pwc-archive/papers-with-abstracts huggingface dataset:
Review of the State-of-the-art on Bio-signal-based Brain-controlled Vehicles
Authors: Amin Hekmatmanesh, Pedro H. J. Nardelli, Heikki Handroos
Date: 2020-06-03 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Brain-controlled vehicle (BCV) is an already established technology usually designed for disabled patients. This review focuses on the most relevant topics on the brain controlling vehicles, especially considering terrestrial BCV (e.g., mobile car, car simulators, real car, graphical and gaming cars) and aerial BCV, also named BCAV (e.g., real quadcopter, drone, fixed wings, graphical helicopter and aircraft) controlled using bio-signals such as electroencephalogram (EEG), electrooculogram and electromyogram. For instance, EEG-based algorithms detect patterns from motor imaginary cortex area of the brain for intention detection, patterns like event related desynchronization/event related synchronization, state visually evoked potentials, P300, and generated local evoked potential patterns. We have identified that the reported best performing approaches employ machine learning and artificial intelligence optimization methods, namely support vector machine, neural network, linear discriminant analysis, k-nearest neighbor, k-means, water drop optimization and chaotic tug of war optimization optimization. We considered the following metrics to analyze the efficiency of the different methods: type and combination of bio-signals, time response, and accuracy values with the statistical analysis. The present work provides an extensive literature review of the key findings of previous ten years, indicating the future perspectives in the field.
On Solving SAR Imaging Inverse Problems Using Non-Convex Regularization with a Cauchy-based Penalty
Authors: Oktay Karakuş, Alin Achim
Date: 2020-05-01 00:00:00
Tasks: Image Reconstruction, Super-Resolution

Synthetic aperture radar (SAR) imagery can provide useful information in a multitude of applications, including climate change, environmental monitoring, meteorology, high dimensional mapping, ship monitoring, or planetary exploration. In this paper, we investigate solutions to a number of inverse problems encountered in SAR imaging. We propose a convex proximal splitting method for the optimization of a cost function that includes a non-convex Cauchy-based penalty. The convergence of the overall cost function optimization is ensured through careful selection of model parameters within a forward-backward (FB) algorithm. The performance of the proposed penalty function is evaluated by solving three standard SAR imaging inverse problems, including super-resolution, image formation, and despeckling, as well as ship wake detection for maritime applications. The proposed method is compared to several methods employing classical penalty functions such as total variation ($TV$) and $L_1$ norms, and to the generalized minimax-concave (GMC) penalty. We show that the proposed Cauchy-based penalty function leads to better image reconstruction results when compared to the reference penalty functions for all SAR imaging inverse problems in this paper.
6G White Paper on Localization and Sensing
Authors: Andre Bourdoux, Andre Noll Barreto, Barend van Liempd, Carlos de Lima, Davide Dardari, Didier Belot, Elana-Simona Lohan, Gonzalo Seco-Granados, Hadi Sarieddeen, Henk Wymeersch, Jaakko Suutala, Jani Saloranta, Maxime Guillaud, Minna Isomursu, Mikko Valkama, Muhammad Reza Kahar Aziz, Rafael Berkvens, Tachporn Sanguanpuak, Tommy Svensson, Yang Miao
Date: 2020-06-02 00:00:00

