pwc-archive/papers-with-abstracts huggingface dataset:
Reverberant sound field equalisation for an enhanced stereo playback experience
Authors: James Brooks-Park, Steven van de Par
Date: 2023-11-01 00:00:00

The topic of room equalisation has been at the forefront of research and product development for many years, with the aim of increasing the playback quality of loudspeakers in reverberant rooms. Traditional room equalisation systems comprise of a number of filters that when applied to the primary loudspeakers, additional room colouration is compensated for. This publication introduces a novel equalisation technique where gammatone filter band energy is added to the reverberant sound field via two surround loudspeakers, leaving the direct sound from the primary loudspeakers unaltered, but the sum of direct and reverberant energy is equalised at the listening position. Unlike traditional systems, this method allows the target function of the direct sound to differ from the reverberant sound field. The proposed method is motivated by the different roles direct and reverberant sound components play in humans perception of sound. Along with introducing the proposed method, results from a subjective listening test are presented, demonstrating the preference towards the proposed technique when compared to a traditional room equalisation technique and stereo playback.
An analysis of large speech models-based representations for speech emotion recognition
Authors: Adrian Bogdan Stânea, Vlad Striletchi, Cosmin Striletchi, Adriana Stan
Date: 2023-11-01 00:00:00
Tasks: Emotion Recognition, Speech Emotion Recognition

Large speech models-derived features have recently shown increased performance over signal-based features across multiple downstream tasks, even when the networks are not finetuned towards the target task. In this paper we show the results of an analysis of several signal- and neural models-derived features for speech emotion recognition. We use pretrained models and explore their inherent potential abstractions of emotions. Simple classification methods are used so as to not interfere or add knowledge to the task. We show that, even without finetuning, some of these large neural speech models' representations can enclose information that enables performances close to, and even beyond state-of-the-art results across six standard speech emotion recognition datasets.
See SIFT in a Rain
Authors: Wei Wu, Hao Chang, Zhu Li
Date: 2023-11-01 00:00:00
Tasks: Rain Removal

Rain streaks bring complicated pixel intensity changes and additional gradients, greatly obstructing the extraction of image features from background. This causes serious performance degradation in feature-based applications. Thus, it is critical to remove rain streaks from a single rainy image to recover image features. Recently, many excellent image deraining methods have made remarkable progress. However, these human visual system-driven approaches mainly focus on improving image quality with pixel recovery as loss function, and neglect how to enhance image feature recovery ability. To address this issue, we propose a task-driven image deraining algorithm to strengthen image feature supply for subsequent feature-based applications. Due to the extensive use and strong practicability of Scale-Invariant Feature Transform (SIFT), we first propose two separate networks using distinct losses and modules to achieve two goals, respectively. One is difference of Gaussian (DoG) pyramid recovery network (DPRNet) for SIFT detection, and the other gradients of Gaussian images recovery network (GGIRNet) for SIFT description. Second, in the DPRNet we propose an alternative interest point loss that directly penalizes scale response extrema to recover the DoG pyramid. Third, we advance a gradient attention module in the GGIRNet to recover those gradients of Gaussian images. Finally, with the recovered DoG pyramid and gradients, we can regain SIFT key points. This divide-and-conquer scheme to set different objectives for SIFT detection and description leads to good robustness. Compared with state-of-the-art methods, experimental results demonstrate that our proposed algorithm achieves better performance in both the number of recovered SIFT key points and their accuracy.
UAV Immersive Video Streaming: A Comprehensive Survey, Benchmarking, and Open Challenges
Authors: Mohit K. Sharma, Chen-Feng Liu, Ibrahim Farhat, Nassim Sehad, Wassim Hamidouche, Merouane Debbah
Date: 2023-10-31 00:00:00
Tasks: Benchmarking

