pwc-archive/papers-with-abstracts huggingface dataset:
Positioning-Aided Channel Estimation for Multi-LEO Satellite Cooperative Communications
Authors: Yuchen Zhang, Pinjun Zheng, Jie Ma, Henk Wymeersch, Tareq Y. Al-Naffouri
Date: 2025-02-09 00:00:00
Tasks: Position

We investigate a multi-low Earth orbit (LEO) satellite system that simultaneously provides positioning and communication services to terrestrial user terminals. To address the challenges of channel estimation in LEO satellite systems, we propose a novel two-timescale positioning-aided channel estimation framework, exploiting the distinct variation rates of position-related parameters and channel gains inherent in LEO satellite channels. Using the misspecified Cram\'er-Rao bound (MCRB) theory, we systematically analyze positioning performance under practical imperfections, such as inter-satellite clock bias and carrier frequency offset. Furthermore, we theoretically demonstrate how position information derived from downlink positioning can enhance uplink channel estimation accuracy, even in the presence of positioning errors, through an MCRB-based analysis. To overcome the constraints of limited link budgets and communication rates associated with single-satellite-based communication, we develop a multi-LEO satellite cooperative beamforming strategy for downlink communication, capitalizing on the benefit of cluster-wise satellites cooperation. Theoretical analyses and numerical results confirm the effectiveness of the proposed framework in achieving high-precision downlink positioning under practical imperfections, facilitating uplink channel estimation, and enabling efficient downlink communication.
Explainable and Class-Revealing Signal Feature Extraction via Scattering Transform and Constrained Zeroth-Order Optimization
Authors: Naoki Saito, David Weber
Date: 2025-02-08 00:00:00
Tasks: Classification, regression, Time Series Classification

We propose a new method to extract discriminant and explainable features from a particular machine learning model, i.e., a combination of the scattering transform and the multiclass logistic regression. Although this model is well-known for its ability to learn various signal classes with high classification rate, it remains elusive to understand why it can generate such successful classification, mainly due to the nonlinearity of the scattering transform. In order to uncover the meaning of the scattering transform coefficients selected by the multiclass logistic regression (with the Lasso penalty), we adopt zeroth-order optimization algorithms to search an input pattern that maximizes the class probability of a class of interest given the learned model. In order to do so, it turns out that imposing sparsity and smoothness of input patterns is important. We demonstrate the effectiveness of our proposed method using a couple of synthetic time-series classification problems.
TrackDiffuser: Nearly Model-Free Bayesian Filtering with Diffusion Model
Authors: Yangguang He, Wenhao Li, Minzhe Li, Juan Zhang, Xiangfeng Wang, Bo Jin
Date: 2025-02-08 00:00:00
Tasks: Autonomous Driving, model, State Estimation

State estimation remains a fundamental challenge across numerous domains, from autonomous driving, aircraft tracking to quantum system control. Although Bayesian filtering has been the cornerstone solution, its classical model-based paradigm faces two major limitations: it struggles with inaccurate state space model (SSM) and requires extensive prior knowledge of noise characteristics. We present TrackDiffuser, a generative framework addressing both challenges by reformulating Bayesian filtering as a conditional diffusion model. Our approach implicitly learns system dynamics from data to mitigate the effects of inaccurate SSM, while simultaneously circumventing the need for explicit measurement models and noise priors by establishing a direct relationship between measurements and states. Through an implicit predict-and-update mechanism, TrackDiffuser preserves the interpretability advantage of traditional model-based filtering methods. Extensive experiments demonstrate that our framework substantially outperforms both classical and contemporary hybrid methods, especially in challenging non-linear scenarios involving non-Gaussian noises. Notably, TrackDiffuser exhibits remarkable robustness to SSM inaccuracies, offering a practical solution for real-world state estimation problems where perfect models and prior knowledge are unavailable.
NOMANet: A Graph Neural Network Enabled Power Allocation Scheme for NOMA
Authors: Yipu Hou, Yang Lu, Wei Chen, Bo Ai, Dusit Niyato, Zhiguo Ding
Date: 2025-02-08 00:00:00
Tasks: Graph Neural Network