This white paper explores future localization and sensing opportunities for beyond 5G wireless communication systems by identifying key technology enablers and discussing their underlying challenges, implementation issues, and identifying potential solutions. In addition, we present exciting new opportunities for localization and sensing applications, which will disrupt traditional design principles and revolutionize the way we live, interact with our environment, and do business. Following the trend initiated in the 5G NR systems, 6G will continue to develop towards even higher frequency ranges, wider bandwidths, and massive antenna arrays. In turn, this will enable sensing solutions with very fine range, Doppler and angular resolutions, as well as localization to cm-level degree of accuracy. Moreover, new materials, device types, and reconfigurable surfaces will allow network operators to reshape and control the electromagnetic response of the environment. At the same time, machine learning and artificial intelligence will leverage the unprecedented availability of data and computing resources to tackle the biggest and hardest problems in wireless communication systems. 6G will be truly intelligent wireless systems that will not only provide ubiquitous communication but also empower high accuracy localization and high-resolution sensing services. They will become the catalyst for this revolution by bringing about a unique new set of features and service capabilities, where localization and sensing will coexist with communication, continuously sharing the available resources in time, frequency and space. This white paper concludes by highlighting foundational research challenges, as well as implications and opportunities related to privacy, security, and trust. Addressing these challenges will undoubtedly require an inter-disciplinary and concerted effort from the research community.
Dilated U-net based approach for multichannel speech enhancement from First-Order Ambisonics recordings
Authors: Amélie Bosca, Alexandre Guérin, Lauréline Perotin, Srđan Kitić
Date: 2020-06-02 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Speech Enhancement, speech-recognition, Speech Recognition

We present a CNN architecture for speech enhancement from multichannel first-order Ambisonics mixtures. The data-dependent spatial filters, deduced from a mask-based approach, are used to help an automatic speech recognition engine to face adverse conditions of reverberation and competitive speakers. The mask predictions are provided by a neural network, fed with rough estimations of speech and noise amplitude spectra, under the assumption of known directions of arrival. This study evaluates the replacing of the recurrent LSTM network previously investigated by a convolutive U-net under more stressing conditions with an additional second competitive speaker. We show that, due to more accurate short-term masks prediction, the U-net architecture brings some improvements in terms of word error rate. Moreover, results indicate that the use of dilated convolutive layers is beneficial in difficult situations with two interfering speakers, and/or where the target and interferences are close to each other in terms of the angular distance. Moreover, these results come with a two-fold reduction in the number of parameters.
Reconfigurable Intelligent Surface Aided Constant-Envelope Wireless Power Transfer
Authors: Huiyuan Yang, Xiaojun Yuan, Jun Fang, Ying-Chang Liang
Date: 2020-06-02 00:00:00

By reconfiguring the propagation environment of electromagnetic waves artificially, reconfigurable intelligent surfaces (RISs) have been regarded as a promising and revolutionary hardware technology to improve the energy and spectrum efficiency of wireless networks. In this paper, we study a RIS aided multiuser multiple-input single-output (MISO) wireless power transfer (WPT) system, where the transmitter is equipped with a constant-envelope analog beamformer. We formulate a novel problem to maximize the total received power of all the users by jointly optimizing the beamformer at transmitter and the phase shifts at the RISs, subject to the individual minimum received power constraints of users. We further solve the problem iteratively with a closed-form expression for each step. Numerical results show the performance gain of deploying RIS and the effectiveness of the proposed algorithm.
Joint Forecasting and Interpolation of Graph Signals Using Deep Learning
Authors: Gabriela Lewenfus, Wallace Alves Martins, Symeon Chatzinotas, Björn Ottersten
Date: 2020-06-02 00:00:00
Tasks: Deep Learning, Time Series, Time Series Analysis, Time Series Prediction

We tackle the problem of forecasting network-signal snapshots using past signal measurements acquired by a subset of network nodes. This task can be seen as a combination of multivariate time-series prediction and graph-signal interpolation. This is a fundamental problem for many applications wherein deploying a high granularity network is impractical. Our solution combines recurrent neural networks with frequency-analysis tools from graph signal processing, and assumes that data is sufficiently smooth with respect to the underlying graph. The proposed approach outperforms state-of-the-art deep learning techniques, especially when only a small fraction of the graph signals is accessible, considering two distinct real world datasets: temperatures in the US and speed flow in Seattle. The results also indicate that our method can handle noisy signals and missing data, making it suitable to many practical applications.
An Overview on Integrated Localization and Communication Towards 6G
Authors: Zhiqiang Xiao, Yong Zeng
Date: 2020-06-02 00:00:00

