pwc-archive/papers-with-abstracts huggingface dataset:
Free-Space Optical MISO Communications With an Array of Detectors
Authors: Muhammad Salman Bashir, Mohamed-Slim Alouini
Date: 2020-09-01 00:00:00
Tasks: Diversity

Multiple-input multiple-output (MIMO) and multiple-input single-output (MISO) schemes have yielded promising results in free space optical (FSO) communications by providing diversity against fading of the received signal intensity. In this paper, we have analyzed the probability of error performance of a \emph{muliple-input single-output} (MISO) free-space optical channel that employs array(s) of detectors at the receiver. In this regard, we have considered the \emph{maximal ratio combiner} (MRC) and \emph{equal gain combiner} (EGC) fusion algorithms for the array of detectors, and we have examined the performance of these algorithms subject to phase and pointing errors for strong atmospheric turbulence conditions. It is concluded that when the variance of the phase and pointing errors are below certain thresholds, signal combining with a single array of detectors yields significantly better performance than a multiple arrays receiver. In the final part of the paper, we examine the probability of error of the single detector array receiver as a function of the beam radius, and the probability of error is minimized by (numerically) optimizing the beam radius of the received signal beams.
SCMA Spectral and Energy Efficiency with QoS
Authors: Samira Jaber, Wen Chen, Kunlun Wang, Qingqing Wu
Date: 2020-04-21 00:00:00

Sparse Code Multiple Access (SCMA) is one of the promising candidates for new radio access interface. The new generation communication system is expected to support massive user access with high capacity. However, there are numerous problems and barriers to achieve optimal performance, e.g., the multiuser interference and high power consumption. In this paper, we present optimization methods to enhance the spectral and energy efficiency for SCMA with individual rate requirements. The proposed method has shown a better network mapping matrix based on power allocation and codebook assignment. Moreover, the proposed method is compared with orthogonal frequency devision multiple access (OFDMA) and code devision multiple access (CDMA) in terms of spectral efficiency (SE) and energy efficiency (EE) respectively. Simulation results show that SCMA performs better than OFDMA and CDMA both in SE and EE.
Application-Based Coexistence of Different Waveforms on Non-orthogonal Multiple Access
Authors: Mehmet Mert Şahin, Hüseyin Arslan
Date: 2020-09-01 00:00:00

The coexistence of different wireless communication systems such as LTE and Wi-Fi by sharing the unlicensed band is well studied in the literature. In these studies, various methods are proposed to support the coexistence of systems, including listen-before-talk mechanism, joint user association and resource allocation. However, in this study, the coexistence of different waveform structures in the same resource elements are studied under the theory of non-orthogonal multiple access. This study introduces a paradigm-shift on NOMA towards the application-centric waveform coexistence. Throughout the paper, the coexistence of different waveforms is explained with two specific use cases, which are power-balanced NOMA and joint radar-sensing and communication with NOMA. In addition, some of the previous works in the literature regarding non-orthogonal waveform coexistence are reviewed. However, the concept is not limited to these use cases. With the rapid development of wireless technology, next-generation wireless systems are proposed to be flexible and hybrid, having different kinds of capabilities such as sensing, security, intelligence, control, and computing. Therefore, the concept of different waveforms' coexistence to meet these concerns are becoming impressive for researchers.
Regional Total Electron Content Map Generation based on Compressive Sensing
Authors: Cansu Sunu, Cenk Toker
Date: 2020-09-01 00:00:00
Tasks: Compressive Sensing

