pwc-archive/papers-with-abstracts huggingface dataset:
Privacy-Preserving Data-Enabled Predictive Leading Cruise Control in Mixed Traffic
Authors: Kaixiang Zhang, Kaian Chen, Zhaojian Li, Jun Chen, Yang Zheng
Date: 2022-05-22 00:00:00
Tasks: Privacy Preserving

Data-driven predictive control of connected and automated vehicles (CAVs) has received increasing attention as it can achieve safe and optimal control without relying on explicit dynamical models. However, employing the data-driven strategy involves the collection and sharing of privacy-sensitive vehicle information, which is vulnerable to privacy leakage and might further lead to malicious activities. In this paper, we develop a privacy-preserving data-enabled predictive control scheme for CAVs in a mixed traffic environment, where human-driven vehicles and CAVs coexist. We tackle external eavesdroppers and honest-but-curious central unit eavesdroppers who wiretap the communication channel of the mixed traffic system and intend to infer the CAVs' state and input information. An affine masking-based privacy protection method is designed to conceal the true state and input signals, and an extended form of the data-enabled predictive leading cruise control under different matrix structures is derived to achieve privacy-preserving optimal control for CAVs. Numerical simulations demonstrate that the proposed scheme can protect the privacy of CAVs against attackers without affecting control performance or incurring heavy computations.
A Hierarchical MPC Approach to Car-Following via Linearly Constrained Quadratic Programming
Authors: Fangyu Wu, Alexandre Bayen
Date: 2022-05-22 00:00:00
Tasks: Autonomous Driving

Single-lane car-following is a fundamental task in autonomous driving. A desirable car-following controller should keep a reasonable range of distances to the preceding vehicle and do so as smoothly as possible. To achieve this, numerous control methods have been proposed: some only rely on local sensing; others also make use of non-local downstream observations. While local methods are capable of attenuating high-frequency velocity oscillation and are economical to compute, non-local methods can dampen a wider spectrum of oscillatory traffic but incur a larger cost in computing. In this article, we design a novel non-local tri-layer MPC controller that is capable of smoothing a wide range of oscillatory traffic and is amenable to real-time applications. At the core of the controller design are 1) an accessible prediction method based on ETA estimation and 2) a robust, light-weight optimization procedure, designed specifically for handling various headway constraints. Numerical simulations suggest that the proposed controller can simultaneously maintain a variable headway while driving with modest acceleration and is robust to imperfect traffic predictions.
Limit Cycles Analysis and Control of Evolutionary Game Dynamics with Environmental Feedback
Authors: Lulu Gong, Weijia Yao, Jian Gao, Ming Cao
Date: 2022-05-22 00:00:00

Recently, an evolutionary game dynamics model taking into account the environmental feedback has been proposed to describe the co-evolution of strategic actions of a population of individuals and the state of the surrounding environment; correspondingly a range of interesting dynamic behaviors have been reported. In this paper, we provide new theoretical insight into such behaviors and discuss control options. Instead of the standard replicator dynamics, we use a more realistic and comprehensive model of replicator-mutator dynamics, to describe the strategic evolution of the population. After integrating the environment feedback, we study the effect of mutations on the resulting closed-loop system dynamics. We prove the conditions for two types of bifurcations, Hopf bifurcation and Heteroclinic bifurcation, both of which result in stable limit cycles. These limit cycles have not been identified in existing works, and we further prove that such limit cycles are in fact persistent in a large parameter space and are almost globally stable. In the end, an intuitive control policy based on incentives is applied, and the effectiveness of this control policy is examined by analysis and simulations.
Economic model predictive control of integrated energy systems: A multi-time-scale framework
Authors: Long Wu, Xunyuan Yin, Lei Pan, Jinfeng Liu
Date: 2022-05-20 00:00:00
Tasks: Decision Making, Model Predictive Control