While the 5G cellular system is being deployed worldwide, researchers have started the investigation of the 6G mobile communication networks. Although the essential requirements and key usage scenarios of 6G are yet to be defined, it is believed that 6G should be able to provide intelligent and ubiquitous wireless connectivity with Tbps data rate and sub-millisecond latency over 3D network coverage. To achieve such goals, acquiring accurate location information of the mobile terminals is becoming extremely useful, not only for location-based services but also for improving wireless communication performance in various ways such as channel estimation, beam alignment, medium access control, routing, and network optimization. On the other hand, the advancement of communication technologies also brings new opportunities to greatly improve the localization performance, as exemplified by the anticipated centimeter-level localization accuracy in 6G by ultra massive MIMO and mmWave technologies. In this regard, a unified study on integrated localization and communication (ILAC) is necessary to unlock the full potential of wireless networks for the best utilization of network infrastructure and radio resources for dual purposes. While there are extensive literatures on wireless localization or communications separately, the research on ILAC is still in its infancy. Therefore, this article aims to give a tutorial overview on ILAC towards 6G wireless networks. After a holistic survey on wireless localization basics, we present the state-of-the-art results on how wireless localization and communication inter-play with each other in various network layers, together with the main architectures and techniques for localization and communication co-design in current 2D and future 3D networks with aerial-ground integration. Finally, we outline some promising future research directions for ILAC.
Practical Modeling and Beamforming for Intelligent Reflecting Surface Aided Wideband Systems
Authors: Wenhao Cai, Hongyu Li, Ming Li, Qian Liu
Date: 2020-06-02 00:00:00

Intelligent reflecting surface (IRS) has emerged as a revolutionizing solution to enhance wireless communications by intelligently changing the propagation environment. Prior studies on IRS are based on an ideal reflection model with a constant amplitude and a variable phase shift. However, it is difficult and unrealistic to implement an IRS satisfying such ideal reflection model in practical applications. In this letter, we aim to investigate the phase-amplitude-frequency relationship of the reflected signals and propose a practical model of reflection coefficient for an IRS-aided wideband system. Then, based on this practical model, joint transmit power allocation of each subcarrier and IRS beamforming optimization are investigated for an IRS-aided wideband orthogonal frequency-division multiplexing (OFDM) system. Simulation results illustrate the importance of the practical model on the IRS designs and validate the effectiveness of our proposed model.
mmWave/THz Channel Estimation Using Frequency-Selective Atomic Norm Minimization
Authors: Yicheng Xu, Hongyun Chu, Xiaodong Wang
Date: 2019-08-27 00:00:00

We propose a MIMO channel estimation method for millimeter-wave (mmWave) and terahertz (THz) systems based on frequency-selective atomic norm minimization (FS-ANM). For the strong line-of-sight property of the channel in such high-frequency bands, prior knowledge on the ranges of angles of departure/arrival (AoD/AoA) can be obtained as the prior knowledge, which can be exploited by the proposed channel estimator to improve the estimation accuracy. Simulation results show that the proposed method can achieve considerable performance gain when compared with the existing approaches without incorporating the the strong line-of-sight property.
Energy-Efficient Cyclical Trajectory Design for UAV-Aided Maritime Data Collection in Wind
Authors: Yifan Zhang, Jiangbin Lyu, Liqun Fu
Date: 2020-06-02 00:00:00
Tasks: Scheduling

