pwc-archive/papers-with-abstracts huggingface dataset:
On-Request Wireless Charging and Partial Computation Offloading In Multi-Access Edge Computing Systems
Authors: Rafia Malik, Mai Vu
Date: 2020-12-10 00:00:00
Tasks: Edge-computing

Wireless charging coupled with computation offloading in edge networks offers a promising solution for realizing power-hungry and computation intensive applications on user devices. We consider a multi-access edge computing (MEC) system with collocated MEC server and base-station/access point, each equipped with a massive MIMO antenna array, supporting multiple users requesting data computation and wireless charging. The goal is to minimize the energy consumption for computation offloading and maximize the received energy at the user from wireless charging. The proposed solution is a novel two-stage algorithm employing nested primal-dual and linear programming techniques to perform data partitioning and time allocation for computation offloading and design the optimal energy beamforming for wireless charging, all within MEC-AP transmit power and latency constraints. Algorithm results show that optimal energy beamforming significantly outperforms other schemes such as isotropic or directed charging without beam power allocation. Compared to binary offloading, data partition in partial offloading leads to lower energy consumption and more charging time, and hence offers better wireless charging performance. The charged energy over an extended period of time both with and without computation offloading can be substantial. Opportunistic wireless charging from MEC-AP thus offers a viable untethered approach for supplying energy to user-devices.
Nonlinear Regression with a Convolutional Encoder-Decoder for Remote Monitoring of Surface Electrocardiograms
Authors: Anton Banta, Romain Cosentino, Mathews M John, Allison Post, Skyler Buchan, Mehdi Razavi, Behnaam Aazhang
Date: 2020-12-10 00:00:00
Tasks: Decoder, Diagnostic, regression

We propose the Nonlinear Regression Convolutional Encoder-Decoder (NRCED), a novel framework for mapping a multivariate input to a multivariate output. In particular, we implement our algorithm within the scope of 12-lead surface electrocardiogram (ECG) reconstruction from intracardiac electrograms (EGM) and vice versa. The goal of performing this task is to allow for improved point-of-care monitoring of patients with an implanted device to treat cardiac pathologies. We will achieve this goal with 12-lead ECG reconstruction and by providing a new diagnostic tool for classifying atypical heartbeats. The algorithm is evaluated on a dataset retroactively collected from 14 patients. Correlation coefficients calculated between the reconstructed and the actual ECG show that the proposed NRCED method represents an efficient, accurate, and superior way to synthesize a 12-lead ECG. We can also achieve the same reconstruction accuracy with only one EGM lead as input. We also tested the model in a non-patient specific way and saw a reasonable correlation coefficient. The model was also executed in the reverse direction to produce EGM signals from a 12-lead ECG and found that the correlation was comparable to the forward direction. Lastly, we analyzed the features learned in the model and determined that the model learns an overcomplete basis of our 12-lead ECG space. We then use this basis of features to create a new diagnostic tool for identifying atypical and diseased heartbeats. This resulted in a ROC curve with an associated area under the curve value of 0.98, demonstrating excellent discrimination between the two classes.
A Spectral Estimation Framework for Phase Retrieval via Bregman Divergence Minimization
Authors: Bariscan Yonel, Birsen Yazıcı
Date: 2020-12-03 00:00:00
Tasks: Retrieval

In this paper, we develop a novel framework to optimally design spectral estimators for phase retrieval given measurements realized from an arbitrary model. We begin by deconstructing spectral methods, and identify the fundamental mechanisms that inherently promote the accuracy of estimates. We then propose a general formalism for spectral estimation as approximate Bregman loss minimization in the range of the lifted forward model that is tractable by a search over rank-1, PSD matrices. Essentially, by the Bregman loss approach we transcend the Euclidean sense alignment based similarity measure between phaseless measurements in favor of appropriate divergence metrics over $\mathbb{R}^M_+$. To this end, we derive spectral methods that perform approximate minimization of KL-divergence, and the Itakura-Saito distance over phaseless measurements by using element-wise sample processing functions. As a result, our formulation relates and extends existing results on model dependent design of optimal sample processing functions in the literature to a model independent sense of optimality. Numerical simulations confirm the effectiveness of our approach in problem settings under synthetic and real data sets.
Online Joint Topology Identification and Signal Estimation from Streams with Missing Data
Authors: Bakht Zaman, Luis Miguel Lopez Ramos, Baltasar Beferull-Lozano
Date: 2020-12-10 00:00:00
Tasks: Denoising, Time Series, Time Series Analysis

