pwc-archive/papers-with-abstracts huggingface dataset:
Beamforming Design for Multiuser Transmission Through Reconfigurable Intelligent Surface
Authors: Zhaohui Yang, Wei Xu, Chongwen Huang, Jianfeng Shi, Mohammad Shikh-Bahaei
Date: 2020-09-24 00:00:00

This paper investigates the problem of resource allocation for multiuser communication networks with a reconfigurable intelligent surface (RIS)-assisted wireless transmitter. In this network, the sum transmit power of the network is minimized by controlling the phase beamforming of the RIS and transmit power of the BS. This problem is posed as a joint optimization problem of transmit power and RIS control, whose goal is to minimize the sum transmit power under signal-to-interference-plus-noise ratio (SINR) constraints of the users. To solve this problem, a dual method is proposed, where the dual problem is obtained as a semidefinite programming problem. After solving the dual problem, the phase beamforming of the RIS is obtained in the closed form, while the optimal transmit power is obtained by using the standard interference function. Simulation results show that the proposed scheme can reduce up to 94% and 27% sum transmit power compared to the maximum ratio transmission (MRT) beamforming and zero-forcing (ZF) beamforming techniques, respectively.
Social Learning in Nonatomic Routing Games
Authors: Emilien Macault, Marco Scarsini, Tristan Tomala
Date: 2020-09-24 00:00:00

We consider a discrete-time nonatomic routing game with variable demand and uncertain costs. Given a routing network with single origin and destination, the cost function of each edge depends on some uncertain persistent state parameter. At every period, a random traffic demand is routed through the network according to a Wardrop equilibrium. The realized costs are publicly observed and the public Bayesian belief about the state parameter is updated. We say that there is strong learning when beliefs converge to the truth and weak learning when the equilibrium flow converges to the complete-information flow. We characterize the networks for which learning occurs. We prove that these networks have a series-parallel structure and provide a counterexample to show that learning may fail in non-series-parallel networks.
Phylosymmetric algebras: mathematical properties of a new tool in phylogenetics
Date: 2020-09-24 00:00:00

In phylogenetics it is of interest for rate matrix sets to satisfy closure
under matrix multiplication as this makes finding the set of corresponding
transition matrices possible without having to compute matrix exponentials. It
is also advantageous to have a small number of free parameters as this, in
applications, will result in a reduction of computation time. We explore a
method of building a rate matrix set from a rooted tree structure by assigning
rates to internal tree nodes and states to the leaves, then defining the rate
of change between two states as the rate assigned to the most recent common
ancestor of those two states. We investigate the properties of these matrix
sets from both a linear algebra and a graph theory perspective and show that
any rate matrix set generated this way is closed under matrix multiplication.
The consequences of setting two rates assigned to internal tree nodes to be
equal are then considered. This methodology could be used to develop
parameterised models of amino acid substitution which have a small number of
parameters but convey biological meaning.
A characterization of absorbing sets in coalition formation games
Authors: Agustin G. Bonifacio, Elena Inarra, Pablo Neme
Date: 2020-09-24 00:00:00

Given a standard myopic dynamic process among coalition structures, an absorbing set is a minimal collection of such structures that is never left once entered through that process. Absorbing sets are an important solution concept in coalition formation games, but they have drawbacks: they can be large and hard to obtain. In this paper, we characterize an absorbing set in terms of a collection consisting of a small number of sets of coalitions that we refer to as a "reduced form" of a game. We apply our characterization to study convergence to stability in several economic environments.
Practical Simplified Indoor Multiwall Path-Loss Model
Authors: Taewon Kang, Jiwon Seo
Date: 2020-09-24 00:00:00
Tasks: model

