pwc-archive/papers-with-abstracts huggingface dataset:
ML-powered KQI estimation for XR services. A case study on 360-Video
Authors: O. S. Peñaherrera-Pulla, Carlos Baena, Sergio Fortes, Raquel Barco
Date: 2022-12-08 00:00:00
Tasks: Feature Engineering, feature selection

The arise of cutting-edge technologies and services such as XR promise to change the concepts of how day-to-day things are done. At the same time, the appearance of modern and decentralized architectures approaches has given birth to a new generation of mobile networks such as 5G, as well as outlining the roadmap for B5G and posterior. These networks are expected to be the enablers for bringing to life the Metaverse and other futuristic approaches. In this sense, this work presents an ML-based (Machine Learning) framework that allows the estimation of service Key Quality Indicators (KQIs). For this, only information reachable to operators is required, such as statistics and configuration parameters from these networks. This strategy prevents operators from avoiding intrusion into the user data and guaranteeing privacy. To test this proposal, 360-Video has been selected as a use case of Virtual Reality (VR), from which specific KQIs are estimated such as video resolution, frame rate, initial startup time, throughput, and latency, among others. To select the best model for each KQI, a search grid with a cross-validation strategy has been used to determine the best hyperparameter tuning. To boost the creation of each KQI model, feature engineering techniques together with cross-validation strategies have been used. The performance is assessed using MAE (Mean Average Error) and the prediction time. The outcomes point out that KNR (K-Near Neighbors) and RF (Random Forest) are the best algorithms in combination with Feature Selection techniques. Likewise, this work will help as a baseline for E2E-Quality-of-Experience-based network management working in conjunction with network slicing, virtualization, and MEC, among other enabler technologies.
Channel charting based beamforming
Authors: Luc Le Magoarou, Taha Yassine, Stephane Paquelet, Matthieu Crussière
Date: 2022-12-06 00:00:00

Channel charting (CC) is an unsupervised learning method allowing to locate users relative to each other without reference. From a broader perspective, it can be viewed as a way to discover a low-dimensional latent space charting the channel manifold. In this paper, this latent modeling vision is leveraged together with a recently proposed location-based beamforming (LBB) method to show that channel charting can be used for mapping channels in space or frequency. Combining CC and LBB yields a neural network resembling an autoencoder. The proposed method is empirically assessed on a channel mapping task whose objective is to predict downlink channels from uplink channels.
Set Membership based Nonlinear Model Predictive Control
Authors: Mattia Boggio, Carlo Novara, Michele Taragna
Date: 2022-12-23 00:00:00
Tasks: model, Model Predictive Control

We present a numerically efficient Nonlinear Model Predictive Control (NMPC) approach, called Set Membership based NMPC (SM-NMPC). In particular, a Set Membership method is used to derive from data an approximation and tight bounds on the optimal NMPC control law. These quantities are used to reduce the dimensionality and volume of the search domain of the NMPC optimization problem, allowing a significant shortening of the computation time. The proposed SM-NMPC strategy is tested in simulation, considering realistic autonomous vehicle scenarios, like parallel parking and lane keeping maneuvers.
Tutorial: Circuit-based Electromagnetic Transient Simulation
Authors: Amritanshu Pandey
Date: 2022-12-23 00:00:00

The growing penetration of inverter-based resources and associated controls necessitates system-wide electromagnetic transient (EMT) analyses. EMT tools and methods today were not designed for the scale of these analyses. In light of the emerging need, there is a great deal of interest in developing new techniques for fast and accurate EMT simulations for large power grids; the foundations of which will be built on current tools and methods. However, we find that educational texts covering the fundamentals and inner workings of current EMT tools are limited. As such, there is a lack of introductory material for students and professionals interested in researching the field. To that end, in this tutorial, we introduce the principles of EMT analyses from the circuit-theoretic viewpoint, mimicking how time-domain analyses are performed in circuit simulation tools like SPICE and Cadence. We perform EMT simulations for two examples, one linear and one nonlinear, including induction motor (IM) from the first principles. By the document's end, we anticipate the readers will have a \textit{basic} understanding of how power grid EMT tools work.
Mesh of Things (MoT) Network-Driven Anomaly Detection in Connected Objects
Authors: Rathinamala Vijay, Prabhakar. T. V.
Date: 2022-12-23 00:00:00
Tasks: Anomaly Detection

