pwc-archive/papers-with-abstracts huggingface dataset:
Deep learning framework for subject-independent emotion detection using wireless signals
Authors: Ahsan Noor Khan, Achintha Avin Ihalage, Yihan Ma, Baiyang Liu, Yujie Liu, Yang Hao
Date: 2020-06-08 00:00:00

Emotion states recognition using wireless signals is an emerging area of research that has an impact on neuroscientific studies of human behaviour and well-being monitoring. Currently, standoff emotion detection is mostly reliant on the analysis of facial expressions and/or eye movements acquired from optical or video cameras. Meanwhile, although they have been widely accepted for recognizing human emotions from the multimodal data, machine learning approaches have been mostly restricted to subject dependent analyses which lack of generality. In this paper, we report an experimental study which collects heartbeat and breathing signals of 15 participants from radio frequency (RF) reflections off the body followed by novel noise filtering techniques. We propose a novel deep neural network (DNN) architecture based on the fusion of raw RF data and the processed RF signal for classifying and visualising various emotion states. The proposed model achieves high classification accuracy of 71.67 % for independent subjects with 0.71, 0.72 and 0.71 precision, recall and F1-score values respectively. We have compared our results with those obtained from five different classical ML algorithms and it is established that deep learning offers a superior performance even with limited amount of raw RF and post processed time-sequence data. The deep learning model has also been validated by comparing our results with those from ECG signals. Our results indicate that using wireless signals for stand-by emotion state detection is a better alternative to other technologies with high accuracy and have much wider applications in future studies of behavioural sciences.
Hybrid Beamforming for Massive MIMO Over-the-Air Computation
Authors: Xiongfei Zhai, Xihan Chen, Jie Xu, Derrick Wing Kwan Ng
Date: 2020-06-08 00:00:00

Over-the-air computation (AirComp) has been recognized as a promising technique in Internet-of-Things (IoT) networks for fast data aggregation from a large number of wireless devices. However, as the number of devices becomes large, the computational accuracy of AirComp would seriously degrade due to the vanishing signal-to-noise ratio (SNR). To address this issue, we exploit the massive multiple-input multiple-output (MIMO) with hybrid beamforming, in order to enhance the computational accuracy of AirComp in a cost-effective manner. In particular, we consider the scenario with a large number of multi-antenna devices simultaneously sending data to an access point (AP) equipped with massive antennas for functional computation over the air. Under this setup, we jointly optimize the transmit digital beamforming at the wireless devices and the receive hybrid beamforming at the AP, with the objective of minimizing the computational mean-squared error (MSE) subject to the individual transmit power constraints at the wireless devices. To solve the non-convex hybrid beamforming design optimization problem, we propose an alternating-optimization-based approach. In particular, we propose two computationally efficient algorithms to handle the challenging receive analog beamforming problem, by exploiting the techniques of successive convex approximation (SCA) and block coordinate descent (BCD), respectively. It is shown that for the special case with a fully-digital receiver at the AP, the achieved MSE of the massive MIMO AirComp system is inversely proportional to the number of receive antennas. Furthermore, numerical results show that the proposed hybrid beamforming design substantially enhances the computation MSE performance as compared to other benchmark schemes, while the SCA-based algorithm performs closely to the performance upper bound achieved by the fully-digital beamforming.
Scheduling in Wireless Networks with Spatial Reuse of Spectrum as Restless Bandits
Authors: Vivek S. Borkar, Shantanu Choudhary, Vaibhav Kumar Gupta, Gaurav S. Kasbekar
Date: 2019-10-10 00:00:00
Tasks: Scheduling

We study the problem of scheduling packet transmissions with the aim of minimizing the energy consumption and data transmission delay of users in a wireless network in which spatial reuse of spectrum is employed. We approach this problem using the theory of Whittle index for cost minimizing restless bandits, which has been used to effectively solve problems in a variety of applications. We design two Whittle index based policies the first by treating the graph representing the network as a clique and the second based on interference constraints derived from the original graph. We evaluate the performance of these two policies via extensive simulations, in terms of average cost and packets dropped, and show that they outperform the well-known Slotted ALOHA and maximum weight scheduling algorithms.
Accelerated Search for Non-Negative Greedy Sparse Decomposition via Dimensionality Reduction
Authors: Konstantinos Voulgaris, Mike E. Davies, Mehrdad Yaghoobi
Date: 2020-06-08 00:00:00
Tasks: Dimensionality Reduction

