pwc-archive/papers-with-abstracts huggingface dataset:
Robust optimal control using dynamic programming and guaranteed Euler's method
Date: 2020-07-27 00:00:00
Tasks: Model Predictive Control

Set-based integration methods allow to prove properties of differential
systems, which take into account bounded disturbances. The systems (either
time-discrete, time-continuous or hybrid) satisfying such properties are said
to be "robust". In the context of optimal control synthesis, the set-based
methods are generally extensions of numerical optimal methods of two classes:
first, methods based on convex optimization; second, methods based on the
dynamic programming principle. Heymann et al. have recently shown that, for
certain systems of low dimension, the second numerical method can give better
solutions than the first one. They have built a solver (Bocop) that implements
both numerical methods. We show in this paper that a set-based extension of a
method of the second class which uses a guaranteed Euler integration method,
allows us to find such good solutions. Besides, these solutions enjoy the
property of robustness against uncertainties on initial conditions and bounded
disturbances. We demonstrate the practical interest of our method on an example
taken from the numerical Bocop solver. We also give a variant of our method,
inspired by the method of Model Predictive Control, that allows us to find more
efficiently an optimal control at the price of losing robustness.
Unconditional Quantile Regression with High Dimensional Data
Authors: Yuya Sasaki, Takuya Ura, Yichong Zhang
Date: 2020-07-27 00:00:00
Tasks: counterfactual, quantile regression, regression, Vocal Bursts Intensity Prediction

This paper considers estimation and inference for heterogeneous counterfactual effects with high-dimensional data. We propose a novel robust score for debiased estimation of the unconditional quantile regression (Firpo, Fortin, and Lemieux, 2009) as a measure of heterogeneous counterfactual marginal effects. We propose a multiplier bootstrap inference and develop asymptotic theories to guarantee the size control in large sample. Simulation studies support our theories. Applying the proposed method to Job Corps survey data, we find that a policy which counterfactually extends the duration of exposures to the Job Corps training program will be effective especially for the targeted subpopulations of lower potential wage earners.
Llums que no són només llums
Authors: Jorge Baranda, Pol Henarejos, Ciprian George Gavrincea, Miguel Ángel Lagunas
Date: 2020-07-15 00:00:00

Visible Light Communications (VLC) is a new paradigm in wireless communications. The characteristics of this technology, which uses light-emitting diode-based lighting devices as transmitting elements, make it possible to be considered a complement to current wireless radio communication systems. ----- Les comunicacions per llum visible o 'Visible Light Communications' (VLC) s\'on un nou paradigma en comunicacions sense fils. Les caracter\'istiques que presenta aquesta tecnologia, que utilitza els dispositius d'il{\lgem{}}luminaci\'o basats en d\'iodes emissors de llum com elements transmissors, fa que es pugui considerar un complement dels actuals sistemes de comunicaci\'o inal`ambrics.
The Spectral Approach to Linear Rational Expectations Models
Authors: Majid M. Al-Sadoon
Date: 2020-07-27 00:00:00

This paper considers linear rational expectations models in the frequency domain. The paper characterizes existence and uniqueness of solutions to particular as well as generic systems. The set of all solutions to a given system is shown to be a finite dimensional affine space in the frequency domain. It is demonstrated that solutions can be discontinuous with respect to the parameters of the models in the context of non-uniqueness, invalidating mainstream frequentist and Bayesian methods. The ill-posedness of the problem motivates regularized solutions with theoretically guaranteed uniqueness, continuity, and even differentiability properties.
Subsampled directed-percolation models explain scaling relations experimentally observed in the brain
Date: 2020-07-27 00:00:00

