pwc-archive/papers-with-abstracts huggingface dataset:
A Chirplet Transform-based Mode Retrieval Method for Multicomponent Signals with Crossover Instantaneous Frequencies
Authors: Lin Li, Ningning Han, Qingtang Jiang, Charles K. Chui
Date: 2020-10-04 00:00:00
Tasks: Retrieval

In nature and engineering world, the acquired signals are usually affected by multiple complicated factors and appear as multicomponent nonstationary modes. In such and many other situations, it is necessary to separate these signals into a finite number of monocomponents to represent the intrinsic modes and underlying dynamics implicated in the source signals. In this paper, we consider the mode retrieval of a multicomponent signal which has crossing instantaneous frequencies (IFs), meaning that some of the components of the signal overlap in the time-frequency domain. We use the chirplet transform (CT) to represent a multicomponent signal in the three-dimensional space of time, frequency and chirp rate and introduce a CT-based signal separation scheme (CT3S) to retrieve modes. In addition, we analyze the error bounds for IF estimation and component recovery with this scheme. We also propose a matched-filter along certain specific time-frequency lines with respect to the chirp rate to make nonstationary signals be further separated and more concentrated in the three-dimensional space of CT. Furthermore, based on the approximation of source signals with linear chirps at any local time, we propose an innovative signal reconstruction algorithm, called the group filter-matched CT3S (GFCT3S), which also takes a group of components into consideration simultaneously. GFCT3S is suitable for signals with crossing IFs. It also decreases component recovery errors when the IFs curves of different components are not crossover, but fast-varying and close to one and other. Numerical experiments on synthetic and real signals show our method is more accurate and consistent in signal separation than the empirical mode decomposition, synchrosqueezing transform, and other approaches
Appliance identification using a histogram post-processing of 2D local binary patterns for smart grid applications
Authors: Yassine Himeur, Abdullah Alsalemi, Faycal Bensaali, Abbes Amira
Date: 2020-10-03 00:00:00
Tasks: Management

Identifying domestic appliances in the smart grid leads to a better power usage management and further helps in detecting appliance-level abnormalities. An efficient identification can be achieved only if a robust feature extraction scheme is developed with a high ability to discriminate between different appliances on the smart grid. Accordingly, we propose in this paper a novel method to extract electrical power signatures after transforming the power signal to 2D space, which has more encoding possibilities. Following, an improved local binary patterns (LBP) is proposed that relies on improving the discriminative ability of conventional LBP using a post-processing stage. A binarized eigenvalue map (BEVM) is extracted from the 2D power matrix and then used to post-process the generated LBP representation. Next, two histograms are constructed, namely up and down histograms, and are then concatenated to form the global histogram. A comprehensive performance evaluation is performed on two different datasets, namely the GREEND and WITHED, in which power data were collected at 1 Hz and 44000 Hz sampling rates, respectively. The obtained results revealed the superiority of the proposed LBP-BEVM based system in terms of the identification performance versus other 2D descriptors and existing identification frameworks.
Blind Numerology Identification for Mixed Numerologies
Authors: Ahmad M. Jaradat, Ebubekir Memisoglu, Huseyin Arslan
Date: 2020-06-16 00:00:00

5G New Radio (NR) introduces new flexibility that different numerologies can be selected to meet the requirements of a wide variety of services. For this new structure, blind numerology identification can increase system efficiency. Therefore, we propose a blind identification method for mixed numerologies. An autocorrelation method is applied in the time domain by correlating the cyclic prefix (CP) signal of the candidate numerology in the received composite signal for numerology type identification. Then, the location of each numerology in the frequency domain is identified by the variance difference in the power spectral density (PSD) of the subbands, on which different numerologies are occupied. The simulation results are obtained under additive white Gaussian noise (AWGN) and frequencyselective channels. The obtained results show that the proposed method has a robust identification accuracy and a satisfactory BER performance as compared to the non-blind identification approach in the conventional mixed-numerology system.
A 5G-NR Satellite Extension for the QuaDRiGa Channel Model
Authors: Stephan Jaeckel, Leszek Raschkowski, Lars Thiele
Date: 2020-10-02 00:00:00