Non-negative signals form an important class of sparse signals. Many algorithms have already beenproposed to recover such non-negative representations, where greedy and convex relaxed algorithms are among the most popular methods. One fast implementation is the FNNOMP algorithm that updates the non-negative coefficients in an iterative manner. Even though FNNOMP is a good approach when working on libraries of small size, the operational time of the algorithm grows significantly when the size of the library is large. This is mainly due to the selection step of the algorithm that relies on matrix vector multiplications. We here introduce the Embedded Nearest Neighbor (E-NN) algorithm which accelerates the search over large datasets while it is guaranteed to find the most correlated atoms. We then replace the selection step of FNNOMP by E-NN. Furthermore we introduce the Update Nearest Neighbor (U-NN) at the look up table of FNNOMP in order to assure the non-negativity criteria of FNNOMP. The results indicate that the proposed methodology can accelerate FNNOMP with a factor 4 on a real dataset of Raman Spectra and with a factor of 22 on a synthetic dataset.
Real-time 112 Gbit/s DMT for Data Center Interconnects
Authors: Annika Dochhan, Nicklas Eiselt, Jim Zou, Helmut Griesser, Michael H. Eiselt, Jörg-Peter Elbers
Date: 2020-06-08 00:00:00

We report on 112 Gbit/s real-time DMT transmission over up to 60 km, targeted at DCI applications. Chromatic dispersion mitigation by vestigial sideband filtering is compared to the use of dispersion compensating fiber.
64 GBd DP-Bipolar-8ASK Transmission over 120 km SSMF Employing a Monolithically Integrated Driver and MZM in 0.25-um SiGe BiCMOS Technology
Authors: Gilda Raoof Mehrpoor, Carsten Schmidt-Langhorst, Benjamin Wohlfeil, Robert Elschner, Danish Rafique, Robert Emmerich, Annika Dochhan, Iria Lopez, Pedro Rito, Despoina Petousi, Dietmar Kissinger, Lars Zimmermann, Colja Schubert, Bernhard Schmauss, Michael Eiselt, Jörg-Peter Elbers
Date: 2020-06-08 00:00:00

We demonstrate 64 GBd signal generation up to bipolar-8-ASK utilizing a single MZM, monolithically integrated with segmented drivers in SiGe. Using polarization multiplexing, 300-Gb/s net data rate transmission over 120 km SSMF is shown.
Metro-haul Project Vertical Service Demo: Video Surveillance Real-time Low-latency Object Tracking
Authors: Annika Dochhan, Johannes K. Fischer, Bodo Lent, Achim Autenrieth, Behnam Shariati, Pablo Wilke Berenguer, Jörg-Peter Elbers
Date: 2020-06-08 00:00:00
Tasks: Object Tracking

We report on the EU H2020 project METRO-HAUL use-case demonstration, including flexible allocation of storage and computing resources in different network locations and deployment of a network slice instance through a programmable multi-layer optical network.
Real-time discrete multi-tone transmission for passive optical networks in C- and O-band
Authors: Annika Dochhan, Tomislav Drenski, Helmut Griesser, Michael H. Eiselt, Jörg-Peter Elbers
Date: 2020-06-08 00:00:00

DMT at 25 Gbit/s, 50 Gbit/s and 100 Gbit/s in passive systems with electro-absorption modulators is investigated. Transmission in C- and O-band are compared, yielding up to 22 km reach, 100 Gbit/s in O-band. All results are obtained with a real-time signal processing ASIC.
Flexible metro network architecture based on wavelength blockers and coherent transmission
Authors: Annika Dochhan, Robert Emmerich, Pablo Wilke Berenguer, Colja Schubert, Johannes K. Fischer, Michael H. Eiselt, Jörg-Peter Elbers
Date: 2020-06-08 00:00:00
Tasks: channel selection