Recent experimental results on spike avalanches measured in the
urethane-anesthetized rat cortex have revealed scaling relations that indicate
a phase transition at a specific level of cortical firing rate variability. The
scaling relations point to critical exponents whose values differ from those of
a branching process, which has been the canonical model employed to understand
brain criticality. This suggested that a different model, with a different
phase transition, might be required to explain the data. Here we show that this
is not necessarily the case. By employing two different models belonging to the
same universality class as the branching process (mean-field directed
percolation) and treating the simulation data exactly like experimental data,
we reproduce most of the experimental results. We find that subsampling the
model and adjusting the time bin used to define avalanches (as done with
experimental data) are sufficient ingredients to change the apparent exponents
of the critical point. Moreover, experimental data is only reproduced within a
very narrow range in parameter space around the phase transition.
A Probabilistic Spectral Analysis of Multivariate Real-Valued Nonstationary Signals
Authors: Bruno Scalzo, Ljubisa Stankovic, Danilo P. Mandic
Date: 2020-07-27 00:00:00

A class of multivariate spectral representations for real-valued nonstationary random variables is introduced, which is characterised by a general complex Gaussian distribution. In this way, the temporal signal properties -- harmonicity, wide-sense stationarity and cyclostationarity -- are designated respectively by the mean, Hermitian variance and pseudo-variance of the associated time-frequency representation (TFR). For rigour, the estimators of the TFR distribution parameters are derived within a maximum likelihood framework and are shown to be statistically consistent, owing to the statistical identifiability of the proposed distribution parametrization. By virtue of the assumed probabilistic model, a generalised likelihood ratio test (GLRT) for nonstationarity detection is also proposed. Intuitive examples demonstrate the utility of the derived probabilistic framework for spectral analysis in low-SNR environments.
Advanced Strategies of Portfolio Management in the Heston Market Model
Authors: Jarosław Gruszka, Janusz Szwabiński
Date: 2020-07-27 00:00:00
Tasks: Diversity, Management

There is a great number of factors to take into account when building and managing an investment portfolio. It is widely believed that a proper set-up of the portfolio combined with a good, robust management strategy is the key to successful investment. In this paper, we aim at an analysis of two aspects that may have an impact on investment performance: diversity of assets and inclusion of cash in the portfolio. We also propose two new management strategies based on the MACD and RSI factors known from technical analysis. Monte Carlo simulations within the Heston model of a market are used to perform numerical experiments.
Local Projection Inference is Simpler and More Robust Than You Think
Authors: José Luis Montiel Olea, Mikkel Plagborg-Møller
Date: 2020-07-27 00:00:00
Tasks: regression, valid

Applied macroeconomists often compute confidence intervals for impulse responses using local projections, i.e., direct linear regressions of future outcomes on current covariates. This paper proves that local projection inference robustly handles two issues that commonly arise in applications: highly persistent data and the estimation of impulse responses at long horizons. We consider local projections that control for lags of the variables in the regression. We show that lag-augmented local projections with normal critical values are asymptotically valid uniformly over (i) both stationary and non-stationary data, and also over (ii) a wide range of response horizons. Moreover, lag augmentation obviates the need to correct standard errors for serial correlation in the regression residuals. Hence, local projection inference is arguably both simpler than previously thought and more robust than standard autoregressive inference, whose validity is known to depend sensitively on the persistence of the data and on the length of the horizon.
Control Barrier Functions for Abstraction-Free Control Synthesis under Temporal Logic Constraints
Date: 2020-07-28 00:00:00

Temporal logic has been widely used to express complex task specifications
for cyber-physical systems (CPSs). One way to synthesize a controller for CPS
under temporal logic constraints is to first abstract the CPS as a discrete
transition system, and then apply formal methods. This approach, however, is
computationally demanding and its scalability suffers due to the curse of
dimensionality. In this paper, we propose a control barrier function (CBF)
approach to abstraction-free control synthesis under a linear temporal logic
(LTL) constraint. We first construct the deterministic Rabin automaton of the
specification and compute an accepting run. We then compute a sequence of LTL
formulae, each of which must be satisfied during a particular time interval,
and prove that satisfying the sequence of formulae is sufficient to satisfy the
LTL specification. Finally, we compute a control policy for satisfying each
formula by constructing an appropriate CBF. We present a quadratic program to
compute the controllers, and show the controllers synthesized using the
proposed approach guarantees the system to satisfy the LTL specification,
provided the quadratic program is feasible at each time step. A numerical case
study is presented to demonstrate the proposed approach.
6G Oriented Wireless Communication Channel Characteristics Analysis and Modeling
Authors: Cheng-Xiang Wang, Jie Huang, Haiming Wang, Xiqi Gao, Xiaohu You, Yang Hao
Date: 2020-07-28 00:00:00
Tasks: All

