pwc-archive/papers-with-abstracts huggingface dataset:
Near-Field THz Bending Beamforming: A Convex Optimization Perspective
Authors: Aoran Liu, Weidong Mei, Peilan Wang, Dong Wang, Ya Fei Wu, Zhi Chen, Boyu Ning
Date: 2025-03-26 00:00:00

Terahertz (THz) communication systems suffer severe blockage issues, which may significantly degrade the communication coverage and quality. Bending beams, capable of adjusting their propagation direction to bypass obstacles, have recently emerged as a promising solution to resolve this issue by engineering the propagation trajectory of the beam. However, traditional bending beam generation methods rely heavily on the specific geometric properties of the propagation trajectory and can only achieve sub-optimal performance. In this paper, we propose a new and general bending beamforming method by adopting the convex optimization techniques. In particular, we formulate the bending beamforming design as a max-min optimization problem, aiming to optimize the analog or digital transmit beamforming vector to maximize the minimum received signal power among all positions along the bending beam trajectory. However, the resulting problem is non-convex and difficult to be solved optimally. To tackle this difficulty, we apply the successive convex approximation (SCA) technique to obtain a high-quality suboptimal solution. Numerical results show that our proposed bending beamforming method outperforms the traditional method and shows robustness to the obstacle in the environment.
Iterative Decoder of Channel-polarized Multilevel Coding for Data Center Networks
Authors: Takeshi Kakizaki, Masanori Nakamura, Fukutaro Hamaoka, Shuto Yamamoto, Etsushi Yamazaki
Date: 2025-03-25 00:00:00
Tasks: Decoder

Data center networks (DCNs) require a low-cost, low-power optical transceiver to handle increased traffic from generative artificial intelligence, video streaming services, and more. Improving the required signal-to-noise ratio (RSNR) by digital signal processing such as forward error correction (FEC) mitigates the requirements for electrical and optical components. The optical transceivers in DCNs exploit a low-complexity soft-decision (SD) FEC, consisting of short block-length linear error-correcting codes and a low-complexity SD decoder (SDD), such as a Chase decoder and ordered statistical decoding. The low complexity SDD efficiently approaches a maximum likelihood decoding (MLD). However, the decoding performance of MLD is limited by its finite block length. In this paper, we describe the detail of our proposed channel-polarized multilevel coding with iterative decoding (CP-MLC-ID), which improves the decoding performance. The 19.5$\%$-OH CP-MLC-ID 128-bit extended Bose-Chaudhuri-Hocquenghem (eBCH) and KP4 codes outperform the concatenated eBCH and KP4 codes with a net coding gain of 0.25 and 0.40 dB for the same and double the number of SDDs, respectively. We also investigate the dependency of the decoding performance on the size of a bit interleaver. The performance degradation of CP-MLC-ID using an 8-bit interleaver is about 0.1 dB compared to using the large-bit interleaver. Our results indicate that even a weak connection by exclusive-OR between codewords improves the decoding performance, compared to simple concatenated codes in the DCNs.
Hybrid FSO and RF Lunar Wireless Power Transfer
Authors: Baris Donmez, Yanni Jiwan-Mercier, Sebastien Loranger, Gunes Karabulut Kurt
Date: 2025-03-26 00:00:00

