pwc-archive/papers-with-abstracts huggingface dataset:
Direction of Arrival Estimation with Sparse Subarrays
Authors: W. Leite, R. C. de Lamare, Y. Zakharov, W. Liu, M. Haardt
Date: 2024-08-17 00:00:00
Tasks: Direction of Arrival Estimation

This paper proposes design techniques for partially-calibrated sparse linear subarrays and algorithms to perform direction-of-arrival (DOA) estimation. First, we introduce array architectures that incorporate two distinct array categories, namely type-I and type-II arrays. The former breaks down a known sparse linear geometry into as many pieces as we need, and the latter employs each subarray such as it fits a preplanned sparse linear geometry. Moreover, we devise two Direction of Arrival (DOA) estimation algorithms that are suitable for partially-calibrated array scenarios within the coarray domain. The algorithms are capable of estimating a greater number of sources than the number of available physical sensors, while maintaining the hardware and computational complexity within practical limits for real-time implementation. To this end, we exploit the intersection of projections onto affine spaces by devising the Generalized Coarray Multiple Signal Classification (GCA-MUSIC) in conjunction with the estimation of a refined projection matrix related to the noise subspace, as proposed in the GCA root-MUSIC algorithm. An analysis is performed for the devised subarray configurations in terms of degrees of freedom, as well as the computation of the Cram\`er-Rao Lower Bound for the utilized data model, in order to demonstrate the good performance of the proposed methods. Simulations assess the performance of the proposed design methods and algorithms against existing approaches.
Csi-LLM: A Novel Downlink Channel Prediction Method Aligned with LLM Pre-Training
Authors: Shilong Fan, Zhenyu Liu, Xinyu Gu, Haozhen Li
Date: 2024-08-15 00:00:00
Tasks: Prediction

Downlink channel temporal prediction is a critical technology in massive multiple-input multiple-output (MIMO) systems. However, existing methods that rely on fixed-step historical sequences significantly limit the accuracy, practicality, and scalability of channel prediction. Recent advances have shown that large language models (LLMs) exhibit strong pattern recognition and reasoning abilities over complex sequences. The challenge lies in effectively aligning wireless communication data with the modalities used in natural language processing to fully harness these capabilities. In this work, we introduce Csi-LLM, a novel LLM-powered downlink channel prediction technique that models variable-step historical sequences. To ensure effective cross-modality application, we align the design and training of Csi-LLM with the processing of natural language tasks, leveraging the LLM's next-token generation capability for predicting the next step in channel state information (CSI). Simulation results demonstrate the effectiveness of this alignment strategy, with Csi-LLM consistently delivering stable performance improvements across various scenarios and showing significant potential in continuous multi-step prediction.
Concentric UCAs Based Low-Order OAM for High Capacity in Radio Vortex Wireless Communications
Authors: Haiyue Jing, Wenchi Cheng, Zan Li, Hailin Zhang
Date: 2024-08-13 00:00:00

Due to the potential capacity-boosting for wireless communications, the Radio vOrtex Wireless COMMunication (RowComm) over orthogonal states/modes of Orbital Angular Momentum (OAM) has been paid much attention in recent years. Uniform circular array (UCA), as an efficient and convenient antenna structure, can transmit/receive multiple OAM beams with different OAM-modes simultaneously when the transmitter and receiver are aligned. However, for high-order OAM-modes, the OAM beams are divergent accompanied by severe attenuations. Thus, it is difficult to directly use high-order OAM-modes to achieve high capacity for RowComms. To obtain high capacity potentially offered by OAM-modes, in this paper we transform the singular UCA into the concentric UCAs, where high capacity can be achieved using multiple parallel low-order OAM-modes instead of all high-order OAM-modes, to increase the capacity of transmitter-receiver aligned RowComms. In particular, we study two cases: 1). Concentric UCAs based RowComms without co-mode interference; 2). Concentric UCAs based RowComms with co-mode interference. We propose a co-mode-interference-free and a co-mode-interference-contained mode-decomposition schemes to recover transmit signals for these two cases, respectively. Also, we develop the optimal power allocation schemes to maximize the capacity for these two cases. Numerical simulations are presented to validate and evaluate that our developed concentric UCAs based low-order RowComms can significantly increase the capacity as compared with that of singular UCA based RowComms.
Coarse Descriptions and Cautious Preferences
Authors: Evan Piermont, Marcus Pivato
Date: 2024-09-09 00:00:00
Tasks: Decision Making, Negation

