pwc-archive/papers-with-abstracts huggingface dataset:
Distributed Resource Allocation Algorithms for Multi-Operator Cognitive Communication Systems
Authors: Ehsan Tohidi, David Gesbert, Philippe Ciblat
Date: 2020-05-06 00:00:00
Tasks: Distributed Optimization

We address the problem of resource allocation (RA) in a cognitive radio (CR) communication system with multiple secondary operators sharing spectrum with an incumbent primary operator. The key challenge of the RA problem is the inter-operator coordination arising in the optimization problem so that the aggregated interference at the primary users (PUs) does not exceed the target threshold. While this problem is easily solvable if a centralized unit could access information of all secondary operators, it becomes challenging in a realistic scenario. In this paper, considering a satellite setting, we alleviate this problem by proposing two approaches to reduce the information exchange level among the secondary operators. In the first approach, we formulate an RA scheme based on a partial information sharing method which enables distributed optimization across secondary operators. In the second approach, instead of exchanging secondary users (SUs) information, the operators only exchange their contributions of the interference-level and RA is performed locally across secondary operators. These two approaches, for the first time in this context, provide a trade-off between performance and level of inter-operator information exchange. Through the numerical simulations, we explain this trade-off and illustrate the penalty resulting from partial information exchange.
Decentralizing Multi-Operator Cognitive Radio Resource Allocation: An Asymptotic Analysis
Authors: Ehsan Tohidi, David Gesbert, Antonio Bazco-Nogueras, Paul de Kerret
Date: 2020-05-06 00:00:00

We address the problem of resource allocation (RA) for spectrum underlay in a cognitive radio (CR) communication system with multiple secondary operators sharing resource with an incumbent primary operator. The multiple secondary operator RA problem is well known to be especially challenging because of the inter-operator coupling constraints arising in the optimization problem, which render impractical inter-operator information exchange necessary. In this paper, we consider a satellite setting for multi-operator CR. In the CR maturation regime, i.e., the period in which the secondary subscriber density is growing yet remains much below that of incumbent users, we show that in fact the inter-operator mutual constraints can be neglected, thus making distributed (across secondary operators) optimization possible. Furthermore, we establish analytically that the mutual constraints asymptotically vanish with the primary user density.
Deep Autoencoders for DOA Estimation of Coherent Sources using Imperfect Antenna Array
Authors: Aya Mostafa Ahmed, Omar Eissa, Aydin Sezgin
Date: 2020-05-06 00:00:00
Tasks: Position

In this paper a robust algorithm for DOA estimation of coherent sources in presence of antenna array imperfections is presented. We exploit the current advances of deep learning to overcome two of the most common problems facing the state of the art DOA algorithms (i.e. coherent sources and array imperfections). We propose a deep auto encoder (AE) that is able to correctly resolve coherent sources without the need of spatial smoothing, hence avoiding possible processing overhead and delays. Moreover, we assumed the presence of array imperfections in the received signal model such as mutual coupling, gain/ phase mismatches, and position errors. The deep AE is trained using the covariance matrix of the received signal, where it alleviates the effect of imperfections, and at the same time act as a filters for the coherent sources. The results show significant improvement compared to the methods used in the literature.
A Wideband Quasi-Asymmetric Doherty Power Amplifier with a Two-Section Matching-Phase Difference Compensator Network Design Using GaAs Technology
Authors: Seyedehmarzieh Rouhani, Ahmad Ghanaatian, Adib Abrishamifar, Majid Tayarani
Date: 2020-05-06 00:00:00

