pwc-archive/papers-with-abstracts huggingface dataset:
Expanding Over-the-Air Computation with Frequency Modulations
Authors: Marc Martinez-Gost, Ana Pérez-Neira, Miguel Ángel Lagunas
Date: 2024-10-01 00:00:00

In this study we introduce Logarithmic Frequency Shift Keying (Log-FSK), a novel frequency modulation for over-the-air computation (AirComp). Log-FSK leverages non-linear signal processing to produce AirComp in the frequency domain, this is, the maximum frequency of the received signal corresponds to the sum of the individual transmitted frequencies. The demodulation procedure relies on the inverse Discrete Cosine Transform (DCT) and the extraction of the maximum frequency component. Log-FSK enables the computation of functions beyond the sum by incorporating nomographic function representation. Furthermore, unlike existing AirComp modulations, Log-FSK allows to compute several functions in a single transmission. We evaluate the capabilities of the scheme in an additive white Gaussian noise (AWGN) and flat-fading channels. To demonstrate its practicality, we present specific applications and experimental results showcasing the effectiveness of Log-FSK AirComp within linear Wireless Sensor Networks (WSN). Our numerical results show that Log-FSK outperform linear analog modulations in terms of MSE and power consumption.
Poststroke rehabilitative mechanisms in individualized fatigue level-controlled treadmill training -- a Rat Model Study
Authors: Yuchen Xu, Yulong Peng, Yuanfa Yao, Xiaoman Fan, Minmin Wang, Feng Gao, Mohamad Sawan, Shaomin Zhang, Xiaoling Hu
Date: 2025-02-20 00:00:00
Tasks: EEG

Individualized training improved post-stroke motor function rehabilitation efficiency. However, the mechanisms of how individualized training facilitates recovery is not clear. This study explored the cortical and corticomuscular rehabilitative effects in post-stroke motor function recovery during individualized training. Sprague-Dawley rats with intracerebral hemorrhage (ICH) were randomly distributed into two groups: forced training (FOR-T, n=13) and individualized fatigue-controlled training (FAT-C, n=13) to receive training respectively from day 2 to day 14 post-stroke. The FAT-C group exhibited superior motor function recovery and less central fatigue compared to the FOR-T group. EEG PSD slope analysis demonstrated a better inter-hemispheric balance in FAT-C group compare to the FOR-T group. The dCMC analysis indicated that training-induced fatigue led to a short-term down-regulation of descending corticomuscular coherence (dCMC) and an up-regulation of ascending dCMC. In the long term, excessive fatigue hindered the recovery of descending control in the affected hemisphere. The individualized strategy of peripheral fatigue-controlled training achieved better motor function recovery, which could be attributed to the mitigation of central fatigue, optimization of inter-hemispheric balance and enhancement of descending control in the affected hemisphere.
Maximizing Spectrum Efficiency of Data-Carrying Reference Signals via Bayesian Optimization
Authors: Taiki Kato, Hiroki Iimori, Chandan Pradhan, Szabolcs Malomsoky, Naoki Ishikawa
Date: 2025-02-20 00:00:00
Tasks: Bayesian Optimization

Data-carrying reference signals are a type of reference signal (RS) constructed on the Grassmann manifold, which allows for simultaneous data transmission and channel estimation to achieve boosted spectral efficiency at high signal-to-noise ratios (SNRs). However, they do not improve spectral efficiency at low to middle SNRs compared with conventional RSs. To address this problem, we propose a numerical optimization-based Grassmann constellation design on the Grassmann manifold that accounts for both data transmission and channel estimation. In our numerical optimization, we derive an upper bound on the normalized mean squared error (NMSE) of estimated channel matrices and a lower bound on the noncoherent average mutual information (AMI), and these bounds are optimized simultaneously by using a Bayesian optimization technique. The proposed objective function outperforms conventional design metrics in obtaining Pareto-optimal constellations for NMSE and AMI. The constellation obtained by our method achieves an NMSE comparable to conventional non-data-carrying RSs while enabling data transmission, resulting in superior AMI performance and improved spectral efficiency even at middle SNRs.
Evaluating Multi-Sensor Placement and Neural Network Architectures for Physical Activity Level Classification
Authors: Bo Cui, Xiaowen Song, Tabak Monique, Bert-Jan van Beijnum, Ying Wang
Date: 2025-02-20 00:00:00
Tasks: Classification, Management