This paper proposes a graph neural network (GNN) enabled power allocation scheme for non-orthogonal multiple access (NOMA) networks. In particular, a downlink scenario with one base station serving multiple users over several subchannels is considered, where the number of subchannels is less than the number of users, and thus, some users have to share a subchannel via NOMA. Our goal is to maximize the system energy efficiency subject to the rate requirement of each user and the overall budget. We propose a deep learning based approach termed NOMA net (NOMANet) to address the considered problem. Particularly, NOMANet is GNN-based, which maps channel state information to the desired power allocation scheme for all subchannels. The multi-head attention and the residual/dense connection are adopted to enhance the feature extraction. The output of NOMANet is guaranteed to be feasible via the customized activation function and the penalty method. Numerical results show that NOMANet trained unsupervised achieves performance close to that of the successive convex approximation method but with a faster inference speed by about $700$ times. Besides, NOMANet is featured by its scalability to both users and subchannels.
Efficient Sampling Allocation Strategies for General Graph-Filter-Based Signal Recovery
Authors: Lital Dabush, Tirza Routtenberg
Date: 2025-02-08 00:00:00

Sensor placement plays a crucial role in graph signal recovery in underdetermined systems. In this paper, we present the graph-filtered regularized maximum likelihood (GFR-ML) estimator of graph signals, which integrates general graph filtering with regularization to enhance signal recovery performance under a limited number of sensors. Then, we investigate task-based sampling allocation aimed at minimizing the mean squared error (MSE) of the GFR-ML estimator by wisely choosing sensor placement. Since this MSE depends on the unknown graph signals to be estimated, we propose four cost functions for the optimization of the sampling allocation: the biased Cram$\acute{\text{e}}$r-Rao bound (bCRB), the worst-case MSE (WC-MSE), the Bayesian MSE (BMSE), and the worst-case BMSE (WC-BMSE), where the last two assume a Gaussian prior. We investigate the properties of these cost functions and develop two algorithms for their practical implementation: 1) the straightforward greedy algorithm; and 2) the alternating projection gradient descent (PGD) algorithm that reduces the computational complexity. Simulation results on synthetic and real-world datasets of the IEEE 118-bus power system and the Minnesota road network demonstrate that the proposed sampling allocation methods reduce the MSE by up to $50\%$ compared to the common sampling methods A-design, E-design, and LR-design in the tested scenarios. Thus, the proposed methods improve the estimation performance and reduce the required number of measurements in graph signal processing (GSP)-based signal recovery in the case of underdetermined systems.
Channel Estimation for RIS-Aided MU-MIMO mmWave Systems with Practical Hybrid Architecture
Authors: Liuchang Zhuo, Cunhua Pan, Hong Ren, Ruisong Weng, Shi Jin, A. Lee Swindlehurst, Jiangzhou Wang
Date: 2025-02-08 00:00:00
Tasks: Compressive Sensing

This paper proposes a correlation-based three-stage channel estimation strategy with low pilot overhead for reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) multi-user (MU) MIMO systems, in which both users and base station (BS) are equipped with a hybrid RF architecture. In Stage I, all users jointly transmit pilots and recover the uncompressed received signals to estimate the angle of arrival (AoA) at the BS using the discrete Fourier transform (DFT). Based on the observation that the overall cascaded MIMO channel can be decomposed into multiple sub-channels, the cascaded channel for a typical user is estimated in Stage II. Specifically, using the invariance of angles and the linear correlation of gains related to different cascaded subchannels, we use compressive sensing (CS), least squares (LS), and a one-dimensional search to estimate the Angles of Departure (AoDs), based on which the overall cascaded channel is obtained. In Stage III, the remaining users independently transmit pilots to estimate their individual cascaded channel with the same approach as in Stage II, which exploits the equivalent common RIS-BS channel obtained in Stage II to reduce the pilot overhead. In addition, the hybrid combining matrix and the RIS phase shift matrix are designed to reduce the noise power, thereby further improving the estimation performance. Simulation results demonstrate that the proposed algorithm can achieve high estimation accuracy especially when the number of antennas at the users is small, and reduce pilot overhead by more than five times compared with the existing benchmark approach.
Modeling of Core Loss Based on Machine Learning and Deep Learning
Authors: Junqi He, Yifeng Wei, Daiguang Jin
Date: 2025-02-08 00:00:00