A flexible semi-filterless architecture for metro DWDM networks with ring- or horseshoe topologies is presented. ROADM nodes are based on wavelength blockers. Coherent transmission enables filterless channel selection. Critical parameters are determined and a 4-span, 160-km horseshoe with 100Gbit/s DP-QPSK and 200Gbit/s DP-16QAM is demonstrated.
13.16 Tbit/s Free-space Optical Transmission over 10.45 km for Geostationary Satellite Feeder-links
Authors: Annika Dochhan, Juraj Poliak, Janis Surof, Mathias Richerzhagen, Helawae Friew Kelemu, Ramon Mata Calvo
Date: 2020-06-08 00:00:00

We report a 13.16 Tbit/s 54-channel DWDM free-space optical data transmission field trial emulating the worst-case scenario for optical GEO satellite uplinks through the atmosphere, and show the system performance under turbulent conditions.
In-Ear Measurement of Blood Oxygen Saturation: An Ambulatory Tool Needed To Detect The Delayed Life-Threatening Hypoxaemia in COVID-19
Authors: Harry J. Davies, Ian Williams, Nicholas S. Peters, Danilo P. Mandic
Date: 2020-06-07 00:00:00
Tasks: SpO2 estimation

Non-invasive ambulatory estimation of blood oxygen saturation has emerged as an important clinical requirement to detect hypoxemia in the delayed post-infective phase of COVID-19, where dangerous hypoxia may occur in the absence of subjective breathlessness. This immediate clinical driver, combined with the general quest for more personalised health data, means that pulse oximetry measurement of capillary oxygen saturation (SpO2) will likely expand into both the clinical and consumer market of wearable health technology in the near future. In this study, we set out to establish the feasibility of SpO2 measurement from the ear canal as a convenient site for long term monitoring, and perform a comprehensive comparison with the right index finger - the conventional clinical measurement site. During resting SpO2 estimation, we found a root mean square difference of 1.47% between the two measurement sites, with a mean difference of 0.23% higher SpO2 in the right ear canal. Through the simultaneous recording of pulse oximetry from both the right ear canal and index finger during breath holds, we observe a substantial improvement in response time between the ear and finger that has a mean of 12.4 seconds and a range of 4.2 - 24.2 seconds across all subjects. Factors which influence this response time, termed SpO2 delay, such as the sex of a subject are also explored. Furthermore, we examine the potential downsides of ear canal blood oxygen saturation measurement, namely the lower photoplethysmogram amplitude, and suggest ways to mitigate this disadvantage. These results are presented in conjunction with previously discovered benefits such as robustness to temperature, making the case for measurement of SpO2 from the ear canal being both convenient and superior to conventional finger measurement sites for continuous non-intrusive long term monitoring in both clinical and everyday-life settings.
6G Communication: Envisioning the Key Issues and Challenges
Authors: Sabuzima Nayak, Ripon Patgiri
Date: 2020-04-07 00:00:00

In 2030, we are going to evidence the 6G mobile communication technology, which will enable the Internet of Everything. Yet 5G has to be experienced by people worldwide and B5G has to be developed; the researchers have already started planning, visioning, and gathering requirements of the 6G. Moreover, many countries have already initiated the research on 6G. 6G promises connecting every smart device to the Internet from smartphone to intelligent vehicles. 6G will provide sophisticated and high QoS such as holographic communication, augmented reality/virtual reality and many more. Also, it will focus on Quality of Experience (QoE) to provide rich experiences from 6G technology. Notably, it is very important to vision the issues and challenges of 6G technology, otherwise, promises may not be delivered on time. The requirements of 6G poses new challenges to the research community. To achieve desired parameters of 6G, researchers are exploring various alternatives. Hence, there are diverse research challenges to envision, from devices to softwarization. Therefore, in this article, we discuss the future issues and challenges to be faced by the 6G technology. We have discussed issues and challenges from every aspect from hardware to the enabling technologies which will be utilized by 6G.
Processing Distribution and Architecture Tradeoff for Large Intelligent Surface Implementation
Authors: Jesus Rodriguez Sanchez, Ove Edfors, Fredrik Rusek, Liang Liu
Date: 2020-01-14 00:00:00

