Unmanned Aerial Vehicle (UAV) has emerged as a promising technology to provide wireless signals from air to the ground users in specific scenarios such as earthquakes, tsunamis and other disasters. The performance of the UAV is degraded when the signals are blocked by obstacles in dense urban scenarios. To address this issue and enhance the signal quality available to the ground users, Reconfigurable Intelligent Surface (RIS) has emerged as a new technological paradigm. It offers an intelligent configuration for the signal propagation environment by redirecting the signals to the users. In this article, we solve a non-convex optimization problem of RIS-assisted UAV network by jointly optimizing the RIS phase shift and 3D trajectory of UAV to maximize the energy efficiency of a rotatory-wing UAV. The considered optimization problem is solved using Deep Reinforcement Learning (DRL) based techniques in an on-line fashion to reduce the computational complexity. We leverage Twin-delayed Deep Deterministic Policy Gradient (TD3) to solve the problem by considering the UAV trajectory as a set of continuous actions. For comparison, we also use the Soft Actor-Critic (SAC), Deep Deterministic Policy Gradient (DDPG) and Double Deep Q-Network (DDQN) for continuous and discrete optimization of the UAV trajectory, respectively. Extensive simulations show that the TD3 outperforms all the considered DRL techniques with the highest energy efficiency and throughput, and the lowest propulsion energy.

We develop and estimate a dynamic game of strategic firm expansion and contraction decisions to study the role of firm size in future profitability and market dominance. Modeling firm size is important because retail chain dynamics are more richly driven by expansion and contraction than de novo entry or permanent exit. Additionally, anticipated size spillovers may influence the strategies of forward-looking firms, making it difficult to analyze the effects of size without explicitly accounting for these in the expectations and, hence, decisions of firms. Expansion may also be profitable for some firms while detrimental for others. Thus, we explicitly model and allow for heterogeneity in the dynamic link between firm size and profits as well as potential for persistent brand effects through firm-specific unobservable factors. As a methodological contribution, we surmount the hurdle of estimating the model by extending a two-step procedure that circumvents solving the game. The first stage combines semiparametric conditional choice probability estimation with a particle filter to eliminate the serially correlated unobservable components. The second stage uses a forward simulation approach to estimate the payoff parameters. Data on Canadian hamburger chains from their inception in 1970 to 2005 provide evidence of firm-specific heterogeneity in brand effects, size spillovers, and persistence in profitability. This heterogeneous dynamic linkage shows how McDonald’s becomes dominant and other chains falter as they evolve, thus affecting market structure and industry concentration. The online appendix is available at https://doi.org/10.1287/mnsc.2017.2814 . This paper was accepted by J. Miguel Villas-Boas, marketing.

This paper presents state-of-the-art user localization technologies that can effectively assist the authorities and first responders to localize individuals during emergency and disaster scenarios. Many of these technologies employ artificial intelligence techniques to improve the localization accuracy using the information collected by a variety of sensing equipment such as sensors, unmanned aerial vehicles, wireless access points, cameras, and smart load meters. The paper also highlights the emerging sixth-generation (6G) wireless technologies such as integrated sensing and localization, THz communications, satellite-based non-terrestrial networks, and reconfigurable intelligent surfaces that can modify the reflection properties of materials to enable device localization. These technologies are expected to play a key role in enabling emergency services. Other localization technologies include WiFi, Bluetooth, radio frequency identification, and long range wide area network. In addition, crowd sensing and smart meter-based non-intrusive load monitoring (NILM) techniques that are supported by deep learning and federated learning techniques are also presented in the context of device localization. To the best of our knowledge, this paper introduces NILM for the first time as a useful technique that can provide information about the inhabitants’ behaviour in residential and commercial buildings to assist in emergency and public safety applications. All the technologies reviewed in this paper can play an effective role in device localization, and are presented to serve as a foundation for researchers to further investigate and conduct research in this domain.