We consider a model where an agent is must choose between alternatives that each provide only an imprecise description of the world (e.g. linguistic expressions). The set of alternatives is closed under logical conjunction and disjunction, but not necessarily negation. (Formally: it is a distributive lattice, but not necessarily a Boolean algebra). In our main result, each alternative is identified with a subset of an (endogenously defined) state space, and two axioms characterize maximin decision making. This means: from the agent's preferences over alternatives, we derive a preference order on the endogenous state space, such that alternatives are ranked in terms of their worst outcomes.
Inertial Coordination Games
Authors: Andrew Koh, Ricky Li, Kei Uzui
Date: 2024-09-12 00:00:00

We analyze inertial coordination games: dynamic coordination games with an endogenously changing state that depends on (i) a persistent fundamental players privately learn about over time; and (ii) past play. The speed of learning determines long-run equilibrium dynamics: the risk-dominant action is played in the limit if and only if learning is slow such that posterior precisions grow sub-quadratically. This generalizes results from static global games and endows them with a learning foundation. Conversely, when learning is fast such that posterior precisions grow super-quadratically, shocks can propagate and generate self-fulfilling spirals.
A Toolkit for Joint Speaker Diarization and Identification with Application to Speaker-Attributed ASR
Authors: Giovanni Morrone, Enrico Zovato, Fabio Brugnara, Enrico Sartori, Leonardo Badino
Date: 2024-09-09 00:00:00
Tasks: Automatic Speech Recognition, speaker-diarization, Speaker Diarization, Speaker Identification, speech-recognition, Speech Recognition

We present a modular toolkit to perform joint speaker diarization and speaker identification. The toolkit can leverage on multiple models and algorithms which are defined in a configuration file. Such flexibility allows our system to work properly in various conditions (e.g., multiple registered speakers' sets, acoustic conditions and languages) and across application domains (e.g. media monitoring, institutional, speech analytics). In this demonstration we show a practical use-case in which speaker-related information is used jointly with automatic speech recognition engines to generate speaker-attributed transcriptions. To achieve that, we employ a user-friendly web-based interface to process audio and video inputs with the chosen configuration.
Unveiling Visual Biases in Audio-Visual Localization Benchmarks
Authors: Liangyu Chen, Zihao Yue, Boshen Xu, Qin Jin
Date: 2024-08-25 00:00:00
Tasks: audio-visual learning, Visual Localization

Audio-Visual Source Localization (AVSL) aims to localize the source of sound within a video. In this paper, we identify a significant issue in existing benchmarks: the sounding objects are often easily recognized based solely on visual cues, which we refer to as visual bias. Such biases hinder these benchmarks from effectively evaluating AVSL models. To further validate our hypothesis regarding visual biases, we examine two representative AVSL benchmarks, VGG-SS and EpicSounding-Object, where the vision-only models outperform all audiovisual baselines. Our findings suggest that existing AVSL benchmarks need further refinement to facilitate audio-visual learning.
Detecting Misinformation in Multimedia Content through Cross-Modal Entity Consistency: A Dual Learning Approach
Authors: Zhe Fu, Kanlun Wang, Wangjiaxuan Xin, Lina Zhou, Shi Chen, Yaorong Ge, Daniel Janies, Dongsong Zhang
Date: 2024-08-16 00:00:00
Tasks: Misinformation, Representation Learning

