pwc-archive/papers-with-abstracts huggingface dataset:
Waveforms and End-to-End Efficiency in RF Wireless Power Transfer Using Digital Radio Transmitter
Authors: Nachiket Ayir, Taneli Riihonen, Markus Allén, Marcelo Fabián Trujillo Fierro
Date: 2020-12-02 00:00:00

We study radio-frequency (RF) wireless power transfer (WPT) using a digital radio transmitter for applications where alternative analog transmit circuits are impractical. An important paramter for assessing the viability of an RF WPT system is its end-to-end efficiency. In this regard, we present a prototype test-bed comprising a software-defined radio (SDR) transmitter and an energy harvesting receiver with a low resistive load; employing an SDR makes our research meaningful for simultaneous wireless information and power transfer (SWIPT). We analyze the effect of clipping and non-linear amplification at the SDR on multisine waveforms. Our experiments suggest that when the DC input power at the transmitter is constant, high peak-to-average power ratio (PAPR) multisine are unsuitable for RF WPT over a flat-fading channel, due to their low average radiated power. The results indicate that the end-to-end efficiency is positively correlated to the average RF power of the waveform, and that it reduces with increasing PAPR. Consequently, digital modulations such as phase-shift keying (PSK) and quadrature amplitude modeulation (QAM) yield better end-to-end efficiency than multisines. Moreover, the end-to-end efficiency of PSK and QAM signals is invariant of the transmission bit rate. An in-depth analysis of the end-to-end efficiency of WPT reveals that the transmitter efficiency is lower than the receiver efficiency. Furthermore, we study the impact of a reflecting surface on the end-to-end efficiency of WPT, and assess the transmission quality of the information signals by evaluating their error vector magnitude (EVM) for SWIPT. Overall, the experimental observations of end-to-end efficiency and EVM suggest that, while employing an SDR transmitter with fixed DC input power, a baseband quadrature PSK signal is most suitable for SWIPT at large, among PSK and QAM signals.
Secure Vehicular Communications through Reconfigurable Intelligent Surfaces
Authors: Yun Ai, Felipe A. P. de Figueiredo, Long Kong, Michael Cheffena, Symeon Chatzinotas, Björn Ottersten
Date: 2020-11-30 00:00:00

Reconfigurable intelligent surfaces (RIS) is considered as a revolutionary technique to improve the wireless system performance by reconfiguring the radio wave propagation environment artificially. Motivated by the potential of RIS in vehicular networks, we analyze the secrecy outage performance of RIS-aided vehicular communications in this paper. More specifically, two vehicular communication scenarios are considered, i.e., a vehicular-to-vehicular (V2V) communication where the RIS acts as a relay and a vehicular-to-infrastructure (V2I) scenario where the RIS functions as the receiver. In both scenarios, a passive eavesdropper is present attempting to retrieve the transmitted information. Closed-form expressions for the secrecy outage probability (SOP) are derived and verified. The results demonstrate the potential of improving secrecy with the aid of RIS under both V2V and V2I communications.
Physical Layer Security Enhancement Exploiting Intelligent Reflecting Surface
Authors: Keming Feng, Xiao Li, Yu Han, Shi Jin, Yijian Chen
Date: 2019-11-07 00:00:00

In this letter, the use of intelligent reflecting surface (IRS) to enhance the physical layer security of downlink wireless communication is investigated. Assuming a single-antenna legitimate user and a multi-antenna eavesdropper, we propose an effective algorithm to jointly optimize the active and passive beamforming. In the proposed algorithm, the optimal transmit beamforming vector at the BS under fixed IRS phase shifts is derived, and a low-complexity algorithm based on fractional programming (FP) and manifold optimization (MO) is proposed to obtain near optimal IRS phase shifts. Simulation results demonstrate that the proposed algorithm can almost achieve the performance upper bound with a fast convergence rate.
Effects of Solar Radio Emissions on Outdoor Propagation Path Loss Models at 60 GHz Bands for Access/Backhaul Links and D2D Communications
Authors: Ahmed Iyanda Sulyman, Hussein Seleem, Abdulmalik Alwarafy, Khaled M. Humadi, Abdulhameed Alsanie
Date: 2020-12-01 00:00:00