This study focuses on the feasibility analyses of the hybrid FSO and RF-based WPT system used in the realistic Cislunar environment, which is established by using STK HPOP software in which many external forces are incorporated. In our proposed multi-hop scheme, a solar-powered satellite (SPS) beams the laser power to the low lunar orbit (LLO) satellite in the first hop, then the harvested power is used as a relay power for RF-based WPT to two critical lunar regions, which are lunar south pole (LSP) (0{\deg}E,90{\deg}S) and Malapert Mountain (0{\deg}E,86{\deg}S), owing to the multi-point coverage feature of RF systems. The end-to-end system is analyzed for two cases, i) the perfect alignment, and ii) the misalignment fading due to the random mechanical vibrations in the optical inter-satellite link. It is found that the harvested power is maximized when the distance between the SPS and LLO satellite is minimized and it is calculated as 331.94 kW, however, when the random misalignment fading is considered, the mean of the harvested power reduces to 309.49 kW for the same distance. In the next hop, the power harvested by the solar array on the LLO satellite is consumed entirely as the relay power. Identical parabolic antennas are considered during the RF-based WPT system between the LLO satellite and the LSP, which utilizes a full-tracking module, and between the LLO satellite and the Malapert Mountain region, which uses a half-tracking module that executes the tracking on the receiver dish only. In the perfectly aligned hybrid WPT system, 19.80 W and 573.7 mW of maximum harvested powers are yielded at the LSP and Mountain Malapert, respectively. On the other hand, when the misalignment fading in the end-to-end system is considered, the mean of the maximum harvested powers degrades to 18.41 W and 534.4 mW for the former and latter hybrid WPT links.
Deep-Learning-Based Classification of Digitally Modulated Signals
Authors: John A. Snoap
Date: 2025-03-25 00:00:00
Tasks: Classification, Deep Learning

This dissertation presents several novel deep-learning (DL)-based approaches for classifying digitally modulated signals, one method of which involves the use of capsule networks (CAPs) together with cyclic cumulant (CC) features of the signals. These were blindly estimated using cyclostationary signal processing (CSP) and were then input into the CAP for training and classification. The results showed that the classification of digitally modulated signals using CAPs and CCs proposed in this dissertation outperformed alternative approaches for classifying digitally modulated signals that included conventional classifiers that employed CSP-based techniques, as well as alternative DL-based classifiers that used various conventional neural networks (NNs) with in-phase/quadrature (I/Q) data used for training and classification. Another method of digital modulation classification presented in this dissertation showcases two novel DL-based classifiers that each use a CAP with custom-designed feature extraction layers. The classifiers take I/Q data as input, and the feature extraction layers are inspired by CSP techniques, which extract the CC features employed by conventional CSP-based approaches to blind modulation classification and signal identification. Specifically, the feature extraction layers implement a proxy of the mathematical functions used in the calculation of the CC features and include a squaring layer, a raise-to-the-power-of-three layer, and a fast-Fourier-transform (FFT) layer, along with additional normalization and warping layers to ensure that the relative signal powers are retained and to prevent the trainable NN layers from diverging in the training process.
An Overview of Low-Rank Structures in the Training and Adaptation of Large Models
Authors: Laura Balzano, Tianjiao Ding, Benjamin D. Haeffele, Soo Min Kwon, Qing Qu, Peng Wang, Zhangyang Wang, Can Yaras
Date: 2025-03-25 00:00:00
Tasks: Self-Supervised Learning

The rise of deep learning has revolutionized data processing and prediction in signal processing and machine learning, yet the substantial computational demands of training and deploying modern large-scale deep models present significant challenges, including high computational costs and energy consumption. Recent research has uncovered a widespread phenomenon in deep networks: the emergence of low-rank structures in weight matrices and learned representations during training. These implicit low-dimensional patterns provide valuable insights for improving the efficiency of training and fine-tuning large-scale models. Practical techniques inspired by this phenomenon, such as low-rank adaptation (LoRA) and training, enable significant reductions in computational cost while preserving model performance. In this paper, we present a comprehensive review of recent advances in exploiting low-rank structures for deep learning and shed light on their mathematical foundations. Mathematically, we present two complementary perspectives on understanding the low-rankness in deep networks: (i) the emergence of low-rank structures throughout the whole optimization dynamics of gradient and (ii) the implicit regularization effects that induce such low-rank structures at convergence. From a practical standpoint, studying the low-rank learning dynamics of gradient descent offers a mathematical foundation for understanding the effectiveness of LoRA in fine-tuning large-scale models and inspires parameter-efficient low-rank training strategies. Furthermore, the implicit low-rank regularization effect helps explain the success of various masked training approaches in deep neural networks, ranging from dropout to masked self-supervised learning.
A Systematic Review of EEG-based Machine Intelligence Algorithms for Depression Diagnosis, and Monitoring
Authors: Amir Nassibi, Christos Papavassiliou, Ildar Rakhmatulin, Danilo Mandic, S. Farokh Atashzar
Date: 2025-03-25 00:00:00
Tasks: Articles, EEG