Low Earth orbit (LEO) satellite networks will become an integral part of the global telecommunication infrastructure. Modeling the radio-links of these networks and their interaction with existing terrestrial systems is crucial for the design, planning and scaling of these networks. The 3rd generation partnership project (3GPP) addressed this by providing guideline for such a radio-channel model. However, the proposed model lacks a satellite orbit model and has some inconsistencies in the provided parameters. This is addressed in this paper. We provide a non-geostationary-satellite model that can be integrated into geometry-based stochastic channel models (GSCMs) such as QuaDRiGa. We then use this model to obtain the GSCM parameters from a simplified environment model and compare the results to the 3GPP parameter-set. This solves the inconsistencies, but our simplified approach does not consider many propagation effects. Future work must therefore rely on measurements or accurate Ray-tracing models to obtain the parameters.
Quantitative assessment of linear noise-reduction filters for spectroscopy
Authors: L. V. Le, Y. D. Kim, D. E. Aspnes
Date: 2020-10-02 00:00:00

Linear noise-reduction filters used in spectroscopy must strike a balance between reducing noise and preserving lineshapes, the two conflicting requirements of interest.
6G Cellular Networks and Connected Autonomous Vehicles
Authors: Jianhua He, Kun Yang, Hsiao-Hwa Chen
Date: 2020-10-02 00:00:00
Tasks: Autonomous Vehicles

With 5G mobile communication systems been commercially rolled out, research discussions on next generation mobile systems, i.e., 6G, have started. On the other hand, vehicular technologies are also evolving rapidly, from connected vehicles as coined by V2X (vehicle to everything) to autonomous vehicles to the combination of the two, i.e., the networks of connected autonomous vehicles (CAV). How fast the evolution of these two areas will go head-in-head is of great importance, which is the focus of this paper. Based on a brief overview on the technological evolution of V2X to CAV and 6G key technologies, this paper explores two complementary research directions, namely, 6G for CAVs versus CAVs for 6G. The former investigates how various 6G key enablers, such as THz, cell free communication and artificial intelligence (AI), can be utilized to provide CAV mission-critical services. The latter discusses how CAVs can facilitate effective deployment and operation of 6G systems. This paper attempts to investigate the interactions between the two technologies to spark more research efforts in these areas.
"Cellular Network Densification Increases Radio-Frequency Pollution": True or False?
Authors: Luca Chiaraviglio, Sara Turco, Giuseppe Bianchi, Nicola Blefari Melazzi
Date: 2020-10-02 00:00:00

A very popular theory circulating among non-scientific communities claims that the massive deployment of Base Stations (BSs) over the territory, a.k.a. cellular network densification, always triggers an uncontrolled and exponential increase of human exposure to Radio Frequency "Pollution" (RFP). To face such concern in a way that can be understood by the layman, in this work we develop a very simple model to compute the RFP, based on a set of worst-case and conservative assumptions. We then provide closed-form expressions to evaluate the RFP variation in a pair of candidate 5G deployments, subject to different densification levels. Results, obtained over a wide set of representative 5G scenarios, dispel the myth: cellular network densification triggers an RFP decrease (up to three orders of magnitude) when the radiated power from the BS is adjusted to ensure a minimum sensitivity at the cell edge. Eventually, we analyze the conditions under which the RFP may increase when the network is densified (e.g., when the radiated power does not scale with the cell size), proving that the amount of RFP is always controlled. Finally, the results obtained by simulation confirm the outcomes of the RFP model.
Impact of Short Blocklength Coding on Stability of an AGV Control System in Industry 4.0
Authors: Shreya Tayade, Peter Rost, Andreas Maeder, Hans D Schotten
Date: 2020-10-02 00:00:00