This paper presents analytical and empirical data documenting the effects of solar radio emissions on outdoor propagation path loss at 60 GHz bands. Both line-of-sight (LOS) and non-LOS scenarios were considered. The setup used in the empirical studies emulates the future fifth-generation cellular systems for both access and backhaul services, as well as for device-to-device communications. Based on the measurement data collected in sunny weather with intense solar activities, we developed large-scale propagation path loss models at 60 GHz, and observed the effects of solar radio emissions on the path loss data. It is shown that solar radio emission can decrease carrier-to-noise ratio, and that this translates into a corresponding increase in the path loss exponent (PLE) values for the large-scale propagation path loss channel models. Empirical data show that 9.0%-15.6% higher PLE values were observed in hot and sunny weather during the day (41{\deg}-42 {\deg}C) compared with the counterpart measurements taken at night in cool and clear weather (20{\deg}-38 {\deg}C). This translates into a corresponding decrease in 60 GHz radio coverage in hot and sunny weather during the day. The empirical data are closely corroborated by analytical estimates presented.
Directional Radio Propagation Path Loss Models for Millimeter-Wave Wireless Networks in the 28-, 60-, and 73-GHz Bands
Authors: Ahmed Iyanda Sulyman, Abdulmalik Alwarafy, George R. MacCartney, Theodore S. Rappaport, Abdulhameed Alsanie
Date: 2020-12-01 00:00:00

Fifth-generation (5G) cellular systems are likely to operate in the centimeter-wave (3-30 GHz) and millimeter-wave (30-300 GHz) frequency bands, where a vast amount of underutilized bandwidth exists world-wide. To assist in the research and development of these emerging wireless systems, a myriad of measurement studies have been conducted to characterize path loss in urban environments at these frequencies. The standard theoretical free space (FS) and Stanford University Interim (SUI) empirical path loss models were recently modified to fit path loss models obtained from measurements performed at 28 GHz and 38 GHz, using simple correction factors. In this paper, we provide similar correction factors for models at 60 GHz and 73 GHz. By imparting slope correction factors on the FS and SUI path loss models to closely match the close-in (CI) free space reference distance path loss models, millimeter-wave path loss can be accurately estimated (with popular models) for 5G cellular planning at 60 GHz and 73 GHz. Additionally, new millimeter-wave beam combining path loss models are provided at 28 GHz and 73 GHz by considering the simultaneous combination of signals from multiple antenna pointing directions between the transmitter and receiver that result in the strongest received power. Such directional channel models are important for future adaptive array systems at millimeter-wave frequencies.
MEG Source Localization via Deep Learning
Authors: Dimitrios Pantazis, Amir Adler
Date: 2020-12-01 00:00:00
Tasks: Deep Learning

We present a deep learning solution to the problem of localization of magnetoencephalography (MEG) brain signals. The proposed deep model architectures are tuned for single and multiple time point MEG data, and can estimate varying numbers of dipole sources. Results from simulated MEG data on the cortical surface of a real human subject demonstrated improvements against the popular RAP-MUSIC localization algorithm in specific scenarios with varying SNR levels, inter-source correlation values, and number of sources. Importantly, the deep learning models had robust performance to forward model errors and a significant reduction in computation time, to a fraction of 1 ms, paving the way to real-time MEG source localization.
Deep Residual Network Empowered Channel Estimation for IRS-Assisted Multi-User Communication Systems
Authors: Chang Liu, Xuemeng Liu, Derrick Wing Kwan Ng, Jinhong Yuan
Date: 2020-12-01 00:00:00
Tasks: Denoising

Channel estimation is of great importance in realizing practical intelligent reflecting surface-assisted multi-user communication (IRS-MC) systems. However, different from traditional communication systems, an IRS-MC system generally involves a cascaded channel with a sophisticated statistical distribution, which hinders the implementations of the Bayesian estimators. To further improve the channel estimation performance, in this paper, we model the channel estimation as a denoising problem and adopt a data-driven approach to realize the channel estimation. Specifically, we propose a convolutional neural network (CNN)-based deep residual network (CDRN) to implicitly learn the residual noise for recovering the channel coefficients from the noisy pilot-based observations. In the proposed CDRN, a CNN denoising block equipped with an element-wise subtraction structure is designed to exploit both the spatial features of the noisy channel matrices and the additive nature of the noise simultaneously, which further improves the estimation accuracy. Simulation results demonstrate that the proposed method can almost achieve the same estimation accuracy as that of the optimal minimum mean square error (MMSE) estimator requiring the knowledge of the channel distribution.
Research on Intelligent Charging System Technology of Automobile Group
Authors: Kedi Yan
Date: 2020-12-01 00:00:00
Tasks: Management

