved the score from 3.15 to 4.42 (p<0.001) for images taken during exercise. Despite lower image quality scores in the patient cohort compared to healthy subjects, likely due to larger body habitus and the difficulty of limiting body motion during exercise, CR effectively suppressed motion artifacts, with all image series from the patient cohort receiving a score of four or higher. Conclusion: Using data from healthy subjects and patients, we demonstrate that the motion artifacts in the reconstructed RT-Cine images can be effectively suppressed significantly with the proposed CR method.
Aperture Selection for CAP Arrays (CAPAs)
Authors: Chongjun Ouyang, Yuanwei Liu, Xingqi Zhang
Date: 2024-05-26 00:00:00
Tasks: Diversity

The concept of aperture selection is proposed for continuous aperture array (CAPA)-based communications. The achieved performance is analyzed in an uplink scenario by considering both line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios. In the LoS scenario, the optimal selection strategy is demonstrated to follow the nearest neighbor criterion, and the resulting signal-to-noise ratio (SNR) is analyzed. In the NLoS scenario, the achieved outage probability along with the diversity order is revealed. Numerical results are provided to demonstrate that aperture selection effectively maintains satisfactory performance by leveraging selection diversity while simultaneously reducing the implementation complexity of CAPAs.
On the Performance of Continuous Aperture Array (CAPA)-Based Wireless Communications
Authors: Chongjun Ouyang, Yuanwei Liu, Xingqi Zhang
Date: 2024-05-26 00:00:00

The performance of continuous aperture array (CAPA)-based wireless communications is analyzed in an uplink scenario. An analytical framework is proposed to characterize uplink CAPA-based transmission using electromagnetic field theories. On this basis, new expressions are derived for the channel capacity in a single-user scenario and the sum-rate capacity in a multiuser scenario, along with the capacity-achieving decoding schemes. These findings are proved to differ greatly from those established for conventional spatially discrete (SPD) arrays. Numerical results are provided to demonstrate that CAPA offers significant capacity gains compared to the SPD array.
Error Performance Analysis of UAV-Mounted RIS for NOMA Systems with Practical Constraints
Authors: Faical Khennoufa, Khelil Abdellatif, Ferdi Kara, Halim Yanikomeroglu, Khaled Rabie, Taissir Y. Elganimi, Safia Beddiaf
Date: 2024-05-26 00:00:00

Uncrewed aerial vehicles (UAVs) have attracted recent attention for sixth-generation (6G) networks due to their low cost and flexible deployment. In order to maximize the ever-increasing data rates, spectral efficiency, and wider coverage, technologies such as reconfigurable intelligent surface (RIS) and non-orthogonal multiple access (NOMA) are adapted with UAVs (UAV-RIS NOMA). However, the error performance of UAV-RIS NOMA has not been considered, yet. In this letter, we investigate the error probability of UAV-RIS NOMA systems. We also consider the practical constraints of hardware impairments (HWI) at the transceivers, inter-cell interference (ICI), and imperfect successive interference cancellation (SIC). The analytical derivations are validated by Monte-Carlo simulations. Our results demonstrate that our proposed system achieves higher performance gain (more than 5 dB with increasing the number of RIS elements) with less error probability compared to UAVs without RIS. Moreover, it is found that the HWI, ICI, and imperfect SIC have shown a negative impact on the system performance.
ECG Semantic Integrator (ESI): A Foundation ECG Model Pretrained with LLM-Enhanced Cardiological Text
Authors: Han Yu, Peikun Guo, Akane Sano
Date: 2024-05-26 00:00:00
Tasks: Arrhythmia Detection, RAG, Representation Learning, Retrieval-augmented Generation, Self-Supervised Learning

The utilization of deep learning on electrocardiogram (ECG) analysis has brought the advanced accuracy and efficiency of cardiac healthcare diagnostics. By leveraging the capabilities of deep learning in semantic understanding, especially in feature extraction and representation learning, this study introduces a new multimodal contrastive pretaining framework that aims to improve the quality and robustness of learned representations of 12-lead ECG signals. Our framework comprises two key components, including Cardio Query Assistant (CQA) and ECG Semantics Integrator(ESI). CQA integrates a retrieval-augmented generation (RAG) pipeline to leverage large language models (LLMs) and external medical knowledge to generate detailed textual descriptions of ECGs. The generated text is enriched with information about demographics and waveform patterns. ESI integrates both contrastive and captioning loss to pretrain ECG encoders for enhanced representations. We validate our approach through various downstream tasks, including arrhythmia detection and ECG-based subject identification. Our experimental results demonstrate substantial improvements over strong baselines in these tasks. These baselines encompass supervised and self-supervised learning methods, as well as prior multimodal pretraining approaches.
A New Solution for MU-MISO Symbol-Level Precoding: Extrapolation and Deep Unfolding
Authors: Mu Liang, Ang Li, Xiaoyan Hu, Christos Masouros
Date: 2024-05-26 00:00:00