Accurate physical activity level (PAL) classification could be beneficial for osteoarthritis (OA) management. This study examines the impact of sensor placement and deep learning models on AL classification using the Metabolic Equivalent of Task values. The results show that the addition of anankle sensor (WA) significantly improves the classification of intensity activities compared to wrist-only configuration(53% to 86.2%). The CNN-LSTM model achieves the highest accuracy (95.09%). Statistical analysis confirms multi-sensor setups outperform single-sensor configurations (p < 0.05). The WA configuration offers a balance between usability and accuracy, making it a cost-effective solution for AL monitoring, particularly in OA management.
A Concise Tutorial for Analyzing Electromagnetic Degrees of Freedom for Continuous-Aperture Array (CAPA) Systems
Authors: Chongjun Ouyang, Boqun Zhao, Xingqi Zhang, Yuanwei Liu
Date: 2025-02-20 00:00:00

A concise tutorial is provided for analysis of the spatial degrees of freedom (DoFs) in continuous-aperture array (CAPA)-based continuous electromagnetic (EM) channels. First, a simplified spatial model is introduced using the Fresnel approximation. By leveraging this model and Landau's theorem, a closed-form expression for the spatial DoFs is derived. The results show that the number of DoFs is proportional to the transmit and receive aperture sizes and inversely proportional to the propagation distance. Numerical results are presented to illustrate the properties of EM DoFs in CAPA-based channels.
Joint Waveform and Beamforming Design in RIS-ISAC Systems: A Model-Driven Learning Approach
Authors: Peng Jiang, Ming Li, Rang Liu, Wei Wang, Qian Liu
Date: 2025-02-20 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated Sensing and Communication (ISAC) has emerged as a key enabler for future wireless systems. The recently developed symbol-level precoding (SLP) technique holds significant potential for ISAC waveform design, as it leverages both temporal and spatial degrees of freedom (DoFs) to enhance multi-user communication and radar sensing capabilities. Concurrently, reconfigurable intelligent surfaces (RIS) offer additional controllable propagation paths, further amplifying interest in their application. However, previous studies have encountered substantial computational challenges due to the complexity of jointly designing SLP-based waveforms and RIS passive beamforming. In this paper, we propose a novel model-driven learning approach that jointly optimizes waveform and beamforming by unfolding the iterative alternative direction method of multipliers (ADMM) algorithm. Two joint design algorithms are developed for radar target detection and direction-of-arrival (DoA) estimation tasks in a cluttered RIS-ISAC system. While ensuring the communication quality-of-service (QoS) requirements, our objectives are: 1) to maximize the radar output signal-to-interference-plus-noise ratio (SINR) for target detection, and 2) to minimize the Cram\'{e}r-Rao bound (CRB) for DoA estimation. Simulation results verify that our proposed model-driven learning algorithms achieve satisfactory communication and sensing performance, while also offering a substantial reduction in computational complexity, as reflected by the average execution time.
Road to 6G Digital Twin Networks: Multi-Task Adaptive Ray-Tracing as a Key Enabler
Authors: Li Yu, Yinghe Miao, Jianhua Zhang, Shaoyi Liu, Yuxiang Zhang, Guangyi Liu
Date: 2025-02-20 00:00:00