In this work, a composite economic model predictive control (CEMPC) is proposed for the optimal operation of a stand-alone integrated energy system (IES). Time-scale multiplicity exists in IESs dynamics is taken into account and addressed using multi-time-scale decomposition. The entire IES is decomposed into three reduced-order subsystems with slow, medium, and fast dynamics. Subsequently, the CEMPC, which includes slow economic model predictive control (EMPC), medium EMPC and fast EMPC, is developed. The EMPCs communicate with each other to ensure consistency in decision-making. In the slow EMPC, the global control objectives are optimized, and the manipulated inputs explicitly affecting the slow dynamics are applied. The medium EMPC optimizes the control objectives correlated with the medium dynamics and applies the corresponding optimal medium inputs to the IES, while the fast EMPC optimizes the fast dynamics relevant objectives and makes a decision on the manipulated inputs directly associated with the fast dynamics. Meanwhile, thermal comfort is integrated into the CEMPC in the form of zone tracking of the building temperature for achieving more control degrees of freedom to prioritize satisfying the electric demand and reducing operating costs of the IES. Moreover, a long-term EMPC based on a simplified slow subsystem model is developed and incorporated into the CEMPC to ensure that the operating state accommodates long-term forecasts for external conditions. Finally, the effectiveness and superiority of the proposed method are demonstrated via simulations and a comparison with a hierarchical real-time optimization mechanism.
Two-Stage Maximum Score Estimator
Authors: Wayne Yuan Gao, Sheng Xu, Kan Xu
Date: 2020-09-07 00:00:00
Tasks: regression, Vocal Bursts Valence Prediction

This paper considers the asymptotic theory of a semiparametric M-estimator that is generally applicable to models that satisfy a monotonicity condition in one or several parametric indexes. We call the estimator two-stage maximum score (TSMS) estimator since our estimator involves a first-stage nonparametric regression when applied to the binary choice model of Manski (1975, 1985). We characterize the asymptotic distribution of the TSMS estimator, which features phase transitions depending on the dimension and thus the convergence rate of the first-stage estimation. Effectively, the first-stage nonparametric estimator serves as an imperfect smoothing function on a non-smooth criterion function, leading to the pivotality of the first-stage estimation error with respect to the second-stage convergence rate and asymptotic distribution
Regime and Treatment Effects in Duration Models: Decomposing Expectation and Transplant Effects on the Kidney Waitlist
Authors: Stephen Kastoryano
Date: 2022-05-23 00:00:00
Tasks: Vocal Bursts Type Prediction

This paper proposes a causal decomposition framework for settings in which an initial regime randomization influences the timing of a treatment duration. The initial randomization and treatment affect in turn a duration outcome of interest. Our empirical application considers the survival of individuals on the kidney transplant waitlist. Upon entering the waitlist, individuals with an AB blood type, who are universal recipients, are effectively randomized to a regime with a higher propensity to rapidly receive a kidney transplant. Our dynamic potential outcomes framework allows us to identify the pre-transplant effect of the blood type, and the transplant effects depending on blood type. We further develop dynamic assumptions which build on the LATE framework and allow researchers to separate effects for different population substrata. Our main empirical result is that AB blood type candidates display a higher pre-transplant mortality. We provide evidence that this effect is due to behavioural changes rather than biological differences.
An optimal test for strategic interaction in social and economic network formation between heterogeneous agents
Authors: Andrin Pelican, Bryan S. Graham
Date: 2020-09-01 00:00:00

Consider a setting where $N$ players, partitioned into $K$ observable types, form a directed network. Agents' preferences over the form of the network consist of an arbitrary network benefit function (e.g., agents may have preferences over their network centrality) and a private component which is additively separable in own links. This latter component allows for unobserved heterogeneity in the costs of sending and receiving links across agents (respectively out- and in- degree heterogeneity) as well as homophily/heterophily across the $K$ types of agents. In contrast, the network benefit function allows agents' preferences over links to vary with the presence or absence of links elsewhere in the network (and hence with the link formation behavior of their peers). In the null model which excludes the network benefit function, links form independently across dyads in the manner described by \cite{Charbonneau_EJ17}. Under the alternative there is interdependence across linking decisions (i.e., strategic interaction). We show how to test the null with power optimized in specific directions. These alternative directions include many common models of strategic network formation (e.g., "connections" models, "structural hole" models etc.). Our random utility specification induces an exponential family structure under the null which we exploit to construct a similar test which exactly controls size (despite the the null being a composite one with many nuisance parameters). We further show how to construct locally best tests for specific alternatives without making any assumptions about equilibrium selection. To make our tests feasible we introduce a new MCMC algorithm for simulating the null distributions of our test statistics.
Productivity Implications of R&D, Innovation, and Capital Accumulation for Incumbents and Entrants: Perspectives from a Catching-up Economy
Authors: Jaan Masso, Amaresh K Tiwari
Date: 2022-05-21 00:00:00