This paper analyzes the smart charging system for dealing with issues related to large parking garages, and analyzes the relevant technical standards of intelligent charging piles application and comprehensive transportation hubs. It mainly includes the number, area and charging method of the charging piles installed in the garages. New forms of construction management is conceived, and the investment and construction are reinforced, apportioning all charging system property rights to investment parties. Simultaneously, the investors take full responsibility for the future management and operation, mainly including the collection of service fees and charging fees.
Reconfigurable Intelligent Surfaces: Principles and Opportunities
Authors: Yuanwei Liu, Xiao Liu, Xidong Mu, Tianwei Hou, Jiaqi Xu, Marco Di Renzo, Naofal Al-Dhahir
Date: 2020-07-07 00:00:00
Tasks: BIG-bench Machine Learning, Management, Survey

Reconfigurable intelligent surfaces (RISs), also known as intelligent reflecting surfaces (IRSs), or large intelligent surfaces (LISs), have received significant attention for their potential to enhance the capacity and coverage of wireless networks by smartly reconfiguring the wireless propagation environment. Therefore, RISs are considered a promising technology for the sixth-generation (6G) of communication networks. In this context, we provide a comprehensive overview of the state-of-the-art on RISs, with focus on their operating principles, performance evaluation, beamforming design and resource management, applications of machine learning to RIS-enhanced wireless networks, as well as the integration of RISs with other emerging technologies. We describe the basic principles of RISs both from physics and communications perspectives, based on which we present performance evaluation of multi-antenna assisted RIS systems. In addition, we systematically survey existing designs for RIS-enhanced wireless networks encompassing performance analysis, information theory, and performance optimization perspectives. Furthermore, we survey existing research contributions that apply machine learning for tackling challenges in dynamic scenarios, such as random fluctuations of wireless channels and user mobility in RIS-enhanced wireless networks. Last but not least, we identify major issues and research opportunities associated with the integration of RISs and other emerging technologies for applications to next-generation networks.
Quantitative Assessment of Adulteration and Reuse of Coconut Oil Using Transmittance Multispectral Imaging
Authors: S. Herath, H. K. Weerasooriya, D. Y. L. Ranasinghe, W. G. C. Bandara, H. M. V. R. Herath, G. M. R. I. Godaliyadda, M. P. B. Ekanayake, Terrence Madhujith
Date: 2020-11-30 00:00:00
Tasks: Clustering

Coconut oil known for its wide range of uses is often adulterated with other edible oils. Repeated use of coconut oil in food preparation could lead to many health issues. Existing methods available for evaluating quality of oil are laborious and time consuming. Therefore, we propose an imaging system hardware and image processing-based algorithm to estimate the adulteration of coconut oil with palm oil as the adulterant. A clear functional relationship between adulteration level and Bhattacharyya distance was observed as R2 = 0.9876 on the training samples. Thereafter, another algorithm is proposed to develop a spectral-clustering based classifier to determine the effect of reheat and reuse of coconut oil. Distinct clusters were obtained for different levels of reheated oil classes and the classification was performed with an accuracy of 0.983 on training samples. Further, the input images for the proposed algorithms were generated using an in-house developed transmittance based multispectral imaging system.
Reconfigurable Intelligent Surfaces for 6G Systems: Principles, Applications, and Research Directions
Authors: Cunhua Pan, Hong Ren, Kezhi Wang, Jonas Florentin Kolb, Maged Elkashlan, Ming Chen, Marco Di Renzo, Yang Hao, Jiangzhou Wang, A. Lee Swindlehurst, Xiaohu You, Lajos Hanzo
Date: 2020-11-09 00:00:00