Identifying the topology underlying a set of time series is useful for tasks such as prediction, denoising, and data completion. Vector autoregressive (VAR) model-based topologies capture dependencies among time series and are often inferred from observed spatio-temporal data. When data are affected by noise and/or missing samples, topology identification and signal recovery (reconstruction) tasks must be performed jointly. Additional challenges arise when i) the underlying topology is time-varying, ii) data become available sequentially, and iii) no delay is tolerated. This study proposes an online algorithm to overcome these challenges in estimating VAR model-based topologies, having constant complexity per iteration, which makes it interesting for big-data scenarios. The inexact proximal online gradient descent framework is used to derive a performance guarantee for the proposed algorithm, in the form of a dynamic regret bound. Numerical tests are also presented, showing the ability of the proposed algorithm to track time-varying topologies with missing data in an online fashion.
Plasma Switch-Based Technology for High-Speed and High-Power Impedance Tuning
Authors: Zach Vander Missen, Sergey Macheret, Abbas Semnani, Dimitrios Peroulis
Date: 2020-12-10 00:00:00
Tasks: Vocal Bursts Intensity Prediction

This paper introduces a new technology for a high-speed, high-power mobile form-factor tuner utilizing gas discharge tube plasma cells as switching components. To the best of our knowledge, this represents the first plasma-enabled RF matching network. Technology development is reviewed, the fabrication and measurement of a proof-of-concept switched stub impedance tuner are presented, and techniques for improvement are discussed. The proof-of-concept impedance tuner functions with a 27% bandwidth from 3 to almost 4 GHz and shows a power gain better than -2.5 dB across all switching state-frequency combinations at a 50 W input power level with spread coverage of the Smith chart. State change transient timing is measured to be on the order of 500 ns. This technology demonstration highlights the potential of miniaturized, rapidly-tunable, high-power, plasma-based RF devices.
Semantic Communications for Speech Signals
Authors: Zhenzi Weng, Zhijin Qin, Geoffrey Ye Li
Date: 2020-12-09 00:00:00
Tasks: Semantic Communication

We consider a semantic communication system for speech signals, named DeepSC-S. Motivated by the breakthroughs in deep learning (DL), we make an effort to recover the transmitted speech signals in the semantic communication systems, which minimizes the error at the semantic level rather than the bit level or symbol level as in the traditional communication systems. Particularly, based on an attention mechanism employing squeeze-and-excitation (SE) networks, we design the transceiver as an end-to-end (E2E) system, which learns and extracts the essential speech information. Furthermore, in order to facilitate the proposed DeepSC-S to work well on dynamic practical communication scenarios, we find a model yielding good performance when coping with various channel environments without retraining process. The simulation results demonstrate that our proposed DeepSC-S is more robust to channel variations and outperforms the traditional communication systems, especially in the low signal-to-noise (SNR) regime.
Distributed and Scalable Uplink Processing for LIS: Algorithm, Architecture, and Design Trade-offs
Authors: Jesus Rodriguez Sanchez, Fredrik Rusek, Ove Edfors, Liang Liu
Date: 2020-12-09 00:00:00