Over the past decade, the utilization of UAVs has witnessed significant growth, owing to their agility, rapid deployment, and maneuverability. In particular, the use of UAV-mounted 360-degree cameras to capture omnidirectional videos has enabled truly immersive viewing experiences with up to 6DoF. However, achieving this immersive experience necessitates encoding omnidirectional videos in high resolution, leading to increased bitrates. Consequently, new challenges arise in terms of latency, throughput, perceived quality, and energy consumption for real-time streaming of such content. This paper presents a comprehensive survey of research efforts in UAV-based immersive video streaming, benchmarks popular video encoding schemes, and identifies open research challenges. Initially, we review the literature on 360-degree video coding, packaging, and streaming, with a particular focus on standardization efforts to ensure interoperability of immersive video streaming devices and services. Subsequently, we provide a comprehensive review of research efforts focused on optimizing video streaming for timevarying UAV wireless channels. Additionally, we introduce a high resolution 360-degree video dataset captured from UAVs under different flying conditions. This dataset facilitates the evaluation of complexity and coding efficiency of software and hardware video encoders based on popular video coding standards and formats, including AVC/H.264, HEVC/H.265, VVC/H.266, VP9, and AV1. Our results demonstrate that HEVC achieves the best trade-off between coding efficiency and complexity through its hardware implementation, while AV1 format excels in coding efficiency through its software implementation, specifically using the libsvt-av1 encoder. Furthermore, we present a real testbed showcasing 360-degree video streaming over a UAV, enabling remote control of the drone via a 5G cellular network.
Supervised Learning Based Real-Time Adaptive Beamforming On-board Multibeam Satellites
Authors: Flor Ortiz, Juan A. Vasquez-Peralvo, Jorge Querol, Eva Lagunas, Jorge L. Gonzalez Rios, Marcele O. K. Mendonca, Luis Garces, Victor Monzon Baeza, Symeon Chatzinotas
Date: 2023-11-02 00:00:00
Tasks: Management

Satellite communications (SatCom) are crucial for global connectivity, especially in the era of emerging technologies like 6G and narrowing the digital divide. Traditional SatCom systems struggle with efficient resource management due to static multibeam configurations, hindering quality of service (QoS) amidst dynamic traffic demands. This paper introduces an innovative solution - real-time adaptive beamforming on multibeam satellites with software-defined payloads in geostationary orbit (GEO). Utilizing a Direct Radiating Array (DRA) with circular polarization in the 17.7 - 20.2 GHz band, the paper outlines DRA design and a supervised learning-based algorithm for on-board beamforming. This adaptive approach not only meets precise beam projection needs but also dynamically adjusts beamwidth, minimizes sidelobe levels (SLL), and optimizes effective isotropic radiated power (EIRP).
Statistical Results of Multivariate Fox-H Function for Exact Performance Analysis of RIS-Assisted Wireless Communication
Authors: vinay kumar chapala, S. M. Zafaruddin
Date: 2023-11-02 00:00:00
Tasks: Diversity

Existing research provides statistical results on the sum of single-variate Fox-H functions to analyze the performance of diversity receivers and reconfigurable intelligent surfaces (RIS) based wireless systems. There is a research gap in exact performance analysis when more than a single-variate Fox-H function represents the statistical characterization of wireless systems. In this paper, we propose a novel approach to obtain the distribution of the sum of independent and non-identically distributed (i.ni.d) random variables characterized by the multivariate Fox-H function. Further, we develop a general framework for an exact analysis of the ergodic capacity when the multivariate Fox-H function characterizes the statistics of signal-to-noise ratio (SNR). We apply the derived results to conduct an exact performance analysis of outage probability and ergodic capacity, taking an example of RIS-assisted communication over Rician fading channels with phase errors. We conduct computer simulations to validate the exact analysis and demonstrate performance of the RIS-assisted system under various practically relevant scenarios for a better performance assessment.
Map-assisted TDOA Localization Enhancement Based On CNN
Authors: YiWen Chen, Tianqi Xiang, Xi Chen, Xin Zhang
Date: 2023-11-02 00:00:00
Tasks: Prediction