With the advent of 5G and beyond, using wireless communication for closed-loop control and automation processes is one of the main aspects of the envisioned Industry 4.0. In this regard, a major challenge is to ensure a robust and stable control system over an unreliable wireless channel. One of the main use-cases in this context is Automated Guided Vehicle (AGV) control in a future factory. Specifically, we consider a system where an AGV controller is placed in an edge cloud in the factory network infrastructure and the control commands are sent over a time-correlated Rayleigh fading channel. In an industrial control, short packets are exchanged between the controller and the actuator. Therefore, in this case, Shannon's assumption for an infinite block length is not applicable. The objective is to analyse the stability performance of an AGV control system in a Finite Block-Length (FBL) regime. We evaluate the coding rate required to maintain a stable edge cloud based AGV system. The results illustrate that adapting the control parameters can lower the stringent requirements on the coding rate. It reveals that a constant stability performance can be achieved even at higher coding rate by increasing the AGV's velocity. Moreover, this paper also determines the maximum number of AGVs that can be served seamlessly over the available communication resources while maintaining a stable control system.
Super-Nyquist Co-Prime Sensing
Authors: Usham V. Dias
Date: 2020-10-02 00:00:00

The theory of co-prime arrays has been studied in the past. Nyquist rate estimation of second order statistics using the combined difference set was demonstrated with low latency. This paper proposes a novel method to reconstruct the second order statistics at a rate that is twice the Nyquist rate using the same sub-Nyquist co-prime samplers. We analyse the difference set, and derive the closed-form expressions for the weight function and the bias of the correlogram estimate. The main lobe width of the bias window is approximately half of the width obtained using the prototype co-prime sampler. Since the proposed scheme employs the same rate prototype co-prime samplers; the number of samples acquired in one co-prime period and hardware cost are unaffected. Super-Nyquist estimation with multiple co-prime periods is also described. Furthermore, n-tuple or multi-level co-prime structure is presented from a super-Nyquist perspective. Here, estimation at a rate q times higher than Nyquist is possible, where q is the number of sub-samplers.
Multidimensional Index Modulation for 5G and Beyond Wireless Networks
Authors: Seda Dogan-Tusha, Armed Tusha, Ertugrul Basar, Huseyin Arslan
Date: 2020-10-02 00:00:00

This study examines the flexible utilization of existing IM techniques in a comprehensive manner to satisfy the challenging and diverse requirements of 5G and beyond services. After spatial modulation (SM), which transmits information bits through antenna indices, application of IM to orthogonal frequency division multiplexing (OFDM) subcarriers has opened the door for the extension of IM into different dimensions, such as radio frequency (RF) mirrors, time slots, codes, and dispersion matrices. Recent studies have introduced the concept of multidimensional IM by various combinations of one-dimensional IM techniques to provide higher spectral efficiency (SE) and better bit error rate (BER) performance at the expense of higher transmitter (Tx) and receiver (Rx) complexity. Despite the ongoing research on the design of new IM techniques and their implementation challenges, proper use of the available IM techniques to address different requirements of 5G and beyond networks is an open research area in the literature. For this reason, we first provide the dimensional-based categorization of available IM domains and review the existing IM types regarding this categorization. Then, we develop a framework that investigates the efficient utilization of these techniques and establishes a link between the IM schemes and 5G services, namely enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communication (URLLC). Additionally, this work defines key performance indicators (KPIs) to quantify the advantages and disadvantages of IM techniques in time, frequency, space, and code dimensions. Finally, future recommendations are given regarding the design of flexible IM-based communication systems for 5G and beyond wireless networks.
Supervised Heart Rate Tracking using Wrist-Type Photoplethysmographic (PPG) Signals during Physical Exercise without Simultaneous Acceleration Signals
Authors: Mahmoud Essalat, Mahdi Boloursaz Mashhadi, Farokh Marvasti
Date: 2020-10-02 00:00:00