The Large Intelligent Surface (LIS) is a promising technology in the areas of wireless communication, remote sensing and positioning. It consists of a continuous radiating surface located in the proximity of the users, with the capability to communicate by transmission and reception (replacing base stations). Despite of its potential, there are numerous challenges from implementation point of view, being the interconnection data-rate, computational complexity, and storage the most relevant ones. In order to address those challenges, hierarchical architectures with distributed processing techniques are envisioned to to be relevant for this task, while ensuring scalability. In this work we perform algorithm-architecture codesign to propose two distributed interference cancellation algorithms, and a tree-based interconnection topology for uplink processing. We also analyze the performance, hardware requirements, and architecture tradeoffs for a discrete LIS, in order to provide concrete case studies and guidelines for efficient implementation of LIS systems.
Magnetic Field Energy Harvesting in Railway
Authors: Asbjørn Engmark Espe, Thomas S. Haugan, Geir Mathisen
Date: 2020-12-09 00:00:00

Magnetic field energy harvesting (MFEH) is a method by which a system can harness an ambient, alternating magnetic field in order to scavenge energy. Presented in this article is a novel application of the concept aimed at the magnetic fields surrounding the rail current in electrified railway. Due to its non-invasive nature, the approach has the potential to be widely deployed as part of low-cost trackside condition monitoring systems in order to increase lifetime and reduce maintenance requirements. In this work, the viability of MFEH in railway is substantiated experimentally -- two different configurations are assessed both in a controlled laboratory environment, as well as in situ along Norwegian railway. When placed near an emulated section of railway carrying 200 A in the laboratory, the power output of the system is up to 40.5 mW at 50 Hz and 4.15 mW at 16 2/3 Hz. In the field, the prototype system harvests 109 mJ from a single freight train passing by, rendering an estimated daily energy output of 1.14 J in a moderately-trafficked location. It is argued that the approach could indeed eliminate the need for battery replacements, and potentially increase the lifetime of an energy-efficient, battery-powered condition monitoring system indefinitely.
Modified Auto Regressive Technique for Univariate Time Series Prediction of Solar Irradiance
Authors: Umar Marikkar, A. S. Jameel Hassan, Mihitha S. Maithripala, Roshan I. Godaliyadda, Parakrama B. Ekanayake, Janaka B. Ekanayake
Date: 2020-12-06 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Prediction

The integration of renewable resources has increased in power generation as a means to reduce the fossil fuel usage and mitigate its adverse effects on the environment. However, renewables like solar energy are stochastic in nature due to its high dependency on weather patterns. This uncertainty vastly diminishes the benefit of solar panel integration and increases the operating costs due to larger energy reserve requirement. To address this issue, a Modified Auto Regressive model, a Convolutional Neural Network and a Long Short Term Memory neural network that can accurately predict the solar irradiance are proposed. The proposed techniques are compared against each other by means of multiple error metrics of validation. The Modified Auto Regressive model has a mean absolute percentage error of 14.2%, 19.9% and 22.4% for 10 minute, 30 minute and 1 hour prediction horizons. Therefore, the Modified Auto Regressive model is proposed as the most robust method, assimilating the state of the art neural networks for the solar forecasting problem.
Meta Learning-based MIMO Detectors: Design, Simulation, and Experimental Test
Authors: Jing Zhang, Yunfeng He, Yu-Wen Li, Chao-Kai Wen, Shi Jin
Date: 2020-12-08 00:00:00
Tasks: Meta-Learning

Deep neural networks (NNs) have exhibited considerable potential for efficiently balancing the performance and complexity of multiple-input and multiple-output (MIMO) detectors. We propose a receiver framework that enables efficient online training by leveraging the following simple observation: although NN parameters should adapt to channels, not all of them are channel-sensitive. In particular, we use a deep unfolded NN structure that represents iterative algorithms in signal detection and channel decoding modules as multi layer deep feed forward networks. An expectation propagation (EP) module, called EPNet, is established for signal detection by unfolding the EP algorithm and rendering the damping factors trainable. An unfolded turbo decoding module, called TurboNet, is used for channel decoding. This component decodes the turbo code, where trainable NN units are integrated into the traditional max-log-maximum a posteriori decoding procedure. We demonstrate that TurboNet is robust for channels and requires only one off-line training. Therefore, only a few damping factors in EPNet must be re-optimized online. An online training mechanism based on meta learning is then developed. Here, the optimizer, which is implemented by long short-term memory NNs, is trained to update damping factors efficiently by using a small training set such that they can quickly adapt to new environments. Simulation results indicate that the proposed receiver significantly outperforms traditional receivers and that the online learning mechanism can quickly adapt to new environments. Furthermore, an over-the-air platform is presented to demonstrate the significant robustness of the proposed receiver in practical deployment.
6G Wireless Channel Measurements and Models: Trends and Challenges
Authors: C. -X. Wang, J. Huang, H. Wang, X. Gao, X. -H. You, Y. Hao
Date: 2020-12-08 00:00:00
Tasks: All

