pwc-archive/papers-with-abstracts huggingface dataset:
Bayesian Optimization Enhanced Deep Reinforcement Learning for Trajectory Planning and Network Formation in Multi-UAV Networks
Authors: Shimin Gong, Meng Wang, Bo Gu, Wenjie Zhang, Dinh Thai Hoang, Dusit Niyato
Date: 2022-12-27 00:00:00
Tasks: Bayesian Optimization, Deep Reinforcement Learning, Trajectory Planning

In this paper, we employ multiple UAVs coordinated by a base station (BS) to help the ground users (GUs) to offload their sensing data. Different UAVs can adapt their trajectories and network formation to expedite data transmissions via multi-hop relaying. The trajectory planning aims to collect all GUs' data, while the UAVs' network formation optimizes the multi-hop UAV network topology to minimize the energy consumption and transmission delay. The joint network formation and trajectory optimization is solved by a two-step iterative approach. Firstly, we devise the adaptive network formation scheme by using a heuristic algorithm to balance the UAVs' energy consumption and data queue size. Then, with the fixed network formation, the UAVs' trajectories are further optimized by using multi-agent deep reinforcement learning without knowing the GUs' traffic demands and spatial distribution. To improve the learning efficiency, we further employ Bayesian optimization to estimate the UAVs' flying decisions based on historical trajectory points. This helps avoid inefficient action explorations and improves the convergence rate in the model training. The simulation results reveal close spatial-temporal couplings between the UAVs' trajectory planning and network formation. Compared with several baselines, our solution can better exploit the UAVs' cooperation in data offloading, thus improving energy efficiency and delay performance.
Population heterogeneity in vaccine coverage impacts epidemic thresholds and bifurcation dynamics
Authors: Alina Glaubitz, Feng Fu
Date: 2022-12-28 00:00:00

Population heterogeneity, especially in individuals' contact networks, plays an important role in transmission dynamics of infectious diseases. For vaccine-preventable diseases, outstanding issues like vaccine hesitancy and availability of vaccines further lead to nonuniform coverage among groups, not to mention the efficacy of vaccines and the mixing pattern varying from one group to another. As the ongoing COVID-19 pandemic transitions to endemicity, it is of interest and significance to understand the impact of aforementioned population heterogeneity on the emergence and persistence of epidemics. Here we analyze epidemic thresholds and characterize bifurcation dynamics by accounting for heterogeneity caused by group-dependent characteristics, including vaccination rate and efficacy as well as disease transmissibility. Our analysis shows that increases in the difference in vaccination coverage among groups can render multiple equilibria of disease burden to exist even if the overall basic reproductive ratio is below one (also known as backward bifurcation). The presence of other heterogeneity factors such as differences in vaccine efficacy, transmission, mixing pattern, and group size can each exhibit subtle impacts on bifurcation. We find that heterogeneity in vaccine efficacy can undermine the condition for backward bifurcations whereas homophily tends to aggravate disease endemicity. Our results have practical implications for improving public health efforts by addressing the role of population heterogeneity in the spread and control of diseases.
Spectral and post-spectral estimators for grouped panel data models
Authors: Denis Chetverikov, Elena Manresa
Date: 2022-12-26 00:00:00

In this paper, we develop spectral and post-spectral estimators for grouped panel data models. Both estimators are consistent in the asymptotics where the number of observations $N$ and the number of time periods $T$ simultaneously grow large. In addition, the post-spectral estimator is $\sqrt{NT}$-consistent and asymptotically normal with mean zero under the assumption of well-separated groups even if $T$ is growing much slower than $N$. The post-spectral estimator has, therefore, theoretical properties that are comparable to those of the grouped fixed-effect estimator developed by Bonhomme and Manresa (2015). In contrast to the grouped fixed-effect estimator, however, our post-spectral estimator is computationally straightforward.
What is expected for China's SARS-CoV-2 epidemic?
Authors: Carlos Hernandez-Suarez, Efren Murillo-Zamora
Date: 2022-12-28 00:00:00

Recently, China announced that its "zero-covid" policy would end, which will bring serious challenges to the country's health system. In here we provide simple calculations that allows us to provide an estimate of what is expected as an outcome in terms of fatalities, using the fact that it is a highly contagious disease that will expose most of a highly vaccinated population to the virus. We use recent findings regarding the amount of reduction in the risk of severe outcome achieved by vaccination and arrive to an estimate of 1.1 m deaths, 60% of these are males. In our model, 84% percent of deaths occur in individuals with age 55 years or older. In a scenario in which this protection is completely lost due to waning and the infection fatality rate of the prevalent strain reaches similar levels to the observed in the beginning of the epidemic, the death toll could reach 2.4 m, 93% in 55 years or older.
Hidden costs of La Mancha's production model and drivers of change
Authors: Máximo Florín, Rafael U. Gosálvez
Date: 2022-12-27 00:00:00

