pwc-archive/papers-with-abstracts huggingface dataset:
Dynamic Edge Computing empowered by Reconfigurable Intelligent Surfaces
Authors: Paolo Di Lorenzo, Mattia Merluzzi, Emilio Calvanese Strinati, Sergio Barbarossa
Date: 2021-12-21 00:00:00
Tasks: CPU, Edge-computing, Stochastic Optimization

In this paper, we propose a novel algorithm for energy-efficient, low-latency dynamic mobile edge computing (MEC), in the context of beyond 5G networks endowed with Reconfigurable Intelligent Surfaces (RISs). In our setting, new computing requests are continuously generated by a set of devices and are handled through a dynamic queueing system. Building on stochastic optimization tools, we devise a dynamic learning algorithm that jointly optimizes the allocation of radio resources (i.e., power, transmission rates, sleep mode and duty cycle), computation resources (i.e., CPU cycles), and RIS reflectivity parameters (i.e., phase shifts), while guaranteeing a target performance in terms of average end-to-end (E2E) delay. The proposed strategy is dynamic, since it performs a low-complexity optimization on a per-slot basis while dealing with time-varying radio channels and task arrivals, whose statistics are unknown. The presence and optimization of RISs helps boosting the performance of dynamic MEC, thanks to the capability to shape and adapt the wireless propagation environment. Numerical results assess the performance in terms of service delay, learning, and adaptation capabilities of the proposed strategy for RIS empowered MEC.
Attention-Based Sensor Fusion for Human Activity Recognition Using IMU Signals
Authors: Wenjin Tao, Haodong Chen, Md Moniruzzaman, Ming C. Leu, Zhaozheng Yi, Ruwen Qin
Date: 2021-12-20 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Sensor Fusion

Human Activity Recognition (HAR) using wearable devices such as smart watches embedded with Inertial Measurement Unit (IMU) sensors has various applications relevant to our daily life, such as workout tracking and health monitoring. In this paper, we propose a novel attention-based approach to human activity recognition using multiple IMU sensors worn at different body locations. Firstly, a sensor-wise feature extraction module is designed to extract the most discriminative features from individual sensors with Convolutional Neural Networks (CNNs). Secondly, an attention-based fusion mechanism is developed to learn the importance of sensors at different body locations and to generate an attentive feature representation. Finally, an inter-sensor feature extraction module is applied to learn the inter-sensor correlations, which are connected to a classifier to output the predicted classes of activities. The proposed approach is evaluated using five public datasets and it outperforms state-of-the-art methods on a wide variety of activity categories.
High-Cardinality Hybrid Shaping for 4D Modulation Formats in Optical Communications Optimized via End-to-End Learning
Authors: Vinícius Oliari, Boris Karanov, Sebastiaan Goossens, Gabriele Liga, Olga Vassilieva, Inwoong Kim, Paparao Palacharla, Chigo Okonkwo, Alex Alvarado
Date: 2021-12-20 00:00:00

In this paper we carry out a joint optimization of probabilistic (PS) and geometric shaping (GS) for four-dimensional (4D) modulation formats in long-haul coherent wavelength division multiplexed (WDM) optical fiber communications using an auto-encoder framework. We propose a 4D 10 bits/symbol constellation which we obtained via end-to-end deep learning over the split-step Fourier model of the fiber channel. The constellation achieved 13.6% reach increase at a data rate of approximately 400 Gbits/second in comparison to the ubiquitously employed polarization multiplexed 32-QAM format at a forward error correction overhead of 20%.
MDG and SNR Estimation in SDM Transmission Based on Artificial Neural Networks
Authors: Ruby S. B. Ospina, Menno van den Hout, Sjoerd van der Heide, John van Weerdenburg, Roland Ryf, Nicolas K. Fontaine, Haoshuo Chen, Rodrigo Amezcua-Correa, Chigo Okonkwo, Darli A. A. Mello
Date: 2021-12-20 00:00:00