Constructive interference (CI) precoding, which converts the harmful multi-user interference into beneficial signals, is a promising and efficient interference management scheme in multi-antenna communication systems. However, CI-based symbol-level precoding (SLP) experiences high computational complexity as the number of symbol slots increases within a transmission block, rendering it unaffordable in practical communication systems. In this paper, we propose a symbol-level extrapolation (SLE) strategy to extrapolate the precoding matrix by leveraging the relationship between different symbol slots within in a transmission block, during which the channel state information (CSI) remains constant, where we design a closed-form iterative algorithm based on SLE for both PSK and QAM modulation. In order to further reduce the computational complexity, a sub-optimal closed-form solution based on SLE is further developed for PSK and QAM, respectively. Moreover, we design an unsupervised SLE-based neural network (SLE-Net) to unfold the proposed iterative algorithm, which helps enhance the interpretability of the neural network. By carefully designing the loss function of the SLE-Net, the time-complexity of the network can be reduced effectively. Extensive simulation results illustrate that the proposed algorithms can dramatically reduce the computational complexity and time complexity with only marginal performance loss, compared with the conventional SLP design methods.
Networked Integrated Sensing and Communications for 6G Wireless Systems
Authors: Jiapeng Li, Xiaodan Shao, Feng Chen, Shaohua Wan, Chang Liu, Zhiqiang Wei, Derrick Wing Kwan Ng
Date: 2024-05-26 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) is envisioned as a key pillar for enabling the upcoming sixth generation (6G) communication systems, requiring not only reliable communication functionalities but also highly accurate environmental sensing capabilities. In this paper, we design a novel networked ISAC framework to explore the collaboration among multiple users for environmental sensing. Specifically, multiple users can serve as powerful sensors, capturing back scattered signals from a target at various angles to facilitate reliable computational imaging. Centralized sensing approaches are extremely sensitive to the capability of the leader node because it requires the leader node to process the signals sent by all the users. To this end, we propose a two-step distributed cooperative sensing algorithm that allows low-dimensional intermediate estimate exchange among neighboring users, thus eliminating the reliance on the centralized leader node and improving the robustness of sensing. This way, multiple users can cooperatively sense a target by exploiting the block-wise environment sparsity and the interference cancellation technique. Furthermore, we analyze the mean square error of the proposed distributed algorithm as a networked sensing performance metric and propose a beamforming design for the proposed network ISAC scheme to maximize the networked sensing accuracy and communication performance subject to a transmit power constraint. Simulation results validate the effectiveness of the proposed algorithm compared with the state-of-the-art algorithms.
Transformer Meets Gated Residual Networks To Enhance Photoplethysmogram Artifact Detection Informed by Mutual Information Neural Estimation
Authors: Thanh-Dung Le, Clara Macabiau, Kévin Albert, Symeon Chatzinotas, Philippe Jouvet, Rita Noumeir
Date: 2024-05-25 00:00:00
Tasks: Artifact Detection, Self-Supervised Learning