The Large Intelligent Surface (LIS) concept has emerged recently as a new paradigm for wireless communication, remote sensing and positioning. It consists of a continuous radiating surface placed relatively close to the users, which is able to communicate with users by independent transmission and reception (replacing base stations). Despite of its potential, there are a lot of challenges from an implementation point of view, with the interconnection data-rate and computational complexity being the most relevant. Distributed processing techniques and hierarchical architectures are expected to play a vital role addressing this while ensuring scalability. In this paper we perform algorithm-architecture codesign and analyze the hardware requirements and architecture trade-offs for a discrete LIS to perform uplink detection. By doing this, we expect to give concrete case studies and guidelines for efficient implementation of LIS systems.
Full-Duplex MIMO Systems with Hardware Limitations and Imperfect Channel Estimation
Authors: Hiroki Iimori, Giuseppe Thadeu Freitas de Abreu, Koji Ishibashi
Date: 2020-06-07 00:00:00

We consider a bidirectional in-band full-duplex (FD) multiple-input multiple-output (MIMO) system subject to imperfect channel state information (CSI), hardware distortion, and limited analog cancellation capability as well as the self-interference (SI) power requirement at the receiver analog domain so as to avoid the saturation of low noise amplifier (LNA). A novel minimum mean square error (MMSE)-based joint design of digital precoder and combiner for SI cancellation is offered, which combines the well-known gradient projection method and non-monotonicity considered in recent machine-learning literature in order to tackle the non-convexity of the optimization problem formulated in this article. Simulation results illustrate the effectiveness of the proposed SI cancellation algorithm.
Discrete-Aware Matrix Completion via Proximal Gradient
Authors: Hiroki Iimori, Giuseppe Thadeu Freitas de Abreu, Omid Taghizadeh, Koji Ishibashi
Date: 2020-06-07 00:00:00
Tasks: Matrix Completion

We present a novel algorithm for the completion of low-rank matrices whose entries are limited to a finite discrete alphabet. The proposed method is based on the recently-emerged proximal gradient (PG) framework of optimization theory, which is applied here to solve a regularized formulation of the completion problem that includes a term enforcing the discrete-alphabet membership of the matrix entries.
I/Q Imbalance Aware Nonlinear Wireless-Powered Relaying of B5G Networks: Security and Reliability Analysis
Authors: Xingwang Li, Mengyan Huang, Yuanwei Liu, Varun G Menon, Anand Paul, Zhiguo Ding
Date: 2020-06-06 00:00:00

Physical layer security is known as a promising paradigm to ensure security for the beyond 5G (B5G) networks in the presence of eavesdroppers. In this paper, we elaborate on a tractable analysis framework to evaluate the reliability and security of wireless-powered decode-and-forward (DF) multi-relay networks. The nonlinear energy harvesters, in-phase and quadrature-phase imbalance (IQI) and channel estimation errors (CEEs) are taken into account in the considered system. To further improve the secure performance, two relay selection strategies are presented: 1) suboptimal relay selection (SRS); 2) optimal relay selection (ORS). Specifically, exact analytical expressions for the outage probability (OP) and the intercept probability (IP) are derived in closed-form. For the IP, we consider that the eavesdropper can wiretap the signal from the source or the relay. In order to obtain more useful insights, we carry out the asymptotic analysis and diversity orders for the OP in the high signal-to-noise ratio (SNR) regime under non-ideal and ideal conditions. Numerical results show that: 1) Although the mismatches of amplitude/phase of transmitter (TX)/receiver (RX) limit the OP performance, it can enhance IP performance; 2) Large number of relays yields better OP performance; 3) There are error floors for the OP because of the CEEs; 4) There is a trade-off for the OP and IO to obtain the balance between reliability and security.
Hybrid Learning Aided Inactive Constraints Filtering Algorithm to Enhance AC OPF Solution Time
Authors: Fouad Hasan, Amin Kargarian, Javad Mohammadi
Date: 2020-06-02 00:00:00
Tasks: regression

The Optimal power flow (OPF) problem contains many constraints. However, equality constraints along with a limited set of active inequality constraints encompass sufficient information to determine the feasible space of the problem. In this paper, a hybrid supervised regression and classification learning based algorithm is proposed to identify active and inactive sets of inequality constraints of AC OPF solely based on nodal power demand information. The proposed algorithm is structured using several classifiers and regression learners. The combination of classifiers with regression learners enhances the accuracy of active / inactive constraints identification procedure. The proposed algorithm modifies the OPF feasible space rather than a direct mapping of OPF results from demand. Inactive constraints are removed from the design space to construct a truncated AC OPF. This truncated optimization problem can be solved faster than the original problem with less computational resources. Numerical results on several test systems show the effectiveness of the proposed algorithm for predicting active and inactive constraints and constructing a truncated AC OPF. We have posted our code for all simulations on arxiv and have uploaded the data used in numerical studies to IEEE DataPort as an open access dataset.
A precise machine learning aided algorithm for land subsidence or upheave prediction from GNSS time series
Authors: M. Kiani
Date: 2020-06-06 00:00:00
Tasks: BIG-bench Machine Learning, Gaussian Processes, Position, Prediction, regression, Time Series, Time Series Analysis

