pwc-archive/papers-with-abstracts huggingface dataset:
Subspace-Informed Matrix Completion
Authors: Hamideh. Sadat Fazael Ardakani, Sajad Daei, Arash Amini, Mikael Skoglund, Gabor Fodor
Date: 2024-05-13 00:00:00
Tasks: Collaborative Filtering, Matrix Completion

In this work, we consider the matrix completion problem, where the objective is to reconstruct a low-rank matrix from a few observed entries. A commonly employed approach involves nuclear norm minimization. For this method to succeed, the number of observed entries needs to scale at least proportional to both the rank of the ground-truth matrix and the coherence parameter. While the only prior information is oftentimes the low-rank nature of the ground-truth matrix, in various real-world scenarios, additional knowledge about the ground-truth low-rank matrix is available. For instance, in collaborative filtering, Netflix problem, and dynamic channel estimation in wireless communications, we have partial or full knowledge about the signal subspace in advance. Specifically, we are aware of some subspaces that form multiple angles with the column and row spaces of the ground-truth matrix. Leveraging this valuable information has the potential to significantly reduce the required number of observations. To this end, we introduce a multi-weight nuclear norm optimization problem that concurrently promotes the low-rank property as well the information about the available subspaces. The proposed weights are tailored to penalize each angle corresponding to each basis of the prior subspace independently. We further propose an optimal weight selection strategy by minimizing the coherence parameter of the ground-truth matrix, which is equivalent to minimizing the required number of observations. Simulation results validate the advantages of incorporating multiple weights in the completion procedure. Specifically, our proposed multi-weight optimization problem demonstrates a substantial reduction in the required number of observations compared to the state-of-the-art methods.
Optimal Transmitter Design and Pilot Spacing in MIMO Non-Stationary Aging Channels
Authors: Sajad Daei, Gabor Fodor, Mikael Skoglund
Date: 2024-05-13 00:00:00

This work considers an uplink wireless communication system where multiple users with multiple antennas transmit data frames over dynamic channels. Previous studies have shown that multiple transmit and receive antennas can substantially enhance the sum-capacity of all users when the channel is known at the transmitter and in the case of uncorrelated transmit and receive antennas. However, spatial correlations stemming from close proximity of transmit antennas and channel variation between pilot and data time slots, known as channel aging, can substantially degrade the transmission rate if they are not properly into account. In this work, we provide an analytical framework to concurrently exploit both of these features. Specifically, we first propose a beamforming framework to capture spatial correlations. Then, based on random matrix theory tools, we introduce a deterministic expression that approximates the average sum-capacity of all users. Subsequently, we obtain the optimal values of pilot spacing and beamforming vectors upon maximizing this expression. Simulation results show the impacts of path loss, velocity of mobile users and Rician factor on the resulting sum-capacity and underscore the efficacy of our methodology compared to prior works.
Beyond Diagonal Reconfigurable Intelligent Surfaces in Wideband OFDM Communications: Circuit-Based Modeling and Optimization
Authors: Hongyu Li, Matteo Nerini, Shanpu Shen, Bruno Clerckx
Date: 2024-05-12 00:00:00

This work investigates the modeling and optimization of beyond diagonal reconfigurable intelligent surface (BD-RIS), which generalizes conventional RIS with diagonal phase shift matrices and provides additional flexibility for manipulating wireless channels, in wideband communication systems. Specifically, we start from the signal modeling of the BD-RIS-aided orthogonal frequency division multiplexing (OFDM) system, which bridges the time-domain and frequency-domain channels, and explicitly shows the frequency dependence of the BD-RIS response. We next characterize the frequency dependence of the BD-RIS response based on circuit models. Benefiting from the admittance parameter analysis, we model individually each tunable admittance component of BD-RIS and derive an approximated linear expression with respect to the frequency of the transmit signals. With the proposed signal model for the BD-RIS-aided OFDM system and the frequency-dependent BD-RIS model, we propose algorithms to optimize the BD-RIS and the power allocation at the transmitter to maximize the average rate for a BD-RIS-aided OFDM system. Finally, simulation results show that BD-RIS outperforms conventional RIS in the OFDM system. More importantly, the impact of wideband modeling of BD-RIS on the system performance becomes more significant as the circuit complexity of BD-RIS architectures increases.
Digital Twin Aided Compressive Sensing: Enabling Site-Specific MIMO Hybrid Precoding
Authors: Hao Luo, Ahmed Alkhateeb
Date: 2024-05-12 00:00:00
Tasks: Compressive Sensing

