pwc-archive/papers-with-abstracts huggingface dataset:
Canceled: A New Reliability Incentive for Energy-Only Electricity Markets
Authors: Devin Mounts, Robin M. Cross
Date: 2024-06-21 00:00:00

This paper considers the reliability problem in energy-only markets. Following widespread blackouts in 2011, Texas introduced a reliability price incentive to attract two GW of net additional natural gas-generating capacity. The incentive is unusual because energy buyers pay the incentive directly to producers in a real-time spot market. The program has created $13 billion in direct payments to generators annually since 2015 and is now being implemented or considered in several major energy markets in the US and abroad. We assess the incentive's impact on the Texas market from three perspectives: First, we derive the incentive's equilibrium effect on the electricity price in a monopolistic market from first principles using a standard partial equilibrium economic model. We then empirically test whether the incentive encouraged net entry into the market or the generating applicant pool, controlling for market and climatic conditions using monthly capacity data. Finally, we look for direct evidence of an incentive response among active traders using real-time market trading data. The three approaches suggest buyers and producers cancel out the incentive, and the price-only program does not encourage new generation capacity to enter the market.
RefXVC: Cross-Lingual Voice Conversion with Enhanced Reference Leveraging
Authors: Mingyang Zhang, Yi Zhou, Yi Ren, Chen Zhang, Xiang Yin, Haizhou Li
Date: 2024-06-24 00:00:00
Tasks: Sentence, Voice Conversion

This paper proposes RefXVC, a method for cross-lingual voice conversion (XVC) that leverages reference information to improve conversion performance. Previous XVC works generally take an average speaker embedding to condition the speaker identity, which does not account for the changing timbre of speech that occurs with different pronunciations. To address this, our method uses both global and local speaker embeddings to capture the timbre changes during speech conversion. Additionally, we observed a connection between timbre and pronunciation in different languages and utilized this by incorporating a timbre encoder and a pronunciation matching network into our model. Furthermore, we found that the variation in tones is not adequately reflected in a sentence, and therefore, we used multiple references to better capture the range of a speaker's voice. The proposed method outperformed existing systems in terms of both speech quality and speaker similarity, highlighting the effectiveness of leveraging reference information in cross-lingual voice conversion. The converted speech samples can be found on the website: \url{http://refxvc.dn3point.com}
DreamVoice: Text-Guided Voice Conversion
Authors: Jiarui Hai, Karan Thakkar, Helin Wang, Zengyi Qin, Mounya Elhilali
Date: 2024-06-24 00:00:00
Tasks: text-guided-generation, Voice Conversion

Generative voice technologies are rapidly evolving, offering opportunities for more personalized and inclusive experiences. Traditional one-shot voice conversion (VC) requires a target recording during inference, limiting ease of usage in generating desired voice timbres. Text-guided generation offers an intuitive solution to convert voices to desired "DreamVoices" according to the users' needs. Our paper presents two major contributions to VC technology: (1) DreamVoiceDB, a robust dataset of voice timbre annotations for 900 speakers from VCTK and LibriTTS. (2) Two text-guided VC methods: DreamVC, an end-to-end diffusion-based text-guided VC model; and DreamVG, a versatile text-to-voice generation plugin that can be combined with any one-shot VC models. The experimental results demonstrate that our proposed methods trained on the DreamVoiceDB dataset generate voice timbres accurately aligned with the text prompt and achieve high-quality VC.
Exploiting Foundation Models and Speech Enhancement for Parkinson's Disease Detection from Speech in Real-World Operative Conditions
Authors: Moreno La Quatra, Maria Francesca Turco, Torbjørn Svendsen, Giampiero Salvi, Juan Rafael Orozco-Arroyave, Sabato Marco Siniscalchi
Date: 2024-06-23 00:00:00
Tasks: Audio Classification, Parkinson Detection from Speech, Speech Enhancement