This study delves into the effectiveness of various learning methods in improving Transformer models, focusing particularly on the Gated Residual Network Transformer (GRN-Transformer) in the context of pediatric intensive care units (PICU) with limited data availability. Our findings indicate that Transformers trained via supervised learning are less effective compared to MLP, CNN, and LSTM networks in such environments. Yet, leveraging unsupervised and self-supervised learning on unannotated data, with subsequent fine-tuning on annotated data, notably enhances Transformer performance, although not to the level of the GRN-Transformer. Central to our research is the analysis of different activation functions for the Gated Linear Unit (GLU), a crucial element of the GRN structure. We also employ Mutual Information Neural Estimation (MINE) to evaluate the GRN's contribution. Additionally, the study examines the effects of integrating GRN within the Transformer's Attention mechanism versus using it as a separate intermediary layer. Our results highlight that GLU with sigmoid activation stands out, achieving 0.98 accuracy, 0.91 precision, 0.96 recall, and 0.94 F1 score. The MINE analysis supports the hypothesis that GRN enhances the mutual information between the hidden representations and the output. Moreover, the use of GRN as an intermediate filter layer proves more beneficial than incorporating it within the Attention mechanism. In summary, this research clarifies how GRN bolsters GRN-Transformer's performance, surpassing other learning techniques. These findings offer a promising avenue for adopting sophisticated models like Transformers in data-constrained environments, such as PPG artifact detection in PICU settings.
Semantic Importance-Aware Communications with Semantic Correction Using Large Language Models
Authors: Shuaishuai Guo, Yanhu Wang, Jia Ye, Anbang Zhang, Kun Xu
Date: 2024-05-25 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Semantic communications, a promising approach for agent-human and agent-agent interactions, typically operate at a feature level, lacking true semantic understanding. This paper explores understanding-level semantic communications (ULSC), transforming visual data into human-intelligible semantic content. We employ an image caption neural network (ICNN) to derive semantic representations from visual data, expressed as natural language descriptions. These are further refined using a pre-trained large language model (LLM) for importance quantification and semantic error correction. The subsequent semantic importance-aware communications (SIAC) aim to minimize semantic loss while respecting transmission delay constraints, exemplified through adaptive modulation and coding strategies. At the receiving end, LLM-based semantic error correction is utilized. If visual data recreation is desired, a pre-trained generative artificial intelligence (AI) model can regenerate it using the corrected descriptions. We assess semantic similarities between transmitted and recovered content, demonstrating ULSC's superior ability to convey semantic understanding compared to feature-level semantic communications (FLSC). ULSC's conversion of visual data to natural language facilitates various cognitive tasks, leveraging human knowledge bases. Additionally, this method enhances privacy, as neither original data nor features are directly transmitted.
Exploring Age-of-Information Weighting in Federated Learning under Data Heterogeneity
Authors: Kaidi Wang, Zhiguo Ding, Daniel K. C. So, Zhi Ding
Date: 2024-05-24 00:00:00
Tasks: Federated Learning

This paper investigates federated learning in a wireless communication system, where random device selection is employed with non-independent and identically distributed (non-IID) data. The analysis indicates that while training deep learning networks using federated stochastic gradient descent (FedSGD) on non-IID datasets, device selection can generate gradient errors that accumulate, leading to potential weight divergence. To mitigate training divergence, we design an age-weighted FedSGD to scale local gradients according to the previous state of devices. To further improve learning performance by increasing device participation under the maximum time consumption constraint, we formulate an energy consumption minimization problem by including resource allocation and sub-channel assignment. By transforming the resource allocation problem into convex and utilizing KKT conditions, we derived the optimal resource allocation solution. Moreover, this paper develops a matching based algorithm to generate the enhanced sub-channel assignment. Simulation results indicate that i) age-weighted FedSGD is able to outperform conventional FedSGD in terms of convergence rate and achievable accuracy, and ii) the proposed resource allocation and sub-channel assignment strategies can significantly reduce energy consumption and improve learning performance by increasing the number of selected devices.
Pattern-Based Time-Series Risk Scoring for Anomaly Detection and Alert Filtering -- A Predictive Maintenance Case Study
Authors: Elad Liebman
Date: 2024-05-24 00:00:00
Tasks: Anomaly Detection, Fault Detection, Time Series

Fault detection is a key challenge in the management of complex systems. In the context of SparkCognition's efforts towards predictive maintenance in large scale industrial systems, this problem is often framed in terms of anomaly detection - identifying patterns of behavior in the data which deviate from normal. Patterns of normal behavior aren't captured simply in the coarse statistics of measured signals. Rather, the multivariate sequential pattern itself can be indicative of normal vs. abnormal behavior. For this reason, normal behavior modeling that relies on snapshots of the data without taking into account temporal relationships as they evolve would be lacking. However, common strategies for dealing with temporal dependence, such as Recurrent Neural Networks or attention mechanisms are oftentimes computationally expensive and difficult to train. In this paper, we propose a fast and efficient approach to anomaly detection and alert filtering based on sequential pattern similarities. In our empirical analysis section, we show how this approach can be leveraged for a variety of purposes involving anomaly detection on a large scale real-world industrial system. Subsequently, we test our approach on a publicly-available dataset in order to establish its general applicability and robustness compared to a state-of-the-art baseline. We also demonstrate an efficient way of optimizing the framework based on an alert recall objective function.