pwc-archive/papers-with-abstracts huggingface dataset:
BiLiMO: Bit-Limited MIMO Radar via Task-Based Quantization
Authors: Feng Xi, Nir Shlezinger, Yonina C. Eldar
Date: 2020-10-01 00:00:00
Tasks: Quantization

Recent years have witnessed growing interest in reduced cost radar systems operating with low power. Multiple-input multiple-output (MIMO) radar technology is known to achieve high performance sensing by probing with multiple orthogonal waveforms. However, implementing a low cost low power MIMO radar is challenging. One of the reasons for this difficulty stems from the increased cost and power consumption required by analog-to-digital convertors (ADCs) in acquiring the multiple waveforms at the radar receiver. In this work we study reduced cost MIMO radar receivers restricted to operate with low resolution ADCs. We design bit-limited MIMO radar (BiLiMO) receivers which are capable of accurately recovering their targets while operating under strict resolution constraints. This is achieved by applying an additional analog filter to the acquired waveforms, and designing the overall hybrid analog-digital system to facilitate target identification using task-based quantization methods. In particular, we exploit the fact that the target parameters can be recovered from a compressed representation of the received waveforms. We thus tune the acquisition system to recover this representation with minimal distortion, from which the targets can be extracted in digital, and characterize the achievable error in identifying the targets. Our numerical results demonstrate that the proposed BiLiMO receiver operating with a bit budget of one bit per sample achieves target recovery performance which approaches that of costly MIMO radars operating with unlimited resolution ADCs, while substantially outperforming MIMO receivers operating only in the digital domain under the same bit limitations.
A Random Antenna Subset Selection Jamming Method against Multistatic Radar System
Authors: Xiangtuan Wang, Yimin Liu, Tianyao Huang
Date: 2020-09-09 00:00:00

Multistatic radar system (MSRS) is considered an effective scheme to suppress mainlobe jamming, since it has higher spatial resolution enabling jamming cancellation from spatial domain. To develop electronic countermeasures against MSRS, a random array subset selection (RASS)jamming method is proposed in this paper. In the RASS jammer, elements of the array antenna are activated randomly, leading to stable mainlobe and random sidelobes, different from the traditional jammer that applies the complete antenna array enjoying constant mainlobe and sidelobes. We study the covariance matrix of jamming signals received by radars, and derive its rank, revealing that the covariance matrix is of full rank. We also calculate the output jamming to signal and noise ratio (JSNR) after the subspace-based jamming suppression methods used in MSRS under the proposed jamming method, which demonstrates that the full rank property invalidates such suppression methods. Numerical results verify our analytical deduction and exhibit the improved countermeasure performance of our proposed RASS jamming method compared to the traditional one.
An Infrared Communication System based on Handstand Pendulum
Authors: Xingchen Li, Changlu Li, Yun Wang, Mengqi Lei
Date: 2020-09-09 00:00:00

This paper mainly introduces an infrared optical communication system based on stable and handstand pendulum. This system adopts the method of loading the infrared light emitting end on an handstand pendulum to realize the stability and controllability of the infrared light transmission light path. In this system, 940nm infrared light is mainly used for audio signal transmission, and an handstand pendulum based on PID is used to control the angle and stability of infrared light emission. Experimental results show that the system can effectively ensure the stability of the transmission optical path and is suitable for accurate and stable signal transmission in bumpy environments.
A Prospective Look: Key Enabling Technologies, Applications and Open Research Topics in 6G Networks
Authors: Lina Bariah, Lina Mohjazi, Sami Muhaidat, Paschalis C. Sofotasios, Gunes Karabulut Kurt, Halim Yanikomeroglu, Octavia A. Dobre
Date: 2020-04-09 00:00:00

