pwc-archive/papers-with-abstracts huggingface dataset:
When NOMA Meets AIGC: Enhanced Wireless Federated Learning
Authors: Ding Xu, Lingjie Duan, Hongbo Zhu
Date: 2024-06-16 00:00:00
Tasks: Federated Learning

Wireless federated learning (WFL) enables devices to collaboratively train a global model via local model training, uploading and aggregating. However, WFL faces the data scarcity/heterogeneity problem (i.e., data are limited and unevenly distributed among devices) that degrades the learning performance. In this regard, artificial intelligence generated content (AIGC) can synthesize various types of data to compensate for the insufficient local data. Nevertheless, downloading synthetic data or uploading local models iteratively takes a lot of time, especially for a large amount of devices. To address this issue, we propose to leverage non-orthogonal multiple access (NOMA) to achieve efficient synthetic data and local model transmission. This paper is the first to combine AIGC and NOMA with WFL to maximally enhance the learning performance. For the proposed NOMA+AIGC-enhanced WFL, the problem of jointly optimizing the synthetic data distribution, two-way communication and computation resource allocation to minimize the global learning error is investigated. The problem belongs to NP-hard mixed integer nonlinear programming, whose optimal solution is intractable to find. We first employ the block coordinate descent method to decouple the complicated-coupled variables, and then resort to our analytical method to derive an efficient low-complexity local optimal solution with partial closed-form results. Extensive simulations validate the superiority of the proposed scheme compared to the existing and benchmark schemes such as the frequency/time division multiple access based AIGC-enhanced schemes.
Fair Computation Offloading for RSMA-Assisted Mobile Edge Computing Networks
Authors: Ding Xu, Lingjie Duan, Haitao Zhao, Hongbo Zhu
Date: 2024-06-16 00:00:00
Tasks: Edge-computing, Fairness, Hypergraph Matching

Rate splitting multiple access (RSMA) provides a flexible transmission framework that can be applied in mobile edge computing (MEC) systems. However, the research work on RSMA-assisted MEC systems is still at the infancy and many design issues remain unsolved, such as the MEC server and channel allocation problem in general multi-server and multi-channel scenarios as well as the user fairness issues. In this regard, we study an RSMA-assisted MEC system with multiple MEC servers, channels and devices, and consider the fairness among devices. A max-min fairness computation offloading problem to maximize the minimum computation offloading rate is investigated. Since the problem is difficult to solve optimally, we develop an efficient algorithm to obtain a suboptimal solution. Particularly, the time allocation and the computing frequency allocation are derived as closed-form functions of the transmit power allocation and the successive interference cancellation (SIC) decoding order, while the transmit power allocation and the SIC decoding order are jointly optimized via the alternating optimization method, the bisection search method and the successive convex approximation method. For the channel and MEC server allocation problem, we transform it into a hypergraph matching problem and solve it by matching theory. Simulation results demonstrate that the proposed RSMA-assisted MEC system outperforms current MEC systems under various system setups.
Noise reduction in ISAR imaging of UAVs using weighted atomic norm minimization and 2D-ADMM algorithm
Authors: Mohammad Roueinfar, Mohammad Hossein Kahaei
Date: 2024-06-16 00:00:00
Tasks: Denoising, Image Reconstruction, SSIM

The effect of noise on the Inverse Synthetic Aperture Radar (ISAR) with sparse apertures is a challenging issue for image reconstruction with high resolution at low Signal-to-Noise Ratios (SNRs). It is well-known that the image resolution is affected by the bandwidth of the transmitted signal and the Coherent Processing Interval (CPI) in two dimensions, range and azimuth, respectively. To reduce the noise effect and thus increase the two-dimensional resolution of Unmanned Aerial Vehicles (UAVs) images, we propose the Fast Reweighted Atomic Norm Denoising (FRAND) algorithm by incorporating the weighted atomic norm minimization. To solve the problem, the Two-Dimensional Alternating Direction Method of Multipliers (2D-ADMM) algorithm is developed to speed up the implementation procedure. Assuming sparse apertures for ISAR images of UAVs, we compare the proposed method with the MUltiple SIgnal Classification (MUSIC), Cadzow, and SL0 methods in different SNRs. Simulation results show the superiority of FRAND at low SNRs based on the Mean-Square Error (MSE), Peak Signal-to-Noise ratio (PSNR) and Structural Similarity Index Measure (SSIM) criteria.
Digital Wireless Image Transmission via Distribution Matching
Authors: Pujing Yang, Guangyi Zhang, Yunlong Cai
Date: 2024-06-16 00:00:00