We study the productivity implications of R&D, capital accumulation, and innovation output for entrants and incumbents in Estonia. First, in contrast to developed economies, a small percentage of firm engage in formal R&D, but a much larger percentage innovate. Second, while we find no difference in the R&D elasticity of productivity for the entrants and incumbents, the impact of innovation output - many of which are a result of 'doing, using and interacting' (DUI) mode of innovation - is found to be higher for the entrants. Entrants who innovate are 21% to 30% more productive than entrants who do not; the corresponding figures for the incumbents are 10% to 13%. Third, despite the adverse sectoral composition typical of catching-up economies, Estonian incumbents, who are the primary carriers of 'scientific and technologically-based innovative' (STI) activities, are comparable to their counterparts in developed economies in translating STI activities into productivity gains. Fourth, while embodied technological change through capital accumulation is found to be more effective in generating productivity growth than R&D, the effectiveness is higher for firms engaging in R&D. Finally, our results suggest that certain policy recommendations for spurring productivity growth in technologically advanced economies may not be applicable for catching-up economies.
Mechanisms without transfers for fully biased agents
Authors: Deniz Kattwinkel, Axel Niemeyer, Justus Preusser, Alexander Winter
Date: 2022-05-22 00:00:00

A principal must decide between two options. Which one she prefers depends on the private information of two agents. One agent always prefers the first option; the other always prefers the second. Transfers are infeasible. One application of this setting is the efficient division of a fixed budget between two competing departments. We first characterize all implementable mechanisms under arbitrary correlation. Second, we study when there exists a mechanism that yields the principal a higher payoff than she could receive by choosing the ex-ante optimal decision without consulting the agents. In the budget example, such a profitable mechanism exists if and only if the information of one department is also relevant for the expected returns of the other department. We generalize this insight to derive necessary and sufficient conditions for the existence of a profitable mechanism in the n-agent allocation problem with independent types.
Information Design of Dynamic Mechanisms
Authors: Soo Hong Chew, Wenqian Wang
Date: 2022-05-22 00:00:00

Two dynamic game forms are said to be behaviorally equivalent if they share the "same" profiles of structurally reduced strategies (Battigalli et al., 2020). In the context of dynamic implementation, behaviorally equivalent game forms are interchangeable under a wide range of solution concepts for the purpose of implementing a social choice function. A gradual mechanism (Chew and Wang, 2022), which designs a procedure of information transmission mediated by a central administrator, enables a formal definition of information flow. We provide a characterization of behavioral equivalence between gradual mechanisms in terms of their informational equivalence -- each agent is designed the "same" information flow. Information flow also helps in defining an intuitive notion of immediacy for gradual mechanisms which is equivalent to their game structures being minimal. Given that the class of gradual mechanisms serves as a revelation principle for dynamic implementation (Li, 2017; Akbarpour and Li, 2020; Mackenzie, 2020; Chew and Wang, 2022), the class of immediate gradual mechanisms provides a refined revelation principle.
A Simple Characterization of Supply Correspondences
Authors: Alexey Kushnir, Vinod Krishnamoorthy
Date: 2022-05-21 00:00:00

We prove that supply correspondences are characterized by two properties: the law of supply and being homogeneous of degree zero.
Predicting Choice from Information Costs
Authors: Elliot Lipnowski, Doron Ravid
Date: 2022-05-20 00:00:00