This paper presents a hybrid Mesh of Things (MoT) network performance model to evaluate the end-to-end Packet Delivery Ratio (PDR) and latency. These PDR and latency measures are used to identify both a de-tangled mesh as well as to track the mesh successfully. A de-tangled mesh is a mesh with an anomaly where one or more nodes are separated from the rest of the mesh network. We demonstrate the performance model of a hybrid BLE mesh-PLC network by considering an air cargo monitoring application and validate with experimental PDR and latency data. The link uncertainty in Bluetooth Low Energy (BLE) mesh may be attributed to (a) RF interference,(b)~Transmitter's vicinity range, and (c) Receiver sensitivity. In contrast, the link uncertainty in Power Line Communication (PLC) may be attributed to: (a) Colored background noise, (b)~Channel frequency response, and (c) Impulse noise appearing due to load state as well as variations in the powerline. In our work, we construct an equivalent Bayesian network for the mesh to be tracked, capture the uncertainty within the mesh links using the Noisy-OR and the Noisy-Integer addition model and perform belief propagation to detect and localize a network anomaly.
Monotonous Parameter Estimation of One Class of Nonlinearly Parameterized Regressions without Overparameterization
Authors: Anton Glushchenko, Konstantin Lastochkin
Date: 2022-12-23 00:00:00
Tasks: parameter estimation, regression

The estimation law of unknown parameters vector ${\theta}$ is proposed for one class of nonlinearly parametrized regression equations $y\left( t \right) = \Omega \left( t \right)\Theta \left( \theta \right)$. We restrict our attention to parametrizations that are widely obtained in practical scenarios when polynomials in $\theta$ are used to form $\Theta \left( \theta \right)$. For them we introduce a new 'linearizability' assumption that a mapping from overparametrized vector of parameters $\Theta \left( \theta \right)$ to original one $\theta$ exists in terms of standard algebraic functions. Under such assumption and weak requirement of the regressor finite excitation, on the basis of dynamic regressor extension and mixing technique we propose a procedure to reduce the nonlinear regression equation to the linear parameterization without application of singularity causing operations and the need to identify the overparametrized parameters vector. As a result, an estimation law with exponential convergence rate is derived, which, unlike known solutions, (i) does not require a strict P-monotonicity condition to be met and a priori information about $\theta$ to be known, (ii) ensures elementwise monotonicity for the parameter error vector. The effectiveness of our approach is illustrated with both academic example and 2-DOF robot manipulator control problem.
Frequency Stability-Constrained Unit Commitment: Tight Approximation using Bernstein Polynomials
Authors: Bo Zhou, Ruiwei Jiang, Siqian Shen
Date: 2022-12-23 00:00:00

As we replace conventional synchronous generators with renewable energy, the frequency security of power systems is at higher risk. This calls for a more careful consideration of unit commitment (UC) and primary frequency response (PFR) reserves. This paper studies frequency-secured UC under significant wind power uncertainty. We coordinate the thermal units and wind farms to provide frequency support, wherein we optimize the variable inverter droop factors of the wind farms for higher economy. In addition, we adopt distributionally robust chance constraints (DRCCs) to handle the wind power uncertainty. To depict the frequency dynamics, we incorporate a differential-algebraic equation (DAE) with the dead band into the UC model. Notably, we apply Bernstein polynomials to derive tight inner approximation of the DAE and obtain mixed-integer linear constraints, which can be computed in off-the-shelf solvers. Case studies demonstrate the tightness and effectiveness of the proposed method in guaranteeing frequency security.
A Method for Crash Prediction and Avoidance Using Hidden Markov Models
Authors: Avinash Prabu, Lingxi Li, Brian King, Yaobin Chen
Date: 2022-12-22 00:00:00
Tasks: Pedestrian Detection