The landscape of social media content has evolved significantly, extending from text to multimodal formats. This evolution presents a significant challenge in combating misinformation. Previous research has primarily focused on single modalities or text-image combinations, leaving a gap in detecting multimodal misinformation. While the concept of entity consistency holds promise in detecting multimodal misinformation, simplifying the representation to a scalar value overlooks the inherent complexities of high-dimensional representations across different modalities. To address these limitations, we propose a Multimedia Misinformation Detection (MultiMD) framework for detecting misinformation from video content by leveraging cross-modal entity consistency. The proposed dual learning approach allows for not only enhancing misinformation detection performance but also improving representation learning of entity consistency across different modalities. Our results demonstrate that MultiMD outperforms state-of-the-art baseline models and underscore the importance of each modality in misinformation detection. Our research provides novel methodological and technical insights into multimodal misinformation detection.
TorchDA: A Python package for performing data assimilation with deep learning forward and transformation functions
Authors: Sibo Cheng, Jinyang Min, Che Liu, Rossella Arcucci
Date: 2024-08-30 00:00:00
Tasks: Deep Learning

Data assimilation techniques are often confronted with challenges handling complex high dimensional physical systems, because high precision simulation in complex high dimensional physical systems is computationally expensive and the exact observation functions that can be applied in these systems are difficult to obtain. It prompts growing interest in integrating deep learning models within data assimilation workflows, but current software packages for data assimilation cannot handle deep learning models inside. This study presents a novel Python package seamlessly combining data assimilation with deep neural networks to serve as models for state transition and observation functions. The package, named TorchDA, implements Kalman Filter, Ensemble Kalman Filter (EnKF), 3D Variational (3DVar), and 4D Variational (4DVar) algorithms, allowing flexible algorithm selection based on application requirements. Comprehensive experiments conducted on the Lorenz 63 and a two-dimensional shallow water system demonstrate significantly enhanced performance over standalone model predictions without assimilation. The shallow water analysis validates data assimilation capabilities mapping between different physical quantity spaces in either full space or reduced order space. Overall, this innovative software package enables flexible integration of deep learning representations within data assimilation, conferring a versatile tool to tackle complex high dimensional dynamical systems across scientific domains.
Inverting the sound speed profile from multi-beam echo sounder data and historical measurements -- a simulation study
Authors: Yohann Gourret, Tommi Brander, Karl Thomas Hjelmervik
Date: 2024-09-03 00:00:00

The ocean's opacity poses challenges for security, as new technology, e.g. underwater drones, offers new opportunities for illegal activities, such as smuggling and terrorism. A network of unmanned surface vehicles (USV) and autonomous underwater vehicles (AUV) offers a potential underwater surveillance solution, but demands high autonomy and compact hardware. For improved situational awareness and efficient operation, sonar performance models may provide the network with sensor coverage maps, but this requires constantly updated environmental information, in particular the present sound speed profile (SSP). We propose the inversion of SSPs from multibeam echo sounder (MBES) data in an environment with known topography. The method exploits the two-way travel time from the MBES to the bottom, comparing the measurements to modelled travel time for a proposed SSP model. An acoustic raytracer models the travel time for the SSP model. The inversion problem is shown to be non-unique when basing the cost function on the two-way travel time alone. This is resolved by incorporating a Tikhonov-type regularization term for inclusion of a priori knowledge on the SSPs in addition to the travel time in the final cost function. Empirical orthogonal functions (EOFs) are derived from a historical SSP data set, and variance for the EOF coefficients are determined from the same data set. The EOF coefficient distributions are assumed Gaussian and used in the regularization term to limit the search space of the inversion algorithm to physically feasible SSPs. A neural network determines the regularization parameters. The method's validity and sensitivity to errors is assessed using synthetic sonar data for the Norwegian Trench. The method accurately recovers SSPs with average root mean square errors of 0.83 m/s. For comparison, the error obtained using state-of-the-art climatology (WOA) is 2.6 m/s.
A novel and efficient parameter estimation of the Lognormal-Rician turbulence model based on k-Nearest Neighbor and data generation method
Authors: Maoke Miao, Xinyu Zhang, Bo Liu, Rui Yin, Jiantao Yuan, Feng Gao, Xiao-Yu Chen
Date: 2024-09-03 00:00:00
Tasks: parameter estimation