In this paper, a quasi-asymmetric Doherty power amplifier (PA) is designed without load modulation using the GaAs 0.25{\mu}m pHEMT technology to reach an enlarged output power back-off (OPBO) with circuitry solutions in order to overcome technology restrictions. To prevent power leakage in auxiliary PA (PAaux) due to its extremely large off-state impedance, a Wilkinson power combiner is added to the output. Moreover, an input asymmetric power divider is designed to guarantee that no considerable power is delivered to main PA (PAmain) in the high-power region to make it saturated. A two-section matching network is proposed for PAmain, which simultaneously compensates for phase differences of the main and auxiliary amplification paths. To control the significant impedance variation of PAaux versus sweeping power and the different impedance trajectories of the main and auxiliary amplification paths, the bias and dimension selection of PAaux are analyzed to reach the desired output power profile versus input power. These methods overcome impedance variations and linearity degradation. To achieve the aimed 10% fractional bandwidth, appropriate low-quality LC-networks are selected as matching networks. The simulation results indicate the utility of the proposed structure for microwave link applications. Continuous-wave simulations imply that the Doherty PA has a 33dBm maximum output power and a 13.5dB power gain with less than 1dB power gain compression in the desired frequency range (7.6-8.4GHz). The drain efficiency of 30% at the highest input power, minimum of second and third harmonic powers of -140dBm and -130dBm, respectively, and OPBO of 7.5dB are also obtained.
Network Massive MIMO Transmission Over Millimeter-Wave and Terahertz Bands: Mobility Enhancement and Blockage Mitigation
Authors: Li You, Xu Chen, Xiaohang Song, Fan Jiang, Wenjin Wang, Xiqi Gao, Gerhard Fettweis
Date: 2020-05-06 00:00:00

Mobility and blockage are two critical challenges in wireless transmission over millimeter-wave (mmWave) and Terahertz (THz) bands. In this paper, we investigate network massive multiple-input multiple-output (MIMO) transmission for mmWave/THz downlink in the presence of mobility and blockage. Considering the mmWave/THz propagation characteristics, we first propose to apply per-beam synchronization for network massive MIMO to mitigate the channel Doppler and delay dispersion effects. Accordingly, we establish a transmission model. We then investigate network massive MIMO downlink transmission strategies with only the statistical channel state information (CSI) available at the base stations (BSs), formulating the strategy design problem as an optimization problem to maximize the network sum-rate. We show that the beam domain is favorable to perform transmission, and demonstrate that BSs can work individually when sending signals to user terminals. Based on these insights, the network massive MIMO precoding design is reduced to a network sum-rate maximization problem with respect to beam domain power allocation. By exploiting the sequential optimization method and random matrix theory, an iterative algorithm with guaranteed convergence performance is further proposed for beam domain power allocation. Numerical results reveal that the proposed network massive MIMO transmission approach with the statistical CSI can effectively alleviate the blockage effects and provide mobility enhancement over mmWave and THz bands.
Affective Brain-Computer Interfaces: A Tutorial to Choose Performance Measuring Metric
Authors: Md Rakibul Mowla, Rachael I. Cano, Katie J. Dhuyvetter, David E. Thompson
Date: 2020-05-06 00:00:00
Tasks: Articles, EEG, Electroencephalogram (EEG)

Affective brain-computer interfaces are a relatively new area of research in affective computing. Estimation of affective states can improve human-computer interaction as well as improve the care of people with severe disabilities. To assess the effectiveness of EEG recordings in recognizing affective state, here we used data collected in our lab as well as the publicly available DEAP database. We also reviewed the articles that used the DEAP database and found that a significant number of articles did not consider the presence of the class imbalance in the DEAP. Failing to consider class imbalance creates misleading results. Further, ignoring class imbalance makes the comparing results between studies impossible, since different datasets will have different class imbalances. Class imbalance also shifts the chance level, hence it is vital to consider class bias while determining if the results are above chance. To properly account the effect of class imbalance, we suggest the use of balanced accuracy as a performance metric and its posterior distribution for computing credible intervals. For classification, we used features mentioned in the literature and additionally theta beta-1 ratio. Results from DEAP and our data suggest that the beta band power, theta band power, and theta beta-1 ratio are better feature sets for classifying valence, arousal, and dominance, respectively.
An Experimental Study on Airborne Landmine Detection Using a Circular Synthetic Aperture Radar
Authors: Markus Schartel, Ralf Burr, Rik Bähnemann, Winfried Mayer, Christian Waldschmidt
Date: 2020-05-06 00:00:00
Tasks: Humanitarian, Landmine