In recent years, automotive technology has made a steady progress. In particular, Advanced Driver Assistance System (ADAS) has enabled many safety features in commercial vehicles, for instance, pedestrian detection, lane keeping assist, emergency automatic braking, etc. Although these features provide drivers with a safer operational environment, crashes still happen occasionally due to the complex road conditions and the unpredictable movement of road users including vehicles, pedestrians, bicyclists, and non-motorized vehicles. In this paper, we aim at predicting the possibilities of crashes between vehicles on highway and implementing an appropriate active safety system to prevent the same. In particular, hidden Markov models are developed for the traffic lanes and speed change of vehicles on highway. Algorithms are developed for the prediction of crash probabilities. Simulation experiments are conducted using Matlab, the results illustrate the effectiveness of the proposed research.
A Novel Method for Lane-change Maneuver in Urban Driving Using Predictive Markov Decision Process
Authors: Avinash Prabu, Niranjan Ravi, Lingxi Li
Date: 2022-12-22 00:00:00
Tasks: Autonomous Driving

Lane-change maneuver has always been a challenging task for both manual and autonomous driving, especially in an urban setting. In particular, the uncertainty in predicting the behavior of other vehicles on the road leads to indecisive actions while changing lanes, which, might result in traffic congestion and cause safety concerns. This paper analyzes the factors related to uncertainty such as speed range change and lane change so as to design a predictive Markov decision process for lane-change maneuver in the urban setting. A hidden Markov model is developed for modeling uncertainties of surrounding vehicles. The reward model uses the crash probabilities and the feasibility/distance to the goal as primary parameters. Numerical simulation and analysis of two traffic scenarios are completed to demonstrate the effectiveness of the proposed approach.
Short time extremal response to step stimulus for a single cell {\sl E. coli}
Authors: Sakuntala Chatterjee
Date: 2022-12-23 00:00:00

After application of a step stimulus, in the form of a sudden change in attractant environment, the receptor activity and tumbling bias of an {\sl E. coli} cell change sharply to reach their extremal values before they gradually relax to their post-stimulus adapted levels in the long time limit. We perform numerical simulations and exact calculations to investigate the short time response of the cell. For both activity and tumbling bias, we exactly derive the condition for extremal response and find good agreement with simulations. We also make experimentally verifiable prediction that there is an optimum size of the step stimulus at which the extremal response is reached in the shortest possible time.
Equivalence between the Fitness-Complexity and the Sinkhorn-Knopp algorithms
Authors: Dario Mazzilli, Manuel Sebastian Mariani, Flaviano Morone, Aurelio Patelli
Date: 2022-12-23 00:00:00

We uncover the connection between the Fitness-Complexity algorithm, developed in the economic complexity field, and the Sinkhorn-Knopp algorithm, widely used in diverse domains ranging from computer science and mathematics to economics. Despite minor formal differences between the two methods, both converge to the same fixed-point solution up to normalization. The discovered connection allows us to derive a rigorous interpretation of the Fitness and the Complexity metrics as the potentials of a suitable energy function. Under this interpretation, high-energy products are unfeasible for low-fitness countries, which explains why the algorithm is effective at displaying nested patterns in bipartite networks. We also show that the proposed interpretation reveals the scale invariance of the Fitness-Complexity algorithm, which has practical implications for the algorithm's implementation in different datasets. Further, analysis of empirical trade data under the new perspective reveals three categories of countries that might benefit from different development strategies.
Single-Crossing Differences in Convex Environments
Authors: Navin Kartik, SangMok Lee, Daniel Rappoport
Date: 2022-12-22 00:00:00

An agent's preferences depend on an ordered parameter or type. We characterize the set of utility functions with single-crossing differences (SCD) in convex environments. These include preferences over lotteries, both in expected utility and rank-dependent utility frameworks, and preferences over bundles of goods and over consumption streams. Our notion of SCD does not presume an order on the choice space. This unordered SCD is necessary and sufficient for ''interval choice'' comparative statics. We present applications to cheap talk, observational learning, and collective choice, showing how convex environments arise in these problems and how SCD/interval choice are useful. Methodologically, our main characterization stems from a result on linear aggregations of single-crossing functions.
Large Raw Emotional Dataset with Aggregation Mechanism
Authors: Vladimir Kondratenko, Artem Sokolov, Nikolay Karpov, Oleg Kutuzov, Nikita Savushkin, Fyodor Minkin
Date: 2022-12-23 00:00:00
Tasks: Emotion Recognition, Speech Emotion Recognition