Based on the vision on the 6G wireless communication network, i.e., global coverage, all spectrums and all applications, we comprehensively survey 6G related wireless channel measurements, channel characteristics, and channel models for all frequency bands and all scenarios. Millimeter wave (mmWave), terahertz (THz), optical band, satellite, unmanned aerial vehicle (UAV), maritime, underwater acoustic, high-speed train (HST), vehicle-to-vehicle (V2V), massive/ ultra-massive multiple-input multiple-output (MIMO), orbital angular momentum (OAM), and industry Internet of things (IoT) communication channels were particularly investigated. The related 6G channel measurement and modeling results were also given. Finally, future research challenges on 6G channel measurements and modeling were pointed out.
Neural Kalman Filtering for Speech Enhancement
Authors: Wei Xue, Gang Quan, Chao Zhang, Guohong Ding, Xiaodong He, BoWen Zhou
Date: 2020-07-28 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Speech Enhancement, speech-recognition, Speech Recognition

Statistical signal processing based speech enhancement methods adopt expert knowledge to design the statistical models and linear filters, which is complementary to the deep neural network (DNN) based methods which are data-driven. In this paper, by using expert knowledge from statistical signal processing for network design and optimization, we extend the conventional Kalman filtering (KF) to the supervised learning scheme, and propose the neural Kalman filtering (NKF) for speech enhancement. Two intermediate clean speech estimates are first produced from recurrent neural networks (RNN) and linear Wiener filtering (WF) separately and are then linearly combined by a learned NKF gain to yield the NKF output. Supervised joint training is applied to NKF to learn to automatically trade-off between the instantaneous linear estimation made by the WF and the long-term non-linear estimation made by the RNN. The NKF method can be seen as using expert knowledge from WF to regularize the RNN estimations to improve its generalization ability to the noise conditions unseen in the training. Experiments in different noisy conditions show that the proposed method outperforms the baseline methods both in terms of objective evaluation metrics and automatic speech recognition (ASR) word error rates (WERs).
End-to-End Energy Efficiency Evaluation for B5G Ultra Dense Networks
Authors: Yu Fu, Mohammad Dehghani Soltani, Hamada Alshaer, Cheng-Xiang Wang, Majid Safari, Stephen McLaughlin, Harald Haas
Date: 2020-07-28 00:00:00

Energy efficiency (EE) is a major performance metric for fifth generation (5G) and beyond 5G (B5G) wireless communication systems, especially for ultra dense networks. This paper proposes an end-to-end (e2e) power consumption model and studies the energy efficiency for a heterogeneous B5G cellular architecture that separates the indoor and outdoor communication scenarios in ultra dense networks. In this work, massive multiple-input-multiple-output (MIMO) technologies at conventional sub-6 GHz frequencies are used for long-distance outdoor communications. Light-Fidelity (LiFi) and millimeter wave (mmWave) technologies are deployed to provide a high data rate service to indoor users. Whereas, in the referenced nonseparated system, the indoor users communicate with the outdoor massive MIMO macro base station directly. The performance of these two systems are evaluated and compared in terms of the total power consumption and energy efficiency. The results show that the network architecture which separates indoor and outdoor communication can support a higher data rate transmission for less energy consumption, compared to non-separate communication scenario. In addition, the results show that deploying LiFi and mmWave IAPs can enable users to transmit at a higher data rate and further improve the EE.
Deep Reinforcement Learning for Dynamic Spectrum Sensing and Aggregation in Multi-Channel Wireless Networks
Authors: Yunzeng Li, Wensheng Zhang, Cheng-Xiang Wang, Jian Sun, Yu Liu
Date: 2020-07-28 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning, Reinforcement Learning (RL)