Many countries in the world are contaminated with landmines. Several thousand casualties occur every year. Although there are certain types of mines that can be detected from a safe stand-off position with tools, humanitarian demining is still mostly done by hand. As a new approach, an unmanned aerial system (UAS) equipped with a ground penetrating synthetic aperture radar (GPSAR) was developed, which is used to detect landmines, cluster munition, grenades, and improvised explosive devices (IEDs). The measurement system consists of a multicopter, a total station, an inertial measurement unit (IMU), and a frequency-modulated continuous-wave (FMCW) radar operating from 1 GHz to 4 GHz. The highly accurate localization of the measurement system and the full flexibility of the UAS are used to generate 3D-repeat-pass circular SAR images of buried antipersonnel landmines. In order to demonstrate the functionality of the system, 15 different dummy landmines were buried in a sandbox. The measurement results show the high potential of circular SAR for the detection of minimum metal mines. 11 out of 15 different test objects could be detected unambiguously with cm-level accuracy by examining depth profiles showing the amplitude of the targets response over the processing depth.
Development of a skateboarding trick classifier using accelerometry and machine learning
Authors: Nicholas Kluge Corrêa, Julio Cesar Marques de Lima, Thais Russomano, Marlise Araujo dos Santos
Date: 2020-05-06 00:00:00
Tasks: BIG-bench Machine Learning, Computational Efficiency, Motion Detection

Introduction: Skateboarding is one of the most popular cultures in Brazil, with more than 8.5 million skateboarders. Nowadays, the discipline of street skating has gained recognition among other more classical sports and awaits its debut at the Tokyo 2020 Summer Olympic Games. This study aimed to explore the state-of-the-art for inertial measurement unit (IMU) use in skateboarding trick detection, and to develop new classification methods using supervised machine learning and artificial neural networks (ANN). Methods: State-of-the-art knowledge regarding motion detection in skateboarding was used to generate 543 artificial acceleration signals through signal modeling, corresponding to 181 flat ground tricks divided into five classes (NOLLIE, NSHOV, FLIP, SHOV, OLLIE). The classifier consisted of a multilayer feed-forward neural network created with three layers and a supervised learning algorithm (backpropagation). Results: The use of ANNs trained specifically for each measured axis of acceleration resulted in error percentages inferior to 0.05%, with a computational efficiency that makes real-time application possible. Conclusion: Machine learning can be a useful technique for classifying skateboarding flat ground tricks, assuming that the classifiers are properly constructed and trained, and the acceleration signals are preprocessed correctly.
Similarity and delay between two non-narrow-band time signals
Authors: Zhen Sun, Guocheng Wang, Xiaoqing Su, Xinghui Liang, Lintao Liu
Date: 2020-05-06 00:00:00
Tasks: Vocal Bursts Valence Prediction

Correlation coefficient is usually used to measure the correlation degree between two time signals. However, its performance will drop or even fail if the signals are noised. Based on the time-frequency phase spectrum (TFPS) provided by normal time-frequency transform (NTFT), similarity coefficient is proposed to measure the similarity between two non-narrow-band time signals, even if the signals are noised. The basic idea of the similarity coefficient is to translate the interest part of signal f1(t)'s TFPS along the time axis to couple with signal f2(t)'s TFPS. Such coupling would generate a maximum if f1(t)and f2(t) are really similar to each other in time-frequency structure. The maximum, if normalized, is called similarity coefficient. The location of the maximum indicates the time delay between f1(t) and f2(t). Numerical results show that the similarity coefficient is better than the correlation coefficient in measuring the correlation degree between two noised signals. Precision and accuracy of the time delay estimation (TDE) based on the similarity analysis are much better than those based on cross-correlation (CC) method and generalized CC (GCC) method under low SNR.
Identifying Unused RF Channels Using Least Matching Pursuit
Authors: Emre Gönültaş, Milad Taghavi, Sweta Soni, Alyssa B. Apsel, Christoph Studer
Date: 2020-05-06 00:00:00
Tasks: Compressive Sensing