Ionosphere has an important role in long distance HF communications, satellite communications and global navigation systems. Ionosphere is a plasma medium which arises due to solar and cosmic radiation, and the amount of ionization is highly time and location dependent. Eventually, the current state of the ionosphere should be continuously monitored with high accuracy. Total Electron Content (TEC) maps are being used to investigate the state of the ionosphere. There are online services which provide TEC maps, however they mostly have low spatial and temporal resolution and the techniques used for generating these maps are generally not accessible. TEC maps can also be generated from GNSS/GPS based Continuously Operating Receiver Stations (CORS) network measurements. Unfortunately, the GNSS/GPS receiver networks are not dense enough to form a map directly. Therefore, an algorithm should be used to estimate the TEC values at coordinates without a receiver. Based on the observation that TEC maps possess a high degree of sparsity, we propose a modified compressive sensing technique for generating regional TEC maps by using the sparse dataset obtained from a CORS network. We evaluate the performance of the proposed technique both over synthetically generated TEC maps which mimic the common characteristics of the ionosphere, and also over actual measurements taken over the Turkish National Permanent GPS Network (TNPGN) Active. Our analysis reveals that the proposed technique can produce TEC maps with high accuracy and resolution. We also demonstrate the superiority of our technique over other TEC map generation techniques found in the literature.
Intelligent Reflecting Surface Aided Multicasting with Random Passive Beamforming
Authors: Qin Tao, Shuowen Zhang, Caijun Zhong, Rui Zhang
Date: 2020-09-01 00:00:00

In this letter, we consider a multicast system where a single-antenna transmitter sends a common message to multiple single-antenna users, aided by an intelligent reflecting surface (IRS) equipped with $N$ passive reflecting elements. Prior works on IRS have mostly assumed the availability of channel state information (CSI) for designing its passive beamforming. However, the acquisition of CSI requires substantial training overhead that increases with $N$. In contrast, we propose in this letter a novel \emph{random passive beamforming} scheme, where the IRS performs independent random reflection for $Q\geq 1$ times in each channel coherence interval without the need of CSI acquisition. For the proposed scheme, we first derive a closed-form approximation of the outage probability, based on which the optimal $Q$ with best outage performance can be efficiently obtained. Then, for the purpose of comparison, we derive a lower bound of the outage probability with traditional CSI-based passive beamforming. Numerical results show that a small $Q$ is preferred in the high-outage regime (or with high rate target) and the optimal $Q$ becomes larger as the outage probability decreases (or as the rate target decreases). Moreover, the proposed scheme significantly outperforms the CSI-based passive beamforming scheme with training overhead taken into consideration when $N$ and/or the number of users are large, thus offering a promising CSI-free alternative to existing CSI-based schemes.
Preventing Identity Attacks in RFID Backscatter Communication Systems: A Physical-Layer Approach
Authors: Ahsan Mehmood, Waqas Aman, M. Mahboob Ur Rahman, M. A. Imran, Qammer H. Abbasi
Date: 2020-09-01 00:00:00
Tasks: TAG

This work considers identity attack on a radio-frequency identification (RFID)-based backscatter communication system. Specifically, we consider a single-reader, single-tag RFID system whereby the reader and the tag undergo two-way signaling which enables the reader to extract the tag ID in order to authenticate the legitimate tag (L-tag). We then consider a scenario whereby a malicious tag (M-tag)---having the same ID as the L-tag programmed in its memory by a wizard---attempts to deceive the reader by pretending to be the L-tag. To this end, we counter the identity attack by exploiting the non-reciprocity of the end-to-end channel (i.e., the residual channel) between the reader and the tag as the fingerprint of the tag. The passive nature of the tag(s) (and thus, lack of any computational platform at the tag) implies that the proposed light-weight physical-layer authentication method is implemented at the reader. To be concrete, in our proposed scheme, the reader acquires the raw data via two-way (challenge-response) message exchange mechanism, does least-squares estimation to extract the fingerprint, and does binary hypothesis testing to do authentication. We also provide closed-form expressions for the two error probabilities of interest (i.e., false alarm and missed detection). Simulation results attest to the efficacy of the proposed method.
Underdetermined Blind Identification via $k$-Sparse Component Analysis: RANSAC-driven Orthogonal Subspace Search
Authors: Ehsan Eqlimi, Bahador Makkiabadi, Mayadeh Kouti, Ardeshir Fotouhi, Saeid Sanei
Date: 2020-08-09 00:00:00