An agent acquires a costly flexible signal before making a decision. We explore to what degree knowledge of the agent's information costs helps predict her behavior. We establish an impossibility result: learning costs alone generate no testable restrictions on choice without also imposing constraints on actions' state-dependent utilities. By contrast, choices from a menu often uniquely pin down the agent's decisions in all submenus. To prove the latter result, we define iteratively differentiable cost functions, a tractable class amenable to first-order techniques. Finally, we construct tight tests for a multi-menu data set to be consistent with a given cost.
Signal Restoration and Channel Estimation for Channel Sounding with SDRs
Authors: Julian Ahrens, Lia Ahrens, Michael Zentarra, Hans D. Schotten
Date: 2022-05-23 00:00:00
Tasks: Time Series, Time Series Analysis

In this paper, the task of channel sounding using software defined radios (SDRs) is considered. In contrast to classical channel sounding equipment, SDRs are general purpose devices and require additional steps to be implemented when employed for this task. On top of this, SDRs may exhibit quirks causing signal artefacts that obstruct the effective collection of channel estimation data. Based on these considerations, in this work, a practical algorithm is devised to compensate for the drawbacks of using SDRs for channel sounding encountered in a concrete setup. The proposed approach utilises concepts from time series and Fourier analysis and comprises a signal restoration routine for mitigating artefacts within the recorded signals and an encompassing channel sounding process. The efficacy of the algorithm is evaluated on real measurements generated within the given setup. The empirical results show that the proposed method is able to counteract the shortcomings of the equipment and deliver reasonable channel estimates.
Beam Squint Assisted User Localization in Near-Field Communications Systems
Authors: Hongliang Luo, Feifei Gao
Date: 2022-05-23 00:00:00
Tasks: Position

The beam squint phenomenon in massive multi-input and multi-output wideband communications has been widely concerned recently, which generally deteriorates the beamforming performance. In this paper, we find that with the aid of the time-delay lines (TDs), the range and trajectory of the beam squint of a near-field communications system can be freely controlled, and hence it is possible to reversely utilize the beam squint for user localization. We derive the trajectory equation for near-field beam squint points and design a way to control the trajectory of these beam squint points. With the proposed design, beamforming from different subcarriers would purposely point to different angles and different distances such that users from different positions would receive the maximum power at different subcarriers. Hence, one can simply find the different users' position from the beam squint effect. Simulation results demonstrate the effectiveness of the proposed scheme.
Persistent Weak Interferer Detection in WiFi Networks: A Deep Learning Based Approach
Authors: Andrew Adams, Richard F. Obrecht, Miller Wilt, Daniel Barcklow, Bennett Blitz, Daniel Chew
Date: 2022-05-23 00:00:00
Tasks: Metric Learning

In this paper, we explore the use of multiple deep learning techniques to detect weak interference in WiFi networks. Given the low interference signal levels involved, this scenario tends to be difficult to detect. However, even signal-to-interference ratios exceeding 20 dB can cause significant throughput degradation and latency. Furthermore, the resultant packet error rate may not be enough to force the WiFi network to fallback to a more robust physical layer configuration. Deep learning applied directly to sampled radio frequency data has the potential to perform detection much cheaper than successive interference cancellation, which is important for real-time persistent network monitoring. The techniques explored in this work include maximum softmax probability, distance metric learning, variational autoencoder, and autoreggressive log-likelihood. We also introduce the notion of generalized outlier exposure for these techniques, and show its importance in detecting weak interference. Our results indicate that with outlier exposure, maximum softmax probability, distance metric learning, and autoreggresive log-likelihood are capable of reliably detecting interference more than 20 dB below the 802.11 specified minimum sensitivity levels. We believe this presents a unique software solution to real-time, persistent network monitoring.
Few-bit Quantization of Neural Networks for Nonlinearity Mitigation in a Fiber Transmission Experiment
Authors: Jamal Darweesh, Nelson Costa, Antonio Napoli, Bernhard Spinnler, Yves Jaouen, Mansoor Yousefi, .
Date: 2022-05-23 00:00:00
Tasks: Quantization