As a virtual, synchronized replica of physical network, the digital twin network (DTN) is envisioned to sense, predict, optimize and manage the intricate wireless technologies and architectures brought by 6G. Given that the properties of wireless channel fundamentally determine the system performances from the physical layer to network layer, it is a critical prerequisite that the invisible wireless channel in physical world be accurately and efficiently twinned. To support 6G DTN, this paper first proposes a multi-task adaptive ray-tracing platform for 6G (MART-6G) to generate the channel with 6G features, specially designed for DTN online real-time and offline high-accurate tasks. Specifically, the MART-6G platform comprises three core modules, i.e., environment twin module to enhance the sensing ability of dynamic environment; RT engine module to incorporate the main algorithms of propagations, accelerations, calibrations, 6G-specific new features; and channel twin module to generate channel multipath, parameters, statistical distributions, and corresponding three-dimensional (3D) environment information. Moreover, MART-6G is tailored for DTN tasks through the adaptive selection of proper sensing methods, antenna and material libraries, propagation models and calibration strategy, etc. To validate MART-6G performance, we present two real-world case studies to demonstrate the accuracy, efficiency and generality in both offline coverage prediction and online real-time channel prediction. Finally, some open issues and challenges are outlined to further support future diverse DTN tasks.
A Low-Complexity Placement Design of Pinching-Antenna Systems
Authors: Ximing Xie, Fang Fang, Zhiguo Ding, Xianbin Wang
Date: 2025-02-20 00:00:00

Pinching-antenna systems have recently been proposed as a new candidate for flexible-antenna systems, not only inheriting the reconfiguration capability but also offering a unique feature: establishing line-of-sight links to mitigate large-scale path loss. However, sophisticated optimization of the placement of pinching antennas has very high complexity, which is challenging for practical implementation. This paper proposes a low-complexity placement design, providing the closed-form expression of the placement of pinching antennas, to maximize the sum rate of multiple downlink users. Orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) are both investigated when the pinching-antenna system is only equipped with a single antenna and only the OMA case is studied when there are multiple antennas equipped by the pinching-antenna system. Simulation results indicate pinching-antenna systems can outperform conventional fixed-antenna systems and are more suitable for large service areas.
AFDM-Enabled Integrated Sensing and Communication: Theoretical Framework and Pilot Design
Authors: Fan Zhang, Zhaocheng Wang, Tianqi Mao, Tianyu Jiao, Yinxiao Zhuo, Miaowen Wen, Wei Xiang, Sheng Chen, George K. Karagiannidis
Date: 2025-02-20 00:00:00
Tasks: Integrated sensing and communication, ISAC

The integrated sensing and communication (ISAC) has been envisioned as one representative usage scenario of sixth-generation (6G) network. However, the unprecedented characteristics of 6G, especially the doubly dispersive channel, make classical ISAC waveforms rather challenging to guarantee a desirable performance level. The recently proposed affine frequency division multiplexing (AFDM) can attain full diversity even under doubly dispersive effects, thus becoming a competitive candidate for next-generation ISAC waveforms. Relevant investigations are still at an early stage, which involve only straightforward design lacking explicit theoretical analysis. This paper provides an in-depth investigation on AFDM waveform design for ISAC applications. Specifically, the closed-form Cr\'{a}mer-Rao bounds of target detection for AFDM are derived, followed by a demonstration on its merits over existing counterparts. Furthermore, we formulate the ambiguity function of the pilot-assisted AFDM waveform for the first time, revealing conditions for stable sensing performance. To further enhance both the communication and sensing performance of the AFDM waveform, we propose a novel pilot design by exploiting the characteristics of AFDM signals. The proposed design is analytically validated to be capable of optimizing the ambiguity function property and channel estimation accuracy simultaneously as well as overcoming the sensing and channel estimation range limitation originated from the pilot spacing. Numerical results have verified the superiority of the proposed pilot design in terms of dual-functional performance.
Near-Field Motion Parameter Estimation: A Variational Bayesian Approach
Authors: Chunwei Meng, Zhaolin Wang, Zhiqing Wei, Yuanwei Liu, Zhiyong Feng
Date: 2025-02-20 00:00:00
Tasks: Bayesian Inference, parameter estimation