In this article, we first present our vision on the application scenarios, performance metrics, and potential key technologies of the sixth generation (6G) wireless communication networks. Then, 6G wireless channel measurements, characteristics, and models are comprehensively surveyed for all frequency bands and all scenarios, focusing on millimeter wave (mmWave), terahertz (THz), and optical wireless communication channels under all spectrums, satellite, unmanned aerial vehicle (UAV), maritime, and underwater acoustic communication channels under global coverage scenarios, and high-speed train (HST), vehicle-to-vehicle (V2V), ultra-massive multiple-input multiple-output (MIMO), orbital angular momentum (OAM), and industry Internet of things (IoT) communication channels under full application scenarios. Future research challenges on 6G channel measurements, a general standard 6G channel model framework, channel measurements and models for intelligent reflection surface (IRS) based 6G technologies, and artificial intelligence (AI) enabled channel measurements and models are also given.
An unsupervised capacity identification approach based on Sobol' indices
Authors: Guilherme D. Pelegrina, Leonardo T. Duarte, Michel Grabisch, João M. T. Romano
Date: 2020-12-07 00:00:00

In many ranking problems, some particular aspects of the addressed situation should be taken into account in the aggregation process. An example is the presence of correlations between criteria, which may introduce bias in the derived ranking. In these cases, aggregation functions based on a capacity may be used to overcome this inconvenience, such as the Choquet integral or the multilinear model. The adoption of such strategies requires a stage to estimate the parameters of these aggregation operators. This task may be difficult in situations in which we do not have either further information about these parameters or preferences given by the decision maker. Therefore, the aim of this paper is to deal with such situations through an unsupervised approach for capacity identification based on the multilinear model. Our goal is to estimate a capacity that can mitigate the bias introduced by correlations in the decision data and, therefore, to provide a fairer result. The viability of our proposal is attested by numerical experiments with synthetic data
Muticriteria decision making based on independent component analysis: A preliminary investigation considering the TOPSIS approach
Authors: Guilherme D. Pelegrina, Leonardo T. Duarte, João M. T. Romano
Date: 2020-12-07 00:00:00
Tasks: Decision Making

This work proposes the application of independent component analysis to the problem of ranking different alternatives by considering criteria that are not necessarily statistically independent. In this case, the observed data (the criteria values for all alternatives) can be modeled as mixtures of latent variables. Therefore, in the proposed approach, we perform ranking by means of the TOPSIS approach and based on the independent components extracted from the collected decision data. Numerical experiments attest the usefulness of the proposed approach, as they show that working with latent variables leads to better results compared to already existing methods
Fuzzy model identification based on mixture distribution analysis for bearings remaining useful life estimation using small training data set
Authors: Fei Huang, Alexandre Sava, Kondo H. Adjallah, Wang Zhouhang
Date: 2020-12-07 00:00:00
Tasks: Clustering

The research work presented in this paper proposes a data-driven modeling method for bearings remaining useful life estimation based on Takagi-Sugeno (T-S) fuzzy inference system (FIS). This method allows identifying the parameters of a classic T-S FIS, starting with a small quantity of data. In this work, we used the vibration signals data from a small number of bearings over an entire period of run-to-failure. The FIS model inputs are features extracted from the vibration signals data observed periodically on the training bearings. The number of rules and the input parameters of each rule of the FIS model are identified using the subtractive clustering method. Furthermore, we propose to use the maximum likelihood method of mixture distribution analysis to calculate the parameters of clusters on the time axis and the probability corresponding to rules on degradation stages. Based on this result, we identified the output parameters of each rule using a weighted least square estimation. We then benchmarked the proposed method with some existing methods from the literature, through numerical experiments conducted on available datasets to highlight its effectiveness.
Reconfigurable Intelligent Surface Aided Constant-Envelope Wireless Power Transfer
Authors: Huiyuan Yang, Xiaojun Yuan, Jun Fang, Ying-Chang Liang
Date: 2020-12-07 00:00:00
Tasks: Fairness, Quantization