We present a new data set for speech emotion recognition (SER) tasks called Dusha. The corpus contains approximately 350 hours of data, more than 300 000 audio recordings with Russian speech and their transcripts. Therefore it is the biggest open bi-modal data collection for SER task nowadays. It is annotated using a crowd-sourcing platform and includes two subsets: acted and real-life. Acted subset has a more balanced class distribution than the unbalanced real-life part consisting of audio podcasts. So the first one is suitable for model pre-training, and the second is elaborated for fine-tuning purposes, model approbation, and validation. This paper describes pre-processing routine, annotation, and experiment with a baseline model to demonstrate some actual metrics which could be obtained with the Dusha data set.
Grant-free Random Access with Self-conjugating Metasurfaces
Authors: Davide Dardari, Marina Lotti, Nicolò Decarli, Gianni Pasolini
Date: 2022-12-23 00:00:00

Recently, grant-free random access schemes have received significant attention in the scientific community as a solution for extremely low-latency massive communications in new industrial Internet-of-things (IIoT) and digital twins applications. Unfortunately, the adoption of such schemes in the mmWave and THz bands is challenging because massive antenna arrays are needed to counteract the high path loss and provide massive access with consequent significant signaling overhead for channel estimation and slow beam alignment procedures between the base station (BS) and user equipments (UEs), which are in contrast to the ultra-low-latency requirement, as well as to the need for low hardware complexity and energy consumption. In this paper, we propose the adoption of a self-conjugating metasurface (SCM) at the UE side, where the signal sent by the BS is reflected after being conjugated and phase-modulated according to the UE data. Then, a novel grant-free random access protocol is presented capable to detect new UEs and establish parallel multiple-input multiple-output (MIMO) uplink communications with almost zero latency and jitter. This is done in a blind way without the need for RF/ADC chains at the UE side as well as without explicit channel estimation and time-consuming beam alignment schemes.
Radio SLAM for 6G Systems at THz Frequencies: Design and Experimental Validation
Authors: Marina Lotti, Gianni Pasolini, Anna Guerra, Francesco Guidi, Raffaele D'Errico, Davide Dardari
Date: 2022-12-23 00:00:00
Tasks: Simultaneous Localization and Mapping

Next-generation wireless networks will see the convergence of communication and sensing, also exploiting the availability of large bandwidths in the Terahertz (THz) spectrum and electrically large antenna arrays on handheld devices. In particular, it is envisaged that user devices will be able to automatically scan their surroundings by steering a very narrow antenna beam and collecting echoes reflected by objects and walls to derive a map of indoors and infer users' trajectories using simultaneous localization and mapping (SLAM) techniques. In this paper, we address this scenario by proposing original radioSLAM (R-SLAM) algorithms, derived from image processing techniques, to map the environment and pinpoint the device position in the map starting from measurements sensed by a mobile THz radar. Initially, to fully understand the THz backscattering phenomenon, we provide an experimental characterization of the THz backscattering channel in indoor environments. Then, the performance of the proposed algorithms is assessed using real-world THz radar measurements and is compared with state-of-the-art SLAM techniques, demonstrating the superiority of the proposed approaches.
a cognitive frequency allocation strategy for multi-carrier radar against communication interference
Authors: Zhao Shan, Lei Wang, PengFei Liu, Tianyao Huang, Yimin Liu
Date: 2022-12-23 00:00:00
Tasks: Deep Reinforcement Learning

Modern radars often adopt multi-carrier waveform which has been widely discussed in the literature. However, with the development of civil communication, more and more spectrum resource has been occupied by communication networks. Thus, avoiding the interference from communication users is an important and challenging task for the application of multi-carrier radar. In this paper, a novel frequency allocation strategy based on the historical experiences is proposed, which is formulated as a Markov decision process (MDP). In a decision step, the multi-carrier radar needs to choose more than one frequencies, leading to a combinatorial action space. To address this challenge, we use a novel iteratively selecting technique which breaks a difficult decision task into several easy tasks. Moreover, an efficient deep reinforcement learning algorithm is adopted to handle the complicated spectrum dynamics. Numerical results show that our proposed method outperforms the existing ones.
Trainable Proximal Gradient Descent Based Channel Estimation for mmWave Massive MIMO Systems
Authors: Peicong Zheng, Xuantao Lyu, Yi Gong
Date: 2022-12-23 00:00:00