Depression disorder is a serious health condition that has affected the lives of millions of people around the world. Diagnosis of depression is a challenging practice that relies heavily on subjective studies and, in most cases, suffers from late findings. Electroencephalography (EEG) biomarkers have been suggested and investigated in recent years as a potential transformative objective practice. In this article, for the first time, a detailed systematic review of EEG-based depression diagnosis approaches is conducted using advanced machine learning techniques and statistical analyses. For this, 938 potentially relevant articles (since 1985) were initially detected and filtered into 139 relevant articles based on the review scheme 'preferred reporting items for systematic reviews and meta-analyses (PRISMA).' This article compares and discusses the selected articles and categorizes them according to the type of machine learning techniques and statistical analyses. Algorithms, preprocessing techniques, extracted features, and data acquisition systems are discussed and summarized. This review paper explains the existing challenges of the current algorithms and sheds light on the future direction of the field. This systematic review outlines the issues and challenges in machine intelligence for the diagnosis of EEG depression that can be addressed in future studies and possibly in future wearable technologies.
Recover from Horcrux: A Spectrogram Augmentation Method for Cardiac Feature Monitoring from Radar Signal Components
Authors: Yuanyuan Zhang, Sijie Xiong, Rui Yang, EngGee Lim, Yutao Yue
Date: 2025-03-25 00:00:00
Tasks: Data Augmentation, Diversity, regression

Radar-based wellness monitoring is becoming an effective measurement to provide accurate vital signs in a contactless manner, but data scarcity retards the related research on deep-learning-based methods. Data augmentation is commonly used to enrich the dataset by modifying the existing data, but most augmentation techniques can only couple with classification tasks. To enable the augmentation for regression tasks, this research proposes a spectrogram augmentation method, Horcrux, for radar-based cardiac feature monitoring (e.g., heartbeat detection, electrocardiogram reconstruction) with both classification and regression tasks involved. The proposed method is designed to increase the diversity of input samples while the augmented spectrogram is still faithful to the original ground truth vital sign. In addition, Horcrux proposes to inject zero values in specific areas to enhance the awareness of the deep learning model on subtle cardiac features, improving the performance for the limited dataset. Experimental result shows that Horcrux achieves an overall improvement of 16.20% in cardiac monitoring and has the potential to be extended to other spectrogram-based tasks. The code will be released upon publication.
Noise Resilient Over-The-Air Federated Learning In Heterogeneous Wireless Networks
Authors: Zubair Shaban, Nazreen Shah, Ranjitha Prasad
Date: 2025-03-25 00:00:00
Tasks: Federated Learning, Privacy Preserving

In 6G wireless networks, Artificial Intelligence (AI)-driven applications demand the adoption of Federated Learning (FL) to enable efficient and privacy-preserving model training across distributed devices. Over-The-Air Federated Learning (OTA-FL) exploits the superposition property of multiple access channels, allowing edge users in 6G networks to efficiently share spectral resources and perform low-latency global model aggregation. However, these advantages come with challenges, as traditional OTA-FL techniques suffer due to the joint effects of Additive White Gaussian Noise (AWGN) at the server, fading, and both data and system heterogeneity at the participating edge devices. In this work, we propose the novel Noise Resilient Over-the-Air Federated Learning (NoROTA-FL) framework to jointly tackle these challenges in federated wireless networks. In NoROTA-FL, the local optimization problems find controlled inexact solutions, which manifests as an additional proximal constraint at the clients. This approach provides robustness against straggler-induced partial work, heterogeneity, noise, and fading. From a theoretical perspective, we leverage the zeroth- and first-order inexactness and establish convergence guarantees for non-convex optimization problems in the presence of heterogeneous data and varying system capabilities. Experimentally, we validate NoROTA-FL on real-world datasets, including FEMNIST, CIFAR10, and CIFAR100, demonstrating its robustness in noisy and heterogeneous environments. Compared to state-of-the-art baselines such as COTAF and FedProx, NoROTA-FL achieves significantly more stable convergence and higher accuracy, particularly in the presence of stragglers.
Exploring Textual Semantics Diversity for Image Transmission in Semantic Communication Systems using Visual Language Model
Authors: Peishan Huang, Dong Li
Date: 2025-03-25 00:00:00
Tasks: Diversity, Language Modeling, Language Modelling, Semantic Communication, Semantic Segmentation