Deep learning-based joint source-channel coding (JSCC) is emerging as a potential technology to meet the demand for effective data transmission, particularly for image transmission. Nevertheless, most existing advancements only consider analog transmission, where the channel symbols are continuous, making them incompatible with practical digital communication systems. In this work, we address this by involving the modulation process and consider mapping the continuous channel symbols into discrete space. Recognizing the non-uniform distribution of the output channel symbols in existing methods, we propose two effective methods to improve the performance. Firstly, we introduce a uniform modulation scheme, where the distance between two constellations is adjustable to match the non-uniform nature of the distribution. In addition, we further design a non-uniform modulation scheme according to the output distribution. To this end, we first generate the constellations by performing feature clustering on an analog image transmission system, then the generated constellations are employed to modulate the continuous channel symbols. For both schemes, we fine-tune the digital system to alleviate the performance loss caused by modulation. Here, the straight-through estimator (STE) is considered to overcome the non-differentiable nature. Our experimental results demonstrate that the proposed schemes significantly outperform existing digital image transmission systems.
Integrating sensing and communications: Simultaneously transmitting and reflecting digital coding metasurfaces
Authors: Francesco Verde, Vincenzo Galdi, Lei Zhang, Tie Jun Cui
Date: 2024-06-16 00:00:00
Tasks: Integrated sensing and communication, ISAC

Wireless networks are undergoing a transformative shift, driven by the crucial factors of cost effectiveness and sustainability. Digital coding metasurfaces (DCMs) might play a key role in realizing cost-effective digital modulators by harnessing energy embedded in electromagnetic waves traversing through the air. Integrated sensing and communication (ISAC) optimize power and spectral resources by combining sensing and communication functionalities on a shared hardware platform. This article presents a tutorial-style overview of the applications and advantages of DCMs in ISAC-based networks. Emphasis is placed on the dual-functionality of ISAC, necessitating the design of DCMs with simultaneously transmitting and reflecting (STAR) capabilities for comprehensive space control. Additionally, the article explores key signal processing challenges and outlines future research directions stemming from the convergence of ISAC and emerging STAR-DCM technologies.
Linear Inverse Problems Using a Generative Compound Gaussian Prior
Authors: Carter Lyons, Raghu G. Raj, Margaret Cheney
Date: 2024-06-16 00:00:00
Tasks: Compressive Sensing

Since most inverse problems arising in scientific and engineering applications are ill-posed, prior information about the solution space is incorporated, typically through regularization, to establish a well-posed problem with a unique solution. Often, this prior information is an assumed statistical distribution of the desired inverse problem solution. Recently, due to the unprecedented success of generative adversarial networks (GANs), the generative network from a GAN has been implemented as the prior information in imaging inverse problems. In this paper, we devise a novel iterative algorithm to solve inverse problems in imaging where a dual-structured prior is imposed by combining a GAN prior with the compound Gaussian (CG) class of distributions. A rigorous computational theory for the convergence of the proposed iterative algorithm, which is based upon the alternating direction method of multipliers, is established. Furthermore, elaborate empirical results for the proposed iterative algorithm are presented. By jointly exploiting the powerful CG and GAN classes of image priors, we find, in compressive sensing and tomographic imaging problems, our proposed algorithm outperforms and provides improved generalizability over competitive prior art approaches while avoiding performance saturation issues in previous GAN prior-based methods.
Notch Filters without Transient Effects: A Constrained Optimization Design
Authors: Reza Sameni
Date: 2024-06-15 00:00:00