By reconfiguring the propagation environment of electromagnetic waves artificially, reconfigurable intelligent surfaces (RISs) have been regarded as a promising and revolutionary hardware technology to improve the energy and spectrum efficiency of wireless networks. In this paper, we study a RIS aided multiuser multiple-input multiple-output (MIMO) wireless power transfer (WPT) system, where the transmitter is equipped with a constant-envelope analog beamformer. First, we maximize the total received power of the users by jointly optimizing the beamformer at transmitter and the phase-shifts at the RIS, and propose two alternating optimization based suboptimal solutions by leveraging the semidefinite relaxation (SDR) and the successive convex approximation (SCA) techniques respectively. Then, considering the user fairness, we formulate another problem to maximize the total received power subject to the users' individual minimum received power constraints. A low complexity iterative algorithm based on both alternating direction method of multipliers (ADMM) and SCA techniques is proposed to solve this problem. In the case of multiple users, we further analyze the asymptotic performance as the number of RIS elements approaches infinity, and bound the performance loss caused by RIS phase quantization. Numerical results show the correctness of the analysis results and the effectiveness of the proposed algorithms.
Multicell Power Control under Rate Constraints with Deep Learning
Authors: Yinghan Li, Shengqian Han, Chenyang Yang
Date: 2020-12-07 00:00:00
Tasks: Deep Learning

In the paper we study a deep learning based method to solve the multicell power control problem for sum rate maximization subject to per-user rate constraints and per-base station (BS) power constraints. The core difficulty of this problem is how to ensure that the learned power control results by the deep neural network (DNN) satisfy the per-user rate constraints. To tackle the difficulty, we propose to cascade a projection block after a traditional DNN, which projects the infeasible power control results onto the constraint set. The projection block is designed based on a geometrical interpretation of the constraints, which is of low complexity, meeting the real-time requirement of online applications. Explicit-form expression of the backpropagated gradient is derived for the proposed projection block, with which the DNN can be trained to directly maximize the sum rate via unsupervised learning. We also develop a heuristic implementation of the projection block to reduce the size of DNN. Simulation results demonstrate the advantages of the proposed method over existing deep learning and numerical optimization~methods, and show the robustness of the proposed method with the model mismatch between training and testing~datasets.
Statistical Learning for End-to-End Simulations
Authors: J. Vicent, J. Verrelst, J. P. Rivera-Caicedo, N. Sabater, J. Muñoz-Marí, G. Camps-Valls, J. Moreno
Date: 2020-12-07 00:00:00
Tasks: Earth Observation

End-to-end mission performance simulators (E2ES) are suitable tools to accelerate satellite mission development from concet to deployment. One core element of these E2ES is the generation of synthetic scenes that are observed by the various instruments of an Earth Observation mission. The generation of these scenes rely on Radiative Transfer Models (RTM) for the simulation of light interaction with the Earth surface and atmosphere. However, the execution of advanced RTMs is impractical due to their large computation burden. Classical interpolation and statistical emulation methods of pre-computed Look-Up Tables (LUT) are therefore common practice to generate synthetic scenes in a reasonable time. This work evaluates the accuracy and computation cost of interpolation and emulation methods to sample the input LUT variable space. The results on MONDTRAN-based top-of-atmosphere radiance data show that Gaussian Process emulators produced more accurate output spectra than linear interpolation at a fraction of its time. It is concluded that emulation can function as a fast and more accurate alternative to interpolation for LUT parameter space sampling.