Two primary families of methods exist for underdetermined blind identification (UBI) based on the sparsity of the source matrix: sparse component analysis (SCA) and $k$-SCA. SCA assumes one active source at each time instant, while $k$-SCA allows for varying numbers of active sources represented by $k$. However, existing $k$-SCA methods, which claim to solve UBI problems by accommodating $k$-sparse sources, predominantly rely on $1$-sparse sources, limiting their effectiveness in real-world scenarios with high noise levels. In this paper, we propose an effective and computationally less complex approach for UBI, specifically focusing on the challenging case when the number of active sources is equal to the number of sensors minus one ($k=m-1$). Our approach overcomes limitations by using a two-step scenario: (1) estimating the orthogonal complement subspaces of the overall space and (2) identifying the mixing vectors. We present an integrated algorithm based on the Gram-Schmidt process and random sample consensus (RANSAC) method to solve both steps. Experimental results using simulated data demonstrate the superior effectiveness of our proposed method compared to existing algorithms.
A Modular Approach for Synchronized Wireless Multimodal Multisensor Data Acquisition in Highly Dynamic Social Settings
Authors: Chirag Raman, Stephanie Tan, Hayley Hung
Date: 2020-08-09 00:00:00

Existing data acquisition literature for human behavior research provides wired solutions, mainly for controlled laboratory setups. In uncontrolled free-standing conversation settings, where participants are free to walk around, these solutions are unsuitable. While wireless solutions are employed in the broadcasting industry, they can be prohibitively expensive. In this work, we propose a modular and cost-effective wireless approach for synchronized multisensor data acquisition of social human behavior. Our core idea involves a cost-accuracy trade-off by using Network Time Protocol (NTP) as a source reference for all sensors. While commonly used as a reference in ubiquitous computing, NTP is widely considered to be insufficiently accurate as a reference for video applications, where Precision Time Protocol (PTP) or Global Positioning System (GPS) based references are preferred. We argue and show, however, that the latency introduced by using NTP as a source reference is adequate for human behavior research, and the subsequent cost and modularity benefits are a desirable trade-off for applications in this domain. We also describe one instantiation of the approach deployed in a real-world experiment to demonstrate the practicality of our setup in-the-wild.
An Embedded System for Monitoring Industrial Air Dehumidifiers using a Mobile Android Application for IEEE 802.11 Networks
Authors: Erik de Oliveira Rosa, Lincoln Cezar Grabarski, Marcos Fernando Fragoso, Allan Cristian Krainski Ferrari, Jefferson Rodrigo Schuertz, Carlos Alexandre Gouvea da Silva
Date: 2020-08-09 00:00:00

The constant technological evolution allowed significant advances and improvements in the processes of industries, mainly in areas that demand greater control and environmental air efficiency. In this way, Embedded Systems allows the development of products and services that aim to solve or propose solutions in these industrial environments. This article presents the development of an Embedded System with a Programmable Logic Controller (PLC) and Arduino for industrial air dehumidifier, which allows the monitoring of failures remotely from a reliable data communication in a mobile application for Android operating system (OS) on a wireless network IEEE 802.11. As a result, a prototype of the test bench for the Embedded System is presented in which the main parameters of temperature sensors and operating conditions of the dehumidifiers are checked.
Discontinuous Computation Offloading for Energy-Efficient Mobile Edge Computing
Authors: Mattia Merluzzi, Nicola di Pietro, Paolo Di Lorenzo, Emilio Calvanese Strinati, Sergio Barbarossa
Date: 2020-08-08 00:00:00
Tasks: Edge-computing, Stochastic Optimization

We propose a novel strategy for energy-efficient dynamic computation offloading, in the context of edge-computing-aided beyond 5G networks. The goal is to minimize the energy consumption of the overall system, comprising multiple User Equipment (UE), an access point (AP), and an edge server (ES), under constraints on the end-to-end service delay and the packet error rate performance over the wireless interface. To reduce the energy consumption, we exploit low-power sleep operation modes for the users, the AP and the ES, shifting the edge computing paradigm from an always on to an always available architecture, capable of guaranteeing an on-demand target service quality with the minimum energy consumption. To this aim, we propose an online algorithm for dynamic and optimal orchestration of radio and computational resources called Discontinuous Computation Offloading (DisCO). In such a framework, end-to-end delay constraints translate into constraints on overall queueing delays, including both the communication and the computation phases of the offloading service. DisCO hinges on Lyapunov stochastic optimization, does not require any prior knowledge on the statistics of the offloading traffic or the radio channels, and satisfies the long-term performance constraints imposed by the users. Several numerical results illustrate the advantages of the proposed method.
Intelligent Physical Layer Security Approach for V2X Communication
Authors: Haji M. Furqan, Muhammad Sohaib J. Solaija, Jehad M. Hamamreh, Huseyin Arslan
Date: 2019-05-13 00:00:00