A near-field motion parameter estimation method is proposed. In contract to far-field sensing systems, the near-field sensing system leverages spherical-wave characteristics to enable full-vector location and velocity estimation. Despite promising advantages, the near-field sensing system faces a significant challenge, where location and velocity parameters are intricately coupled within the signal. To address this challenge, a novel subarray-based variational message passing (VMP) method is proposed for near-field joint location and velocity estimation. First, a factor graph representation is introduced, employing subarray-level directional and Doppler parameters as intermediate variables to decouple the complex location-velocity dependencies. Based on this, the variational Bayesian inference is employed to obtain closed-form posterior distributions of subarray-level parameters. Subsequently, the message passing technique is employed, enabling tractable computation of location and velocity marginal distributions. Two implementation strategies are proposed: 1) System-level fusion that aggregates all subarray posteriors for centralized estimation, or 2) Subarray-level fusion where locally processed estimates from subarrays are fused through Guassian product rule. Cram\'er-Rao bounds for location and velocity estimation are derived, providing theoretical performance limits. Numerical results demonstrate that the proposed VMP method outperforms existing approaches while achieving a magnitude lower complexity. Specifically, the proposed VMP method achieves centimeter-level location accuracy and sub-m/s velocity accuracy. It also demonstrates robust performance for high-mobility targets, making the proposed VMP method suitable for real-time near-field sensing and communication applications.
Zero loss guarantees and explicit minimizers for generic overparametrized Deep Learning networks
Authors: Thomas Chen, Andrew G. Moore
Date: 2025-02-19 00:00:00

We determine sufficient conditions for overparametrized deep learning (DL) networks to guarantee the attainability of zero loss in the context of supervised learning, for the $\mathcal{L}^2$ cost and {\em generic} training data. We present an explicit construction of the zero loss minimizers without invoking gradient descent. On the other hand, we point out that increase of depth can deteriorate the efficiency of cost minimization using a gradient descent algorithm by analyzing the conditions for rank loss of the training Jacobian. Our results clarify key aspects on the dichotomy between zero loss reachability in underparametrized versus overparametrized DL.
Counting spinal phylogenetic networks
Authors: Andrew Francis, Michael Hendriksen
Date: 2025-02-20 00:00:00

Phylogenetic networks are an important way to represent evolutionary histories that involve reticulations such as hybridization or horizontal gene transfer, yet fundamental questions such as how many networks there are that satisfy certain properties are very difficult. A new way to encode a large class of networks, using expanding covers, may provide a way to approach such problems. Expanding covers encode a large class of phylogenetic networks, called labellable networks. This class does not include all networks, but does include many familiar classes, including orchard, normal, tree-child and tree-sibling networks. As expanding covers are a combinatorial structure, it is possible that they can be used as a tool for counting such classes for a fixed number of leaves and reticulations, for which, in many cases, a closed formula has not yet been found. More recently, a new class of networks was introduced, called spinal networks, which are analogous to caterpillar trees for phylogenetic trees and can be fully described using covers. In the present article, we describe a method for counting networks that are both spinal and belong to some more familiar class, with the hope that these form a base case from which to attack the more general classes.
EquivaMap: Leveraging LLMs for Automatic Equivalence Checking of Optimization Formulations
Authors: Haotian Zhai, Connor Lawless, Ellen Vitercik, Liu Leqi
Date: 2025-02-20 00:00:00
Tasks: Combinatorial Optimization, valid

A fundamental problem in combinatorial optimization is identifying equivalent formulations. Despite the growing need for automated equivalence checks -- driven, for example, by optimization copilots, which generate problem formulations from natural language descriptions -- current approaches rely on simple heuristics that fail to reliably check formulation equivalence. Inspired by Karp reductions, in this work we introduce Quasi-Karp equivalence, a formal criterion for determining when two optimization formulations are equivalent based on the existence of a mapping between their decision variables. We propose EquivaMap, a framework that leverages large language models to automatically discover such mappings for scalable, reliable equivalence checking, with a verification stage that ensures mapped solutions preserve feasibility and optimality without additional solver calls. To evaluate our approach, we construct EquivaFormulation, the first open-source dataset of equivalent optimization formulations, generated by applying transformations such as adding slack variables or valid inequalities to existing formulations. Empirically, EquivaMap significantly outperforms existing methods, achieving substantial improvements in correctly identifying formulation equivalence.
Variance Reduction Methods Do Not Need to Compute Full Gradients: Improved Efficiency through Shuffling
Authors: Daniil Medyakov, Gleb Molodtsov, Savelii Chezhegov, Alexey Rebrikov, Aleksandr Beznosikov
Date: 2025-02-20 00:00:00