In this paper, the problem of dynamic spectrum sensing and aggregation is investigated in a wireless network containing N correlated channels, where these channels are occupied or vacant following an unknown joint 2-state Markov model. At each time slot, a single cognitive user with certain bandwidth requirement either stays idle or selects a segment comprising C (C < N) contiguous channels to sense. Then, the vacant channels in the selected segment will be aggregated for satisfying the user requirement. The user receives a binary feedback signal indicating whether the transmission is successful or not (i.e., ACK signal) after each transmission, and makes next decision based on the sensing channel states. Here, we aim to find a policy that can maximize the number of successful transmissions without interrupting the primary users (PUs). The problem can be considered as a partially observable Markov decision process (POMDP) due to without full observation of system environment. We implement a Deep Q-Network (DQN) to address the challenge of unknown system dynamics and computational expenses. The performance of DQN, Q-Learning, and the Improvident Policy with known system dynamics is evaluated through simulations. The simulation results show that DQN can achieve near-optimal performance among different system scenarios only based on partial observations and ACK signals.
Portfolio Optimization on the Dispersion Risk and the Asymmetric Tail Risk
Authors: Young Shin Kim
Date: 2020-07-28 00:00:00
Tasks: Management, Portfolio Optimization

In this paper, we propose a market model with returns assumed to follow a multivariate normal tempered stable distribution defined by a mixture of the multivariate normal distribution and the tempered stable subordinator. This distribution is able to capture two stylized facts: fat-tails and asymmetry, that have been empirically observed for asset return distributions. On the new market model, we discuss a new portfolio optimization method, which is an extension of Markowitz's mean-variance optimization. The new optimization method considers not only reward and dispersion but also asymmetry. The efficient frontier is also extended to a curved surface on three-dimensional space of reward, dispersion, and asymmetry. We also propose a new performance measure which is an extension of the Sharpe Ratio. Moreover, we derive closed-form solutions for two important measures used by portfolio managers in portfolio construction: the marginal Value-at-Risk (VaR) and the marginal Conditional VaR (CVaR). We illustrate the proposed model using stocks comprising the Dow Jones Industrial Average. First, perform the new portfolio optimization and then demonstrating how the marginal VaR and marginal CVaR can be used for portfolio optimization under the model. Based on the empirical evidence presented in this paper, our framework offers realistic portfolio optimization and tractable methods for portfolio risk management.
Multi-Frequency Multi-Scenario Millimeter Wave MIMO Channel Measurements and Modeling for B5G Wireless Communication Systems
Authors: Jie Huang, Cheng-Xiang Wang, Hengtai Chang, Jian Sun, Xiqi Gao
Date: 2020-07-28 00:00:00

Millimeter wave (mmWave) bands have been utilized for the fifth generation (5G) communication systems and will no doubt continue to be deployed for beyond 5G (B5G). However, the underlying channels are not fully investigated at multifrequency bands and in multi-scenarios by using the same channel sounder, especially for the outdoor, multiple-input multiple-output (MIMO), and vehicle-to-vehicle (V2V) conditions. In this paper, we conduct multi-frequency multi-scenario mmWave MIMO channel measurements with 4*4 antennas at 28, 32, and 39 GHz bands for three cases, i.e., the human body and vehicle blockage measurements, outdoor path loss measurements, and V2V measurements. The channel characteristics, including blockage effect, path loss and coverage range, and non-stationarity and spatial consistency, are thoroughly studied. The blockage model, path loss model, and time-varying channel model are proposed for mmWave MIMO channels. The channel measurement and modeling results will be of great importance for further mmWave communication system deployments in indoor hotspot, outdoor, and vehicular network scenarios for B5G.
A Non-Stationary VVLC MIMO Channel Model for Street Corner Scenarios
Authors: Qingshan Chen, Cheng-Xiang Wang, Jian Sun, Wensheng Zhang, Qiuming Zhu
Date: 2020-07-28 00:00:00