The increase in capacity provided by coupled SDM systems is fundamentally limited by MDG and ASE noise. Therefore, monitoring MDG and optical SNR is essential for accurate performance evaluation and troubleshooting. Recent works show that the conventional MDG estimation method based on the transfer matrix of MIMO equalizers optimizing the MMSE underestimates the actual value at low SNR. Besides, estimating the optical SNR itself is not a trivial task in SDM systems, as MDG strongly influences the electrical SNR after the equalizer. In a recent work we propose an MDG and SNR estimation method using ANN. The proposed ANN-based method processes features extracted at the receiver after DSP. In this paper, we discuss the ANN-based method in detail, and validate it in an experimental 73-km 3-mode transmission link with controlled MDG and SNR. After validation, we apply the method in a case study consisting of an experimental long-haul 6-mode link. The results show that the ANN estimates both MDG and SNR with high accuracy, outperforming conventional methods.
The two-dimensional OLCT of angularly periodic functions in polar coordinates
Authors: Hui Zhao, Bing-Zhao Li
Date: 2021-12-19 00:00:00
Tasks: Vocal Bursts Valence Prediction

The two-dimensional (2D) offset linear canonical transform (OLCT) in polar coordinates plays an important role in many fields of optics and signal processing. This paper studies the 2D OLCT in polar coordinates. Firstly, we extend the 2D OLCT to the polar coordinate system, and obtain the offset linear canonical Hankel transform (OLCHT) formula. Secondly, through the angular periodic function with a period of 2{\pi}, the relationship between the 2D OLCT and the OLCHT is revealed. Finally, the spatial shift and convolution theorems for the 2D OLCT are proposed by using this relationship.
Digital RIS (DRIS): The Future of Digital Beam Management in RIS-Assisted OWC Systems
Authors: Alain R. Ndjiongue, Telex M. N. Ngatched, Octavia A. Dobre, Harald Haas
Date: 2021-12-18 00:00:00
Tasks: Management

Reconfigurable intelligent surfaces (RIS) have been recently introduced to optical wireless communication (OWC) networks to resolve skip areas and improve the signal-to-noise ratio at the user's end. In OWC networks, RIS are based on mirrors or metasurfaces. Metasurfaces have evolved significantly over the last few years. As a result, coding, digital, programmable, and information metamaterials have been developed. The advantage of these materials is that they can enable digital signal processing (DSP) techniques. For the first time, this paper proposes the use of digital RIS (DRIS) in OWC systems. We discuss the concept of DRIS and the application of DSP methods to the physical material. In addition, we examine metamaterials for optical DRIS with liquid crystals serving as the front row material. Finally, we present a design example and discuss future research directions.
Learning to Model the Relationship Between Brain Structural and Functional Connectomes
Authors: Yang Li, Gonzalo Mateos, Zhengwu Zhang
Date: 2021-12-18 00:00:00
Tasks: Decoder, Functional Connectivity, Graph Representation Learning, Representation Learning

Recent advances in neuroimaging along with algorithmic innovations in statistical learning from network data offer a unique pathway to integrate brain structure and function, and thus facilitate revealing some of the brain's organizing principles at the system level. In this direction, we develop a supervised graph representation learning framework to model the relationship between brain structural connectivity (SC) and functional connectivity (FC) via a graph encoder-decoder system, where the SC is used as input to predict empirical FC. A trainable graph convolutional encoder captures direct and indirect interactions between brain regions-of-interest that mimic actual neural communications, as well as to integrate information from both the structural network topology and nodal (i.e., region-specific) attributes. The encoder learns node-level SC embeddings which are combined to generate (whole brain) graph-level representations for reconstructing empirical FC networks. The proposed end-to-end model utilizes a multi-objective loss function to jointly reconstruct FC networks and learn discriminative graph representations of the SC-to-FC mapping for downstream subject (i.e., graph-level) classification. Comprehensive experiments demonstrate that the learnt representations of said relationship capture valuable information from the intrinsic properties of the subject's brain networks and lead to improved accuracy in classifying a large population of heavy drinkers and non-drinkers from the Human Connectome Project. Our work offers new insights on the relationship between brain networks that support the promising prospect of using graph representation learning to discover more about human brain activity and function.
A Joint Beamforming Design and Integrated CPM-LFM Signal for Dual-functional Radar-communication Systems
Authors: Yu Cao, QiYue Yu
Date: 2021-12-18 00:00:00