Compressive sensing is a promising solution for the channel estimation in multiple-input multiple-output (MIMO) systems with large antenna arrays and constrained hardware. Utilizing site-specific channel data from real-world systems, deep learning can be employed to learn the compressive sensing measurement vectors with minimum redundancy, thereby focusing sensing power on promising spatial directions of the channel. Collecting real-world channel data, however, is challenging due to the high overhead resulting from the large number of antennas and hardware constraints. In this paper, we propose leveraging a site-specific digital twin to generate synthetic channel data, which shares a similar distribution with real-world data. The synthetic data is then used to train the deep learning models for learning measurement vectors and hybrid precoder/combiner design in an end-to-end manner. We further propose a model refinement approach to fine-tune the model pre-trained on the digital twin data with a small amount of real-world data. The evaluation results show that, by training the model on the digital twin data, the learned measurement vectors can be efficiently adapted to the environment geometry, leading to high performance of hybrid precoding for real-world deployments. Moreover, the model refinement approach can enable the digital twin aided model to achieve comparable performance to the model trained on the real-world dataset with a significantly reduced amount of real-world data.
Energy Reduction in Cell-Free Massive MIMO through Fine-Grained Resource Management
Authors: Özlem Tuğfe Demir, Lianet Méndez-Monsanto, Nicola Bastianello, Emma Fitzgerald, Gilles Callebaut
Date: 2024-05-11 00:00:00
Tasks: Management

The physical layer foundations of cell-free massive MIMO (CF-mMIMO) have been well-established. As a next step, researchers are investigating practical and energy-efficient network implementations. This paper focuses on multiple sets of access points (APs) where user equipments (UEs) are served in each set, termed a federation, without inter-federation interference. The combination of federations and CF-mMIMO shows promise for highly-loaded scenarios. Our aim is to minimize the total energy consumption while adhering to UE downlink data rate constraints. The energy expenditure of the full system is modelled using a detailed hardware model of the APs. We jointly design the AP-UE association variables, determine active APs, and assign APs and UEs to federations. To solve this highly combinatorial problem, we develop a novel alternating optimization algorithm. Simulation results for an indoor factory demonstrate the advantages of considering multiple federations, particularly when facing large data rate requirements. Furthermore, we show that adopting a more distributed CF-mMIMO architecture is necessary to meet the data rate requirements. Conversely, if feasible, using a less distributed system with more antennas at each AP is more advantageous from an energy savings perspective.
Speaker Embeddings With Weakly Supervised Voice Activity Detection For Efficient Speaker Diarization
Authors: Jenthe Thienpondt, Kris Demuynck
Date: 2024-05-15 00:00:00
Tasks: Action Detection, Activity Detection, speaker-diarization, Speaker Diarization, Speaker Verification

Current speaker diarization systems rely on an external voice activity detection model prior to speaker embedding extraction on the detected speech segments. In this paper, we establish that the attention system of a speaker embedding extractor acts as a weakly supervised internal VAD model and performs equally or better than comparable supervised VAD systems. Subsequently, speaker diarization can be performed efficiently by extracting the VAD logits and corresponding speaker embedding simultaneously, alleviating the need and computational overhead of an external VAD model. We provide an extensive analysis of the behavior of the frame-level attention system in current speaker verification models and propose a novel speaker diarization pipeline using ECAPA2 speaker embeddings for both VAD and embedding extraction. The proposed strategy gains state-of-the-art performance on the AMI, VoxConverse and DIHARD III diarization benchmarks.
A tunable binaural audio telepresence system capable of balancing immersive and enhanced modes
Authors: Yicheng Hsu, Mingsian R. Bai
Date: 2024-05-14 00:00:00