Reconfigurable intelligent surfaces (RISs) or intelligent reflecting surfaces (IRSs), are regarded as one of the most promising and revolutionizing techniques for enhancing the spectrum and/or energy efficiency of wireless systems. These devices are capable of reconfiguring the wireless propagation environment by carefully tuning the phase shifts of a large number of low-cost passive reflecting elements. In this article, we aim for answering four fundmental questions: 1) Why do we need RISs? 2) What is an RIS? 3) What are RIS's applications? 4) What are the relevant challenges and future research directions? In response, eight promising research directions are pointed out.
COded Taking And Giving (COTAG): Enhancing Transport Layer Performance over Indoor Millimeter Wave Access Networks
Authors: Zongshen Wu, Chin-Ya Huang, Parameswaran Ramanathan
Date: 2019-07-05 00:00:00

Millimeter wave (mmWave) access networks have the potential to meet the high-throughput and low-latency needs of immersive applications. However, due to the highly directional nature of the mmWave beams and their susceptibility to beam misalignment and blockage resulting from user movements and rotations, the associated mmWave links are vulnerable to large channel fluctuations. These fluctuations result in disproportionately adverse effects on performance of transport layer protocols such as Transmission Control Protocol (TCP). To overcome this challenge, we propose a network layer solution, COded Taking And Giving (COTAG) scheme to sustain low-latency and high-throughput end-to-end TCP performance in dually connected networks. In particular, COTAG creates network encoded packets at the network gateway and each access point (AP) aiming to adaptively take the spare bandwidth on each link for transmission. Further, if one link bandwidth drops due to user movements, COTAG actively abandons the transmission opportunity by conditionally dropping packets. Consequently, COTAG actively adapts to link quality changes in mmWave access network and enhances the TCP performance without jeopardizing the latency of immersive content delivery. To evaluate the effectiveness of the proposed COTAG, we conduct experiments using off-the-shelf APs and network simulations. The evaluation results show that COTAG improves end-to-end TCP performance significantly on both throughput and latency.
Enriching Load Data Using Micro-PMUs and Smart Meters
Authors: Fankun Bu, Kaveh Dehghanpour, Zhaoyu Wang
Date: 2020-11-29 00:00:00

In modern distribution systems, load uncertainty can be fully captured by micro-PMUs, which can record high-resolution data; however, in practice, micro-PMUs are installed at limited locations in distribution networks due to budgetary constraints. In contrast, smart meters are widely deployed but can only measure relatively low-resolution energy consumption, which cannot sufficiently reflect the actual instantaneous load volatility within each sampling interval. In this paper, we have proposed a novel approach for enriching load data for service transformers that only have low-resolution smart meters. The key to our approach is to statistically recover the high-resolution load data, which is masked by the low-resolution data, using trained probabilistic models of service transformers that have both high and low-resolution data sources, i.e, micro-PMUs and smart meters. The overall framework consists of two steps: first, for the transformers with micro-PMUs, a Gaussian Process is leveraged to capture the relationship between the maximum/minimum load and average load within each low-resolution sampling interval of smart meters; a Markov chain model is employed to characterize the transition probability of known high-resolution load. Next, the trained models are used as teachers for the transformers with only smart meters to decompose known low-resolution load data into targeted high-resolution load data. The enriched data can recover instantaneous load uncertainty and significantly enhance distribution system observability and situational awareness. We have verified the proposed approach using real high- and low-resolution load data.
Reconstruction Condition of Quantized Signals in Unlimited Sampling Framework
Authors: Yan He, Jifang Qiu, Chang Liu, Yue Liu, Jian Wu
Date: 2020-11-29 00:00:00
Tasks: Quantization

The latest theoretical advances in the field of unlimited sampling framework (USF) show the potential to avoid clipping problems of analog-to-digital converters (ADC). To date, most of the related works have focused on real-valued modulo samples, but little has been reported about the impact of quantization. In this paper, we study more practical USF system where modulo samples are quantized to a finite number of bits. In particular, we present a new requirement about the lower bound of sampling rate to ensure exact recovery of original signals from quantized modulo samples. The minimum sampling rate is jointly determined by signal bandwidth and quantization bits. Numerical results show that in the presence of quantization noise, signals with different waveforms and bandwidths are recovered perfectly at the new minimum sampling rate while the recovery fails at minimum sampling rate before modification, which also verifies the correctness of the theory. The trade-offs of sampling rates, quantization bits and computational complexity of recovery algorithm are also given for practitioners to weigh.
Factorization over interpolation: A fast continuous orthogonal matching pursuit
Authors: Gilles Monnoyer de Galland, Luc Vandendorpe, Laurent Jacques
Date: 2020-07-02 00:00:00