The dual-functional radar-communication (DFRC) system is an attractive technique, since it can support both wireless communications and radar by a unified hardware platform with real-time cooperation. Considering the appealing feature of multiple beams, this paper proposes a precoding scheme that simultaneously support multiuser transmission and target detection, with an integrated continuous phase modulation (CPM) and linear frequency modulation (LFM) signal, based on the designed dual mode framework. Similarly to the conception of communication rate, this paper defines radar rate to unify the DFRC system. Then, the maximum sum-rate that includes both the communication and radar rates is set to be the objective function. Regarding as the optimal issue is non-convex, the optimal problem is divided into two sub-issues, one is the user selection issue, and the other is the joint beamforming design and power allocation issue. A successive maximum iteration (SMI) algorithm is presented for the former issue, which can balance the performances between the sum-rate and complexity; and maximum minimization Lagrange multiplier (MMLM) iteration algorithm is utilized to solve the latter optimal issue. Moreover, we deduce the spectrum characteristic, bit error rate (BER) and ambiguity function (AF) for the proposed system. Simulation results show that our proposed system can provide appreciated sum-rate than the classical schemes, validating the efficiency of the proposed system.
Robust Distributed Bayesian Learning with Stragglers via Consensus Monte Carlo
Authors: Hari Hara Suthan Chittoor, Osvaldo Simeone
Date: 2021-12-17 00:00:00

This paper studies distributed Bayesian learning in a setting encompassing a central server and multiple workers by focusing on the problem of mitigating the impact of stragglers. The standard one-shot, or embarrassingly parallel, Bayesian learning protocol known as consensus Monte Carlo (CMC) is generalized by proposing two straggler-resilient solutions based on grouping and coding. Two main challenges in designing straggler-resilient algorithms for CMC are the need to estimate the statistics of the workers' outputs across multiple shots, and the joint non-linear post-processing of the outputs of the workers carried out at the server. This is in stark contrast to other distributed settings like gradient coding, which only require the per-shot sum of the workers' outputs. The proposed methods, referred to as Group-based CMC (G-CMC) and Coded CMC (C-CMC), leverage redundant computing at the workers in order to enable the estimation of global posterior samples at the server based on partial outputs from the workers. Simulation results show that C-CMC may outperform G-CMC for a small number of workers, while G-CMC is generally preferable for a larger number of workers.
Comparative Analysis of Radar Cross Section Based UAV Classification Techniques
Authors: Martins Ezuma, Chethan Kumar Anjinappa, Vasilii Semkin, Ismail Guvenc
Date: 2021-12-17 00:00:00
Tasks: Classification

This work investigates the problem of unmanned aerial vehicles (UAVs) identification using their radar crosssection (RCS) signature. The RCS of six commercial UAVs are measured at 15 GHz and 25 GHz in an anechoic chamber, for both vertical-vertical and horizontal-horizontal polarization. The RCS signatures are used to train 15 different classification algorithms, each belonging to one of three different categories: statistical learning (SL), machine learning (ML), and deep learning (DL). The study shows that while the classification accuracy of all the algorithms increases with the signal-to-noise ratio (SNR), the ML algorithm achieved better accuracy than the SL and DL algorithms. For example, the classification tree ML achieves an accuracy of 98.66% at 3 dB SNR using the 15 GHz VV-polarized RCS test data from the UAVs. We investigate the classification accuracy using Monte Carlo analysis with the aid of boxplots, confusion matrices, and classification plots. On average, the accuracy of the classification tree ML model performed better than the other algorithms, followed by the Peter Swerling statistical models and the discriminant analysis ML model. In general, the classification accuracy of the ML and SL algorithms outperformed the DL algorithms (Squeezenet, Googlenet, Nasnet, and Resnet 101) considered in the study. Furthermore, the computational time of each algorithm is analyzed. The study concludes that while the SL algorithms achieved good classification accuracy, the computational time was relatively long when compared to the ML and DL algorithms. Also, the study shows that the classification tree achieved the fastest average classification time of about 0.46 ms.
Learning in Restless Bandits under Exogenous Global Markov Process
Authors: Tomer Gafni, Michal Yemini, Kobi Cohen
Date: 2021-12-17 00:00:00