The fifth generation (5G) mobile networks are envisaged to enable a plethora of breakthrough advancements in wireless technologies, providing support of a diverse set of services over a single platform. While the deployment of 5G systems is scaling up globally, it is time to look ahead for beyond 5G systems. This is driven by the emerging societal trends, calling for fully automated systems and intelligent services supported by extended reality and haptics communications. To accommodate the stringent requirements of their prospective applications, which are data-driven and defined by extremely low-latency, ultra-reliable, fast and seamless wireless connectivity, research initiatives are currently focusing on a progressive roadmap towards the sixth generation (6G) networks. In this article, we shed light on some of the major enabling technologies for 6G, which are expected to revolutionize the fundamental architectures of cellular networks and provide multiple homogeneous artificial intelligence-empowered services, including distributed communications, control, computing, sensing, and energy, from its core to its end nodes. Particularly, this paper aims to answer several 6G framework related questions: What are the driving forces for the development of 6G? How will the enabling technologies of 6G differ from those in 5G? What kind of applications and interactions will they support which would not be supported by 5G? We address these questions by presenting a profound study of the 6G vision and outlining five of its disruptive technologies, i.e., terahertz communications, programmable metasurfaces, drone-based communications, backscatter communications and tactile internet, as well as their potential applications. Then, by leveraging the state-of-the-art literature surveyed for each technology, we discuss their requirements, key challenges, and open research problems.
First results from three years of GNSS Interference Monitoring from Low Earth Orbit
Authors: Matthew J. Murrian, Lakshay Narula, Peter A. Iannucci, Scott Budzien, Brady W. O'Hanlon, Mark L. Psiaki, Todd E. Humphreys
Date: 2020-09-09 00:00:00

Observation of terrestrial GNSS interference (jamming and spoofing) from low-earth orbit (LEO) is a uniquely effective technique for characterizing the scope, strength, and structure of interference and for estimating transmitter locations. Such details are useful for situational awareness, interference deterrence, and for developing interference-hardened GNSS receivers. This paper presents the results of a three-year study of global interference, with emphasis on a particularly powerful interference source active in Syria since 2017. It then explores the implications of such interference for GNSS receiver operation and design.
Secrecy Rate Maximization for Intelligent Reflecting Surface Aided SWIPT Systems
Authors: Wei Sun, Qingyang Song, Lei Guo, Jun Zhao
Date: 2020-07-22 00:00:00

Simultaneous wireless information and power transfer (SWIPT) and intelligent reflecting surface (IRS) are two promising techniques for providing enhanced wireless communication capability and sustainable energy supply to energy-constrained wireless devices. Moreover, the combination of the IRS and the SWIPT can create the "one plus one greater than two" effect. However, due to the broadcast nature of wireless media, the IRS-aided SWIPT systems are vulnerable to eavesdropping. In this paper, we study the security issue of the IRS-aided SWIPT systems. The objective is to maximize the secrecy rate by jointly designing the transmit beamforming and artificial noise (AN) covariance matrix at a base station (BS) and reflective beamforming at an IRS, under transmit power constraint at the BS and energy harvesting (EH) constraints at multiple energy receivers. To tackle the formulated non-convex problem, we first employ an alternating optimization (AO) algorithm to decouple the coupling variables. Then, reflective beamforming, transmit beamforming and AN covariance matrix can be optimized by using a penalty-based algorithm and semidefinite relaxation (SDR) method, respectively. Simulation results demonstrate the effectiveness of the proposed scheme over baseline schemes.
Modeling of Vertical Dipole Above Lossy Dielectric Half-Space: Characteristic Mode Theory
Authors: Sandip Ghosal, Arijit De, Raed M. Shubair, Ajay Chakrabarty
Date: 2020-09-08 00:00:00

This work introduces a theoretical extension of the characteristic mode formulation for analysing the vertical electric dipole lying above a lossy dielectric half-space. As the conventional characteristic formulation fails to maintain the orthogonality of the characteristic field modes over the infinite sphere, an alternate modal formulation is proposed here to maintain the orthogonality for both the current and field modes. The modal results are found to match closely with its method of moment counterparts. Later, the modes of an isolated dipole with no ground plane have been used to predict the role of the lossy ground plane through a theory of the linear combination of the eigenvectors. The proposed formulations have been studied with different heights from the ground plane and are compared with the direct modal solutions to validate its accuracy. It helps to provide a thorough understanding of how the isolated modes interact among each other to constitute the perturbed modes in the presence of the lossy half-space. It can find application to include the lossy earth effect in the study of the lightning fields and the path loss modelling of the antennas over the lossy ground.
Towards new forms of particle sensing and manipulation and 3D imaging on a smartphone for healthcare applications
Authors: Jinlong Zhu, Ni Zhao, Renjie Zhou
Date: 2020-09-08 00:00:00
Tasks: Medical Diagnosis