Over the past few decades, attempts had been made to build a suitable channel prediction model to optimize radio transmission systems. It is particularly essential to predict the path loss due to the blockage of the signal, in indoor radio system applications. This paper proposed a multiwall path-loss propagation model for an indoor environment, operating at a transmission frequency of 2.45 GHz in the industrial, scientific, and medical (ISM) radio band. The effects of the number of the walls to be traversed along the radio propagation path are considered in the model. To propose the model, the previous works on well-known indoor path loss models are discussed. Then, the path loss produced by the intervening walls in the propagation path is measured, and the terms representing the loss factors in the theoretical pathloss model are modified. The analyzed results of the path loss factors acquired at 2.45 GHz are presented. The proposed path-loss model simplifies the loss factor term with an admissible assumption of the indoor environment and predicts the path-loss factor accurately.
Effects of Initial Attitude Estimation Errors on Loosely Coupled Smartphone GPS/IMU Integration System
Authors: Kwansik Park, Woohyun Kim, Jiwon Seo
Date: 2020-09-24 00:00:00
Tasks: State Estimation

Global Positioning System (GPS) and inertial measurement unit (IMU) sensors are commonly integrated using the extended Kalman filter (EKF), for achieving better navigation performance. However, because of nonlinearity, the performance of the EKF is affected by the initial state estimation errors, and the navigation solutions, including the attitude, diverge rapidly as the initial errors increase. This paper analyzes the data obtained from an outdoor experiment, and investigates the effect of the initial errors on the attitude estimation performance using EKF, which is used in loosely coupled low-cost smartphone GPS/IMU sensors.
Effect of Outlier Removal from Temporal ASF Corrections on Multichain Loran Positioning Accuracy
Authors: Jongmin Park, Pyo-Woong Son, Woohyun Kim, Joon Hyo Rhee, Jiwon Seo
Date: 2020-09-24 00:00:00

The widely used global navigation satellite systems (GNSSs) are vulnerable to radio frequency interference (RFI). Long-range navigation (Loran), a terrestrial navigation system, can compensate for this weakness; however, it suffers from low positioning accuracy, and studies are under way to improve its positioning performance. One such study has proposed the multichain Loran positioning method that uses the signals of transmitting stations belonging to different chains. Although the multichain Loran positioning performance is superior to the performance of conventional methods, the additional secondary factor (ASF) can still degrade its positioning accuracy. To mitigate the effects of temporal ASF, which is one of the ASF components, it is necessary to obtain temporal correction data from a nearby reference station at a known location. In this study, an experiment is performed to verify the effect of removing the outliers in the temporal correction data on the multichain Loran positioning accuracy.
Neural Network-Based Ranging with LTE Channel Impulse Response for Localization in Indoor Environments
Authors: Halim Lee, Ali A. Abdallah, Jongmin Park, Jiwon Seo, Zaher M. Kassas
Date: 2020-09-24 00:00:00
Tasks: Indoor Localization

A neural network (NN)-based approach for indoor localization via cellular long-term evolution (LTE) signals is proposed. The approach estimates, from the channel impulse response (CIR), the range between an LTE eNodeB and a receiver. A software-defined radio (SDR) extracts the CIR, which is fed to a long short-term memory model (LSTM) recurrent neural network (RNN) to estimate the range. Experimental results are presented comparing the proposed approach against a baseline RNN without LSTM. The results show a receiver navigating for 100 m in an indoor environment, while receiving signals from one LTE eNodeB. The ranging root-mean squared error (RMSE) and ranging maximum error along the receiver's trajectory were reduced from 13.11 m and 55.68 m, respectively, in the baseline RNN to 9.02 m and 27.40 m, respectively, with the proposed RNN-LSTM.
A Preliminary Study of Machine-Learning-Based Ranging with LTE Channel Impulse Response in Multipath Environment
Authors: Halim Lee, Jiwon Seo
Date: 2020-09-24 00:00:00
Tasks: BIG-bench Machine Learning

Alternative navigation technology to global navigation satellite systems (GNSSs) is required for unmanned ground vehicles (UGVs) in multipath environments (such as urban areas). In urban areas, long-term evolution (LTE) signals can be received ubiquitously at high power without any additional infrastructure. We present a machine learning approach to estimate the range between the LTE base station and UGV based on the LTE channel impulse response (CIR). The CIR, which includes information of signal attenuation from the channel, was extracted from the LTE physical layer using a software-defined radio (SDR). We designed a convolutional neural network (CNN) that estimates ranges with the CIR as input. The proposed method demonstrated better ranging performance than a received signal strength indicator (RSSI)-based method during our field test.
RSS-based LTE Base Station Localization Using Single Receiver in Environment with Unknown Path-Loss Exponent
Authors: Suhui Jeong, Halim Lee, Taewon Kang, Jiwon Seo
Date: 2020-09-24 00:00:00