Cognitive radio aims at identifying unused radio-frequency (RF) bands with the goal of re-using them opportunistically for other services. While compressive sensing (CS) has been used to identify strong signals (or interferers) in the RF spectrum from sub-Nyquist measurements, identifying unused frequencies from CS measurements appears to be uncharted territory. In this paper, we propose a novel method for identifying unused RF bands using an algorithm we call least matching pursuit (LMP). We present a sufficient condition for which LMP is guaranteed to identify unused frequency bands and develop an improved algorithm that is inspired by our theoretical result. We perform simulations for a CS-based RF whitespace detection task in order to demonstrate that LMP is able to outperform black-box approaches that build on deep neural networks.
On Deep Learning for Radio Resource Management in A Non-stationary Radio Environment
Authors: Suren Sritharan, Harshana Weligampola, Haris Gacanin
Date: 2020-05-05 00:00:00
Tasks: Dataset Generation, Management

This paper studies practical limitations of learning methods for resource management in non-stationary radio environment. We propose two learning models carefully designed to support rate maximization objective under user mobility. We study the effects of practical systems such as latency and reliability on the rate maximization with deep learning models. For common testing in the non-stationary environment we present a generic dataset generation method to benchmark across different learning models versus traditional optimal resource management solutions. Our results indicate that learning models have practical challenges related to training limiting their applications. The models need environment-specific design to reach the accuracy of an optimal algorithm.
E-Quarantine: A Smart Health System for Monitoring Coronavirus Patients for Remotely Quarantine
Authors: Doaa Mohey El-Din, Aboul Ella Hassanein, Ehab E. Hassanien, Walaa M. E. Hussein
Date: 2020-05-05 00:00:00

Coronavirus becomes officially a global pandemic due to the speed spreading off in various countries. An increasing number of infected with this disease causes the Inability problem to fully care in hospitals and afflict many doctors and nurses inside the hospitals. This paper proposes a smart health system that monitors the patients holding the Coronavirus remotely. Due to protect the lives of the health services members (like physicians and nurses) from infection. This smart system observes the people with this disease based on putting many sensors to record many features of their patients in every second. These parameters include measuring the patient's temperature, respiratory rate, pulse rate, blood pressure, and time. The proposed system saves lives and improves making decisions in dangerous cases. It proposes using artificial intelligence and Internet-of-things to make remotely quarantine and develop decisions in various situations. It provides monitoring patients remotely and guarantees giving patients medicines and getting complete health care without anyone getting sick with this disease. It targets two people's slides the most serious medical conditions and infection and the lowest serious medical conditions in their houses. Observing in hospitals for the most serious medical cases that cause infection in thousands of healthcare members so there is a big need to uses it. Other less serious patients slide, this system enables physicians to monitor patients and get the healthcare from patient's houses to save places for the critical cases in hospitals.
Aerial Spectrum Surveying: Radio Map Estimation with Autonomous UAVs
Authors: Daniel Romero, Raju Shrestha, Yves Teganya, Sundeep Prabhakar Chepuri
Date: 2020-05-05 00:00:00

Radio maps are emerging as a popular means to endow next-generation wireless communications with situational awareness. In particular, radio maps are expected to play a central role in unmanned aerial vehicle (UAV) communications since they can be used to determine interference or channel gain at a spatial location where a UAV has not been before. Existing methods for radio map estimation utilize measurements collected by sensors whose locations cannot be controlled. In contrast, this paper proposes a scheme in which a UAV collects measurements along a trajectory. This trajectory is designed to obtain accurate estimates of the target radio map in a short time operation. The route planning algorithm relies on a map uncertainty metric to collect measurements at those locations where they are more informative. An online Bayesian learning algorithm is developed to update the map estimate and uncertainty metric every time a new measurement is collected, which enables real-time operation.
Reducing Uncertainty by Fusing Dynamic Occupancy Grid Maps in a Cloud-based Collective Environment Model
Authors: Bastian Lampe, Raphael van Kempen, Timo Woopen, Alexandru Kampmann, Bassam Alrifaee, Lutz Eckstein
Date: 2020-05-05 00:00:00
Tasks: Specificity