In recent years, the rapid development of machine learning has brought reforms and challenges to traditional communication systems. Semantic communication has appeared as an effective strategy to effectively extract relevant semantic signals semantic segmentation labels and image features for image transmission. However, the insufficient number of extracted semantic features of images will potentially result in a low reconstruction accuracy, which hinders the practical applications and still remains challenging for solving. In order to fill this gap, this letter proposes a multi-text transmission semantic communication (Multi-SC) system, which uses the visual language model (VLM) to assist in the transmission of image semantic signals. Unlike previous image transmission semantic communication systems, the proposed system divides the image into multiple blocks and extracts multiple text information from the image using a modified large language and visual assistant (LLaVA), and combines semantic segmentation tags with semantic text for image recovery. Simulation results show that the proposed text semantics diversity scheme can significantly improve the reconstruction accuracy compared with related works.
RIS-Assisted Passive Localization (RAPL): An Efficient Zero-Overhead Framework Using Conditional Sample Mean
Authors: Jiawei Yao, Yijie Mao, Mingzhe Chen, Ye Hu
Date: 2025-03-25 00:00:00

Reconfigurable Intelligent Surface (RIS) has been recognized as a promising solution for enhancing localization accuracy. Traditional RIS-based localization methods typically rely on prior channel knowledge, beam scanning, and pilot-based assistance. These approaches often result in substantial energy and computational overhead, and require real-time coordination between the base station (BS) and the RIS. To address these challenges, in this work, we move beyond conventional methods and introduce a novel data-driven, multiple RISs-assisted passive localization approach (RAPL). The proposed method includes two stages, the angle-of-directions (AoDs) between the RISs and the user is estimated by using the conditional sample mean in the first stage, and then the user's position is determined based on the estimated multiple AoD pairs in the second stage. This approach only utilizes the existing communication signals between the user and the BS, relying solely on the measurement of received signal power at each BS antenna for a set of randomly generated phase shifts across all RISs. Moreover, by obviating the need for real-time RIS phase shift optimization or user-to-BS pilot transmissions, the method introduces no additional communication overhead, making it highly suitable for deployment in real-world networks. The proposed scheme is then extended to multi-RIS scenarios considering both parallel and cascaded RIS topologies. Numerical results show that the proposed RAPL improves localization accuracy while significantly reducing energy and signaling overhead compared to conventional methods.
Parameter Design for Secure Affine Frequency Division Multiplexing Waveform
Authors: Zhang Di, Wang Zeyin, Tang Yanqun, Wu Dongdong, Yuan Muzi
Date: 2025-03-25 00:00:00

The secure affine frequency division multiplexing (AFDM) waveform design is a main concern in high-mobility networks. In this article, we employ the four key parameters in AFDM to design secure waveforms, and afterward we analyze the role of the four parameters to reveal the design guideline. We find that c1 is bounded by the Doppler shifts and preset guard. The parameter c2 exhibits a minimum periodicity of 1, with the effective range [0, 1] rather than any real number. The adjustable parameters c1 and c2 introduce additional degrees of freedom to the AFDM waveform, thereby enhancing the anti-eavesdropping performance. In addition, excessive Lmax that determines the preset guard interval leads to a security-risk interval and poses eavesdropping risks. Therefore, the optimal Lmax equals maximum delay. Numerical simulations verify the accuracy of our analysis.
Joint Sparse Graph for Enhanced MIMO-AFDM Receiver Design
Authors: Qu Luo, Jing Zhu, Zilong Liu, Yanqun Tang, Pei Xiao, Gaojie Chen, Jia Shi
Date: 2025-03-24 00:00:00
Tasks: Variational Inference

