pwc-archive/papers-with-abstracts huggingface dataset:
Indoor and Outdoor Physical Channel Modeling and Efficient Positioning for Reconfigurable Intelligent Surfaces in mmWave Bands
Authors: E. Basar, I. Yildirim, F. Kilinc
Date: 2020-05-31 00:00:00

Reconfigurable intelligent surface (RIS)-assisted communication appears as one of the potential enablers for sixth generation (6G) wireless networks by providing a new degree of freedom in the system design to telecom operators. Particularly, RIS-empowered millimeter wave (mmWave) communication systems can be a remedy to provide broadband and ubiquitous connectivity. This paper aims to fill an important gap in the open literature by providing a physical, accurate, open-source, and widely applicable RIS channel model for mmWave frequencies. Our model is not only applicable in various indoor and outdoor environments but also includes the physical characteristics of wireless propagation in the presence of RISs by considering 5G radio channel conditions. Various deployment scenarios are presented for RISs and useful insights are provided for system designers from the perspective of potential RIS use-cases and their efficient positioning. The scenarios in which the use of an RIS makes a big difference or might not have a big impact on the communication system performance, are revealed. The open-source and comprehensive SimRIS Channel Simulator is also introduced in this paper.
American Sign Language Recognition Using RF Sensing
Authors: Sevgi Z. Gurbuz, Ali C. Gurbuz, Evie A. Malaia, Darrin J. Griffin, Chris Crawford, M. Mahbubur Rahman, Emre Kurtoglu, Ridvan Aksu, Trevor Macks, Robiulhossain Mdrafi
Date: 2020-09-02 00:00:00
Tasks: Sign Language Recognition

Many technologies for human-computer interaction have been designed for hearing individuals and depend upon vocalized speech, precluding users of American Sign Language (ASL) in the Deaf community from benefiting from these advancements. While great strides have been made in ASL recognition with video or wearable gloves, the use of video in homes has raised privacy concerns, while wearable gloves severely restrict movement and infringe on daily life. Methods: This paper proposes the use of RF sensors for HCI applications serving the Deaf community. A multi-frequency RF sensor network is used to acquire non-invasive, non-contact measurements of ASL signing irrespective of lighting conditions. The unique patterns of motion present in the RF data due to the micro-Doppler effect are revealed using time-frequency analysis with the Short-Time Fourier Transform. Linguistic properties of RF ASL data are investigated using machine learning (ML). Results: The information content, measured by fractal complexity, of ASL signing is shown to be greater than that of other upper body activities encountered in daily living. This can be used to differentiate daily activities from signing, while features from RF data show that imitation signing by non-signers is 99\% differentiable from native ASL signing. Feature-level fusion of RF sensor network data is used to achieve 72.5\% accuracy in classification of 20 native ASL signs. Implications: RF sensing can be used to study dynamic linguistic properties of ASL and design Deaf-centric smart environments for non-invasive, remote recognition of ASL. ML algorithms should be benchmarked on native, not imitation, ASL data.
Determining the Number of Sinusoids Measured with Errors
Authors: Aleksandr Kharin, Oleg V. Chernoyarov
Date: 2020-09-02 00:00:00

This paper describes how a priori information about the signal parameters can influence the accuracy of estimating the number of these signals. This study considers sinusoidal signals and it is supposed that the parameters (amplitudes, frequencies and phases) of the received signals are known up to a certain error. The error probability of the maximum likelihood estimation of the number of sinusoids is calculated under this condition.
A General Framework for RIS-Aided mmWave Communication Networks: Channel Estimation and Mobile User Tracking
Authors: Salah Eddine Zegrar, Liza Afeef, Huseyin Arslan
Date: 2020-09-02 00:00:00