In this letter, we address the problem of millimeter-Wave channel estimation in massive MIMO communication systems. Leveraging the sparsity of the mmWave channel in the beamspace, we formulate the estimation problem as a sparse signal recovery problem. To this end, we propose a deep learning based trainable proximal gradient descent network (TPGD-Net). The TPGD-Net unfolds the iterative proximal gradient descent (PGD) algorithm into a layer-wise network, with the gradient descent step size set as a trainable parameter. Additionally, we replace the proximal operator in the PGD algorithm with a neural network that exploits data-driven prior channel information to perform the proximal operation implicitly. To further enhance the transfer of feature information across layers, we introduce the cross-layer feature attention fusion module into the TPGD-Net. Our simulation results on the Saleh-Valenzuela channel model and the DeepMIMO dataset demonstrate the superior performance of TPGD-Net compared to state-of-the-art mmWave channel estimators.
Design of a Single-Element Dynamic Antenna for Secure Wireless Applications
Authors: Amer Abu Arisheh, Jason M. Merlo, Jeffrey A. Nanzer
Date: 2022-12-23 00:00:00

We introduce a new technique for secure wireless applications using a single dynamic antenna. The dynamic antenna supports a constantly changing current distribution that generates a radiation pattern that is static in a desired direction and dynamic elsewhere, thereby imparting additional modulation on the signal and obscuring information transmitted or received outside of the secure spatial region. Dynamic currents are supported by a single feed that is switched between separate ports on a single antenna, generating two different radiation patterns. We introduce the theoretical concept by exploring an ideal complex dynamic radiation pattern that remains static in a narrow desired direction and is dynamic elsewhere. The impact on the transmission of information is analyzed, showing that the secure region narrows as the modulation order increases, and design constraints on the spatial width of the secure region as a function of modulation format are determined. We design and analyze a 2.3 GHz two-state dynamic dipole antenna and experimentally demonstrate secure wireless transmission. We show the ability to steer the secure region experimentally, and to maintain high throughput in the secure region while obscuring the information elsewhere. Our approach introduces a novel single-element technique for secure wireless applications that can be used independently from the rest of the wireless system, essentially operating as a $``$black box$"$ for an additional layer of security.
AMDET: Attention based Multiple Dimensions EEG Transformer for Emotion Recognition
Authors: Yongling Xu, Yang Du, Jing Zou, Tianying Zhou, Lushan Xiao, Li Liu, Pengcheng
Date: 2022-12-23 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG), Emotion Recognition

Affective computing is an important branch of artificial intelligence, and with the rapid development of brain computer interface technology, emotion recognition based on EEG signals has received broad attention. It is still a great challenge to effectively explore the multi-dimensional information in the EEG data in spite of a large number of deep learning methods. In this paper, we propose a deep model called Attention-based Multiple Dimensions EEG Transformer (AMDET), which can exploit the complementarity among the spectral-spatial-temporal features of EEG data by employing the multi-dimensional global attention mechanism. We transformed the original EEG data into 3D temporal-spectral-spatial representations and then the AMDET would use spectral-spatial transformer encoder layer to extract effective features in the EEG signal and concentrate on the critical time frame with a temporal attention layer. We conduct extensive experiments on the DEAP, SEED, and SEED-IV datasets to evaluate the performance of AMDET and the results outperform the state-of-the-art baseline on three datasets. Accuracy rates of 97.48%, 96.85%, 97.17%, 87.32% were achieved in the DEAP-Arousal, DEAP-Valence, SEED, and SEED-IV datasets, respectively. We also conduct extensive experiments to explore the possible brain regions that influence emotions and the coupling of EEG signals. AMDET can perform as well even with few channels which are identified by visualizing what learned model focus on. The accuracy could achieve over 90% even with only eight channels and it is of great use and benefit for practical applications.