This work is concerned with devising a robust Parkinson's (PD) disease detector from speech in real-world operating conditions using (i) foundational models, and (ii) speech enhancement (SE) methods. To this end, we first fine-tune several foundational-based models on the standard PC-GITA (s-PC-GITA) clean data. Our results demonstrate superior performance to previously proposed models. Second, we assess the generalization capability of the PD models on the extended PC-GITA (e-PC-GITA) recordings, collected in real-world operative conditions, and observe a severe drop in performance moving from ideal to real-world conditions. Third, we align training and testing conditions applaying off-the-shelf SE techniques on e-PC-GITA, and a significant boost in performance is observed only for the foundational-based models. Finally, combining the two best foundational-based models trained on s-PC-GITA, namely WavLM Base and Hubert Base, yielded top performance on the enhanced e-PC-GITA.
Text-Queried Target Sound Event Localization
Authors: Jinzheng Zhao, Xinyuan Qian, Yong Xu, Haohe Liu, Yin Cao, Davide Berghi, Wenwu Wang
Date: 2024-06-23 00:00:00
Tasks: Room Impulse Response (RIR), Sound Event Localization and Detection, Sound Source Localization

Sound event localization and detection (SELD) aims to determine the appearance of sound classes, together with their Direction of Arrival (DOA). However, current SELD systems can only predict the activities of specific classes, for example, 13 classes in DCASE challenges. In this paper, we propose text-queried target sound event localization (SEL), a new paradigm that allows the user to input the text to describe the sound event, and the SEL model can predict the location of the related sound event. The proposed task presents a more user-friendly way for human-computer interaction. We provide a benchmark study for the proposed task and perform experiments on datasets created by simulated room impulse response (RIR) and real RIR to validate the effectiveness of the proposed methods. We hope that our benchmark will inspire the interest and additional research for text-queried sound source localization.
Approximate DCT and Quantization Techniques for Energy-Constrained Image Sensors
Authors: Ming-Che Li, Archisman Ghosh, Shreyas Sen
Date: 2024-06-24 00:00:00
Tasks: Quantization

Recent expansions in multimedia devices gather enormous amounts of real-time images for processing and inference. The images are first compressed using compression schemes, like JPEG, to reduce storage costs and power for transmitting the captured data. Due to inherent error resilience and imperceptibility in images, JPEG can be approximated to reduce the required computation power and area. This work demonstrates the first end-to-end approximation computing-based optimization of JPEG hardware using i) an approximate division realized using bit-shift operators to reduce the complexity of the quantization block, ii) loop perforation, and iii) precision scaling on top of a multiplier-less fast DCT architecture to achieve an extremely energy-efficient JPEG compression unit which will be a perfect fit for power/bandwidth-limited scenario. Furthermore, a gradient descent-based heuristic composed of two conventional approximation strategies, i.e., Precision Scaling and Loop Perforation, is implemented for tuning the degree of approximation to trade off energy consumption with the quality degradation of the decoded image. The entire RTL design is coded in Verilog HDL, synthesized, mapped to TSMC 65nm CMOS technology, and simulated using Cadence Spectre Simulator under 25$^{\circ}$\textbf{C}, TT corner. The approximate division approach achieved around $\textbf{28\%}$ reduction in the active design area. The heuristic-based approximation technique combined with accelerator optimization achieves a significant energy reduction of $\textbf{36\%}$ for a minimal image quality degradation of $\textbf{2\%}$ SAD. Simulation results also show that the proposed architecture consumes 15uW at the DCT and quantization stages to compress a colored 480p image at 6fps.
Reducing the Sampling Burden of Fourier Sensing with a Non-rectangular Field-of-View
Authors: Nicholas Dwork, Erin K. Englund, Alex J. Barker
Date: 2024-06-23 00:00:00

With Fourier sensing, it is commonly the case that the field-of-view (FOV), the area of space to be imaged, is known prior to reconstruction. To date, reconstruction algorithms have focused on FOVs with simple geometries: a rectangle or a hexagon. This yields sampling patterns that are more burdensome than necessary. Due to the reduced area of imaging possible with an arbitrary (e.g., non-rectangular) FOV, the number of samples required for a high-quality images is reduced. However, when an arbitrary FOV has been considered, the reconstruction algorithm is computationally expensive. In this manuscript, we present a method to reduce the sampling pattern for an arbitrary FOV with an accompanying direct (non-iterative) reconstruction algorithm. We also present a method to decrease the computational cost of the (iterative) model-based reconstruction (MBR) algorithm. We present results using MRI data of an ankle, a pineapple, and a brain.
Stationary and Sparse Denoising Approach for Corticomuscular Causality Estimation
Authors: Farwa Abbas, Verity McClelland, Zoran Cvetkovic, Wei Dai
Date: 2024-06-24 00:00:00
Tasks: Computational Efficiency, Denoising, EEG, Electroencephalogram (EEG)