With the increasing demand for location-based services, localization technology research has recently intensified. Received signal strength (RSS)-based localization has the advantage of simplicity. However, as RSS-based localization requires the path-loss model parameters, it is difficult to use in place on which those parameters are unknown. In prior research, a transmitter localization algorithm with multiple stationary receivers was proposed for use under unknown path-loss exponent (PLE) conditions. However, if a mobile receiver is utilized, the localization would be possible with a single receiver alone. In this paper, we suggest a method of RSS-based LTE base station (BS) localization with a single mobile receiver when the PLE is unknown. We also propose an efficient mobile-receiver movement method to improve the PLE estimation and BS localization accuracy. Simulation results demonstrate the performance of the proposed methods.
Resting state-fMRI approach towards understanding impairments in mTLE
Date: 2020-09-24 00:00:00

Mesial temporal lobe epilepsy (mTLE) is the most common form of epilepsy.
While it is characterized by an epileptogenic focus in the mesial temporal
lobe, it is increasingly understood as a network disorder. Hence, understanding
the nature of impairments on a network level is essential for its diagnosis and
treatment. In this work, we review recent works that apply resting-state
functional MRI to provide key insights into the impairments to the functional
architecture in mTLE. We discuss changes on both regional and global scales.
Finally, we describe how Machine Learning can be applied to rs-fMRI data to
extract resting-state networks specific to mTLE and for automated diagnosis of
this disease.
A distributed service-matching coverage via heterogeneous mobile agents
Date: 2020-09-24 00:00:00
Tasks: Event Detection

We propose a distributed deployment solution for a group of mobile agents
that should provide a service for a dense set of targets. The agents are
heterogeneous in a sense that their quality of service (QoS), modeled as a
spatial Gaussian distribution, is different. To provide the best service, the
objective is to deploy the agents such that their collective QoS distribution
is as close as possible to the density distribution of the targets. We propose
a distributed consensus-based expectation-maximization (EM) algorithm to
estimate the target density distribution, modeled as a Gaussian mixture model
(GMM). The GMM not only gives an estimate of the targets' distribution, but
also partitions the area to subregions, each of which is represented by one of
the GMM's Gaussian bases. We use the Kullback-Leibler divergence (KLD) to
evaluate the similarity between the QoS distribution of each agent and each
Gaussian basis/subregion. Then, a distributed assignment problem is formulated
and solved as a discrete optimal mass transport problem that allocates each
agent to a subregion by taking the KLD as the assignment cost. We demonstrate
our results by a sensor deployment for event detection where the sensor's QoS
is modeled as an anisotropic Gaussian distribution.
Event-Driven Receding Horizon Control for Distributed Estimation in Network Systems
Authors: Shirantha Welikala, Christos G. Cassandras
Date: 2020-09-24 00:00:00
Tasks: Computational Efficiency

We consider the problem of estimating the states of a distributed network of nodes (targets) through a team of cooperating agents (sensors) persistently visiting the nodes so that an overall measure of estimation error covariance evaluated over a finite period is minimized. We formulate this as a multi-agent persistent monitoring problem where the goal is to control each agent's trajectory defined as a sequence of target visits and the corresponding dwell times spent making observations at each visited target. A distributed on-line agent controller is developed where each agent solves a sequence of receding horizon control problems (RHCPs) in an event-driven manner. A novel objective function is proposed for these RHCPs so as to optimize the effectiveness of this distributed estimation process and its unimodality property is established under some assumptions. Moreover, a machine learning solution is proposed to improve the computational efficiency of this distributed estimation process by exploiting the history of each agent's trajectory. Finally, extensive numerical results are provided indicating significant improvements compared to other state-of-the-art agent controllers.
Privacy-Preserving Push-sum Average Consensus via State Decomposition
Authors: Xiaomeng Chen, Lingying Huang, Kemi Ding, Subhrakanti Dey, Ling Shi
Date: 2020-09-25 00:00:00
Tasks: Privacy Preserving