Accurate environment perception is essential for automated vehicles. Since occlusions and inaccuracies regularly occur, the exchange and combination of perception data of multiple vehicles seems promising. This paper describes a method to combine perception data of automated and connected vehicles in the form of evidential Dynamic Occupany Grid Maps (DOGMas) in a cloud-based system. This system is called the Collective Environment Model and is part of the cloud system developed in the project UNICARagil. The presented concept extends existing approaches that fuse evidential grid maps representing static environments of a single vehicle to evidential grid maps computed by multiple vehicles in dynamic environments. The developed fusion process additionally incorporates self-reported data provided by connected vehicles instead of only relying on perception data. We show that the uncertainty in a DOGMa described by Shannon entropy as well as the uncertainty described by a non-specificity measure can be reduced. This enables automated and connected vehicles to behave in ways not before possible due to unknown but relevant information about the environment.
NOMA Visible Light Communication System with Angle Diversity Receivers
Authors: Mansourah K. Aljohani, Osama Zwaid Alsulami, Khulood D. Alazwary, Mohamed O. I. Musa, T. E. H. El-Gorashi, Mohammed T. Alresheedi, Jaafar M. H. Elmirghani
Date: 2020-05-05 00:00:00
Tasks: Diversity

In this paper, a non-orthogonal multiple access (NOMA) visible light communication (VLC) system is investigated. The system uses angle diversity receivers (ADRs) to provide high data rates. The ADR has 4 branches, each directed to a different direction. An 8m x 4m sized room is modelled to study the resource allocation to users according to their channel conditions to maximize the data rate. The results show that using ADRs improves the data rate by an average of 35% compared to a system using wide FOV receivers.
Beyond trans-dimensional RJMCMC with a case study in impulsive data modeling
Authors: Oktay Karakuş, Ercan E. Kuruoğlu, Mustafa A. Altınkaya
Date: 2017-11-09 00:00:00

Reversible jump Markov chain Monte Carlo (RJMCMC) is a Bayesian model estimation method which has been used for trans-dimensional sampling. In this study, we propose utilization of RJMCMC beyond trans-dimensional sampling. This new interpretation, which we call trans-space RJMCMC, reveals the undiscovered potential of RJMCMC by exploiting the original formulation to explore spaces of different classes or structures. This provides flexibility in using different types of candidate classes in the combined model space such as spaces of linear and nonlinear models or of various distribution families. As an application for the proposed method, we have performed a special case of trans-space sampling, namely trans-distributional RJMCMC in impulsive data modeling. In many areas such as seismology, radar, image, using Gaussian models is a common practice due to analytical ease. However, many noise processes do not follow a Gaussian character and generally exhibit events too impulsive to be successfully described by the Gaussian model. We test the proposed method to choose between various impulsive distribution families to model both synthetically generated noise processes and real-life measurements on power line communications (PLC) impulsive noises and 2-D discrete wavelet transform (2-D DWT) coefficients.
Potential, Challenges and Future Directions for Deep Learning in Prognostics and Health Management Applications
Authors: Olga Fink, Qin Wang, Markus Svensén, Pierre Dersin, Wan-Jui Lee, Melanie Ducoffe
Date: 2020-05-05 00:00:00
Tasks: Deep Learning, Management, Natural Language Understanding

Deep learning applications have been thriving over the last decade in many different domains, including computer vision and natural language understanding. The drivers for the vibrant development of deep learning have been the availability of abundant data, breakthroughs of algorithms and the advancements in hardware. Despite the fact that complex industrial assets have been extensively monitored and large amounts of condition monitoring signals have been collected, the application of deep learning approaches for detecting, diagnosing and predicting faults of complex industrial assets has been limited. The current paper provides a thorough evaluation of the current developments, drivers, challenges, potential solutions and future research needs in the field of deep learning applied to Prognostics and Health Management (PHM) applications.