This paper is aimed at the problem of predicting the land subsidence or upheave in an area, using GNSS position time series. Since machine learning algorithms have presented themselves as strong prediction tools in different fields of science, we employ them to predict the next values of the GNSS position time series. For this reason, we present an algorithm that takes advantage of the machine learning algorithms for the prediction of positions in a GNSS time series. The proposed algorithm has two steps-preprocessing and prediction. In the preprocessing phase, the periodic tidal and atmospheric signals in the time series are removed and coordinates are transferred to the local coordinate system. In the prediction phase, eight different machine learning algorithms are used, namely, multilayer perceptron, Bayesian neural network, radial basis functions, Gaussian processes, k-nearest neighbor, generalized regression neural network, classification and regression trees, and support vector regression. We show the superiority of the Gaussian processes algorithm, compared to other methods, in 14 different real GNSS time series studies. The proposed algorithm can achieve up to 4 millimeters in accuracy, with the average accuracy as 2 centimeters across all time series.
A General Architecture for Behavior Modeling of Nonlinear Power Amplifier using Deep Convolutional Neural Network
Authors: Xin Hu, Zhijun Liu, You Li, Lexi Xu, Sun Zhang, Qinlong Li, Jia Hu, WenHua Chen, Weidong Wang, Mohamed Helaoui, Fadhel M. Ghannouchi
Date: 2020-06-06 00:00:00

Nonlinearity of power amplifier is one of the major limitations to the achievable capacity in wireless transmission systems. Nonlinear impairments are determined by the nonlinear distortions of the power amplifier and modulator imperfections. The Volterra model, several compact Volterra models and neural network models to establish a nonlinear model of power amplifier have all been demonstrated. However, the computational cost of these models increases and their implementation demands more signal processing resources as the signal bandwidth gets wider or the number of carrier aggregation. A completely different approach uses deep convolutional neural network to learn from the training data to figure out the nonlinear distortion. In this work, a low complexity, general architecture based on the deep real-valued convolutional neural network (DRVCNN) is proposed to build the nonlinear behavior of the power amplifier. With each of the multiple inputs equivalent to an input vector, the DRVCNN tensor weights are constructed from training data thanks to the current and historical envelope-dependent terms, I, and Q, which are components of the input. The effectiveness of the general framework in modeling single-carrier and multi-carrier power amplifiers is verified.
An Interference-Free Filter-Bank Multicarrier System Applicable for MIMO Channels
Authors: Mohammad Towliat
Date: 2020-06-06 00:00:00
Tasks: Diversity

In filter-bank multicarrier (FBMC) systems the intrinsic interference is the major challenge to achieve a full gain of diversity over multi-input multi-output (MIMO) channels. In this paper, we develop a novel multicarrier system called FBMC offset upper-lower polyphase network (FBMC/OULP) in which, to eliminate the intrinsic interference, the complex-valued symbols are alternatively transmitted via upper and lower half of polyphase network branches with an offset time. The symbol density of the FBMC/OULP system is equal to one complex-valued symbol in time-frequency lattice. Also, for transmission over frequency selective channels, a minimum mean square error (MMSE) estimator is employed at the receiver of the FBMC/OULP system to eliminate the interference caused by the frequency selectivity of the channel. The proposed scheme mitigates the produced interference between symbols in the upper and lower polyphase branches, based on the circular convolutional property. As a result of using complex-valued symbols and diminishing the interference, the full diversity gain of the orthogonal space-time block codes (OSTBC) can be achieved in MIMO channels by a low complex maximum likelihood (ML) detector. In comparison with the orthogonal frequency division multiplexing (OFDM) system, simulation results indicate that the proposed system achieves a superior performance in fast multi-path fading channels and a competitive performance in slow multi-path fading channels.