Affine frequency division multiplexing (AFDM) is a promising chirp-assisted multicarrier waveform for future high-mobility communications. This paper is devoted to enhanced receiver design for multiple input and multiple output AFDM (MIMO-AFDM) systems. Firstly, we introduce a unified variational inference (VI) approach to approximate the target posterior distribution, under which the belief propagation (BP) and expectation propagation (EP)-based algorithms are derived. As both VI-based detection and low-density parity-check (LDPC) decoding can be expressed by bipartite graphs in MIMO-AFDM systems, we construct a joint sparse graph (JSG) by merging the graphs of these two for low-complexity receiver design. Then, based on this graph model, we present the detailed message propagation of the proposed JSG. Additionally, we propose an enhanced JSG (E-JSG) receiver based on the linear constellation encoding model. The proposed E-JSG eliminates the need for interleavers, de-interleavers, and log-likelihood ratio transformations, thus leading to concurrent detection and decoding over the integrated sparse graph. To further reduce detection complexity, we introduce a sparse channel method by approaximating multiple graph edges with insignificant channel coefficients into a single edge on the VI graph. Simulation results show the superiority of the proposed receivers in terms of computational complexity, detection and decoding latency, and error rate performance compared to the conventional ones.
Tracking the topology of neural manifolds across populations
Authors: Iris H. R. Yoon, Gregory Henselman-Petrusek, Yiyi Yu, Robert Ghrist, Spencer LaVere Smith, Chad Giusti
Date: 2025-03-26 00:00:00
Tasks: Dimensionality Reduction

Neural manifolds summarize the intrinsic structure of the information encoded by a population of neurons. Advances in experimental techniques have made simultaneous recordings from multiple brain regions increasingly commonplace, raising the possibility of studying how these manifolds relate across populations. However, when the manifolds are nonlinear and possibly code for multiple unknown variables, it is challenging to extract robust and falsifiable information about their relationships. We introduce a framework, called the method of analogous cycles, for matching topological features of neural manifolds using only observed dissimilarity matrices within and between neural populations. We demonstrate via analysis of simulations and \emph{in vivo} experimental data that this method can be used to correctly identify multiple shared circular coordinate systems across both stimuli and inferred neural manifolds. Conversely, the method rejects matching features that are not intrinsic to one of the systems. Further, as this method is deterministic and does not rely on dimensionality reduction or optimization methods, it is amenable to direct mathematical investigation and interpretation in terms of the underlying neural activity. We thus propose the method of analogous cycles as a suitable foundation for a theory of cross-population analysis via neural manifolds.
TempTest: Local Normalization Distortion and the Detection of Machine-generated Text
Authors: Tom Kempton, Stuart Burrell, Connor Cheverall
Date: 2025-03-26 00:00:00

Existing methods for the zero-shot detection of machine-generated text are dominated by three statistical quantities: log-likelihood, log-rank, and entropy. As language models mimic the distribution of human text ever closer, this will limit our ability to build effective detection algorithms. To combat this, we introduce a method for detecting machine-generated text that is entirely agnostic of the generating language model. This is achieved by targeting a defect in the way that decoding strategies, such as temperature or top-k sampling, normalize conditional probability measures. This method can be rigorously theoretically justified, is easily explainable, and is conceptually distinct from existing methods for detecting machine-generated text. We evaluate our detector in the white and black box settings across various language models, datasets, and passage lengths. We also study the effect of paraphrasing attacks on our detector and the extent to which it is biased against non-native speakers. In each of these settings, the performance of our test is at least comparable to that of other state-of-the-art text detectors, and in some cases, we strongly outperform these baselines.