Reconfigurable intelligent surface (RIS) has been widely discussed as new technology to improve wireless communication performance. Based on the unique design of RIS, its elements can reflect, refract, absorb, or focus the incoming waves toward any desired direction. These functionalities turned out to be a major solution to overcome millimeter-wave (mmWave)'s high propagation conditions including path attenuation and blockage. However, channel estimation in RIS-aided communication is still a major concern due to the passive nature of RIS elements, and estimation overhead that arises with multiple-input multiple-output (MIMO) system. As a consequence, user tracking has not been analyzed yet. This paper is the first work that addresses channel estimation, beamforming, and user tracking under practical mmWave RIS-MIMO systems. By providing the mathematical relation of RIS design with a MIMO system, a three-stage framework is presented. Starting with estimating the channel between a base station (BS) and RIS using hierarchical beam searching, followed by estimating the channel between RIS and user using an iterative resolution algorithm. Lastly, a popular tracking algorithm is employed to track channel parameters between the RIS and the user. System analysis demonstrates the robustness and the effectiveness of the proposed framework in real-time scenarios.
A Review of Wind Speed and Wind Power Forecasting Techniques
Authors: Harsh S. Dhiman, Dipankar Deb
Date: 2020-09-02 00:00:00
Tasks: Scheduling, Time Series, Time Series Analysis

Forecasting a particular variable can depend upon temporal or spatial scale. Temporal variations that indicate variations with time, reflect the stochasticity present in the variable. Spatial variation usually are dominant in climatology and meteorology. Temporal scale for a variable can be modeled in terms of time-series. A time series is a successively ordered sequence of numerical data points, and can be taken on any variable changing with time. Wind speed forecasting applications lie majorly in the area of electricity market clearing, economic load dispatch and scheduling, and sometimes to provide ancillary support. Thus, a proper classification based on the prediction horizon i.e. the duration of prediction becomes important for various transmission system operators.
SAR Tomography via Nonlinear Blind Scatterer Separation
Authors: Yuanyuan Wang, Xiao Xiang Zhu
Date: 2020-09-02 00:00:00

Layover separation has been fundamental to many synthetic aperture radar applications, such as building reconstruction and biomass estimation. Retrieving the scattering profile along the mixed dimension (elevation) is typically solved by inversion of the SAR imaging model, a process known as SAR tomography. This paper proposes a nonlinear blind scatterer separation method to retrieve the phase centers of the layovered scatterers, avoiding the computationally expensive tomographic inversion. We demonstrate that conventional linear separation methods, e.g., principle component analysis (PCA), can only partially separate the scatterers under good conditions. These methods produce systematic phase bias in the retrieved scatterers due to the nonorthogonality of the scatterers' steering vectors, especially when the intensities of the sources are similar or the number of images is low. The proposed method artificially increases the dimensionality of the data using kernel PCA, hence mitigating the aforementioned limitations. In the processing, the proposed method sequentially deflates the covariance matrix using the estimate of the brightest scatterer from kernel PCA. Simulations demonstrate the superior performance of the proposed method over conventional PCA-based methods in various respects. Experiments using TerraSAR-X data show an improvement in height reconstruction accuracy by a factor of one to three, depending on the used number of looks.
Communication and networking technologies for UAVs: A survey
Authors: Abhishek Sharma, Pankhuri Vanjani, Nikhil Paliwal, Chathuranga M. Wijerathna Basnayaka, Dushantha Nalin K. Jayakody, Hwang-Cheng Wang, P. Muthuchidambaranathane
Date: 2020-09-02 00:00:00
Tasks: Edge-computing, Management, Survey

With the advancement in drone technology, in just a few years, drones will be assisting humans in every domain. But there are many challenges to be tackled, communication being the chief one. This paper aims at providing insights into the latest UAV (Unmanned Aerial Vehicle) communication technologies through investigation of suitable task modules, antennas, resource handling platforms, and network architectures. Additionally, we explore techniques such as machine learning and path planning to enhance existing drone communication methods. Encryption and optimization techniques for ensuring long lasting and secure communications, as well as for power management, are discussed. Moreover, applications of UAV networks for different contextual uses ranging from navigation to surveillance, URLLC (Ultra reliable and low latency communications), edge computing and work related to artificial intelligence are examined. In particular, the intricate interplay between UAV, advanced cellular communication, and internet of things constitutes one of the focal points of this paper. The survey encompasses lessons learned, insights, challenges, open issues, and future directions in UAV communications. Our literature review reveals the need for more research work on drone to drone and drone to device communications.
Reconfigurable Intelligent Surfaces for Smart Cities: Research Challenges and Opportunities
Authors: Steven Kisseleff, Wallace A. Martins, Hayder Al-Hraishawi, Symeon Chatzinotas, Björn Ottersten
Date: 2020-09-02 00:00:00