Binaural Audio Telepresence (BAT) aims to encode the acoustic scene at the far end into binaural signals for the user at the near end. BAT encompasses an immense range of applications that can vary between two extreme modes of Immersive BAT (I-BAT) and Enhanced BAT (E-BAT). With I-BAT, our goal is to preserve the full ambience as if we were at the far end, while with E-BAT, our goal is to enhance the far-end conversation with significantly improved speech quality and intelligibility. To this end, this paper presents a tunable BAT system to vary between these two AT modes with a desired application-specific balance. Microphone signals are converted into binaural signals with prescribed ambience factor. A novel Spatial COherence REpresentation (SCORE) is proposed as an input feature for model training so that the network remains robust to different array setups. Experimental results demonstrated the superior performance of the proposed BAT, even when the array configurations were not included in the training phase.
Neural Speech Coding for Real-time Communications using Constant Bitrate Scalar Quantization
Authors: Andreas Brendel, Nicola Pia, Kishan Gupta, Lyonel Behringer, Guillaume Fuchs, Markus Multrus
Date: 2024-05-14 00:00:00
Tasks: Quantization, Scheduling

Neural audio coding has emerged as a vivid research direction by promising good audio quality at very low bitrates unachievable by classical coding techniques. Here, end-to-end trainable autoencoder-like models represent the state of the art, where a discrete representation in the bottleneck of the autoencoder is learned. This allows for efficient transmission of the input audio signal. The learned discrete representation of neural codecs is typically generated by applying a quantizer to the output of the neural encoder. In almost all state-of-the-art neural audio coding approaches, this quantizer is realized as a Vector Quantizer (VQ) and a lot of effort has been spent to alleviate drawbacks of this quantization technique when used together with a neural audio coder. In this paper, we propose and analyze simple alternatives to VQ, which are based on projected Scalar Quantization (SQ). These quantization techniques do not need any additional losses, scheduling parameters or codebook storage thereby simplifying the training of neural audio codecs. For real-time speech communication applications, these neural codecs are required to operate at low complexity, low latency and at low bitrates. We address those challenges by proposing a new causal network architecture that is based on SQ and a Short-Time Fourier Transform (STFT) representation. The proposed method performs particularly well in the very low complexity and low bitrate regime.
Semantic MIMO Systems for Speech-to-Text Transmission
Authors: Zhenzi Weng, Zhijin Qin, Huiqiang Xie, Xiaoming Tao, Khaled B. Letaief
Date: 2024-05-13 00:00:00
Tasks: Semantic Communication, Speech-to-Text

Semantic communications have been utilized to execute numerous intelligent tasks by transmitting task-related semantic information instead of bits. In this article, we propose a semantic-aware speech-to-text transmission system for the single-user multiple-input multiple-output (MIMO) and multi-user MIMO communication scenarios, named SAC-ST. Particularly, a semantic communication system to serve the speech-to-text task at the receiver is first designed, which compresses the semantic information and generates the low-dimensional semantic features by leveraging the transformer module. In addition, a novel semantic-aware network is proposed to facilitate transmission with high semantic fidelity by identifying the critical semantic information and guaranteeing its accurate recovery. Furthermore, we extend the SAC-ST with a neural network-enabled channel estimation network to mitigate the dependence on accurate channel state information and validate the feasibility of SAC-ST in practical communication environments. Simulation results will show that the proposed SAC-ST outperforms the communication framework without the semantic-aware network for speech-to-text transmission over the MIMO channels in terms of the speech-to-text metrics, especially in the low signal-to-noise regime. Moreover, the SAC-ST with the developed channel estimation network is comparable to the SAC-ST with perfect channel state information.
Evaluating Speech Enhancement Systems Through Listening Effort
Authors: Femke B. Gelderblom, Tron V. Tronstad, Iván López-Espejo
Date: 2024-05-13 00:00:00
Tasks: Speech Enhancement