Objective: Cortico-muscular communication patterns are instrumental in understanding movement control. Estimating significant causal relationships between motor cortex electroencephalogram (EEG) and surface electromyogram (sEMG) from concurrently active muscles presents a formidable challenge since the relevant processes underlying muscle control are typically weak in comparison to measurement noise and background activities. Methodology: In this paper, a novel framework is proposed to simultaneously estimate the order of the autoregressive model of cortico-muscular interactions along with the parameters while enforcing stationarity condition in a convex program to ensure global optimality. The proposed method is further extended to a non-convex program to account for the presence of measurement noise in the recorded signals by introducing a wavelet sparsity assumption on the excitation noise in the model. Results: The proposed methodology is validated using both simulated data and neurophysiological signals. In case of simulated data, the performance of the proposed methods has been compared with the benchmark approaches in terms of order identification, computational efficiency, and goodness of fit in relation to various noise levels. In case of physiological signals our proposed methods are compared against the state-of-the-art approaches in terms of the ability to detect Granger causality. Significance: The proposed methods are shown to be effective in handling stationarity and measurement noise assumptions, revealing significant causal interactions from brain to muscles and vice versa.
Decentralized RL-Based Data Transmission Scheme for Energy Efficient Harvesting
Authors: Rafaela Scaciota, Glauber Brante, Richard Souza, Onel Lopez, Septimia Sarbu, Mehdi Bennis, Sumudu Samarakoon
Date: 2024-06-24 00:00:00
Tasks: Reinforcement Learning (RL)

The evolving landscape of the Internet of Things (IoT) has given rise to a pressing need for an efficient communication scheme. As the IoT user ecosystem continues to expand, traditional communication protocols grapple with substantial challenges in meeting its burgeoning demands, including energy consumption, scalability, data management, and interference. In response to this, the integration of wireless power transfer and data transmission has emerged as a promising solution. This paper considers an energy harvesting (EH)-oriented data transmission scheme, where a set of users are charged by their own multi-antenna power beacon (PB) and subsequently transmits data to a base station (BS) using an irregular slotted aloha (IRSA) channel access protocol. We propose a closed-form expression to model energy consumption for the present scheme, employing average channel state information (A-CSI) beamforming in the wireless power channel. Subsequently, we employ the reinforcement learning (RL) methodology, wherein every user functions as an agent tasked with the goal of uncovering their most effective strategy for replicating transmissions. This strategy is devised while factoring in their energy constraints and the maximum number of packets they need to transmit. Our results underscore the viability of this solution, particularly when the PB can be strategically positioned to ensure a strong line-of-sight connection with the user, highlighting the potential benefits of optimal deployment.
The Derivation of The Probability of Error for BPSK, 16-QAM and 64-QAM in Rayleigh Fading Channel: A Unified Approach
Authors: Amr Abdelbari, Bülent Bilgehan
Date: 2024-06-24 00:00:00

Understanding the probability of error is paramount in the design and analysis of digital communication systems, particularly in Rayleigh fading channels where signal impairments are prevalent. This article presents a unified approach for deriving the probability of error formulations applicable to Binary Phase Shift Keying (BPSK), 16-Quadrature Amplitude Modulation (16-QAM), and 64-QAM in Rayleigh fading channels. This article presents a general approach to derive the probability of error formulations applicable to Binary Phase Shift Keying (BPSK), 16-Quadruple Amplitude Modulation (16-QAM) and 64-QAM in Rayleigh fading channels. The derivation process encompasses the unique characteristics of each modulation scheme and the statistical properties of Rayleigh fading providing a comprehensive framework to analyze error performance. By establishing a unified formulation, this approach simplifies the analysis and facilitates a deeper understanding of error probability behavior across different modulation schemes. The derived formulations offer insights into the impact of fading-induced impairments on system performance, allowing for accurate prediction and optimization of communication systems in real-world fading environments. The insights and techniques presented herein serve as a valuable resource for researchers, engineers, and practitioners engaged in the design and optimization of communication systems operating in challenging fading environments.
Robust NLoS Localization in 5G mmWave Networks: Data-based Methods and Performance
Authors: Roman Klus, Jukka Talvitie, Julia Vinogradova, Gabor Fodor, Johan Torsner, Mikko Valkama
Date: 2024-06-24 00:00:00
Tasks: Transfer Learning