Close to half of the world population have smartphones, while a typical flagship smartphone today has been integrated with more than 20 smart components and sensors, making a smartphone a highly integrated platform that can potentially mimic the five senses of humans. Recent advancement in achieving high compactness, high performance computing, high flexibility, and multiplexed functionality in smartphones have enabled them for many cutting-edge healthcare applications, such as single-molecule imaging, medical diagnosis, and biosensing, which were conventionally done with bulky and sophisticated devices. Most of the current healthcare applications are developed based on using the photon-sensitive components, such as CMOS sensors, flash & fill lights, lens modules, and LED lights in the screen, leaving the rest of the smart and high-performance sensors rarely explored. In this Perspective, we review recent progresses in advanced sensors in modern smartphones and discuss how those sensors have great, as yet unmet, promise to offer widespread and easy-to-implement solutions to many emerging healthcare applications, including nanoscale sensing, point-of-care testing, pollution monitoring, etc.
High-Bandwidth Spatial Equalization for mmWave Massive MU-MIMO with Processing-In-Memory
Authors: Oscar Castañeda, Sven Jacobsson, Giuseppe Durisi, Tom Goldstein, Christoph Studer
Date: 2020-09-08 00:00:00

All-digital basestation (BS) architectures enable superior spectral efficiency compared to hybrid solutions in massive multi-user MIMO systems. However, supporting large bandwidths with all-digital architectures at mmWave frequencies is challenging as traditional baseband processing would result in excessively high power consumption and large silicon area. The recently-proposed concept of finite-alphabet equalization is able to address both of these issues by using equalization matrices that contain low-resolution entries to lower the power and complexity of high-throughput matrix-vector products in hardware. In this paper, we explore two different finite-alphabet equalization hardware implementations that tightly integrate the memory and processing elements: (i) a parallel array of multiply-accumulate (MAC) units and (ii) a bit-serial processing-in-memory (PIM) architecture. Our all-digital VLSI implementation results in 28nm CMOS show that the bit-serial PIM architecture reduces the area and power consumption up to a factor of 2x and 3x, respectively, when compared to a parallel MAC array that operates at the same throughput.
Model Fusion to Enhance the Clinical Acceptability of Long-Term Glucose Predictions
Authors: Maxime De Bois, Mounîm A. El Yacoubi, Mehdi Ammi
Date: 2020-09-08 00:00:00
Tasks: Prediction

This paper presents the Derivatives Combination Predictor (DCP), a novel model fusion algorithm for making long-term glucose predictions for diabetic people. First, using the history of glucose predictions made by several models, the future glucose variation at a given horizon is predicted. Then, by accumulating the past predicted variations starting from a known glucose value, the fused glucose prediction is computed. A new loss function is introduced to make the DCP model learn to react faster to changes in glucose variations. The algorithm has been tested on 10 \textit{in-silico} type-1 diabetic children from the T1DMS software. Three initial predictors have been used: a Gaussian process regressor, a feed-forward neural network and an extreme learning machine model. The DCP and two other fusion algorithms have been evaluated at a prediction horizon of 120 minutes with the root-mean-squared error of the prediction, the root-mean-squared error of the predicted variation, and the continuous glucose-error grid analysis. By making a successful trade-off between prediction accuracy and predicted-variation accuracy, the DCP, alongside with its specifically designed loss function, improves the clinical acceptability of the predictions, and therefore the safety of the model for diabetic people.
Study of Short-Term Personalized Glucose Predictive Models on Type-1 Diabetic Children
Authors: Maxime De Bois, Mounîm A. El Yacoubi, Mehdi Ammi
Date: 2020-09-08 00:00:00
Tasks: Gaussian Processes