We propose a fast greedy algorithm to compute sparse representations of signals from continuous dictionaries that are factorizable, i.e., with atoms that can be separated as a product of sub-atoms. Existing algorithms strongly reduce the computational complexity of the sparse decomposition of signals in discrete factorizable dictionaries. On another flavour, existing greedy algorithms use interpolation strategies from a discretization of continuous dictionaries to perform off-the-grid decomposition. Our algorithm aims to combine the factorization and the interpolation concepts to enable low complexity computation of continuous sparse representation of signals. The efficiency of our algorithm is highlighted by simulations of its application to a radar system.
Non-Contact Vital Signs Detection with UAV-Borne Radars
Authors: Yu Rong, Richard M. Gutierrez, Kumar Vijay Mishra, Daniel W. Bliss
Date: 2020-11-27 00:00:00
Tasks: Disaster Response

Airborne radar carried on-board unmanned aerial vehicles (UAV) is serving as the harbinger of new remote sensing applications for security and rescue in inclement environments. The mobility and agility of UAVs along with intelligent on-board sensors (cameras, acoustics, and radar) are more effective during the early stages of disaster response. The ability of radars to penetrate through objects and operate during low visibility conditions enables detection of occluded human subjects on and under debris when other sensing modalities fail. Recently, radars have been deployed on UAVs to measure minute human physiological parameters such as respiratory and heart rates while sensing through clothing and building materials. Aggregating radar measurements with the information from other sensors is broadening the applications of drones in life-critical situations. Signal processing techniques are critical in enabling UAV-borne radars for human vital sign detection (VSD) in multiple operation modes. Novel radar configurations such as in a UAV swarm and tethered UAVs are required to facilitate multi-tasking and high endurance, respectively. This paper provides an overview of recent advances in UAV-borne VSD with a focus on deployment modes and processing methods.
Uplink Achievable Rate Maximization for Reconfigurable Intelligent Surface Aided Millimeter Wave Systems with Resolution-Adaptive ADCs
Authors: Yue Xiu, Jun Zhao, Ertugrul Basar, Marco Di Renzo, Wei Sun, Guan Gui, Ning Wei
Date: 2020-11-27 00:00:00
Tasks: Quantization

In this letter, we investigate the uplink of a reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) multi-user system. In the considered system, however, problems with hardware cost and power consumption arise when massive antenna arrays coupled with power-demanding analog-to-digital converters (ADCs) are employed. To account for practical hardware complexity, we consider that the access point (AP) is equipped with resolution-adaptive analog-to-digital converters (RADCs). We maximize the achievable rate under hardware constraints by jointly optimizing the ADC quantization bits, the RIS phase shifts, and the beam selection matrix. Due to the non-convexity of the feasible set and objective function, the formulated problem is non-convex and difficult to solve. To efficiently tackle this problem, a block coordinated descent (BCD)-based algorithm is proposed. Simulations demonstrate that an RIS can mitigate the hardware loss due to use of RADCs, and that the proposed BCD-based algorithm outperforms state-of-the-art algorithms.
Near- and Far-Field Communications with Large Intelligent Surfaces
Authors: Andrea de Jesus Torres, Luca Sanguinetti, Emil Björnson
Date: 2020-11-27 00:00:00

This paper studies the uplink spectral efficiency (SE) achieved by two single-antenna user equipments (UEs) communicating with a Large Intelligent Surface (LIS), defined as a planar array consisting of $N$ antennas that each has area $A$. The analysis is carried out with a deterministic line-of-sight propagation channel model that captures key fundamental aspects of the so-called geometric near-field of the array. Maximum ratio (MR) and minimum mean squared error (MMSE) combining schemes are considered. With both schemes, the signal and interference terms are numerically analyzed as a function of the position of the transmitting devices when the width/height $L = \sqrt{NA}$ of the square-shaped array grows large. The results show that an exact near-field channel model is needed to evaluate the SE whenever the distance of transmitting UEs is comparable with the LIS' dimensions. It is shown that, if $L$ grows, the UEs are eventually in the geometric near-field and the interference does not vanish. MMSE outperforms MR for an LIS of practically large size.