This article proposes a Mix Neural Network (MNN) based on CNN-FCNN for predicting magnetic loss of different materials. In traditional magnetic core loss models, empirical equations usually need to be regressed under the same external conditions. When the magnetic core material is different, it needs to be classified and discussed. If external factors increase, multiple models need to be proposed for classification and discussion, making the modeling process extremely cumbersome. And traditional empirical equations still has the problem of low accuracy, although various correction equations have been introduced later, the accuracy has always been unsatisfactory. By introducing machine learning and deep learning, it is possible to simultaneously solve prediction problems with low accuracy of empirical equations and complex conditions. Based on the MagNet database, through the training of the newly proposed MNN, it is found that a single model is sufficient to make predictions for at least four different materials under varying temperatures, frequencies, and waveforms, with accuracy far exceeding that of traditional models. At the same time, we also used three other machine learning and deep learning models (Random Forest, XGBoost, MLP-LSTM) for training, all of which had much higher accuracy than traditional models. On the basis of the predicted results, a hybrid model combining MNN and XGBoost was proposed, which predicted through weighting and found that the accuracy could continue to improve. This provides a solution for modeling magnetic core loss under different materials and operating modes.
Graph Neural Network Enabled Pinching Antennas
Authors: Xinke Xie, Yang Lu, Zhiguo Ding
Date: 2025-02-08 00:00:00
Tasks: Computational Efficiency, Graph Attention, Graph Neural Network

The pinching-antenna system is a novel flexible-antenna technology, which has the capabilities not only to combat large-scale path loss, but also to reconfigure the antenna array in a flexible manner. The key idea of pinching antennas is to apply small dielectric particles on a waveguide of arbitrary length, so that they can be positioned close to users to avoid significant large-scale path loss. This paper investigates the graph neural network (GNN) enabled transmit design for the joint optimization of antenna placement and power allocation in pinching-antenna systems. We formulate the downlink communication system equipped with pinching antennas as a bipartite graph, and propose a graph attention network (GAT) based model, termed bipartite GAT (BGAT), to solve an energy efficiency (EE) maximization problem. With the tailored readout processes, the BGAT guarantees a feasible solution, which also facilitates the unsupervised training. Numerical results demonstrate the effectiveness of pinching antennas in enhancing the system EE as well as the proposed BGAT in terms of optimality, scalability and computational efficiency.
Gait Disorder Assessment Based on a Large-Scale Clinical Trial: WiFi vs. Video vs. Doctor's Visual Inspection
Authors: Alireza Parsay, Mert Torun, Philip R. Delio, Yasamin Mostofi
Date: 2025-02-07 00:00:00

Neurological gait disorders can affect a large population, with devastating consequences on the quality of life. In this paper, we are interested in fundamentally understanding the potential of emerging sensing modalities (e.g., WiFi) for neurological gait disorder assessment. Towards this goal, we conduct a one-year-long clinical trial in collaboration with the Neurology Associates of Santa Barbara, the results of which are detailed in this paper. More specifically, our medical campaign encompasses 114 real subjects and a wide spectrum of disorders (e.g., Parkinson's, Neuropathy, Post Stroke, Dementia, and Arthritis). We then develop the first WiFi-based gait disorder sensing system of its kind, distinguished by its scope of validation with a large and diverse patient cohort. To ensure generalizability, we mainly leverage publicly-accessible online videos of gait disorders for training, and develop a video-to-RF pipeline to convert them to synthetic RF data. We then extensively test the system in a neurology center (i.e., the Neurology Associates of Santa Barbara). In addition, we provide a 1-1 comparison with a vision-based system, by developing a vision-based gait disorder assessment system under the same exact conditions (e.g., same subjects, etc), a comparison crucial for developing smart health systems and the first of its kind to our knowledge. We finally contrast both systems with the accuracy of neurologists when basing evaluation solely on the visual inspection of the gait, by designing a large survey and distributing it to an extensive network of neurologists, thus offering the first such apples-to-apples comparison of these three sensing modalities. Overall, the findings can shape the integration of new sensing modalities into medical practice, and pave the way toward equitable healthcare.
An Elliptic Curve Based Solution to the Perspective-Three-Point Problem
Authors: Michael Q. Rieck
Date: 2025-02-11 00:00:00