For signal processing related to localization technologies, non line of sight (NLOS) multipaths have a significant impact on the localization error level. This study proposes a localization correction method based on convolution neural network (CNN), which extracts obstacle features from maps to predict the localization errors caused by NLOS effects. A novel compensation scheme is developed and structured around the localization error in terms of distance and azimuth angle predicted by the CNN. Four prediction tasks are executed over different building distributions within the maps for typical urban scenario, resulting in CNN models with high prediction accuracy. Finally, a thorough comparison of the accuracy performance between the time difference of arrival (TDOA) localization algorithm and the results after the error compensation reveals that, generally, the CNN prediction approach demonstrates great localization error correction performance, improving TDOA accuracy by 75%. It can be observed that the powerful feature extraction capability of CNN can be exploited by processing surrounding maps to predict the localization error distribution, showing great potential for further enhancement of TDOA performance under challenging scenarios with rich multipath propagation.
Decentralized Federated Learning on the Edge over Wireless Mesh Networks
Authors: Abdelaziz Salama, Achilleas Stergioulis, Syed Ali Zaidi, Des McLernon
Date: 2023-11-02 00:00:00
Tasks: Benchmarking, Federated Learning

The rapid growth of Internet of Things (IoT) devices has generated vast amounts of data, leading to the emergence of federated learning as a novel distributed machine learning paradigm. Federated learning enables model training at the edge, leveraging the processing capacity of edge devices while preserving privacy and mitigating data transfer bottlenecks. However, the conventional centralized federated learning architecture suffers from a single point of failure and susceptibility to malicious attacks. In this study, we delve into an alternative approach called decentralized federated learning (DFL) conducted over a wireless mesh network as the communication backbone. We perform a comprehensive network performance analysis using stochastic geometry theory and physical interference models, offering fresh insights into the convergence analysis of DFL. Additionally, we conduct system simulations to assess the proposed decentralized architecture under various network parameters and different aggregator methods such as FedAvg, Krum and Median methods. Our model is trained on the widely recognized EMNIST dataset for benchmarking handwritten digit classification. To minimize the model's size at the edge and reduce communication overhead, we employ a cutting-edge compression technique based on genetic algorithms. Our simulation results reveal that the compressed decentralized architecture achieves performance comparable to the baseline centralized architecture and traditional DFL in terms of accuracy and average loss for our classification task. Moreover, it significantly reduces the size of shared models over the wireless channel by compressing participants' local model sizes to nearly half of their original size compared to the baselines, effectively reducing complexity and communication overhead.
Combating Inter-Operator Pilot Contamination in Reconfigurable Intelligent Surfaces Assisted Multi-Operator Networks
Authors: Doğa Gürgünoğlu, Emil Björnson, Gábor Fodor
Date: 2023-11-02 00:00:00

In this paper, we study a new kind of pilot contamination appearing in multi-operator reconfigurable intelligent surfaces (RIS) assisted networks, where multiple operators provide services to their respective served users. The operators use dedicated frequency bands, but each RIS inadvertently reflects the transmitted uplink signals of the user equipment devices in multiple bands. Consequently, the concurrent reflection of pilot signals during the channel estimation phase introduces a new inter-operator pilot contamination effect. We investigate the implications of this effect in systems with either deterministic or correlated Rayleigh fading channels, specifically focusing on its impact on channel estimation quality, signal equalization, and channel capacity. The numerical results demonstrate the substantial degradation in system performance caused by this phenomenon and highlight the pressing need to address inter-operator pilot contamination in multi-operator RIS deployments. To combat the negative effect of this new type of pilot contamination, we propose to use orthogonal RIS configurations during uplink pilot transmission, which can mitigate or eliminate the negative effect of inter-operator pilot contamination at the expense of some inter-operator information exchange and orchestration.
Noncontact Detection of Sleep Apnea Using Radar and Expectation-Maximization Algorithm
Authors: Takato Koda, Shigeaki Okumura, Hirofumi Taki, Satoshi Hamada, Hironobu Sunadome, Susumu Sato, Kazuo Chin, Takuya Sakamoto
Date: 2023-11-02 00:00:00