Ensuring smooth mobility management while employing directional beamformed transmissions in 5G millimeter-wave networks calls for robust and accurate user equipment (UE) localization and tracking. In this article, we develop neural network-based positioning models with time- and frequency-domain channel state information (CSI) data in harsh non-line-of-sight (NLoS) conditions. We propose a novel frequency-domain feature extraction, which combines relative phase differences and received powers across resource blocks, and offers robust performance and reliability. Additionally, we exploit the multipath components and propose an aggregate time-domain feature combining time-of-flight, angle-of-arrival and received path-wise powers. Importantly, the temporal correlations are also harnessed in the form of sequence processing neural networks, which prove to be of particular benefit for vehicular UEs. Realistic numerical evaluations in large-scale line-of-sight (LoS)-obstructed urban environment with moving vehicles are provided, building on full ray-tracing based propagation modeling. The results show the robustness of the proposed CSI features in terms of positioning accuracy, and that the proposed models reliably localize UEs even in the absence of a LoS path, clearly outperforming the state-of-the-art with similar or even reduced processing complexity. The proposed sequence-based neural network model is capable of tracking the UE position, speed and heading simultaneously despite the strong uncertainties in the CSI measurements. Finally, it is shown that differences between the training and online inference environments can be efficiently addressed and alleviated through transfer learning.
Polar-Coded Tensor-Based Unsourced Random Access with Soft Decoding
Authors: Jiaqi Fang, Yan Liang, Gangle Sun, Hongwei Hou, Yafei Wang, Li You, Wenjin Wang
Date: 2024-06-24 00:00:00
Tasks: Decoder, Tensor Decomposition

The unsourced random access (URA) has emerged as a viable scheme for supporting the massive machine-type communications (mMTC) in the sixth generation (6G) wireless networks. Notably, the tensor-based URA (TURA), with its inherent tensor structure, stands out by simultaneously enhancing performance and reducing computational complexity for the multi-user separation, especially in mMTC networks with a large numer of active devices. However, current TURA scheme lacks the soft decoder, thus precluding the incorporation of existing advanced coding techniques. In order to fully explore the potential of the TURA, this paper investigates the Polarcoded TURA (PTURA) scheme and develops the corresponding iterative Bayesian receiver with feedback (IBR-FB). Specifically, in the IBR-FB, we propose the Grassmannian modulation-aided Bayesian tensor decomposition (GM-BTD) algorithm under the variational Bayesian learning (VBL) framework, which leverages the property of the Grassmannian modulation to facilitate the convergence of the VBL process, and has the ability to generate the required soft information without the knowledge of the number of active devices. Furthermore, based on the soft information produced by the GM-BTD, we design the soft Grassmannian demodulator in the IBR-FB. Extensive simulation results demonstrate that the proposed PTURA in conjunction with the IBR-FB surpasses the existing state-of-the-art unsourced random access scheme in terms of accuracy and computational complexity.
Low-Complexity CSI Feedback for FDD Massive MIMO Systems via Learning to Optimize
Authors: Yifan Ma, Hengtao He, Shenghui Song, Jun Zhang, Khaled B. Letaief
Date: 2024-06-24 00:00:00
Tasks: Compressive Sensing, Decoder