The territory of La Mancha, its rural areas, and its landscapes suffer a kind of atherosclerosis ("the silent killer") because of the increase in artificial surfaces, the fragmentation of the countryside by various infrastructures, the abandonment of small and medium-sized farms and the loss of agricultural, material, and intangible heritage. At the same time, agricultural industrialization hides, behind a supposed productive efficiency, the deterioration of the quantitative and qualitative ecological status of surface and groundwater bodies, and causes air pollution, greenhouse gas emissions, loss of soil fertility, drainage and plowing of wetlands, forgetfulness of the ancestral environmental heritage, of the emergence of uses and customs of collective self-government and reduction of the adaptive capacity of traditional agroecosystems. This work aims, firstly, to shed light on the true costs of the main causes of environmental degradation in the territory of La Mancha, while deteriorating relations between rural and urban areas and determining the loss of territorial identity of La Mancha. the population. In addition, drivers of change toward a more sustainable social, economic, hydrological, environmental, and cultural production model are identified.
Motivations and locational factors of FDI in CIS countries: Empirical evidence from South Korean FDI in Kazakhstan, Russia, and Uzbekistan
Authors: Han-Sol Lee, Sergey U. Chernikov, Szabolcs Nagy
Date: 2022-12-26 00:00:00

Considering the growing significance of Eurasian economic ties because of South Korea s New Northern Policy and Russia s New Eastern Policy, this study investigates the motivations and locational factors of South Korean foreign direct investment (FDI) in three countries in the Commonwealth of Independent States (CIS: Kazakhstan, Russia, and Uzbekistan) by employing panel analysis (pooled ordinary least squares (OLS), fixed effects, random effects) using data from 1993 to 2017. The results show the positive and significant coefficients of GDP, resource endowments, and inflation. Unlike conventional South Korean outward FDI, labour-seeking is not defined as a primary purpose. Exchange rates, political rights, and civil liberties are identified as insignificant. The authors conclude that South Korean FDI in Kazakhstan, Russia, and Uzbekistan is associated with market-seeking (particularly in Kazakhstan and Russia) and natural resource-seeking, especially the former. From a policy perspective, our empirical evidence suggests that these countries host governments could implement mechanisms to facilitate the movement of goods across regions and countries to increase the attractiveness of small local markets. The South Korean government could develop financial support and risk sharing programmes to enhance natural resource-seeking investments and mutual exchange programmes to overcome the red syndrome complex in South Korean society.
The relationship between social innovation and digital economy and society
Authors: Szabolcs Nagy, Mariann Veresne Somosi
Date: 2022-12-26 00:00:00

The information age is also an era of escalating social problems. The digital transformation of society and the economy is already underway in all countries, although the progress in this transformation can vary widely. There are more social innovation projects addressing global and local social problems in some countries than in others. This suggests that different levels of digital transformation might influence the social innovation potential. Using the International Digital Economy and Society Index and the Social Innovation Index, this study investigates how digital transformation of the economy and society affects the capacity for social innovation. A dataset of 29 countries was analysed using both simple and multiple linear regressions and Pearsons correlation. Based on the research findings, it can be concluded that the digital transformation of the economy and society has a significant positive impact on the capacity for social innovation. It was also found that the integration of digital technology plays a critical role in digital transformation. Therefore, the progress in digital transformation is beneficial to social innovation capacity. In line with the research findings, this study outlines the implications and possible directions for policy.
The Impact of the Pharmaceutical Industry on the Innovation Performance of European Countries
Authors: Szabolcs Nagy, Sergey U. Chernikov, Ekaterina Degtereva
Date: 2022-12-26 00:00:00

There are significant differences in innovation performance between countries. Additionally, the pharmaceutical sector is stronger in some countries than in others. This suggests that the development of the pharmaceutical industry can influence a country's innovation performance. Using the Global Innovation Index and selected performance measures of the pharmaceutical sector, this study examines how the pharmaceutical sector influences the innovation performance of countries from the European context. The dataset of 27 European countries was analysed using simple, and multiple linear regressions and Pearson correlation. Our findings show that only three indicators of the pharmaceutical industry, more precisely pharmaceutical Research and Development, pharmaceutical exports, and pharmaceutical employment explain the innovation performance of a country largely. Pharmaceutical Research and Development and exports have a significant positive impact on a country's innovation performance, whereas employment in the pharmaceutical industry has a slightly negative impact. Additionally, global innovation performance has been found to positively influence life expectancy. We further outline the implications and possible policy directions based on these findings.
Distributed Active Noise Control System Based on a Block Diffusion FxLMS Algorithm with Bidirectional Communication
Authors: Tianyou Li, Hongji Duan, Sipei Zhao, Jing Lu, Ian S. Burnett
Date: 2022-12-28 00:00:00