Sleep apnea syndrome requires early diagnosis because this syndrome can lead to a variety of health problems. If sleep apnea events can be detected in a noncontact manner using radar, we can then avoid the discomfort caused by the contact-type sensors that are used in conventional polysomnography. This study proposes a novel radar-based method for accurate detection of sleep apnea events. The proposed method uses the expectation-maximization algorithm to extract the respiratory features that form normal and abnormal breathing patterns, resulting in an adaptive apnea detection capability without any requirement for empirical parameters. We conducted an experimental quantitative evaluation of the proposed method by performing polysomnography and radar measurements simultaneously in five patients with the symptoms of sleep apnea syndrome. Through these experiments, we show that the proposed method can detect the number of apnea and hypopnea events per hour with an error of 4.8 times/hour; this represents an improvement in the accuracy by 1.8 times when compared with the conventional threshold-based method and demonstrates the effectiveness of our proposed method.
Continuous Fluid Antenna Systems: Modeling and Analysis
Authors: Constantinos Psomas, Peter J. Smith, Himal A. Suraweera, Ioannis Krikidis
Date: 2023-11-02 00:00:00

Fluid antennas (FAs) is a promising technology for introducing flexibility and reconfigurability in wireless networks. Recent research efforts have highlighted the potential gains that can be achieved in comparison to conventional antennas. These works assume that the FA has a discrete number of positions that the liquid can take. However, from a practical standpoint, the liquid moves in a continuous fashion to any point inside the FA. In this paper, we focus on a continuous FA system (CFAS) and present a general framework for its design and analytical evaluation. In particular, we derive closed-form analytical expressions for the level crossing rate (LCR) and the average fade duration of the continuous signal-to-interference ratio (SIR) process over the FA's length. Then, by leveraging the LCR expression, we characterize the system's outage performance with a bound on the cumulative distribution function of the SIR's supremum. Our results confirm that the CFAS outperforms its discrete counterpart and thus provides the performance limits of FA-based systems.
Channel Estimation for Reconfigurable Intelligent Surface MIMO with Tensor Signal Modelling
Authors: Alexander James Fernandes, Ioannis Psaromiligkos
Date: 2023-11-01 00:00:00

We consider a narrowband MIMO reconfigurable intelligent surface (RIS)-assisted wireless communication system and use tensor signal modelling techniques to individually estimate all communication channels including the non-RIS channels (direct path) and decoupled RIS channels. We model the received signal as a third-order tensor composed of two CANDECOMP/PARAFAC decomposition terms for the non-RIS and the RIS-assisted links, respectively, and we propose two channel estimation methods based on an iterative alternating least squares (ALS) algorithm: The two-stage RIS OFF-ON method estimates each of the non-RIS and RIS-assisted terms in two pilot training stages, whereas the enhanced alternating least squares (E-ALS) method improves upon the ALS algorithm to jointly estimate all channels over the full training duration. A key benefit of both methods compared to the traditional least squares (LS) solution is that they exploit the structure of the tensor model to obtain decoupled estimates of all communication channels. We provide the computational complexities to obtain each of the channel estimates for our two proposed methods. Numerical simulations are used to evaluate the accuracy and verify the computational complexities of the proposed two-stage RIS OFF-ON, and E-ALS, and compare them to the traditional LS methods. Results show that E-ALS will obtain the most accurate estimate while only having a slightly higher run-time than the two-stage method.
Generating HSR Bogie Vibration Signals via Pulse Voltage-Guided Conditional Diffusion Model
Authors: Xuan Liu, Jinglong Chen, Jingsong Xie, Yuanhong Chang
Date: 2023-11-01 00:00:00
Tasks: Denoising, Fault Diagnosis