In frequency-division duplex (FDD) massive multiple-input multiple-output (MIMO) systems, the growing number of base station antennas leads to prohibitive feedback overhead for downlink channel state information (CSI). To address this challenge, state-of-the-art (SOTA) fully data-driven deep learning (DL)-based CSI feedback schemes have been proposed. However, the high computational complexity and memory requirements of these methods hinder their practical deployment on resource-constrained devices like mobile phones. To solve the problem, we propose a model-driven DL-based CSI feedback approach by integrating the wisdom of compressive sensing and learning to optimize (L2O). Specifically, only a linear learnable projection is adopted at the encoder side to compress the CSI matrix, thereby significantly cutting down the user-side complexity and memory expenditure. On the other hand, the decoder incorporates two specially designed components, i.e., a learnable sparse transformation and an element-wise L2O reconstruction module. The former is developed to learn a sparse basis for CSI within the angular domain, which explores channel sparsity effectively. The latter shares the same long short term memory (LSTM) network across all elements of the optimization variable, eliminating the retraining cost when problem scale changes. Simulation results show that the proposed method achieves a comparable performance with the SOTA CSI feedback scheme but with much-reduced complexity, and enables multiple-rate feedback.
Hybrid Precoding With Low-Resolution PSs for Wideband Terahertz Communication Systems in The Face of Beam Squint
Authors: Yang Wang, Chuang Yang, Mugen Peng
Date: 2024-06-24 00:00:00

Terahertz (THz) communication is considered one of the most critical technologies for 6G because of its abundant bandwidth. To compensate the high propagation of THz, analog/digital hybrid precoding for THz massive multiple input multiple output (MIMO) is proposed to focus signals and extend communication range. Notably, considering hardware cost and power consumption, infinite and high-resolution phase shifters (PSs) are difficult to implement in THz massive MIMO and low-resolution PSs are typically adopted in practice. However, low-resolution PSs cause severe performance degradation. Moreover, the beam squint in wideband THz massive MIMO increases the performance degradation because of the frequency independence of the analog PSs. Motivated by the above factors, in this paper, we firstly propose a heuristic algorithm under fully connected (FC) structure, which optimize the digital precoder and the analog precoder alternately. Then we migrate the proposed heuristic algorithm to the partially-connected (PC) architecture. To further improve the performance, we extend our design to dynamic subarrays in which each RF chain is connected to any antenna that does not duplicate. The numerical results demonstrate that our proposed wideband hybrid precoding with low-resolution PSs achieves better performance to the comparisons for both FC structure and PC structure.
Complex-Valued Kernel-based Phase and Amplitude Distortion Compensation in Parametrically Amplified Optical Links
Authors: Long Hoang Nguyen, Sonia Boscolo, Stylianos Sygletos
Date: 2024-06-23 00:00:00

We develop a complex-valued kernel-adaptive-filtering based method for phase and amplitude distortion compensation in cascaded fibre-optical parametric amplifier (FOPA) links. Our algorithm predicts and cancels both distortions induced by pump-phase modulation across all amplification stages, achieving more than an order of magnitude improvement in BER.
Federated Transfer Learning Aided Interference Classification in GNSS Signals
Authors: Min Jiang, Ziqiang Ye, Yue Xiao, Xiaogang Gou
Date: 2024-06-23 00:00:00
Tasks: Classification, Federated Learning, Transfer Learning

This study delves into the classification of interference signals to global navigation satellite systems (GNSS) stemming from mobile jammers such as unmanned aerial vehicles (UAVs) across diverse wireless communication zones, employing federated learning (FL) and transfer learning (TL). Specifically, we employ a neural network classifier, enhanced with FL to decentralize data processing and TL to hasten the training process, aiming to improve interference classification accuracy while preserving data privacy. Our evaluations span multiple data scenarios, incorporating both independent and identically distributed (IID) and non-identically distributed (non-IID), to gauge the performance of our approach under different interference conditions. Our results indicate an improvement of approximately $8\%$ in classification accuracy compared to basic convolutional neural network (CNN) model, accompanied by expedited convergence in networks utilizing pre-trained models. Additionally, the implementation of FL not only developed privacy but also matched the robustness of centralized learning methods, particularly under IID scenarios. Moreover, the federated averaging (FedAvg) algorithm effectively manages regional interference variability, thereby enhancing the regional communication performance indicator, $C/N_0$, by roughly $5\text{dB}\cdot \text{Hz}$ compared to isolated setups.