PPG based heart rate (HR) monitoring has recently attracted much attention with the advent of wearable devices such as smart watches and smart bands. However, due to severe motion artifacts (MA) caused by wristband stumbles, PPG based HR monitoring is a challenging problem in scenarios where the subject performs intensive physical exercises. This work proposes a novel approach to the problem based on supervised learning by Neural Network (NN). By simulations on the benchmark datasets [1], we achieve acceptable estimation accuracy and improved run time in comparison with the literature. A major contribution of this work is that it alleviates the need to use simultaneous acceleration signals. The simulation results show that although the proposed method does not process the simultaneous acceleration signals, it still achieves the acceptable Mean Absolute Error (MAE) of 1.39 Beats Per Minute (BPM) on the benchmark data set.
A Modified Schmidl-Cox OFDM Timing Detector
Authors: Stephen G. Wilson, Rui Shang
Date: 2020-10-02 00:00:00

We describe a simple modification of the Schmidl-Cox detector for establishing timing in OFDM transmissions that stabilizes performance in transitions from no-signal to signal, or vice-versa. Moreover, the proposed modification scales the detector's metric between 0 and 1 for all scenarios, simplifying threshold setting, and improves timing detector SNR.
Performance Analysis and Optimization of NOMA with HARQ for Short Packet Communications in Massive IoT
Authors: Fatemeh Ghanami, Ghosheh Abed Hodtani, Branka Vucetic, Mahyar Shirvanimoghaddam
Date: 2020-10-01 00:00:00

In this paper, we consider the massive non-orthogonal multiple access (NOMA) with hybrid automatic repeat request (HARQ) for short packet communications. To reduce the latency, each user can perform one re-transmission provided that the previous packet was not decoded successfully. The system performance is evaluated for both coordinated and uncoordinated transmissions. We first develop a Markov model (MM) to analyze the system dynamics and characterize the packet error rate (PER) and throughput of each user in the coordinated scenario. The power levels are then optimized for two scenarios, including the power constrained and reliability constrained scenarios. A simple yet efficient dynamic cell planning is also designed for the uncoordinated scenario. Numerical results show that both coordinated and uncoordinated NOMA-HARQ with a limited number of retransmissions can achieve the desired level of reliability with the guaranteed latency using a proper power control strategy. Results also show that NOMA-HARQ achieves a higher throughput compared to the orthogonal multiple access scheme with HARQ under the same average received power constraint at the base station.
High Accuracy VLP based on Image Sensor using Error Calibration Method
Authors: Shihuan Chen, Weipeng Guan
Date: 2020-10-01 00:00:00
Tasks: Vocal Bursts Intensity Prediction

In this paper, visible light positioning (VLP) where the receiver adopts a commercial image sensor is considered. We firstly analyze the theoretical limits and error source of the VLP system using image sensor. And then, we develop a VLP positioning model on the receiver movement and further propose two novel error calibration algorithms, namely Rotation Calibration Method and dispersion circle calibration method. The rotation algorithm estimates the rotation center in the image instead of treating the image center as the rotation center, leading to reduced positioning error. For the dispersion circle, it can offset the shift error created by the conversation between different coordinate during the positioning calculation. According to the experimental results, the average positioning error of the proposed methods can be reduced to 0.82cm, which achieve state-of-the-art in the VLP field.
Channel Modeling for IRS-Assisted FSO Systems
Authors: Hedieh Ajam, Marzieh Najafi, Vahid Jamali, Robert Schober
Date: 2020-10-01 00:00:00
Tasks: valid

In this paper, we develop an analytical channel model for intelligent reflecting surface (IRS)-assisted free space optical (FSO) systems. Unlike IRS-assisted radio frequency systems, where it is typically assumed that a plane wave is incident on the IRS, in FSO systems, the incident wave is a Gaussian beam with non-uniform power distribution across the IRS. Taking this property into account, we develop an analytical end-to-end channel model for IRS-assisted FSO systems based on the Huygens-Fresnel principle. Our analytical model reveals the impact of the size, position, orientation, and phase-shift configuration of the IRS on the end-to-end channel. Furthermore, we show that results obtained based on geometric optics under the far-field approximation are only valid for a specific range of IRS-receiver lens distances depending on the IRS size, incident beam width, and wavelength. Simulation results validate the accuracy of the proposed analytical results for the FSO beam reflected from the IRS and compare the bit error rate performance obtained for the proposed analytical channel model with that obtained for geometric optics under the far-field approximation.
On the Advantage of Coherent LoRa Detection in the Presence of Interference
Authors: Orion Afisiadis, Sitian Li, Andreas Burg, Alexios Balatsoukas-Stimming
Date: 2020-10-01 00:00:00