Generative Adversarial Networks (GANs) for producing realistic signals, have substantially improved fault diagnosis algorithms in various Internet of Things (IoT) systems. However, challenges such as training instability and dynamical inaccuracy limit their utility in high-speed rail (HSR) bogie fault diagnosis. To address these challenges, we introduce the Pulse Voltage-Guided Conditional Diffusion Model (VGCDM). Unlike traditional implicit GANs, VGCDM adopts a sequential U-Net architecture, facilitating multi-phase denoising diffusion for generation, which bolsters training stability and mitigate convergence issues. VGCDM also incorporates control pulse voltage by cross-attention mechanism to ensure the alignment of vibration with voltage signals, enhancing the Conditional Diffusion Model's progressive controlablity. Consequently, solely straightforward sampling of control voltages, ensuring the efficient transformation from Gaussian Noise to vibration signals. This adaptability remains robust even in scenarios with time-varying speeds. To validate the effectiveness, we conducted two case studies using SQ dataset and high-simulation HSR bogie dataset. The results of our experiments unequivocally confirm that VGCDM outperforms other generative models, achieving the best RSME, PSNR, and FSCS, showing its superiority in conditional HSR bogie vibration signal generation. For access, our code is available at https://github.com/xuanliu2000/VGCDM.
Deriving Characteristic Mode Eigenvalue Behavior Using Subduction of Group Representations
Authors: Lukas Grundmann, Lukas Warkentin, Dirk Manteuffel
Date: 2023-11-01 00:00:00

A method to derive features of modal eigenvalue traces from known and understood solutions is proposed. It utilizes the concept of subduction from point group theory to obtain the symmetry properties of a target structure from those of a structure with a higher order of symmetry. This is applied exemplary to the analytically known characteristic modes (CMs) of the spherical shell. {The eigenvalue behavior of a cube in free-space is derived from it numerically. In this process, formerly crossing eigenvalue traces are found to split up, forming a macroscopic crossing avoidance (MACA). This finding is used to explain indentations in eigenvalue traces observed for 3-D structures, which are of increasing interest in recent literature. The utility of this knowledge is exemplified through a demonstrator antenna design. Here, the subduction procedure is used to analytically predict the eigenvalues of a cuboid on a perfectly electrically conducting plane. The a priori knowledge about the MACA is used to avoid its negative impact on input matching and the frequency stability of the far-field patterns, by choosing the dimensions of the antenna structure so the MACA is outside the target frequency range.
On the Semi-Blind Mutually Referenced Equalizers for MIMO Systems
Authors: Do Hai Son, Karim Abed-Meraim, Tran Trong Duy, Nguyen Linh Trung, Tran Thi Thuy Quynh
Date: 2023-11-01 00:00:00

Minimizing training overhead in channel estimation is a crucial challenge in wireless communication systems. This paper presents an extension of the traditional blind algorithm, called "Mutually referenced equalizers" (MRE), specifically designed for MIMO systems. Additionally, we propose a novel semi-blind method, SB-MRE, which combines the benefits of pilot-based and MRE approaches to achieve enhanced performance while utilizing a reduced number of pilot symbols. Moreover, the SB-MRE algorithm helps to minimize complexity and training overhead and to remove the ambiguities inherent to blind processing. The simulation results demonstrated that SB-MRE outperforms other linear algorithms, i.e., MMSE, ZF, and MRE, in terms of training overhead symbols and complexity, thereby offering a promising solution to address the challenge of minimizing training overhead in channel estimation for wireless communication systems.
Improving MIMO channel estimation via receive power feedback
Authors: Chao Zhang, Hang Zou, Samson Lasaulce, Lucas Saludjian
Date: 2023-11-01 00:00:00

Estimating the channel state is known to be an important problem in wireless networks. To this end, it matters to exploit all the available information to improve channel estimation accuracy as much as possible. It turns out that the problem of exploiting the information associated with the receive power feedback (e.g., the received signal strength indicator -RSSI-) has not been identified and solved; in this setup, the transmitter is assumed to receive feedback from all the receivers in presence. As shown in this paper, to solve this problem, classical estimation tools can be used. Using the corresponding MMSE is shown to be always beneficial, whereas the relevance of using the MAP estimator would depend on the operating SNR.