Research in diabetes, especially when it comes to building data-driven models to forecast future glucose values, is hindered by the sensitive nature of the data. Because researchers do not share the same data between studies, progress is hard to assess. This paper aims at comparing the most promising algorithms in the field, namely Feedforward Neural Networks (FFNN), Long Short-Term Memory (LSTM) Recurrent Neural Networks, Extreme Learning Machines (ELM), Support Vector Regression (SVR) and Gaussian Processes (GP). They are personalized and trained on a population of 10 virtual children from the Type 1 Diabetes Metabolic Simulator software to predict future glucose values at a prediction horizon of 30 minutes. The performances of the models are evaluated using the Root Mean Squared Error (RMSE) and the Continuous Glucose-Error Grid Analysis (CG-EGA). While most of the models end up having low RMSE, the GP model with a Dot-Product kernel (GP-DP), a novel usage in the context of glucose prediction, has the lowest. Despite having good RMSE values, we show that the models do not necessarily exhibit a good clinical acceptability, measured by the CG-EGA. Only the LSTM, SVR and GP-DP models have overall acceptable results, each of them performing best in one of the glycemia regions.
Joint deconvolution and blind source separation on the sphere with an application to radio-astronomy
Authors: R. Carloni Gertosio, J. Bobin
Date: 2020-09-08 00:00:00
Tasks: Astronomy, blind source separation

Blind source separation is one of the major analysis tool to extract relevant information from multichannel data. While being central, joint deconvolution and blind source separation (DBSS) methods are scarce. To that purpose, a DBSS algorithm coined SDecGMCA is proposed. It is designed to process data sampled on the sphere, allowing large-field data analysis in radio-astronomy.
A Constrained Convex Optimization Approach to Hyperspectral Image Restoration with Hybrid Spatio-Spectral Regularization
Authors: Saori Takeyama, Shunsuke Ono, Itsuo Kumazawa
Date: 2019-07-31 00:00:00
Tasks: Image Restoration

We propose a new constrained optimization approach to hyperspectral (HS) image restoration. Most existing methods restore a desirable HS image by solving some optimization problem, which consists of a regularization term(s) and a data-fidelity term(s). The methods have to handle a regularization term(s) and a data-fidelity term(s) simultaneously in one objective function, and so we need to carefully control the hyperparameter(s) that balances these terms. However, the setting of such hyperparameters is often a troublesome task because their suitable values depend strongly on the regularization terms adopted and the noise intensities on a given observation. Our proposed method is formulated as a convex optimization problem, where we utilize a novel hybrid regularization technique named Hybrid Spatio-Spectral Total Variation (HSSTV) and incorporate data-fidelity as hard constraints. HSSTV has a strong ability of noise and artifact removal while avoiding oversmoothing and spectral distortion, without combining other regularizations such as low-rank modeling-based ones. In addition, the constraint-type data-fidelity enables us to translate the hyperparameters that balance between regularization and data-fidelity to the upper bounds of the degree of data-fidelity that can be set in a much easier manner. We also develop an efficient algorithm based on the alternating direction method of multipliers (ADMM) to efficiently solve the optimization problem. Through comprehensive experiments, we illustrate the advantages of the proposed method over various HS image restoration methods including state-of-the-art ones.
Are wave union methods still suitable for 20 nm FPGA-based high-resolution (< 2 ps) time-to-digital converters?
Authors: Wujun Xie, Haochang Chen, David Day-Uei Li
Date: 2020-09-08 00:00:00

This paper presents several new structures to pursue high-resolution (< 2 ps) time-to-digital converters (TDCs) in Xilinx 20 nm UltraScale field-programmable gate arrays (FPGAs). The proposed TDCs combined the advantages of 1) our newly proposed sub-tapped delay line (sub-TDL) architecture effective in removing bubbles and zero-bins and 2) the wave union (WU) A method to improve the resolution and reduce the impact introduced from ultrawide bins. We also compared the proposed WU/sub-TDL TDC with the TDC combining the dual sampling (DS) structure and the sub-TDL technique. Moreover, we introduced a binning method to improve the linearity and derived a formula of the total measurement uncertainty for a single-stage TDL-TDC to obtain its root-mean-square (RMS) resolution. Results conclude that the proposed designs are cost-effective in logic resources and have the potential for multiple-channel implementations. Different from the conclusions from a previous study, we found that the wave union is still influential in UltraScale devices when combining with our sub-TDL structure. We also compared with other published TDCs to demonstrate where the proposed TDCs stand.
Securing Mobile Multiuser Transmissions with UAVs in the Presence of Multiple Eavesdroppers
Authors: Aly Sabri Abdalla, Vuk Marojevic
Date: 2020-09-08 00:00:00
Tasks: Position