Transient responses are an inherent property of recursive filters due to unknown or incorrectly selected initial conditions. Well-designed stable filters are less affected by transient responses, as the impact of initial conditions diminishes over time. However, applications that require very short data acquisition periods (for example, as short as ten seconds), such as biosignals recorded and processed by wearable technologies, can be significantly impacted by transient effects. But how feasible is it to design filters without transient responses? We propose a well-known filter design scheme based on constrained least squares (CLS) optimization to create zero-transient effect notch filters for powerline noise cancellation. We demonstrate that this filter is equivalent to the optimal Wiener smoother in the stationary case. We also discuss its limitations in removing powerline noise with nonstationary amplitude, where a Kalman filter-based formulation can be used instead.
Semantic Communication for Edge Intelligence Enabled Autonomous Driving System
Authors: Yunqi Feng, Hesheng Shen, Zhendong Shan, Qianqian Yang, Xiufang Shi
Date: 2024-06-15 00:00:00
Tasks: Autonomous Driving, Decision Making, Semantic Communication

Expected to provide higher transportation efficiency and security, autonomous driving has attracted substantial attentions from both industry and academia. Meanwhile, the emergence of edge intelligence has further introduced significant advancements to this field. However, the crucial demands of ultra-reliable and low-latency communications (URLLC) among the vehicles and edge servers have hindered the development of autonomous driving. In this article, we provide a brief overview of edge intelligence enabled autonomous driving system and current vehicle-to-everything (V2X) technologies. Moreover, challenges associated with massive data transmission in autonomous driving are highlighted from three perspectives: multi-modal data transmission and fusion, multi-user collaboration and connection, and multi-task training and execution. To cope with these challenges, we propose to incorporate semantic communication into autonomous driving to achieve highly efficient and task-oriented data transmission. Unlike traditional communications, semantic communication extracts task-relevant semantic feature from multi-sensory data. Specifically, a unified multi-user semantic communication system for transmitting multi-modal data and performing multi-task execution is designed for collaborative data transmission and decision making in autonomous driving. Simulation results demonstrate that the proposed system can significantly reduce data transmission volume without compromising task performance, as evidenced by the realization of a cooperative multi-vehicle target classification and detection task.
A Rao-Blackwellized Particle Filter for Superelliptical Extended Target Tracking
Authors: Oğul Can Yurdakul, Mehmet Çetinkaya, Enescan Çelebi, Emre Özkan
Date: 2024-06-14 00:00:00
Tasks: Object

In this work, we propose a new method to track extended targets of different shapes such as ellipses, rectangles and rhombi. We provide an analytical framework to express these shapes as superelliptical contours and propose a Bayesian filtering scheme that can handle measurements from the contour of the object. The method utilizes the Rao-Blackwellized particle filtering algorithm with novel sensor-object geometry constraints. The success of the algorithm is demonstrated using both simulations and real-data experiments, and the algorithm has been demonstrated to be of high performance in various challenging scenarios.
Semi-Blind Multi-Tag Ambient Backscatter Communications Using Radar Signals
Authors: Luca Venturino, Emanuele Grossi, Jeremy Johnston, Marco Lops, Xiaodong Wang
Date: 2024-06-14 00:00:00
Tasks: TAG

In this work, we consider a backscatter communication system wherein multiple asynchronous sources (tags) exploit the reverberation generated by a nearby radar transmitter as an ambient carrier to deliver a message to a common destination (reader) through a number of available subchannels. We propose a new encoding strategy wherein each tag transmits both pilot and data symbols on each subchannel and repeats some of the data symbols on multiple subchannels. We then exploit this signal structure to derive two semi-blind iterative algorithms for joint estimation of the data symbols and the subchannel responses that are also able to handle some missing measurements. The proposed encoding/decoding strategies are scalable with the number of tags and their payload and can achieve different tradeoffs in terms of transmission and error rates. Some numerical examples are provided to illustrate the merits of the proposed solutions.
Machine Learning-based Near-field Emitter Location Sensing via Grouped Hybrid Analog and Digital XL-MIMO Receive Array
Authors: Jiatong Bai, YiFan Li, Feng Shu, Kang Wei, Cunhua Pan, Yongpeng Wu, Yaoliang Song, Jiangzhou Wang
Date: 2024-06-14 00:00:00
Tasks: Clustering, Position

