A Wasserstein distance-based double-bootstrap method for comparing spatial simulation output

Microscopic traffic simulation models have been playing an important role in the evaluation of transportation engineering and planning practices for the past few decades, particularly in cases in which field implementation is difficult or expensive to conduct. To achieve high fidelity and credibility for a traffic simulation model, model calibration and validation are of utmost importance. Most calibration efforts reported in the literature have focused on the informal practice, and they have seldom proposed a systematic procedure or guideline for the calibration and validation of simulation models. This paper proposes a procedure for microscopic simulation model calibration. The validity of the proposed procedure was demonstrated by use of a case study of an actuated signalized intersection by using a widely used microscopic traffic simulation model, Verkehr in Staedten Simulation (VISSIM). The simulation results were compared with multiple days of field data to determine the performance of the calibrate...

This paper gives the history of verification and validation of discrete-event simulation models as seen through the eyes of its authors and their experiences. The history is divided into three time periods: the early era covering years up to 1970, the awareness era covering the years of the 1970s and 1980s, and the modern era covering the years of 1990 to the present.

The main purpose of this article is to build a general platform for Unmanned Aerial Vehicle (UAV) simulation especially for vision algorithms. This general simulation platform can also connect to realistic UAVs for further tests. The platform enables UAVs controlled by a variety of advanced vision algorithms to accomplish certain tasks. Unlike the previous simulation systems, which can only achieve tasks with certain drones, we are committed to build a platform to support a variety of vision based UAVs simulation platform. And if the simulation test is successful, we can apply vision algorithms to realistic UAVs' tests based on our platform with little adaption. We use open source Gazebo as a simulator and combined with ROS (Robot Operating System) to implement UAVs simulation. Besides, we build a general UAVs link interface to connect realistic UAVs with ROS. All these works construct the general simulation platform.

Wireless communication is an essential technology to unlock the full potential of unmanned aerial vehicles (UAVs) in numerous applications and has thus received unprecedented attention recently. Although technologies such as direct link, WiFi, and satellite communications are still useful in some remote scenarios where cellular services are unavailable, it is believed that exploiting the thriving 5G and beyond cellular networks to support UAV communications is the most promising and cost-effective approach, especially when the number of UAVs grows dramatically. On the one hand, to guarantee safe and efficient flight operations of multiple UAVs, it is of paramount importance to provide secure and ultra-reliable communication links between the UAVs and their ground pilots or control stations for conveying command and control signals, especially in beyond-visual-line-of-sight (BVLOS) scenarios. On the other hand, because of advances in communication equipment miniaturization as well as UAV manufacturing, mounting compact and lightweight base stations (BSs) or relays on UAVs becomes increasingly feasible. This has led to two promising research paradigms for UAV communications, namely, UAV-assisted cellular communications and cellular-connected UAVs, where UAVs are integrated into cellular networks as aerial communication platforms and aerial users, respectively. As such, integrating UAVs into cellular networks is believed to be a win-win technology for both UAV-related industries and cellular network operators, which not only creates plenty of new business opportunities but also benefits the communication performance of 3-D wireless networks. In addition, UAV related sensing and computing are also helpful for achieving efficient and reliable communication (e.g., in avoiding coverage holes) as well as smart UAV coordination, positioning, and trajectory design. However, 5G and beyond wireless networks with UAVs significantly differs from traditional communication systems, because of the high altitude and high maneuverability of UAVs, the unique UAV-ground channels, the diversified quality of service (QoS) requirements for downlink command and control (C&C) and uplink mission-related data transmission, the stringent constraints imposed by the size, weight, and power (SWAP) limitations of UAVs, as well as the new design degrees of freedom enabled by joint UAV mobility control and communication resource allocation.

In this paper we discuss validation and verification of simulation models. Four different approaches to deciding model validity are described; two different paradigms that relate validation and verification to the model development process are presented; various validation techniques are defined; conceptual model validity, model verification, operational validity, and data validity are discussed; a way to document results is given; a recommended procedure for model validation is presented; and accreditation is briefly discussed.

In this expository paper we give a general introduction to verification and validation of simulation models, define the various validation techniques, and present a recommended model validation procedure.

Robust calibration of macroscopic traffic simulation models using stochastic collocation

In many applications, there is a need to compare multiple simulation models or parameter configurations in order to choose the model(s) whose output best resembles the historical data available from the system under study. This paper presents a novel framework that enables such analysis for multidimensional output statistics such as those found in weather/climate, epidemic, swarm/crowd control, social systems, communication networks, and other simulation applications with spatial output statistics that are distributed across various locations or geographical regions. The proposed framework uses the Wasserstein distance from the target data as the primary performance metric and statistically compares the models to determine the best-performing candidate(s). The efficacy of the proposed framework is illustrated through three examples related to random walk of swarm particles on a two-dimensional space, Monte Carlo simulation with correlated bivariate output, and a realistic engineering application involving simulation of unmanned aerial vehicle (UAV) communication systems with spatial output.