The Perspective-Three-Point Problem (P3P) is solved by first focusing on determining the directions of the lines through pairs of control points, relative to the camera, rather than the distances from the camera to the control points. The analysis of this produces an efficient, accurate and reasonably simple P3P solver, which is compared with a state-of-the-art P3P solver, "Lambda Twist." Both methods depend on the accurate computation of a single root of a cubic polynomial. They have been implemented and tested for a wide range of control-point triangles, and under certain reasonable restrictions, the new method is noticably more accurate than Lambda Twist, though it is slower. However, the principal value of the present work is not in introducing yet another P3P solver, but lies rather in the discovery of an intimate connection between the P3P problem and a special family of elliptic curves that includes curves utilized in cryptography. This holds the potential for further advances in a number of directions. To make this connection, an interesting spherical analogue of an ancient "sliding" problem is stated and solved.
HiPoNet: A Multi-View Simplicial Complex Network for High Dimensional Point-Cloud and Single-Cell Data
Authors: Siddharth Viswanath, Hiren Madhu, Dhananjay Bhaskar, Jake Kovalic, David R Johnson, Christopher Tape, Ian Adelstein, Rex Ying, Michael Perlmutter, Smita Krishnaswamy
Date: 2025-02-11 00:00:00
Tasks: Representation Learning

In this paper, we propose HiPoNet, an end-to-end differentiable neural network for regression, classification, and representation learning on high-dimensional point clouds. Our work is motivated by single-cell data which can have very high-dimensionality --exceeding the capabilities of existing methods for point clouds which are mostly tailored for 3D data. Moreover, modern single-cell and spatial experiments now yield entire cohorts of datasets (i.e., one data set for every patient), necessitating models that can process large, high-dimensional point-clouds at scale. Most current approaches build a single nearest-neighbor graph, discarding important geometric and topological information. In contrast, HiPoNet models the point-cloud as a set of higher-order simplicial complexes, with each particular complex being created using a reweighting of features. This method thus generates multiple constructs corresponding to different views of high-dimensional data, which in biology offers the possibility of disentangling distinct cellular processes. It then employs simplicial wavelet transforms to extract multiscale features, capturing both local and global topology from each view. We show that geometric and topological information is preserved in this framework both theoretically and empirically. We showcase the utility of HiPoNet on point-cloud level tasks, involving classification and regression of entire point-clouds in data cohorts. Experimentally, we find that HiPoNet outperforms other point-cloud and graph-based models on single-cell data. We also apply HiPoNet to spatial transcriptomics datasets using spatial coordinates as one of the views. Overall, HiPoNet offers a robust and scalable solution for high-dimensional data analysis.
Poincaré Inequality for Local Log-Polyak-Lojasiewicz Measures : Non-asymptotic Analysis in Low-temperature Regime
Authors: Yun Gong, Zebang Shen, Niao He
Date: 2025-02-08 00:00:00

Potential functions in highly pertinent applications, such as deep learning in over-parameterized regime, are empirically observed to admit non-isolated minima. To understand the convergence behavior of stochastic dynamics in such landscapes, we propose to study the class of \logPLmeasure\ measures $\mu_\epsilon \propto \exp(-V/\epsilon)$, where the potential $V$ satisfies a local Polyak-{\L}ojasiewicz (P\L) inequality, and its set of local minima is provably \emph{connected}. Notably, potentials in this class can exhibit local maxima and we characterize its optimal set S to be a compact $\mathcal{C}^2$ \emph{embedding submanifold} of $\mathbb{R}^d$ without boundary. The \emph{non-contractibility} of S distinguishes our function class from the classical convex setting topologically. Moreover, the embedding structure induces a naturally defined Laplacian-Beltrami operator on S, and we show that its first non-trivial eigenvalue provides an \emph{$\epsilon$-independent} lower bound for the \Poincare\ constant in the \Poincare\ inequality of $\mu_\epsilon$. As a direct consequence, Langevin dynamics with such non-convex potential $V$ and diffusion coefficient $\epsilon$ converges to its equilibrium $\mu_\epsilon$ at a rate of $\tilde{\mathcal{O}}(1/\epsilon)$, provided $\epsilon$ is sufficiently small. Here $\tilde{\mathcal{O}}$ hides logarithmic terms.
Flowing Through Layers: A Continuous Dynamical Systems Perspective on Transformers
Authors: Jacob Fein-Ashley
Date: 2025-02-08 00:00:00