This paper discusses the problem of securing the transmissions of multiple ground users against eavesdropping attacks. We propose and optimize the deployment of a single unmanned aerial vehicle (UAV), which serves as an aerial relay between the user cluster and the base station. The focus is on maximizing the secrecy energy efficiency by jointly optimizing the uplink transmission powers of the ground users and the position of the UAV. The joint optimization problem is nonconvex; therefore we split it into two subproblems and solve them using an iterative algorithm.
Towards Ubiquitous Positioning by Leveraging Reconfigurable Intelligent Surface
Authors: Haobo Zhang, Hongliang Zhang, Boya Di, Kaigui Bian, Zhu Han, Lingyang Song
Date: 2020-07-10 00:00:00

The received signal strength (RSS) based technique is widely utilized for ubiquitous positioning due to its advantage of simple implementability. However, its accuracy is limited because the RSS values of adjacent locations can be very difficult to distinguish. Against this background, we propose the novel RSS-based positioning scheme enabled by reconfigurable intelligent surface (RIS). By modifying the reflection coefficient of the RIS, the propagation channels are programmed in such a way that the differences between the RSS values of adjacent locations can be enlarged to improve the positioning accuracy. New challenge lies in the selection of suitable reflection coefficients for high-accuracy positioning. To tackle this challenge, we formulate the RIS-aided positioning problem and design an iterative algorithm to solve the problem. The effectiveness of the proposed positioning scheme is validated through simulations.
A Lightweight CNN Model for Detecting Respiratory Diseases from Lung Auscultation Sounds using EMD-CWT-based Hybrid Scalogram
Authors: Samiul Based Shuvo, Shams Nafisa Ali, Soham Irtiza Swapnil, Taufiq Hasan, Mohammed Imamul Hassan Bhuiyan
Date: 2020-09-08 00:00:00
Tasks: Lung Sound Classification

Listening to lung sounds through auscultation is vital in examining the respiratory system for abnormalities. Automated analysis of lung auscultation sounds can be beneficial to the health systems in low-resource settings where there is a lack of skilled physicians. In this work, we propose a lightweight convolutional neural network (CNN) architecture to classify respiratory diseases using hybrid scalogram-based features of lung sounds. The hybrid scalogram features utilize the empirical mode decomposition (EMD) and continuous wavelet transform (CWT). The proposed scheme's performance is studied using a patient independent train-validation set from the publicly available ICBHI 2017 lung sound dataset. Employing the proposed framework, weighted accuracy scores of 99.20% for ternary chronic classification and 99.05% for six-class pathological classification are achieved, which outperform well-known and much larger VGG16 in terms of accuracy by 0.52% and 1.77% respectively. The proposed CNN model also outperforms other contemporary lightweight models while being computationally comparable.
Energy-Efficient NOMA Multicasting System for 5G Cellular V2X Communications with Imperfect CSI
Authors: Asim Ihsan, Wen Chen, Shunqing Zhang, Shugong Xu
Date: 2020-09-08 00:00:00

Vehicle-to-everything (V2X) is a modern vehicular technology that improves conventional vehicle systems in traffic and communications. V2X communications demand energyefficient and high-reliability networking because of massive vehicular connections and high mobility wireless channels. Nonorthogonal multiple access (NOMA) is a promising solution for 5G V2X services that intend to guarantee high reliability, quality-of-service (QoS) provisioning, and massive connectivity requirements. In V2X, it is vital to inspect imperfect CSI because the high mobility of vehicles leads to more channel estimation uncertainties. Unlike existing literatures, we propose energy-efficient roadside units (RSUs) assisted NOMA multicasting system for 5G cellular V2X communications, and investigate the energy-efficient power allocation problem. The proposed system multicast the information through low complexity optimal power allocation algorithms used under channel outage probability constraint of vehicles with imperfect CSI, QoS constraints of vehicles, and transmit power limits constraint of RSUs. The formulated problem with the channel outage probability constraint is a nonconvex probabilistic optimization problem. This problem is solved efficiently by converting the probabilistic problem through relaxation into a non-probabilistic problem.