The concept of Smart Cities has been introduced as a way to benefit from the digitization of various ecosystems at a city level. To support this concept, future communication networks need to be carefully designed with respect to the city infrastructure and utilization of resources. Recently, the idea of 'smart' environment, which takes advantage of the infrastructure for better performance of wireless networks, has been proposed. This idea is aligned with the recent advances in design of reconfigurable intelligent surfaces (RISs), which are planar structures with the capability to reflect impinging electromagnetic waves toward preferred directions. Thus, RISs are expected to provide the necessary flexibility for the design of the 'smart' communication environment, which can be optimally shaped to enable cost- and energy-efficient signal transmissions where needed. Upon deployment of RISs, the ecosystem of the Smart Cities would become even more controllable and adaptable, which would subsequently ease the implementation of future communication networks in urban areas and boost the interconnection among private households and public services. In this paper, we describe our vision of the application of RISs in future Smart Cities. In particular, the research challenges and opportunities are addressed. The contribution paves the road to a systematic design of RIS-assisted communication networks for Smart Cities in the years to come.
Radio Frequency Speed Bumps for Near-Zero Speed Zone Control in Nigeria
Authors: Olaide Ayodeji Agbolade
Date: 2020-09-02 00:00:00

Over speeding is an important problem in Nigeria. The Nigerian Federal Road Safety Commission report shows that over speeding is directly responsible for over 50 percent of road crashes in the country. Speed bumps particularly on highways have not proved to be the solution; they rather contribute more to the problem. Based the advent of the dedicated short range communications (DSRC) systems defined by the IEEE 802.11p standard, we proposed a radio frequency based speed bump to mimic the operation of traditional speed breakers. The model consists of a roadside unit and an in-vehicle unit that works with the already installed speed limiter in vehicles. In our work, we showed that the RF based model is able to decelerate the vehicle at a recommended rate of 10 m/s2 over a convenient stopping distance of 80 m. Our work also showed that signal strength based adaptive speed limiter systems can be used as a reliable substitute to GPS thus eradicating the need for additional hardware in the design.
HydraWave: Multi-Group Multicast Hybrid Precoding and Low-Latency Scheduling for Ubiquitous Industry 4.0 mmWave Communication
Authors: Luis F. Abanto-Leon, Matthias Hollick, Gek Hong Sim
Date: 2020-02-03 00:00:00
Tasks: Robust Design, Scheduling

Industry 4.0 anticipates massive interconnectivity of industrial devices (e.g., sensors, actuators) to support factory automation and production. Due to the rigidity of wired connections to harmonize with automation, wireless information transfer has attracted substantial attention. However, existing solutions for the manufacturing sector face critical issues in coping with the key performance demands: ultra-low latency, high throughput, and high reliability. Besides, recent advancements in wireless millimeter-wave technology advocates hybrid precoding with affordable hardware and outstanding spatial multiplexing performance. Thus, we present HYDRAWAVE -- a new paradigm that contemplates the joint design of group scheduling and hybrid precoding for multi-group multicasting to support ubiquitous low-latency communications. Our hybrid precoder, based on semidefinite relaxation and Cholesky matrix factorization, facilitates the robust design of the constant-modulus phase shifts rendering formidable performance at a fraction of the power required by fully-digital precoders. Further, our novel group scheduling formulation minimizes the number of scheduling windows while accounting for the channel correlation of the co-scheduled multicast receivers. Compared to exhaustive search, which renders the optimal scheduling at high overhead, HYDRAWAVE incurs only 9.5% more delay. Notoriously, HYDRAWAVE attains up to 102% gain when compared to the other benchmarked schemes.
Disaggregating Customer-level Behind-the-Meter PV Generation Using Smart Meter Data and Solar Exemplars
Authors: Fankun Bu, Kaveh Dehghanpour, Yuxuan Yuan, Zhaoyu Wang, Yifei Guo
Date: 2020-09-01 00:00:00