Average consensus is extensively used in distributed networks for computation and control, where all the agents constantly communicate with each other and update their states in order to reach an agreement. Under a general average consensus algorithm, information exchanged through wireless or wired communication networks could lead to the disclosure of sensitive and private information. In this paper, we propose a privacy-preserving push-sum approach for directed networks that can protect the privacy of all agents while achieving average consensus simultaneously. Each node decomposes its initial state arbitrarily into two substates, and their average equals to the initial state, guaranteeing that the agent's state will converge to the accurate average consensus. Only one substate is exchanged by the node with its neighbours over time, and the other one is reserved. That is to say, only the exchanged substate would be visible to an adversary, preventing the initial state information from leakage. Different from the existing state-decomposition approach which only applies to undirected graphs, our proposed approach is applicable to strongly connected digraphs. In addition, in direct contrast to offset-adding based privacy-preserving push-sum algorithm, which is vulnerable to an external eavesdropper, our proposed approach can ensure privacy against both an honest-but-curious node and an external eavesdropper. A numerical simulation is provided to illustrate the effectiveness of the proposed approach.
Sensor Fault Detection and Isolation via Networked Estimation: Full-Rank Dynamical Systems
Date: 2020-09-25 00:00:00
Tasks: Fault Detection

This paper considers the problem of simultaneous sensor fault detection,
isolation, and networked estimation of linear full-rank dynamical systems. The
proposed networked estimation is a variant of single time-scale protocol and is
based on (i) consensus on \textit{a-priori} estimates and (ii) measurement
innovation. The necessary connectivity condition on the sensor network and
stabilizing block-diagonal gain matrix is derived based on our previous works.
Considering additive faults in the presence of system and measurement noise,
the estimation error at sensors is derived and proper residuals are defined for
fault detection. Unlike many works in the literature, no simplifying
upper-bound condition on the noise is considered and we assume Gaussian
system/measurement noise. A probabilistic threshold is then defined for fault
detection based on the estimation error covariance norm. Finally, a
graph-theoretic sensor replacement scenario is proposed to recover possible
loss of networked observability due to removing the faulty sensor. We examine
the proposed fault detection and isolation scheme on an illustrative academic
example to verify the results and make a comparison study with related
literature.
EVM-Constrained and Mask-Compliant MIMO-OFDM Spectral Precoding
Authors: Shashi Kant, Mats Bengtsson, Gabor Fodor, Bo Göransson, Carlo Fischione
Date: 2020-09-25 00:00:00

Spectral precoding is a promising technique to suppress out-of-band emissions and comply with leakage constraints over adjacent frequency channels and with mask requirements on the unwanted emissions. However, spectral precoding may distort the original data vector, which is formally expressed as the error vector magnitude (EVM) between the precoded and original data vectors. Notably, EVM has a deleterious impact on the performance of multiple-input multiple-output orthogonal frequency division multiplexing-based systems. In this paper we propose a novel spectral precoding approach which constrains the EVM while complying with the mask requirements. We first formulate and solve the EVM-unconstrained mask-compliant spectral precoding problem, which serves as a springboard to the design of two EVM-constrained spectral precoding schemes. The first scheme takes into account a wideband EVM-constraint which limits the average in-band distortion. The second scheme takes into account frequency-selective EVM-constraints, and consequently, limits the signal distortion at the subcarrier level. Numerical examples illustrate that both proposed schemes outperform previously developed schemes in terms of important performance indicators such as block error rate and system-wide throughput while complying with spectral mask and EVM constraints.
Pareto efficient combinatorial auctions: dichotomous preferences without quasilinearity
Date: 2020-09-25 00:00:00