Understanding degraded speech is demanding, requiring increased listening effort (LE). Evaluating processed and unprocessed speech with respect to LE can objectively indicate if speech enhancement systems benefit listeners. However, existing methods for measuring LE are complex and not widely applicable. In this study, we propose a simple method to evaluate speech intelligibility and LE simultaneously without additional strain on subjects or operators. We assess this method using results from two independent studies in Norway and Denmark, testing 76 (50+26) subjects across 9 (6+3) processing conditions. Despite differences in evaluation setups, subject recruitment, and processing systems, trends are strikingly similar, demonstrating the proposed method's robustness and ease of implementation into existing practices.
IPDnet: A Universal Direct-Path IPD Estimation Network for Sound Source Localization
Authors: Yabo Wang, Bing Yang, Xiaofei Li
Date: 2024-05-11 00:00:00
Tasks: Sound Source Localization

Extracting direct-path spatial feature is crucial for sound source localization in adverse acoustic environments. This paper proposes the IPDnet, a neural network that estimates direct-path inter-channel phase difference (DP-IPD) of sound sources from microphone array signals. The estimated DP-IPD can be easily translated to source location based on the known microphone array geometry. First, a full-band and narrow-band fusion network is proposed for DP-IPD estimation, in which alternating narrow-band and full-band layers are responsible for estimating the rough DP-IPD information in one frequency band and capturing the frequency correlations of DP-IPD, respectively. Second, a new multi-track DP-IPD learning target is proposed for the localization of flexible number of sound sources. Third, the IPDnet is extend to handling variable microphone arrays, once trained which is able to process arbitrary microphone arrays with different number of channels and array topology. Experiments of multiple-moving-speaker localization are conducted on both simulated and real-world data, which show that the proposed full-band and narrow-band fusion network and the proposed multi-track DP-IPD learning target together achieves excellent sound source localization performance. Moreover, the proposed variable-array model generalizes well to unseen microphone arrays.
Exposing and Explaining Fake News On-the-Fly
Authors: Francisco de Arriba-Pérez, Silvia García-Méndez, Fátima Leal, Benedita Malheiro, Juan Carlos Burguillo
Date: 2024-05-03 00:00:00
Tasks: Classification

Social media platforms enable the rapid dissemination and consumption of information. However, users instantly consume such content regardless of the reliability of the shared data. Consequently, the latter crowdsourcing model is exposed to manipulation. This work contributes with an explainable and online classification method to recognize fake news in real-time. The proposed method combines both unsupervised and supervised Machine Learning approaches with online created lexica. The profiling is built using creator-, content- and context-based features using Natural Language Processing techniques. The explainable classification mechanism displays in a dashboard the features selected for classification and the prediction confidence. The performance of the proposed solution has been validated with real data sets from Twitter and the results attain 80 % accuracy and macro F-measure. This proposal is the first to jointly provide data stream processing, profiling, classification and explainability. Ultimately, the proposed early detection, isolation and explanation of fake news contribute to increase the quality and trustworthiness of social media contents.
Perturbed Integrators Chain Control via Barrier Function Adaptation and Lyapunov Redesign
Authors: Manuel A. Estrada, Claudia A. Pérez-Pinacho, Christopher D. Cruz-Ancona, Leonid Fridman
Date: 2024-05-15 00:00:00

Lyapunov redesign is a classical technique that uses a nominal control and its corresponding nominal Lyapunov function to design a discontinuous control, such that it compensates the uncertainties and disturbances. In this paper, the idea of Lyapunov redesign is used to propose an adaptive time-varying gain controller to stabilize a class of perturbed chain of integrators with an unknown control coefficient. It is assumed that the upper bound of the perturbation exists but is unknown. A proportional navigation feedback type gain is used to drive the system's trajectories into a prescribed vicinity of the origin in a predefined time, measured using a quadratic Lyapunov function. Once this neighborhood is reached, a barrier function-based gain is used, ensuring that the system's trajectories never leave this neighborhood despite uncertainties and perturbations. Experimental validation of the proposed controller in Furuta's pendulum is presented.
Analyzing and Enhancing Queue Sampling for Energy-Efficient Remote Control of Bandits
Authors: Hiba Dakdouk, Mohamed Sana, Mattia Merluzzi
Date: 2024-05-15 00:00:00
Tasks: Autonomous Vehicles, Thompson Sampling