A neural network is quantized for the mitigation of nonlinear and components distortions in a 16-QAM 9x50km dual-polarization fiber transmission experiment. Post-training additive power-of-two quantization at 6 bits incurs a negligible Q-factor penalty. At 5 bits, the model size is reduced by 85%, with 0.8 dB penalty.
Structured Sensing Matrix Design for In-sector Compressed mmWave Channel Estimation
Authors: Hamed Masoumi, Nitin Jonathan Myers, Geert Leus, Sander Wahls, Michel Verhaegen
Date: 2022-05-23 00:00:00
Tasks: compressed sensing

Fast millimeter wave (mmWave) channel estimation techniques based on compressed sensing (CS) suffer from low signal-to-noise ratio (SNR) in the channel measurements, due to the use of wide beams. To address this problem, we develop an in-sector CS-based mmWave channel estimation technique that focuses energy on a sector in the angle domain. Specifically, we construct a new class of structured CS matrices to estimate the channel within the sector of interest. To this end, we first determine an optimal sampling pattern when the number of measurements is equal to the sector dimension and then use its subsampled version in the sub-Nyquist regime. Our approach results in low aliasing artifacts in the sector of interest and better channel estimates than benchmark algorithms.
Efficient PHY Layer Abstraction under Imperfect Channel Estimation
Authors: Liu Cao, Lyutianyang Zhang, Sian Jin, Sumit Roy
Date: 2022-05-22 00:00:00

As most existing work investigate the PHY layer abstraction under an assumption of perfect channel estimation, it may become unreliable if there exists channel estimation error in a real communication system. This letter improves an efficient PHY layer method, EESM-log-SGN PHY layer abstraction, by considering the presence of channel estimation error. We develop two methods for implementing the EESM-log-SGN PHY abstraction under imperfect channel estimation. We show that the effective SINR is not impacted by the channel estimation error under multiple-input and single-output (MISO)/single-input and single-output (SISO) configuration, which is also verified by the full PHY simulation. The developed methods are then validated under different orthogonal frequency division multiplexing (OFDM) scenarios.
Antenna Selection in Switch-Based MIMO Arrays via DOA threshold region Approximation
Authors: Hui Chen, Tarig Ballal, Mohammed E. Eltayeb, Tareq Y. Al-Naffouri
Date: 2022-05-22 00:00:00

Direction-of-arrival (DOA) information is vital for multiple-input-multiple-output (MIMO) systems to complete localization and beamforming tasks. Switched antenna arrays have recently emerged as an effective solution to reduce the cost and power consumption of MIMO systems. Switch-based array architectures connect a limited number of radio frequency chains to a subset of the antenna elements forming a subarray. This paper addresses the problem of antenna selection to optimize DOA estimation performance. We first perform a subarray layout alignment process to remove subarrays with identical beampatterns and create a unique subarray set. By using this set, and based on a DOA threshold region performance approximation, we propose two antenna selection algorithms; a greedy algorithm and a deep-learning-based algorithm. The performance of the proposed algorithms is evaluated numerically. The results show a significant performance improvement over selected benchmark approaches in terms of DOA estimation in the threshold region and computational complexity.
6G Radio Requirements to Support Integrated Communication, Localization, and Sensing
Authors: Henk Wymeersch, Aarno Pärssinen, Traian E. Abrudan, Andreas Wolfgang, Katsuyuki Haneda, Muris Sarajlic, Marko E. Leinonen, Musa Furkan Keskin, Hui Chen, Simon Lindberg, Pekka Kyösti, Tommy Svensson, Xinxin Yang
Date: 2022-05-22 00:00:00

6G will be characterized by extreme use cases, not only for communication, but also for localization, and sensing. The use cases can be directly mapped to requirements in terms of standard key performance indicators (KPIs), such as data rate, latency, or localization accuracy. The goal of this paper is to go one step further and map these standard KPIs to requirements on signals, on hardware architectures, and on deployments. Based on this, system solutions can be identified that can support several use cases simultaneously. Since there are several ways to meet the KPIs, there is no unique solution and preferable configurations will be discussed.