It has been shown that the coherent detection of LoRa signals only provides marginal gains of around 0.7 dB on the additive white Gaussian noise (AWGN) channel. However, ALOHA-based massive Internet of Things systems, including LoRa, often operate in the interference-limited regime. Therefore, in this work, we examine the performance of the LoRa modulation with coherent detection in the presence of interference from another LoRa user with the same spreading factor. We derive rigorous symbol- and frame error rate expressions as well as bounds and approximations for evaluating the error rates. The error rates predicted by these approximations are compared against error rates found by Monte Carlo simulations and shown to be very accurate. We also compare the performance of LoRa with coherent and non-coherent receivers and we show that the coherent detection of LoRa is significantly more beneficial in interference scenarios than in the presence of only AWGN. For example, we show that coherent detection leads to a 2.5 dB gain over the standard non-coherent detection for a signal-to-interference ratio (SIR) of 3 dB and up to a 10 dB gain for an SIR of 0 dB.
The RFML Ecosystem: A Look at the Unique Challenges of Applying Deep Learning to Radio Frequency Applications
Authors: Lauren J. Wong, William H. Clark IV, Bryse Flowers, R. Michael Buehrer, Alan J. Michaels, William C. Headley
Date: 2020-10-01 00:00:00
Tasks: BIG-bench Machine Learning

While deep machine learning technologies are now pervasive in state-of-the-art image recognition and natural language processing applications, only in recent years have these technologies started to sufficiently mature in applications related to wireless communications. In particular, recent research has shown deep machine learning to be an enabling technology for cognitive radio applications as well as a useful tool for supplementing expertly defined algorithms for spectrum sensing applications such as signal detection, estimation, and classification (termed here as Radio Frequency Machine Learning, or RFML). A major driver for the usage of deep machine learning in the context of wireless communications is that little, to no, a priori knowledge of the intended spectral environment is required, given that there is an abundance of representative data to facilitate training and evaluation. However, in addition to this fundamental need for sufficient data, there are other key considerations, such as trust, security, and hardware/software issues, that must be taken into account before deploying deep machine learning systems in real-world wireless communication applications. This paper provides an overview and survey of prior work related to these major research considerations. In particular, we present their unique considerations in the RFML application space, which are not generally present in the image, audio, and/or text application spaces.
Fast Uplink Grant-Free NOMA with Sinusoidal Spreading Sequences
Authors: Shah Mahdi Hasan, Kaushik Mahata, Md Mashud Hyder
Date: 2020-10-01 00:00:00
Tasks: Compressive Sensing

Uplink (UL) dominated sporadic transmission and stringent latency requirement of massive machine type communication (mMTC) forces researchers to abandon complicated grant-acknowledgment based legacy networks. UL grant-free non-orthogonal multiple access (NOMA) provides an array of features which can be harnessed to efficiently solve the problem of massive random connectivity and latency. Because of the inherent sparsity in user activity pattern in mMTC, the trend of existing literature specifically revolves around compressive sensing based multi user detection (CS-MUD) and Bayesian framework paradigm which employs either random or Zadoff-Chu spreading sequences for non-orthogonal multiple access. In this work, we propose sinusoidal code as candidate spreading sequences. We show that, sinusoidal codes allow some non-iterative algorithms to be employed in context of active user detection, channel estimation and data detection in a UL grant-free mMTC system. This relaxes the requirement of several impractical assumptions considered in the state-of-art algorithms with added advantages of performance guarantees and lower computational cost. Extensive simulation results validate the performance potential of sinusoidal codes in realistic mMTC environments.