Recently, distributed active noise control systems based on diffusion adaptation have attracted significant research interest due to their balance between computational complexity and stability compared to conventional centralized and decentralized adaptation schemes. However, the existing diffusion FxLMS algorithm employs node-specific adaptation and neighborhood-wide combination, and assumes that the control filters of neighbor nodes are similar to each other. This assumption is not true in practical applications, and it leads to inferior performance to the centralized controller approach. In contrast, this paper proposes a Block Diffusion FxLMS algorithm with bidirectional communication, which uses neighborhood-wide adaptation and node-specific combination to update the control filters. Simulation results validate that the proposed algorithm converges to the solution of the centralized controller with reduced computational burden.
A Distributed Game-Theoretic Solution for Power Management in the Uplink of Cell-Free Systems
Authors: Juno V. Saraiva, Roberto P. Antonioli, Gábor Fodor, Walter C. Freitas Jr., Yuri C. B. Silva
Date: 2022-12-28 00:00:00
Tasks: Management

This paper investigates cell-free massive multiple input multiple output systems with a particular focus on uplink power allocation. In these systems, uplink power control is highly non-trivial, since a single user terminal is associated with multiple intended receiving base stations. In addition, in cell-free systems, distributed power control schemes that address the inherent spectral and energy efficiency targets are desirable. By utilizing tools from game theory, we formulate our proposal as a noncooperative game, and using the best-response dynamics, we obtain a distributed power control mechanism. To ensure that this power control game converges to a Nash equilibrium, we apply the theory of potential games. Differently from existing gamebased schemes, interestingly, our proposed potential function has a scalar parameter that controls the power usage of the users. Numerical results confirm that the proposed approach improves the use of the energy stored in the battery of user terminals and balances between spectral and energy efficiency.
Efficient Battery Usage in Wireless-Powered Cell-Free Systems with Self-Energy Recycling
Authors: Iran M. Braga Jr., Roberto P. Antonioli, Gabor Fodor, Yuri C. B. Silva, Walter C. Freitas Jr
Date: 2022-12-28 00:00:00

This paper investigates wireless-powered cell-free systems, in which the users send their uplink data signal while simultaneously harvesting energy from network nodes and user terminals - including the transmitting user terminal itself - by performing self-energy recycling. In this rather general setting, a closed-form lower bound of the amount of harvested energy and the achieved signal-to-interference-plus-noise ratio expressions are derived. Then, to improve the energy efficiency, we formulate the problem of minimizing the users' battery energy usage while satisfying minimum data rate requirements. Due to the non-convexity of the problem, a novel alternating optimization algorithm is proposed, and its proof of convergence is provided. Finally, numerical results show that the proposed method is more efficient than a state-of-art algorithm in terms of battery energy usage and outage rate.
VAFER: Signal Decomposition based Mutual Interference Suppression in FMCW Radars
Authors: Abhilash Gaur, Po-Hsuan Tseng, Kai-Ten Feng, Seshan Srirangarajan
Date: 2022-12-28 00:00:00
Tasks: Autonomous Vehicles

With increasing application of frequency-modulated continuous wave (FMCW) radars in autonomous vehicles, mutual interference among FMCW radars poses a serious threat. Through this paper, we present a novel approach to effectively and elegantly suppress mutual interference in FMCW radars. We first decompose the received signal into modes using variational mode decomposition (VMD) and perform time-frequency analysis using Fourier synchrosqueezed transform (FSST). The interference-suppressed signal is then reconstructed by applying a proposed energy-entropy-based thresholding operation on the time-frequency spectra of VMD modes. The effectiveness of proposed method is measured in terms of signal-to-interference plus noise ratio (SINR) and correlation coefficient for both simulated and experimental automotive radar data in the presence of FMCW interference. Compared to other existing literature, our proposed method demonstrates significant improvement in the output SINR by at least 14.07 dB for simulated data and 9.87 dB for experimental data.
Joint Receive Antenna Selection and Beamforming in RIS-Aided MIMO Systems
Authors: Chongjun Ouyang, Ali Bereyhi, Saba Asaad, Ralf R. Müller, Hongwen Yang
Date: 2022-12-28 00:00:00