Customer-level rooftop photovoltaic (PV) has been widely integrated into distribution systems. In most cases, PVs are installed behind-the-meter (BTM), and only the net demand is recorded. Therefore, the native demand and PV generation are unknown to utilities. Separating native demand and solar generation from net demand is critical for improving grid-edge observability. In this paper, a novel approach is proposed for disaggregating customer-level BTM PV generation using low-resolution but widely available hourly smart meter data. The proposed approach exploits the strong correlation between monthly nocturnal and diurnal native demands and the high similarity among PV generation profiles. First, a joint probability density function (PDF) of monthly nocturnal and diurnal native demands is constructed for customers without PVs, using Gaussian mixture modeling (GMM). Deviation from the constructed PDF is utilized to probabilistically assess the monthly solar generation of customers with PVs. Then, to identify hourly BTM solar generation for these customers, their estimated monthly solar generation is decomposed into an hourly timescale; to do this, we have proposed a maximum likelihood estimation (MLE)-based technique that utilizes hourly typical solar exemplars. Leveraging the strong monthly native demand correlation and high PV generation similarity enhances our approach's robustness against the volatility of customers' hourly load and enables highly accurate disaggregation. The proposed approach has been verified using real native demand and PV generation data.
Channel Estimation and Detection in FBMC/OQAM System with Affine Precoding and Decoding
Authors: Radhashyam Patra, Arunanshu Mohapatra
Date: 2020-09-01 00:00:00

We derive the mathematical equations required for channel estimation and data detection of filter bank multi-carrier (FBMC) offset quadrature amplitude modulation (OQAM) systems with affine precoding and decoding. The mean square error (MSE) in least square (LS) channel estimation and bit error rate (BER) of the system is found for different training power coefficients. The proposed system gives better BER performance compared to other cutting edge FBMC systems. The optimum training power coefficient $(\sigma_{c_{opt}}^2)$ is also found from the simulation results. The band width efficiency of the system is also calculated.
Covid 19 and A Wavelet Analysis of the Total Deaths per Month in Brazil since 2015
Authors: Alexandre Barbosa de Lima
Date: 2020-09-01 00:00:00

We investigate the historical series of the total number of deaths per month in Brazil since 2015 using the wavelet transform, in order to assess whether the COVID-19 pandemic caused any change point in that series. Our wavelet analysis shows that the series has a change point in the variance. However, it occurred long before the pandemic began.
Linear Receivers for Massive MIMO Systems with One-Bit ADCs
Authors: Ly V. Nguyen, Duy H. N. Nguyen
Date: 2019-07-15 00:00:00

In this letter, we propose three linear receivers including Bussgang-based Maximal Ratio Combining (BMRC), Bussgang-based Zero-Forcing (BZF), and Bussgang-based Minimum Mean Squared Error (BMMSE) for massive MIMO systems with one-bit analog-to-digital converters (ADCs). Closed-form expressions of the proposed receivers are obtained by using the Bussgang decomposition to cope with the non-linear effect of the one-bit ADCs. Simulation results show significantly lower bit error rate floors obtained by the proposed receivers than those of conventional linear receivers.
Neural Network-Optimized Channel Estimator and Training Signal Design for MIMO Systems with Few-Bit ADCs
Authors: Duy H. N. Nguyen
Date: 2020-03-19 00:00:00

This paper is concerned with channel estimation in MIMO systems with few-bit ADCs. In these systems, a linear minimum mean-squared error (MMSE) channel estimator obtained in closed-form is not an optimal solution. We first consider a deep neural network (DNN) and train it as a non-linear MMSE channel estimator for few-bit MIMO systems. We then present a first attempt to use DNN in optimizing the training signal and the MMSE channel estimator concurrently. Specifically, we propose an autoencoder with a specialized first layer, whose weights embed the training signal matrix. Consequently, the trained autoencoder prompts a new training signal design that is customized for the MIMO channel model under consideration.
Linear and Deep Neural Network-based Receivers for Massive MIMO Systems with One-Bit ADCs
Authors: Ly V. Nguyen, A. Lee Swindlehurst, Duy H. N. Nguyen
Date: 2020-08-09 00:00:00