We consider a combinatorial auction model where preferences of agents over
bundles of objects and payments need not be quasilinear. However, we restrict
the preferences of agents to be dichotomous. An agent with dichotomous
preference partitions the set of bundles of objects as acceptable} and
unacceptable, and at the same payment level, she is indifferent between bundles
in each class but strictly prefers acceptable to unacceptable bundles. We show
that there is no Pareto efficient, dominant strategy incentive compatible
(DSIC), individually rational (IR) mechanism satisfying no subsidy if the
domain of preferences includes all dichotomous preferences. However, a
generalization of the VCG mechanism is Pareto efficient, DSIC, IR and satisfies
no subsidy if the domain of preferences contains only positive income effect
dichotomous preferences. We show the tightness of this result: adding any
non-dichotomous preference (satisfying some natural properties) to the domain
of quasilinear dichotomous preferences brings back the impossibility result.
A Machine Learning Based Regulatory Risk Index for Cryptocurrencies
Authors: Xinwen Ni, Wolfgang Karl Härdle, Taojun Xie
Date: 2020-09-25 00:00:00
Tasks: BIG-bench Machine Learning

Cryptocurrencies' values often respond aggressively to major policy changes, but none of the existing indices informs on the market risks associated with regulatory changes. In this paper, we quantify the risks originating from new regulations on FinTech and cryptocurrencies (CCs), and analyse their impact on market dynamics. Specifically, a Cryptocurrency Regulatory Risk IndeX (CRRIX) is constructed based on policy-related news coverage frequency. The unlabeled news data are collected from the top online CC news platforms and further classified using a Latent Dirichlet Allocation model and Hellinger distance. Our results show that the machine-learning-based CRRIX successfully captures major policy-changing moments. The movements for both the VCRIX, a market volatility index, and the CRRIX are synchronous, meaning that the CRRIX could be helpful for all participants in the cryptocurrency market. The algorithms and Python code are available for research purposes on www.quantlet.de.
A first econometric analysis of the CRIX family
Authors: Shi Chen, Cathy Yi-Hsuan Chen, Wolfgang Karl Härdle
Date: 2020-09-25 00:00:00
Tasks: Clustering, Diagnostic, Model Selection, Time Series, Time Series Analysis

In order to price contingent claims one needs to first understand the dynamics of these indices. Here we provide a first econometric analysis of the CRIX family within a time-series framework. The key steps of our analysis include model selection, estimation and testing. Linear dependence is removed by an ARIMA model, the diagnostic checking resulted in an ARIMA(2,0,2) model for the available sample period from Aug 1st, 2014 to April 6th, 2016. The model residuals showed the well known phenomenon of volatility clustering. Therefore a further refinement lead us to an ARIMA(2,0,2)-t-GARCH(1,1) process. This specification conveniently takes care of fat-tail properties that are typical for financial markets. The multivariate GARCH models are implemented on the CRIX index family to explore the interaction.
A Comprehensive Survey of the Tactile Internet: State of the art and Research Directions
Authors: N. Promwongsa, A. Ebrahimzadeh, D. Naboulsi, S. Kianpisheh, F. Belqasmi, R. Glitho, N. Crespi, O. Alfandi
Date: 2020-09-22 00:00:00
Tasks: Survey

The Internet has made several giant leaps over the years, from a fixed to a mobile Internet, then to the Internet of Things, and now to a Tactile Internet. The Tactile Internet goes far beyond data, audio and video delivery over fixed and mobile networks, and even beyond allowing communication and collaboration among things. It is expected to enable haptic communication and allow skill set delivery over networks. Some examples of potential applications are tele-surgery, vehicle fleets, augmented reality and industrial process automation. Several papers already cover many of the Tactile Internet-related concepts and technologies, such as haptic codecs, applications, and supporting technologies. However, none of them offers a comprehensive survey of the Tactile Internet, including its architectures and algorithms. Furthermore, none of them provides a systematic and critical review of the existing solutions. To address these lacunae, we provide a comprehensive survey of the architectures and algorithms proposed to date for the Tactile Internet. In addition, we critically review them using a well-defined set of requirements and discuss some of the lessons learned as well as the most promising research directions.