As a green MIMO structure, the partially-connected hybrid analog and digital (PC-HAD) structure has been widely used in the far-field (FF) scenario for it can significantly reduce the hardware cost and complexity of large-scale or extremely large-scale MIMO (XL-MIMO) array. Recently, near-field (NF) emitter localization including direction-of-arrival (DOA) and range estimations has drawn a lot of attention, but is rarely explored via PC-HAD structure. In this paper, we first analyze the impact of PC-HAD structure on the NF emitter localization and observe that the phase ambiguity (PA) problem caused by PC-HAD structure can be removed inherently with low-latency in the NF scenario. To obtain the exact NF DOA estimation results, we propose a grouped PC-HAD structure, which is capable of dividing the NF DOA estimation problem into multiple FF DOA estimation problems via partitioning the large-scale PC-HAD array into small-scale groups. An angle calibration method is developed to address the inconsistency among these FF DOA estimation problems. Then, to eliminate PA and improve the NF emitter localization performance, we develop three machine learning (ML)-based methods, i.e., two low-complexity data-driven clustering-based methods and one model-driven regression method, namely RegNet. Furthermore, the Cramer-Rao lower bound (CRLB) of NF emitter localization for the proposed grouped PC-HAD structure is derived and reveals that localization performance will decrease with the increasing of the number of groups. The simulation results show that the proposed methods can achieve CRLB at different SNR regions, the RegNet has great performance advantages at low SNR regions and the clustering-based methods have much lower computation complexity.
Enhancing Activity Recognition After Stroke: Generative Adversarial Networks for Kinematic Data Augmentation
Authors: Aaron J. Hadley, Christopher L. Pulliam
Date: 2024-06-12 00:00:00
Tasks: Activity Recognition, Data Augmentation

The generalizability of machine learning (ML) models for wearable monitoring in stroke rehabilitation is often constrained by the limited scale and heterogeneity of available data. Data augmentation addresses this challenge by adding computationally derived data to real data to enrich the variability represented in the training set. Traditional augmentation methods, such as rotation, permutation, and time-warping, have shown some benefits in improving classifier performance, but often fail to produce realistic training examples. This study employs Conditional Generative Adversarial Networks (cGANs) to create synthetic kinematic data from a publicly available dataset, closely mimicking the experimentally measured reaching movements of stroke survivors. This approach not only captures the complex temporal dynamics and common movement patterns after stroke, but also significantly enhances the training dataset. By training deep learning models on both synthetic and experimental data, we enhanced task classification accuracy: models incorporating synthetic data attained an overall accuracy of 80.0%, significantly higher than the 66.1% seen in models trained solely with real data. These improvements allow for more precise task classification, offering clinicians the potential to monitor patient progress more accurately and tailor rehabilitation interventions more effectively.
A topological analysis of the space of recipes
Authors: Emerson G. Escolar, Yuta Shimada, Masahiro Yuasa
Date: 2024-06-12 00:00:00
Tasks: Combinatorial Optimization, Topological Data Analysis

In recent years, the use of data-driven methods has provided insights into underlying patterns and principles behind culinary recipes. In this exploratory work, we introduce the use of topological data analysis, especially persistent homology, in order to study the space of culinary recipes. In particular, persistent homology analysis provides a set of recipes surrounding the multiscale "holes" in the space of existing recipes. We then propose a method to generate novel ingredient combinations using combinatorial optimization on this topological information. We made biscuits using the novel ingredient combinations, which were confirmed to be acceptable enough by a sensory evaluation study. Our findings indicate that topological data analysis has the potential for providing new tools and insights in the study of culinary recipes.
Robust dividend policy: Equivalence of Epstein-Zin and Maenhout preferences
Authors: Kexin Chen, Kyunghyun Park, Hoi Ying Wong
Date: 2024-06-18 00:00:00