We consider an extension to the restless multi-armed bandit (RMAB) problem with unknown arm dynamics, where an unknown exogenous global Markov process governs the rewards distribution of each arm. Under each global state, the rewards process of each arm evolves according to an unknown Markovian rule, which is non-identical among different arms. At each time, a player chooses an arm out of $N$ arms to play, and receives a random reward from a finite set of reward states. The arms are restless, that is, their local state evolves regardless of the player's actions. Motivated by recent studies on related RMAB settings, the regret is defined as the reward loss with respect to a player that knows the dynamics of the problem, and plays at each time $t$ the arm that maximizes the expected immediate value. The objective is to develop an arm-selection policy that minimizes the regret. To that end, we develop the Learning under Exogenous Markov Process (LEMP) algorithm. We analyze LEMP theoretically and establish a finite-sample bound on the regret. We show that LEMP achieves a logarithmic regret order with time. We further analyze LEMP numerically and present simulation results that support the theoretical findings and demonstrate that LEMP significantly outperforms alternative algorithms.
Joint Transmit Waveform and Passive Beamforming Design for RIS-Aided DFRC Systems
Authors: Rang Liu, Ming Li, Yang Liu, Qingqing Wu, Qian Liu
Date: 2021-12-16 00:00:00

Reconfigurable intelligent surface (RIS) is a promising technology for 6G networks owing to its superior ability to enhance the capacity and coverage of wireless communications by smartly creating a favorable propagation environment. In this paper, we investigate the potential of employing RIS in dual-functional radar-communication (DFRC) systems for improving both radar sensing and communication functionalities. In particular, we consider a RIS-assisted DFRC system in which the multi-antenna base station (BS) simultaneously performs both multi-input multi-output (MIMO) radar sensing and multi-user multi-input single-output (MU-MISO) communications using the same hardware platform. We aim to jointly design the dual-functional transmit waveform and the passive beamforming of RIS to maximize the radar output signal-to-interference-plus-noise ratio (SINR) achieved by space-time adaptive processing (STAP), while satisfying the communication quality-of-service (QoS) requirement under one of three metrics, the constant-modulus constraint on the transmit waveform, and the unit-modulus constraint of RIS reflecting coefficients. An efficient algorithm framework based on the alternative direction method of multipliers (ADMM) and majorization-minimization (MM) methods is developed to solve the complicated non-convex optimization problem. Simulation results verify the advancement of the proposed RIS-assisted DRFC scheme and the effectiveness of the developed ADMM-MM-based joint transmit waveform and passive beamforming design algorithm.
A Multi-band Solution for Interacting with Energy-Neutral Devices
Authors: Chesney Buyle, Bert Cox, Liesbet Van der Perre, Lieven De Strycker
Date: 2021-12-16 00:00:00

RF Wireless Power Transfer (WPT) emerges as a technology for charging autonomous devices, enabling simultaneous power and information transfer. However, with increasing distance, single-input, single-channel rectenna systems are not able to meet the power requirements of large scale IoT applications. In this paper, we tackle this problem on two levels. First, we minimize the energy consumption at the energy-constrained device on three levels. Second, we evolve to a dual-band solution increasing RF WPT. One frequency band is used to provide a base charge to many nodes in a shared transmission. Beam steering, on the other hand, allows for more power hungry operations while introducing as minimal interference as possible. We showcase this method for a hybrid RF-acoustic positioning system. Practical measurements conducted in a multi-antenna indoor testbed (Techtile) show the additional power gain and positioning rate.
Joint Waveform and Filter Designs for STAP-SLP-based MIMO-DFRC Systems
Authors: R. Liu, M. Li, Q. Liu, A. L. Swindlehurst
Date: 2021-12-16 00:00:00
Tasks: Integrated sensing and communication, ISAC