In this paper, we propose a novel and efficient parameter estimator based on $k$-Nearest Neighbor ($k$NN) and data generation method for the Lognormal-Rician turbulence channel. The Kolmogorov-Smirnov (KS) goodness-of-fit statistical tools are employed to investigate the validity of $k$NN approximation under different channel conditions and it is shown that the choice of $k$ plays a significant role in the approximation accuracy. We present several numerical results to illustrate that solving the constructed objective function can provide a reasonable estimate for the actual values. The accuracy of the proposed estimator is investigated in terms of the mean square error. The simulation results show that increasing the number of generation samples by two orders of magnitude does not lead to a significant improvement in estimation performance when solving the optimization problem by the gradient descent algorithm. However, the estimation performance under the genetic algorithm (GA) approximates to that of the saddlepoint approximation and expectation-maximization estimators. Therefore, combined with the GA, we demonstrate that the proposed estimator achieves the best tradeoff between the computation complexity and the accuracy.
Methods based on Radon transform for non-affine deformable image registration of noisy images
Authors: Daniel E. Hurtado, Axel Osses, Rodrigo Quezada
Date: 2024-08-18 00:00:00
Tasks: Image Registration

Deformable image registration is a standard engineering problem used to determine the distortion experienced by a body by comparing two images of it in different states. This study introduces two new DIR methods designed to capture non-affine deformations using Radon transform-based similarity measures and a classical regularizer based on linear elastic deformation energy. It establishes conditions for the existence and uniqueness of solutions for both methods and presents synthetic experimental results comparing them with a standard method based on the sum of squared differences similarity measure. These methods have been tested to capture various non-affine deformations in images, both with and without noise, and their convergence rates have been analyzed. Furthermore, the effectiveness of these methods was also evaluated in a lung image registration scenario.
Clutter Suppression, Time-Frequency Synchronization, and Sensing Parameter Association in Asynchronous Perceptive Vehicular Networks
Authors: Xiao-Yang Wang, Shaoshi Yang, Jianhua Zhang, Christos Masouros, Ping Zhang
Date: 2024-09-02 00:00:00
Tasks: Integrated sensing and communication

Significant challenges remain for realizing precise positioning and velocity estimation in perceptive vehicular networks (PVN) enabled by the emerging integrated sensing and communication technology. First, complicated wireless propagation environment generates undesired clutter, which degrades the vehicular sensing performance and increases the computational complexity. Second, in practical PVN, multiple types of parameters individually estimated are not well associated with specific vehicles, which may cause error propagation in multiple-vehicle positioning. Third, radio transceivers in a PVN are naturally asynchronous, which causes strong range and velocity ambiguity. To overcome these challenges, 1) we introduce a moving target indication based joint clutter suppression and sensing algorithm, and analyze its clutter-suppression performance and the Cramer-Rao lower bound of the paired range-velocity estimation upon using the proposed clutter suppression algorithm; 2) we design algorithms for associating individual direction-of-arrival estimates with the paired range-velocity estimates based on "domain transformation"; 3) we propose the first viable carrier frequency offset (CFO) and time offset (TO) estimation algorithm that supports passive vehicular sensing in non-line-of-sight environments. This algorithm treats the delay-Doppler spectrum of the signals reflected by static objects as an environment-specific "fingerprint spectrum", which is shown to exhibit a circular shift property upon changing the CFO and/or TO. Then, the CFO and TO are efficiently estimated by acquiring the number of circular shifts, and we also analyse the mean squared error performance of the proposed time-frequency synchronization algorithm. Simulation results demonstrate the performance advantages of our algorithms under diverse configurations, while corroborating the theoretical analysis.
Windowing Optimization for Fingerprint-Spectrum-Based Passive Sensing in Perceptive Mobile Networks
Authors: Xiao-Yang Wang, Shaoshi Yang, Hou-Yu Zhai, Christos Masouros, J. Andrew Zhang
Date: 2024-09-02 00:00:00