This work studies a low-complexity design for reconfigurable intelligent surface (RIS)-aided multiuser multiple-input multiple-output systems. The base station (BS) applies receive antenna selection to connect a subset of its antennas to the available radio frequency chains. For this setting, the BS switching network, uplink precoders, and RIS phase-shifts are jointly designed, such that the uplink sum-rate is maximized. The principle design problem reduces to an NP-hard mixed-integer optimization. We hence invoke the weighted minimum mean squared error technique and the penalty dual decomposition method to develop a tractable iterative algorithm that approximates the optimal design effectively. Our numerical investigations verify the efficiency of the proposed algorithm and its superior performance as compared with the benchmark.
Reconfigurable Holographic Surface: A New Paradigm to Implement Holographic Radio
Authors: Ruoqi Deng, Yutong Zhang, Haobo Zhang, Boya Di, Hongliang Zhang, Lingyang Song
Date: 2022-12-28 00:00:00
Tasks: Diversity

Ultra-massive multiple-input multiple-output (MIMO) is one of the key enablers in the forthcoming 6G networks to provide high-speed data services by exploiting spatial diversity. In this article, we consider a new paradigm termed holographic radio for ultra-massive MIMO, where numerous tiny and inexpensive antenna elements are integrated to realize high directive gain with low hardware cost. We propose a practical way to enable holographic radio by a novel metasurface-based antenna, i.e., reconfigurable holographic surface (RHS). Specifically, RHSs incorporating densely packed tunable metamaterial elements are capable of holographic beamforming. Based on the working principle and hardware design of RHSs, we conduct full-wave analyses of RHSs and build an RHS-aided point-to-point communication platform supporting real-time data transmission. Both simulated and experimental results show that the RHS has great potential to achieve high directive gain with a limited size, thereby substantiating the feasibility of RHS-enabled holographic radio. Moreover, future research directions for RHS-enabled holographic radio are also discussed.
On Ambiguity in Linear Inverse Problems: Entrywise Bounds on Nearly Data-Consistent Solutions and Entrywise Condition Numbers
Authors: Justin P. Haldar
Date: 2022-12-27 00:00:00
Tasks: valid

Ill-posed linear inverse problems appear frequently in various signal processing applications. It can be very useful to have theoretical characterizations that quantify the level of ill-posedness for a given inverse problem and the degree of ambiguity that may exist about its solution. Traditional measures of ill-posedness, such as the condition number of a matrix, provide characterizations that are global in nature. While such characterizations can be powerful, they can also fail to provide full insight into situations where certain entries of the solution vector are more or less ambiguous than others. In this work, we derive novel theoretical lower- and upper-bounds that apply to individual entries of the solution vector, and are valid for all potential solution vectors that are nearly data-consistent. These bounds are agnostic to the noise statistics and the specific method used to solve the inverse problem, and are also shown to be tight. In addition, our results also lead us to introduce an entrywise version of the traditional condition number, which provides a substantially more nuanced characterization of scenarios where certain elements of the solution vector are less sensitive to perturbations than others. Our results are illustrated in an application to magnetic resonance imaging reconstruction, and we include discussions of practical computation methods for large-scale inverse problems, connections between our new theory and the traditional Cram\'{e}r-Rao bound under statistical modeling assumptions, and potential extensions to cases involving constraints beyond just data-consistency.
Near-Field Sparse Channel Representation and Estimation in 6G Wireless Communications
Authors: Xing Zhang, Haiyang Zhang, Yonina C. Eldar
Date: 2022-12-27 00:00:00
Tasks: Dictionary Learning

The employment of extremely large antenna arrays and high-frequency signaling makes future 6G wireless communications likely to operate in the near-field region. In this case, the spherical wave assumption which takes into account both the user angle and distance is more accurate than the conventional planar one that is only related to the user angle. Therefore, the conventional planar wave based far-field channel model as well as its associated estimation algorithms needs to be reconsidered. Here we first propose a distance-parameterized angular-domain sparse model to represent the near-field channel. In this model, the user distance is included in the dictionary as an unknown parameter, so that the number of dictionary columns depends only on the angular space division. This is different from the existing polar-domain near-field channel model where the dictionary is constructed on an angle-distance two-dimensional (2D) space. Next, based on this model, joint dictionary learning and sparse recovery based channel estimation methods are proposed for both line of sight (LoS) and multi-path settings. To further demonstrate the effectiveness of the suggested algorithms, recovery conditions and computational complexity are studied. Our analysis shows that with the decrease of distance estimation error in the dictionary, the angular-domain sparse vector can be exactly recovered after a few iterations. The high storage burden and dictionary coherence issues that arise in the polar-domain 2D representation are well addressed. Finally, simulations in multi-user communication scenarios support the superiority of the proposed near-field channel sparse representation and estimation over the existing polar-domain method in channel estimation error.