Unmanned aerial vehicles (UAVs), especially fixed-wing ones that withstand strong winds, have great potential for oceanic exploration and research. This paper studies a UAV-aided maritime data collection system with a fixed-wing UAV dispatched to collect data from marine buoys. We aim to minimize the UAV's energy consumption in completing the task by jointly optimizing the communication time scheduling among the buoys and the UAV's flight trajectory subject to wind effect, which is a non-convex problem and difficult to solve optimally. Existing techniques such as the successive convex approximation (SCA) method provide efficient sub-optimal solutions for collecting small/moderate data volume, whereas the solution heavily relies on the trajectory initialization and has not explicitly considered the wind effect, while the computational complexity and resulted trajectory complexity both become prohibitive for the task with large data volume. To this end, we propose a new cyclical trajectory design framework that can handle arbitrary data volume efficiently subject to wind effect. Specifically, the proposed UAV trajectory comprises multiple cyclical laps, each responsible for collecting only a subset of data and thereby significantly reducing the computational/trajectory complexity, which allows searching for better trajectory initialization that fits the buoys' topology and the wind. Numerical results show that the proposed cyclical scheme outperforms the benchmark one-flight-only scheme in general. Moreover, the optimized cyclical 8-shape trajectory can proactively exploit the wind and achieve lower energy consumption compared with the case without wind.
Semi-Blind Post-Equalizer SINR Estimation and Dual CSI Feedback for Radar-Cellular Coexistence
Authors: Raghunandan M. Rao, Vuk Marojevic, Jeffrey H. Reed
Date: 2020-06-02 00:00:00
Tasks: Quantization, Scheduling

Current cellular systems use pilot-aided statistical-channel state information (S-CSI) estimation and limited feedback schemes to aid in link adaptation and scheduling decisions. However, in the presence of pulsed radar signals, pilot-aided S-CSI is inaccurate since interference statistics on pilot and non-pilot resources can be different. Moreover, the channel will be bimodal as a result of the periodic interference. In this paper, we propose a max-min heuristic to estimate the post-equalizer SINR in the case of non-pilot pulsed radar interference, and characterize its distribution as a function of noise variance and interference power. We observe that the proposed heuristic incurs low computational complexity, and is robust beyond a certain SINR threshold for different modulation schemes, especially for QPSK. This enables us to develop a comprehensive semi-blind framework to estimate the wideband SINR metric that is commonly used for S-CSI quantization in 3GPP Long-Term Evolution (LTE) and New Radio (NR) networks. Finally, we propose dual CSI feedback for practical radar-cellular spectrum sharing, to enable accurate CSI acquisition in the bimodal channel. We demonstrate significant improvements in throughput, block error rate and retransmission-induced latency for LTE-Advanced Pro when compared to conventional pilot-aided S-CSI estimation and limited feedback schemes.
Graph Unrolling Networks: Interpretable Neural Networks for Graph Signal Denoising
Authors: Siheng Chen, Yonina C. Eldar, Lingxiao Zhao
Date: 2020-06-01 00:00:00
Tasks: Denoising, Graph Neural Network, Rolling Shutter Correction

We propose an interpretable graph neural network framework to denoise single or multiple noisy graph signals. The proposed graph unrolling networks expand algorithm unrolling to the graph domain and provide an interpretation of the architecture design from a signal processing perspective. We unroll an iterative denoising algorithm by mapping each iteration into a single network layer where the feed-forward process is equivalent to iteratively denoising graph signals. We train the graph unrolling networks through unsupervised learning, where the input noisy graph signals are used to supervise the networks. By leveraging the learning ability of neural networks, we adaptively capture appropriate priors from input noisy graph signals, instead of manually choosing signal priors. A core component of graph unrolling networks is the edge-weight-sharing graph convolution operation, which parameterizes each edge weight by a trainable kernel function where the trainable parameters are shared by all the edges. The proposed convolution is permutation-equivariant and can flexibly adjust the edge weights to various graph signals. We then consider two special cases of this class of networks, graph unrolling sparse coding (GUSC) and graph unrolling trend filtering (GUTF), by unrolling sparse coding and trend filtering, respectively. To validate the proposed methods, we conduct extensive experiments on both real-world datasets and simulated datasets, and demonstrate that our methods have smaller denoising errors than conventional denoising algorithms and state-of-the-art graph neural networks. For denoising a single smooth graph signal, the normalized mean square error of the proposed networks is around 40% and 60% lower than that of graph Laplacian denoising and graph wavelets, respectively.
RSSI Fingerprinting-based Localization Using Machine Learning in LoRa Networks
Authors: Mahnoor Anjum, Muhammad Abdullah Khan, Syed Ali Hassan, Aamir Mahmood, Hassaan Khaliq Qureshi, Mikael Gidlund
Date: 2020-06-01 00:00:00
Tasks: BIG-bench Machine Learning, Ensemble Learning