Perceptive mobile networks (PMN) have been widely recognized as a pivotal pillar for the sixth generation (6G) mobile communication systems. However, the asynchronicity between transmitters and receivers results in velocity and range ambiguity, which seriously degrades the sensing performance. To mitigate the ambiguity, carrier frequency offset (CFO) and time offset (TO) synchronizations have been studied in the literature. However, their performance can be significantly affected by the specific choice of the window functions harnessed. Hence, we set out to find superior window functions capable of improving the performance of CFO and TO estimation algorithms. We firstly derive a near-optimal window, and the theoretical synchronization mean square error (MSE) when utilizing this window. However, since this window is not practically achievable, we then test a practical "window function" by utilizing the multiple signal classification (MUSIC) algorithm, which may lead to excellent synchronization performance.
Analysis of Status Update in Wireless Networks with Successive Interference Cancellation
Authors: Asmad Bin Abdul Razzaque, Andrea Baiocchi
Date: 2024-08-30 00:00:00

Data collection in an IoT environment requires simple and effective communication solutions to address resource constraints, ensure network efficiency, while achieving scalability. Efficiency is evaluated based on the timeliness of collected data (Age of Information), the energy spent per delivered unit of data, and the effectiveness in utilizing spectrum resources. This paper addresses a random multiple access adaptive system, in which a large number of devices send sporadic messages in non-periodic pattern. In particular, our analysis highlights the potential of Successive Interference Cancellation and identifies an adaptive parameter setting to maximize its benefits as the level of contention on the shared channel varies. An analytical model is defined, easily scalable with the number of nodes and yielding all the relevant metrics. Evidence of the accuracy of the model is given by comparing predicted results against simulations. The model is utilized to assess the trade-off between Age of Information and energy consumption, revealing a sharp relationship between the two. The considered approach lends itself to many generalizations and applications to massive machine-type communications and IoT networks.
SGP-RI: A Real-Time-Trainable and Decentralized IoT Indoor Localization Model Based on Sparse Gaussian Process with Reduced-Dimensional Inputs
Authors: Zhe Tang, Sihao Li, Zichen Huang, Guandong Yang, Kyeong Soo Kim, Jeremy S. Smith
Date: 2024-08-24 00:00:00
Tasks: Indoor Localization

Internet of Things (IoT) devices are deployed in the filed, there is an enormous amount of untapped potential in local computing on those IoT devices. Harnessing this potential for indoor localization, therefore, becomes an exciting research area. Conventionally, the training and deployment of indoor localization models are based on centralized servers with substantial computational resources. This centralized approach faces several challenges, including the database's inability to accommodate the dynamic and unpredictable nature of the indoor electromagnetic environment, the model retraining costs, and the susceptibility of centralized servers to security breaches. To mitigate these challenges we aim to amalgamate the offline and online phases of traditional indoor localization methods using a real-time-trainable and decentralized IoT indoor localization model based on Sparse Gaussian Process with Reduced-dimensional Inputs (SGP-RI), where the number and dimension of the input data are reduced through reference point and wireless access point filtering, respectively. The experimental results based on a multi-building and multi-floor static database as well as a single-building and single-floor dynamic database, demonstrate that the proposed SGP-RI model with less than half the training samples as inducing inputs can produce comparable localization performance to the standard Gaussian Process model with the whole training samples. The SGP-RI model enables the decentralization of indoor localization, facilitating its deployment to resource-constrained IoT devices, and thereby could provide enhanced security and privacy, reduced costs, and network dependency. Also, the model's capability of real-time training makes it possible to quickly adapt to the time-varying indoor electromagnetic environment.