In recent years, the application potential of visible light communication (VLC) technology as an alternative and supplement to radio frequency (RF) technology has attracted people's attention. The study of the underlying VLC channel is the basis for designing the VLC communication system. In this paper, a new non-stationary geometric street corner model is proposed for vehicular VLC (VVLC) multiple-input multiple-output (MIMO) channel. The proposed model takes into account changes in vehicle speed and direction. The category of scatterers includes fixed scatterers and mobile scatterers (MS). Based on the proposed model, we derive the channel impulse response (CIR) and explore the statistical characteristics of the VVLC channel. The channel gain and root mean square (RMS) delay spread of the VVLC channel are studied. In addition, the influence of velocity change on the statistical characteristics of the model is also investigated. The proposed channel model can guide future vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) optical communication system design.
Semiglobal exponential input-to-state stability of sampled-data systems based on approximate discrete-time models
Authors: Alexis J. Vallarella, Paula Cardone, Hernan Haimovich
Date: 2020-07-28 00:00:00

Exact discrete-time models of nonlinear systems are difficult or impossible to obtain, and hence approximate models may be employed for control design. Most existing results provide conditions under which the stability of the approximate model in closed-loop carries over to the stability of the (unknown) exact model but only in a practical sense, i.e. the trajectories of the closed-loop system are ensured to converge to a bounded region whose size can be made as small as desired by limiting the maximum sampling period. In addition, some very stringent conditions exist for the exact model to exhibit exactly the same type of asymptotic stability as the approximate model. In this context, our main contribution consists in providing less stringent conditions by considering semiglobal exponential input-to-state stability (SE-ISS), where the inputs can successfully represent state-measurement and actuation errors. These conditions are based on establishing SE-ISS for an adequate approximate model and are applicable both under uniform and nonuniform sampling. As a second contribution, we show that explicit Runge-Kutta models satisfy our conditions and can hence be employed. An example of control design for stabilization based on approximate discrete-time models is also given.
Effects of Digital Map on the RT-based Channel Model for UAV mmWave Communications
Authors: Qiuming Zhu, Shan Jiang, Cheng-Xiang Wang, Boyu Hua, Kai Mao, Xiaomin Chen, Weizhi Zhong
Date: 2020-07-28 00:00:00

Based on the geometry and ray tracing (RT) theory, a millimeter wave (mmWave) channel model and parameter computation method for unmanned aerial vehicle (UAV) assisted air-to-ground (A2G) communications are proposed in this paper. In order to speed up the parameter calculation, a reconstruction process of scene database on the original digital map is developed. Moreover, the effects of reconstruction accuracy on the channel parameter and characteristic are analyzed by extensive simulations at 28 GHz under the campus scene. The simulation and analysis results show that the simplified database can save up to 50% time consumption. However, the difference of statistical properties is slight in the campus scenario.
BER Performance of Spatial Modulation Systems Under a Non-Stationary Massive MIMO Channel Model
Authors: Yu Fu, Cheng-Xiang Wang, Xuming Fang, Li Yan, Stephen McLaughlin
Date: 2020-07-28 00:00:00

In this paper, the bit error rate (BER) performance of spatial modulation (SM) systems is investigated both theoretically and by simulation in a non-stationary Kronecker-based massive multiple-input-multiple-output (MIMO) channel model in multi-user (MU) scenarios. Massive MIMO SM systems are considered in this paper using both a time-division multiple access (TDMA) scheme and a block diagonalization (BD) based precoding scheme, for different system settings. Their performance is compared with a vertical Bell labs layered space-time (V-BLAST) architecture based system and a conventional channel inversion system. It is observed that a higher cluster evolution factor can result in better BER performance of SM systems due to the low correlation among sub-channels. Compared with the BD-SM system, the SM system using the TDMA scheme obtains a better BER performance but with a much lower total system data rate. The BD-MU-SM system achieves the best trade-off between the data rate and the BER performance among all of the systems considered. When compared with the V-BLAST system and the channel inversion system, SM approaches offer advantages in performance for MU massive MIMO systems.