Intelligent transportation systems (ITS) with advanced sensing and computing technologies are expected to support a whole new set of services including pedestrian and vehicular safety, internet access for vehicles, and eventually, driverless cars. Wireless communication is a major driving factor behind ITS, enabling reliable communication between vehicles, infrastructure, pedestrians and network, generally referred to as vehicle to everything (V2X) communication. However, the broadcast nature of wireless communication renders it prone to jamming, eavesdropping and spoofing attacks which can adversely affect ITS. Keeping in view this issue, we suggest the use of an intelligent security framework for V2X communication security, referred to as intelligent V2X security (IV2XS), to provide a reliable and robust solution capable of adapting to different conditions, scenarios and user requirements. We also identify the conditions that impact the security and describe the open challenges in achieving a realistic IV2XS system.
A Robust Color Edge Detection Algorithm Based on Quaternion Hardy Filter
Authors: Wenshan Bi, Dong Cheng, Kit Ian Kou
Date: 2020-01-06 00:00:00
Tasks: Edge Detection

This paper presents a robust filter called quaternion Hardy filter (QHF) for color image edge detection. The QHF can be capable of color edge feature enhancement and noise resistance. It is flexible to use QHF by selecting suitable parameters to handle different levels of noise. In particular, the quaternion analytic signal, which is an effective tool in color image processing, can also be produced by quaternion Hardy filtering with specific parameters. Based on the QHF and the improved Di Zenzo gradient operator, a novel color edge detection algorithm is proposed. Importantly, it can be efficiently implemented by using the fast discrete quaternion Fourier transform technique. The experiments demonstrate that the proposed algorithm outperforms several widely used algorithms.
Application of the Non-Hermitian Singular Spectrum Analysis to the exponential retrieval problem
Authors: D. J Nicolsky, G. S. Tipenko
Date: 2020-08-08 00:00:00
Tasks: Retrieval, Time Series, Time Series Analysis

We present a new approach to solve the exponential retrieval problem. We derive a stable technique, based on the singular value decomposition (SVD) of lag-covariance and crosscovariance matrices consisting of covariance coefficients computed for index translated copies of an initial time series. For these matrices a generalized eigenvalue problem is solved. The initial signal is mapped into the basis of the generalized eigenvectors and phase portraits are consequently analyzed. Pattern recognition techniques could be applied to distinguish phase portraits related to the exponentials and noise. Each frequency is evaluated by unwrapping phases of the corresponding portrait, detecting potential wrapping events and estimation of the phase slope. Efficiency of the proposed and existing methods is compared on the set of examples, including the white Gaussian and auto-regressive model noise.
Rounded Hartley Transform: A Quasi-involution
Authors: R. J. Cintra, H. M. de Oliveira, C. O. Cintra
Date: 2020-08-07 00:00:00
Tasks: Decision Making

A new multiplication-free transform derived from DHT is introduced: the RHT. Investigations on the properties of the RHT led us to the concept of weak-inversion. Using new constructs, we show that RHT is not involutional like the DHT, but exhibits quasi-involutional property, a new definition derived from the periodicity of matrices. Thus instead of using the actual inverse transform, the RHT is viewed as an involutional transform, allowing the use of direct (multiplication-free) to evaluate the inverse. A fast algorithm to compute RHT is presented. This algorithm show embedded properties. We also extended RHT to the two-dimensional case. This permitted us to perform a preliminary analysis on the effects of RHT on images. Despite of some SNR loss, RHT can be very interesting for applications involving image monitoring associated to decision making, such as military applications or medical imaging.
Power Quality Monitor for Residential Voltage
Authors: Daniel Pérez
Date: 2020-05-12 00:00:00