In a continuous-time economy, this paper formulates the Epstein-Zin (EZ) preference for discounted dividends received by an investor as an EZ singular control utility. We introduce a backward stochastic differential equation with a aggregator integrated with respect to a singular control, prove its well-posedness, and show that it coincides with the EZ singular control utility. We then establish that this formulation is equivalent to a robust dividend policy chosen by the firm's executive under the Maenhout's ambiguity-averse preference. In particular, the robust dividend policy takes the form of a threshold strategy on the firm's surplus process, where the threshold level is characterized as the free boundary of a Hamilton-Jacobi-Bellman variational inequality. Therefore, dividend-caring investors can choose firms that match their preferences by examining stock's dividend policies and financial statements, whereas executives can make use of dividend to signal their confidence, in the form of ambiguity aversion, on realizing the earnings implied by their financial statements.
Constrained mean-variance investment-reinsurance under the Cramér-Lundberg model with random coefficients
Authors: Xiaomin Shi, Zuo Quan Xu
Date: 2024-06-15 00:00:00

In this paper, we study an optimal mean-variance investment-reinsurance problem for an insurer (she) under a Cram\'er-Lundberg model with random coefficients. At any time, the insurer can purchase reinsurance or acquire new business and invest her surplus in a security market consisting of a risk-free asset and multiple risky assets, subject to a general convex cone investment constraint. We reduce the problem to a constrained stochastic linear-quadratic control problem with jumps whose solution is related to a system of partially coupled stochastic Riccati equations (SREs). Then we devote ourselves to establishing the existence and uniqueness of solutions to the SREs by pure backward stochastic differential equation (BSDE) techniques. We achieve this with the help of approximation procedure, comparison theorems for BSDEs with jumps, log transformation and BMO martingales. The efficient investment-reinsurance strategy and efficient mean-variance frontier are explicitly given through the solutions of the SREs, which are shown to be a linear feedback form of the wealth process and a half-line, respectively.
A Benchmark for Maximum Cut: Towards Standardization of the Evaluation of Learned Heuristics for Combinatorial Optimization
Authors: Ankur Nath, Alan Kuhnle
Date: 2024-06-14 00:00:00
Tasks: Combinatorial Optimization

Recently, there has been much work on the design of general heuristics for graph-based, combinatorial optimization problems via the incorporation of Graph Neural Networks (GNNs) to learn distribution-specific solution structures.However, there is a lack of consistency in the evaluation of these heuristics, in terms of the baselines and instances chosen, which makes it difficult to assess the relative performance of the algorithms. In this paper, we propose an open-source benchmark suite MaxCut-Bench dedicated to the NP-hard Maximum Cut problem in both its weighted and unweighted variants, based on a careful selection of instances curated from diverse graph datasets. The suite offers a unified interface to various heuristics, both traditional and machine learning-based. Next, we use the benchmark in an attempt to systematically corroborate or reproduce the results of several, popular learning-based approaches, including S2V-DQN [31], ECO-DQN [4], among others, in terms of three dimensions: objective value, generalization, and scalability. Our empirical results show that several of the learned heuristics fail to outperform a naive greedy algorithm, and that only one of them consistently outperforms Tabu Search, a simple, general heuristic based upon local search. Furthermore, we find that the performance of ECO-DQN remains the same or is improved if the GNN is replaced by a simple linear regression on a subset of the features that are related to Tabu Search. Code, data, and pretrained models are available at: \url{https://github.com/ankurnath/MaxCut-Bench}.
Computation of Robust Option Prices via Structured Multi-Marginal Martingale Optimal Transport
Authors: Linn Engström, Sigrid Källblad, Johan Karlsson
Date: 2024-06-14 00:00:00

We introduce an efficient computational framework for solving a class of multi-marginal martingale optimal transport problems, which includes many robust pricing problems of large financial interest. Such problems are typically computationally challenging due to the martingale constraint, however, by extending the state space we can identify them with problems that exhibit a certain sequential martingale structure. Our method exploits such structures in combination with entropic regularisation, enabling fast computation of optimal solutions and allowing us to solve problems with a large number of marginals. We demonstrate the method by using it for computing robust price bounds for different options, such as lookback options and Asian options.