In recent years, the integration of communication and control systems has gained significant traction in various domains, ranging from autonomous vehicles to industrial automation and beyond. Multi-armed bandit (MAB) algorithms have proven their effectiveness as a robust framework for solving control problems. In this work, we investigate the use of MAB algorithms to control remote devices, which faces considerable challenges primarily represented by latency and reliability. We analyze the effectiveness of MABs operating in environments where the action feedback from controlled devices is transmitted over an unreliable communication channel and stored in a Geo/Geo/1 queue. We investigate the impact of queue sampling strategies on the MAB performance, and introduce a new stochastic approach. Its performance in terms of regret is evaluated against established algorithms in the literature for both upper confidence bound (UCB) and Thompson Sampling (TS) algorithms. Additionally, we study the trade-off between maximizing rewards and minimizing energy consumption.
On identifying the non-linear dynamics of a hovercraft using an end-to-end deep learning approach
Authors: Roland Schwan, Nicolaj Schmid, Etienne Chassaing, Karim Samaha, Colin N. Jones
Date: 2024-05-15 00:00:00
Tasks: Position

We present the identification of the non-linear dynamics of a novel hovercraft design, employing end-to-end deep learning techniques. Our experimental setup consists of a hovercraft propelled by racing drone propellers mounted on a lightweight foam base, allowing it to float and be controlled freely on an air hockey table. We learn parametrized physics-inspired non-linear models directly from data trajectories, leveraging gradient-based optimization techniques prevalent in machine learning research. The chosen model structure allows us to control the position of the hovercraft precisely on the air hockey table. We then analyze the prediction performance and demonstrate the closed-loop control performance on the real system.
On the impact of the antenna radiation patterns in passive radio sensing
Authors: Federica Fieramosca, Vittorio Rampa, Stefano Savazzi, Michele D'Amico
Date: 2024-05-15 00:00:00

Electromagnetic (EM) body models based on the scalar diffraction theory allow to predict the impact of subject motions on the radio propagation channel without requiring a time-consuming full-wave approach. On the other hand, they are less effective in complex environments characterized by significant multipath effects. Recently, emerging radio sensing applications have proposed the adoption of smart antennas with non-isotropic radiation characteristics to improve coverage.This letter investigates the impact of antenna radiation patterns in passive radio sensing applications. Adaptations of diffraction-based EM models are proposed to account for antenna non-uniform angular filtering. Next, we quantify experimentally the impact of diffraction and multipath disturbance components on radio sensing accuracy in environments with smart antennas.
Full-wave EM simulation analysis of human body blockage by dense 2D antenna arrays
Authors: Federica Fieramosca, Vittorio Rampa, Michele D'Amico, Stefano Savazzi
Date: 2024-05-15 00:00:00
Tasks: Indoor Localization

Recently, proposals of human-sensing-based services for cellular and local area networks have brought indoor localization to the attention of several research groups. In response to these stimuli, various Device-Free Localization (DFL) techniques, also known as Passive Localization methods, have emerged by exploiting ambient signals to locate and track individuals that do not carry any electronic device. This study delves into human passive indoor localization through full-wave electromagnetic simulations. For the scope, we exploit simulations from the commercial tool FEKO software that employs the Method of Moments (MoM). In particular, we collect and analyze the electric field values in a scenario constituted by a dense 2D/3D deployment of receivers in the presence of an anthropomorphic mobile target. The paper describes in detail the collected dataset and provides a first analysis based on a statistical approach. Possible use cases are also investigated through examples in the context of passive localization, sensing, and imaging.