In today's world, machine learning is hard to imagine without large training datasets and models. This has led to the use of stochastic methods for training, such as stochastic gradient descent (SGD). SGD provides weak theoretical guarantees of convergence, but there are modifications, such as Stochastic Variance Reduced Gradient (SVRG) and StochAstic Recursive grAdient algoritHm (SARAH), that can reduce the variance. These methods require the computation of the full gradient occasionally, which can be time consuming. In this paper, we explore variants of variance reduction algorithms that eliminate the need for full gradient computations. To make our approach memory-efficient and avoid full gradient computations, we use two key techniques: the shuffling heuristic and idea of SAG/SAGA methods. As a result, we improve existing estimates for variance reduction algorithms without the full gradient computations. Additionally, for the non-convex objective function, our estimate matches that of classic shuffling methods, while for the strongly convex one, it is an improvement. We conduct comprehensive theoretical analysis and provide extensive experimental results to validate the efficiency and practicality of our methods for large-scale machine learning problems.
A Non-Asymptotic Theory of Seminorm Lyapunov Stability: From Deterministic to Stochastic Iterative Algorithms
Authors: Zaiwei Chen, Sheng Zhang, Zhe Zhang, Shaan ul Haque, Siva Theja Maguluri
Date: 2025-02-20 00:00:00
Tasks: Q-Learning

We study the problem of solving fixed-point equations for seminorm-contractive operators and establish foundational results on the non-asymptotic behavior of iterative algorithms in both deterministic and stochastic settings. Specifically, in the deterministic setting, we prove a fixed-point theorem for seminorm-contractive operators, showing that iterates converge geometrically to the kernel of the seminorm. In the stochastic setting, we analyze the corresponding stochastic approximation (SA) algorithm under seminorm-contractive operators and Markovian noise, providing a finite-sample analysis for various stepsize choices. A benchmark for equation solving is linear systems of equations, where the convergence behavior of fixed-point iteration is closely tied to the stability of linear dynamical systems. In this special case, our results provide a complete characterization of system stability with respect to a seminorm, linking it to the solution of a Lyapunov equation in terms of positive semi-definite matrices. In the stochastic setting, we establish a finite-sample analysis for linear Markovian SA without requiring the Hurwitzness assumption. Our theoretical results offer a unified framework for deriving finite-sample bounds for various reinforcement learning algorithms in the average reward setting, including TD($\lambda$) for policy evaluation (which is a special case of solving a Poisson equation) and Q-learning for control.
Weighted Low-rank Approximation via Stochastic Gradient Descent on Manifolds
Authors: Conglong Xu, Peiqi Yang, Hao Wu
Date: 2025-02-20 00:00:00

We solve a regularized weighted low-rank approximation problem by a stochastic gradient descent on a manifold. To guarantee the convergence of our stochastic gradient descent, we establish a convergence theorem on manifolds for retraction-based stochastic gradient descents admitting confinements. On sample data from the Netflix Prize training dataset, our algorithm outperforms the existing stochastic gradient descent on Euclidean spaces. We also compare the accelerated line search on this manifold to the existing accelerated line search on Euclidean spaces.
Understanding SGD with Exponential Moving Average: A Case Study in Linear Regression
Authors: Xuheng Li, Quanquan Gu
Date: 2025-02-19 00:00:00

Exponential moving average (EMA) has recently gained significant popularity in training modern deep learning models, especially diffusion-based generative models. However, there have been few theoretical results explaining the effectiveness of EMA. In this paper, to better understand EMA, we establish the risk bound of online SGD with EMA for high-dimensional linear regression, one of the simplest overparameterized learning tasks that shares similarities with neural networks. Our results indicate that (i) the variance error of SGD with EMA is always smaller than that of SGD without averaging, and (ii) unlike SGD with iterate averaging from the beginning, the bias error of SGD with EMA decays exponentially in every eigen-subspace of the data covariance matrix. Additionally, we develop proof techniques applicable to the analysis of a broad class of averaging schemes.