We show that the standard discrete update rule of transformer layers can be naturally interpreted as a forward Euler discretization of a continuous dynamical system. Our Transformer Flow Approximation Theorem demonstrates that, under standard Lipschitz continuity assumptions, token representations converge uniformly to the unique solution of an ODE as the number of layers grows. Moreover, if the underlying mapping satisfies a one-sided Lipschitz condition with a negative constant, the resulting dynamics are contractive, causing perturbations to decay exponentially across layers. Beyond clarifying the empirical stability and expressivity of transformer models, these insights link transformer updates to a broader iterative reasoning framework, suggesting new avenues for accelerated convergence and architectural innovations inspired by dynamical systems theory.
An Improved Optimal Proximal Gradient Algorithm for Non-Blind Image Deblurring
Authors: Qingsong Wang, Shengze Xu, Xiaojiao Tong, Tieyong Zeng
Date: 2025-02-11 00:00:00
Tasks: Blind Image Deblurring, Deblurring, Image Deblurring, SSIM

Image deblurring remains a central research area within image processing, critical for its role in enhancing image quality and facilitating clearer visual representations across diverse applications. This paper tackles the optimization problem of image deblurring, assuming a known blurring kernel. We introduce an improved optimal proximal gradient algorithm (IOptISTA), which builds upon the optimal gradient method and a weighting matrix, to efficiently address the non-blind image deblurring problem. Based on two regularization cases, namely the $l_1$ norm and total variation norm, we perform numerical experiments to assess the performance of our proposed algorithm. The results indicate that our algorithm yields enhanced PSNR and SSIM values, as well as a reduced tolerance, compared to existing methods.
Training Deep Learning Models with Norm-Constrained LMOs
Authors: Thomas Pethick, Wanyun Xie, Kimon Antonakopoulos, Zhenyu Zhu, Antonio Silveti-Falls, Volkan Cevher
Date: 2025-02-11 00:00:00
Tasks: Deep Learning

In this work, we study optimization methods that leverage the linear minimization oracle (LMO) over a norm-ball. We propose a new stochastic family of algorithms that uses the LMO to adapt to the geometry of the problem and, perhaps surprisingly, show that they can be applied to unconstrained problems. The resulting update rule unifies several existing optimization methods under a single framework. Furthermore, we propose an explicit choice of norm for deep architectures, which, as a side benefit, leads to the transferability of hyperparameters across model sizes. Experimentally, we demonstrate significant speedups on nanoGPT training using our algorithm, Scion, without any reliance on Adam. The proposed method is memory-efficient, requiring only one set of model weights and one set of gradients, which can be stored in half-precision. The code is available at https://github.com/LIONS-EPFL/scion .
On properties driving diversity index selection
Authors: Martin Frohn, Kerry Manson
Date: 2025-02-11 00:00:00
Tasks: Diversity

Phylogenies are commonly used to represent the evolutionary relationships between species, and often these phylogenies are equipped with edge lengths that indicate degrees of evolutionary difference. Given such a phylogeny, a popular measure for the biodiversity of a subset of the species is the phylogenetic diversity (PD). But if we want to focus conservation efforts on particular species, we may use a phylogenetic diversity index, a function that shares out the PD value of an entire phylogeny across all of its species. With these indices, various species-level conservation strategies can be evaluated. This work explores how the most suitable diversity indices can be found. In particular, how formalizing the requirement for diversity indices to capture high levels of PD, or to maintain a scoring of taxa in the presence of uncertain edge lengths drives the selection of a suitable index. Furthermore, we provide illustrations of these new mechanisms for diversity index selection in a case study. This analysis includes the comparison to popular phylogenetic indices from the conservation literature.