The use of one-bit analog-to-digital converters (ADCs) is a practical solution for reducing cost and power consumption in massive Multiple-Input-Multiple-Output (MIMO) systems. However, the distortion caused by one-bit ADCs makes the data detection task much more challenging. In this paper, we propose a two-stage detection method for massive MIMO systems with one-bit ADCs. In the first stage, we propose several linear receivers based on the Bussgang decomposition, that show significant performance gain over existing linear receivers. Next, we reformulate the maximum-likelihood (ML) detection problem to address its non-robustness. Based on the reformulated ML detection problem, we propose a model-driven deep neural network-based (DNN-based) receiver, whose performance is comparable with an existing support vector machine-based receiver, albeit with a much lower computational complexity. A nearest-neighbor search method is then proposed for the second stage to refine the first stage solution. Unlike existing search methods that typically perform the search over a large candidate set, the proposed search method generates a limited number of most likely candidates and thus limits the search complexity. Numerical results confirm the low complexity, efficiency, and robustness of the proposed two-stage detection method.
Extended (Conventional) Co-Prime Arrays and Difference Set Analysis: Low Latency Approach
Authors: Usham V. Dias
Date: 2020-03-11 00:00:00

The co-prime array is a sub-Nyquist acquisition scheme for the estimation of second order statistics. It cannot generate all the difference values in the co-prime range and hence, one of the sub-array is extended to enable the estimation of the second order statistics at each difference value in the co-prime range. Recently, the difference set for the co-prime array was studied and low latency temporal spectrum estimation was demonstrated. In this paper, the fundamentals of the difference set of the extended co-prime array, also known as the conventional co-prime array, is developed. The closed-form equations for the weight function, correlogram bias window, and the variance are described. This is provided for the entire difference set, continuous difference set and for the prototype co-prime period. It is shown that the choice of $M\approx \frac{N}{2}$ generates a bias function with a large relative amplitude between the main-lobe and side-lobe peaks, where $M$ and $N$ are co-prime pairs. The expressions for the number of multiplications and additions required to compute the autocorrelation for the entire, continuous, and prototype range is derived. Simulation results demonstrate low latency spectrum estimation using the correlogram method.
Online change-point detection with kernels
Authors: André Ferrari, Cédric Richard, Anthony Bourrier, Ikram Bouchikhi
Date: 2020-02-07 00:00:00
Tasks: Change Point Detection, Time Series, Time Series Analysis

Change-points in time series data are usually defined as the time instants at which changes in their properties occur. Detecting change-points is critical in a number of applications as diverse as detecting credit card and insurance frauds, or intrusions into networks. Recently the authors introduced an online kernel-based change-point detection method built upon direct estimation of the density ratio on consecutive time intervals. This paper further investigates this algorithm, making improvements and analyzing its behavior in the mean and mean square sense, in the absence and presence of a change point. These theoretical analyses are validated with Monte Carlo simulations. The detection performance of the algorithm is illustrated through experiments on real-world data and compared to state of the art methodologies.
Denoising Click-evoked Otoacoustic Emission Signals by Optimal Shrinkage
Authors: Tzu-Chi Liu, Yi-Wen Liu, Hau-Tieng Wu
Date: 2020-09-01 00:00:00
Tasks: Denoising

Click-evoked otoacoustic emissions (CEOAEs) are clinically used as an objective way to infer whether cochlear functions are normal. However, because the sound pressure level of CEOAEs is typically much lower than the background noise, it usually takes hundreds, if not thousands of repetitions to estimate the signal with sufficient accuracy. In this paper, we propose to improve the signal-to-noise ratio (SNR) of CEOAE signals within limited measurement time by optimal shrinkage (OS) in two different settings: the covariance-based OS (cOS) and the singular value decomposition (SVD)-based OS (sOS). By simulation and analyzing human CEOAE data, the cOS consistently reduced the noise and enhanced the SNR by 1 to 2 dB from a baseline method (BM) that is based on calculating the median. The sOS achieved an SNR enhancement of 2 to 3 dB in simulation, and demonstrated capability to enhance the SNR in real recordings when the SNR achieved by the BM was below 0 dB. An appealing property of OS is that it produces an estimate of every individual column of the signal matrix. This property makes it possible to investigate CEOAE dynamics across a longer period of time when the cochlear conditions are not strictly stationary.