A simple and inexpensive system capable of displaying relevant power quality data of a residential voltage signal is presented, and it is based on Arduino, Python and Kivy. A circuit for adjusting the voltage signal for sampling is suggested. The software of the system allows the transmission of data with any device capable of serial communication at a 2000000 Baud rate, although an Arduino NANO/UNO board is recommended A board based on an Arduino NANO R3 was used. The software plots the last 6 cycles contained in the data by default, but lets analyze any amount of cycles. It provides the RMS voltage, the peak voltage and the total harmonic distortion of the scrutinized cycles, up to the 25th harmonic. The software is also capable of calculating the fast Fourier transform of the studied cycles, and returning the corresponding plot. A sample of data captured at Maracaibo, Venezuela is shown.
Deep Learning Based Dereverberation of Temporal Envelopesfor Robust Speech Recognition
Authors: Anurenjan Purushothaman, Anirudh Sreeram, Rohit Kumar, Sriram Ganapathy
Date: 2020-08-07 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Deep Learning, Robust Speech Recognition, speech-recognition, Speech Recognition

Automatic speech recognition in reverberant conditions is a challenging task as the long-term envelopes of the reverberant speech are temporally smeared. In this paper, we propose a neural model for enhancement of sub-band temporal envelopes for dereverberation of speech. The temporal envelopes are derived using the autoregressive modeling framework of frequency domain linear prediction (FDLP). The neural enhancement model proposed in this paper performs an envelop gain based enhancement of temporal envelopes and it consists of a series of convolutional and recurrent neural network layers. The enhanced sub-band envelopes are used to generate features for automatic speech recognition (ASR). The ASR experiments are performed on the REVERB challenge dataset as well as the CHiME-3 dataset. In these experiments, the proposed neural enhancement approach provides significant improvements over a baseline ASR system with beamformed audio (average relative improvements of 21% on the development set and about 11% on the evaluation set in word error rates for REVERB challenge dataset).
Evaluating Load Models and Their Impacts on Power Transfer Limits
Authors: Xinan Wang, Yishen Wang, Di Shi, Jianhui Wang, Siqi Wang, Ruisheng Diao, Zhiwei Wang
Date: 2020-08-07 00:00:00
Tasks: Q-Learning

Power transfer limits or transfer capability (TC) directly relate to the system operation and control as well as electricity markets. As a consequence, their assessment has to comply with static constraints, such as line thermal limits, and dynamic constraints, such as transient stability limits, voltage stability limits and small-signal stability limits. Since the load dynamics have substantial impacts on power system transient stability, load models are one critical factor that affects the power transfer limits. Currently, multiple load models have been proposed and adopted in the industry and academia, including the ZIP model, ZIP plus induction motor composite model (ZIP + IM) and WECC composite load model (WECC CLM). Each of them has its unique advantages, but their impacts on the power transfer limits are not yet adequately addressed. One existing challenge is fitting the high-order nonlinear models such as WECC CLM. In this study, we innovatively adopt double deep Q-learning Network (DDQN) agent as a general load modeling tool in the dynamic assessment procedure and fit the same transient field measurements into different load models. A comprehensive evaluation is then conducted to quantify the load models' impacts on the power transfer limits. The simulation environment is the IEEE-39 bus system constructed in Transient Security Assessment Tool (TSAT).
Design of a Sag and Surge Detector For Residential Voltage
Authors: Daniel Pérez
Date: 2020-06-03 00:00:00

An inexpensive protection device capable of disconnecting a load upon detecting a voltage sag or surge is presented. Said device is aimed towards the residential voltage of Venezuela's electrical system. The device is based on the Arduino platform, and uses an ATmega 328P. It detects the presence of a voltage sag or surge by sampling the residential voltage used to feed the load, and continuously measuring the RMS $\frac{1}{2}$ voltage. It disconnects the load after 3 semi-cycles where the residential voltage is outside the boundaries established by Venezuela's national electrical code, and therefore there is the presence of a voltage sag or surge. The device is capable of transmitting the data of the sampled signal, for making further analysis.