The scale of wireless technologies penetration in our daily lives, primarily triggered by the Internet-of-things (IoT)-based smart cities, is beaconing the possibilities of novel localization and tracking techniques. Recently, low-power wide-area network (LPWAN) technologies have emerged as a solution to offer scalable wireless connectivity for smart city applications. LoRa is one such technology that provides energy efficiency and wide-area coverage. This article explores the use of intelligent machine learning techniques, such as support vector machines, spline models, decision trees, and ensemble learning, for received signal strength indicator (RSSI)-based ranging in LoRa networks, on a training dataset collected in two different environments: indoors and outdoors. The suitable ranging model is then used to experimentally evaluate the accuracy of localization and tracking using trilateration in the studied environments. Later, we present the accuracy of LoRa-based positioning system (LPS) and compare it with the existing ZigBee, WiFi, and Bluetooth-based solutions. In the end, we discuss the challenges of satellite-independent tracking systems and propose future directions to improve accuracy and provide deployment feasibility.
Energy Efficiency Optimization for Device-to-Device Communication Underlaying Cellular Networks in Millimeter-Wave
Authors: Negar Zabetian, Abbas Mohammadi, Mohammad Kazemi
Date: 2020-06-01 00:00:00

This paper studies energy efficiency maximization in device-to-device (D2D) communications underlaying cellular networks in millimeter-wave (mm-wave) band. A stochastic geometry framework has been used to extract the results. First, cellular and D2D users are modeled by independent homogeneous Poisson point process; then, exact expressions for successful transmission probability of D2D and cellular users have been derived. Furthermore, the average sum rate and energy efficiency for a typical D2D scenario have been presented. An optimization problem subject to transmission power and quality of service constraints for both cellular and D2D users has been defined and energy efficiency of D2D communication is maximized. Simulation results reveal that by working in millimeter-wave, significant energy efficiency improvement can be attained, e.g., 20\% energy efficiency improvement compared to Rayleigh distribution in the practical scenarios by considering circuit power. Finally, to verify our analytical expressions, the simulation studies are carried out and the excellent agreements have been achieved.
Health Risks Associated with 5G Exposure: A View from the Communications Engineering Perspective
Authors: Luca Chiaraviglio, Ahmed Elzanaty, Mohamed-Slim Alouini
Date: 2020-06-01 00:00:00

The deployment of 5G wireless communication services requires the installation of 5G next-generation Node-B Base Stations (gNBs) over the territory and the wide adoption of 5G User Equipment (UE). In this context, the population is concerned about the potential health risks associated with the Radio Frequency (RF) emissions from 5G equipment, with several communities actively working toward stopping the 5G deployment. To face these concerns, in this work, we analyze the health risks associated with 5G exposure by adopting a new and comprehensive viewpoint, based on the communications engineering perspective. By exploiting our background, we analyze the alleged health effects of 5G exposure and critically review the latest works that are often referenced to support the health concerns from 5G. We then precisely examine the up-to-date metrics, regulations, and assessment of compliance procedures for 5G exposure, by evaluating the latest guidelines from IEEE, ICNIRP, ITU, IEC, and FCC, as well as the national regulations in more than 220 countries. We also thoroughly analyze the main health risks that are frequently associated with specific 5G features (e.g., MIMO, beamforming, cell densification, adoption of millimeter waves, and connection of millions of devices). Finally, we examine the risk mitigation techniques based on communications engineering that can be implemented to reduce the exposure from 5G gNB and UE. Overall, we argue that the widely perceived health risks that are attributed to 5G are not supported by scientific evidence from communications engineering. In addition, we explain how the solutions to minimize the health risks from 5G are already mature and ready to be implemented. Finally, future works, e.g., aimed at evaluating long-term impacts of 5G exposure, as well as innovative solutions to further reduce the RF emissions, are suggested.