Dual-function radar-communication (DFRC), which can simultaneously perform both radar and communication functionalities using the same hardware platform, spectral resource and transmit waveform, is a promising technique for realizing integrated sensing and communication (ISAC). Spacetime adaptive processing (STAP) in multi-antenna radar systems is the primary tool for detecting moving targets in the presence of strong clutter. The idea of joint spatial-temporal optimization in STAP-based radar systems is consistent with the concept of symbol-level precoding (SLP) for multi-input multi-output (MIMO) communications, which optimizes the transmit waveform for each of the transmitted symbols. In this paper, we combine STAP and SLP and propose a novel STAP-SLP-based DFRC system that enjoys the advantages of both techniques. The radar output signal-to-interference-plus-noise ratio (SINR) is maximized by jointly optimizing the transmit waveform and receive filter, while satisfying the communication quality-of-service (QoS) constraint and various waveform constraints including constant-modulus, similarity and peak-to-average power ratio (PAPR). An efficient algorithm framework based on majorization-minimization (MM) and nonlinear equality constrained alternative direction method of multipliers (neADMM) methods is proposed to solve these complicated non-convex optimization problems. Simulation results verify the effectiveness of the proposed STAP-SLP-based MIMO-DRFC scheme and the associate algorithms.
COVID-19 Electrocardiograms Classification using CNN Models
Authors: Ismail Shahin, Ali Bou Nassif, Mohamed Bader Alsabek
Date: 2021-12-15 00:00:00
Tasks: Classification

With the periodic rise and fall of COVID-19 and numerous countries being affected by its ramifications, there has been a tremendous amount of work that has been done by scientists, researchers, and doctors all over the world. Prompt intervention is keenly needed to tackle the unconscionable dissemination of the disease. The implementation of Artificial Intelligence (AI) has made a significant contribution to the digital health district by applying the fundamentals of deep learning algorithms. In this study, a novel approach is proposed to automatically diagnose the COVID-19 by the utilization of Electrocardiogram (ECG) data with the integration of deep learning algorithms, specifically the Convolutional Neural Network (CNN) models. Several CNN models have been utilized in this proposed framework, including VGG16, VGG19, InceptionResnetv2, InceptionV3, Resnet50, and Densenet201. The VGG16 model has outperformed the rest of the models, with an accuracy of 85.92%. Our results show a relatively low accuracy in the rest of the models compared to the VGG16 model, which is due to the small size of the utilized dataset, in addition to the exclusive utilization of the Grid search hyperparameters optimization approach for the VGG16 model only. Moreover, our results are preparatory, and there is a possibility to enhance the accuracy of all models by further expanding the dataset and adapting a suitable hyperparameters optimization technique.
Fast Computation of Generalized Eigenvectors for Manifold Graph Embedding
Authors: Fei Chen, Gene Cheung, Xue Zhang
Date: 2021-12-15 00:00:00
Tasks: Clustering, Graph Embedding

Our goal is to efficiently compute low-dimensional latent coordinates for nodes in an input graph -- known as graph embedding -- for subsequent data processing such as clustering. Focusing on finite graphs that are interpreted as uniform samples on continuous manifolds (called manifold graphs), we leverage existing fast extreme eigenvector computation algorithms for speedy execution. We first pose a generalized eigenvalue problem for sparse matrix pair $(\A,\B)$, where $\A = \L - \mu \Q + \epsilon \I$ is a sum of graph Laplacian $\L$ and disconnected two-hop difference matrix $\Q$. Eigenvector $\v$ minimizing Rayleigh quotient $\frac{\v^{\top} \A \v}{\v^{\top} \v}$ thus minimizes $1$-hop neighbor distances while maximizing distances between disconnected $2$-hop neighbors, preserving graph structure. Matrix $\B = \text{diag}(\{\b_i\})$ that defines eigenvector orthogonality is then chosen so that boundary / interior nodes in the sampling domain have the same generalized degrees. $K$-dimensional latent vectors for the $N$ graph nodes are the first $K$ generalized eigenvectors for $(\A,\B)$, computed in $\cO(N)$ using LOBPCG, where $K \ll N$. Experiments show that our embedding is among the fastest in the literature, while producing the best clustering performance for manifold graphs.