Traditional wireless security focuses on preventing unmanned aerial vehicle (UAV) communications from suspicious eavesdropping and/or jamming attacks. However, there is a growing need for governments to keep malicious UAV communications under legitimate surveillance. This paper first investigates a new surveillance paradigm for monitoring suspicious UAV communications via jamming suspicious UAVs. Due to the power consumption limitation, the choice of eavesdropping and jamming will reflect the performance of the UAVs communication. Therefore, the paper analyses the UAV’s eavesdropping and jamming models in different cases, and then proposes the model to optimize the data package in the constraints of lower power consumption, which can be solved by the proposed selection policy. The simulation results validate our proposed selection policy in terms of power consumption and eavesdropped packets. In different fading models, power consumption increases with time, regardless of distances, and our proposed policy performs better in Weibull fading channels in terms of eavesdropped packets.

Recently, unmanned aerial vehicle (UAV) communications have drawn significant research interests. Meanwhile, cell-free (CF) massive multiple-input multiple-output (MIMO) is proposed as a promising technology to achieve the ultimate performance limits. In this paper, we investigate the UAV communication with wireless power transfer (WPT) aided CF massive MIMO systems, where the harvested energy (HE) from the downlink WPT is used to support both uplink data and pilot transmission. Take hardware impairments of UAV into account, novel closed-form downlink HE and uplink spectral efficiency (SE) expressions are derived. UAV communications with small cell (SC) and cellular massive MIMO enabled WPT systems are also considered for comparison. Our results reveal that CF massive MIMO achieves two and four times higher 95%-likely uplink SE than the ones of SC and cellular massive MIMO, respectively. To this end, SE is a concave function of the time-splitting fraction, and the optimal time-splitting fraction for maximizing SE is determined by the altitude and hardware impairment factor of the UAV.

Unmanned aerial vehicle (UAV) communication has been deemed as a promising technology to collect data for the Internet of Things (IoT) in inaccessible areas. However, due to the limited UAV flight time, traditional UAV communication may not be competent for large-scale IoT data collection. This paper considers integrating non-orthogonal multiple access (NOMA) into UAV communication systems to collect data for large-scale IoT devices within UAV flight time. We aim to minimize the total energy consumption of IoT devices while ensuring data collection, by jointly optimizing UAV trajectory, IoT device scheduling and transmit power. The formulated problem is a mixed integer non-convex problem, which is challenging to solve in general. We propose a data collection optimization algorithm (DCOA) to solve it by applying the Generalized Benders Decomposition (GBD) and successive convex approximation (SCA) techniques. Then, a greedy algorithm (GA) is also proposed to reduce complexity by simplifying the optimization of UAV trajectory and IoT device scheduling. Finally, the numerical results demonstrate that, compared with traditional UAV communication systems, the NOMA-aided UAV system performs better in terms of data collection and lower total energy consumption of IoT devices can be achieved by DCOA.

Since one GRS used for Control and Non-Payload Communication (CNPC) of Unmanned Aerial Vehicle (UAV) cannot support all operational ranges of an UAV, the handover technology of CNPC is essential to operate an UAV in a wide range. However, the CNPC physical layer standard of Radio Technical Commission for Aeronautics (RTCA) does not have a reference signal for measuring the signal strength of a neighboring base station used for handover decision in an existing mobile communication system. Therefore, in this paper, we propose a method to transmit measurement signals for stable handover of CNPC. The proposed method starts transmission of the measurement signal when the UAV enters the handover zone for the preventing waste of power and generates the measurement signal using the same data that is transmitted through the currently connected Ground Radio Station (GRS) for the preventing packet loss during the handover operation.

By exploiting the degree of freedom on the altitude, unmanned aerial vehicle (UAV) communication can provide ubiquitous communication for future wireless networks. In the case of concurrent transmission of multiple UAVs, the directional beamforming formed by multiple antennas is an effective way to reduce co-channel interference. However, factors, such as airflow disturbance or estimation error for UAV communications, can cause the occurrence of beam misalignment. In this article, we investigate the system performance of a multitier UAV communication network with the consideration of unstable beam alignment. In particular, we propose a tractable random model to capture the impacts of beam misalignment in the 3-D space. Based on this, by utilizing stochastic geometry, an analytical framework for obtaining the outage probability in the downlink of a multitier UAV communication network for the closest distance association scheme and the maximum average power association scheme is established. The accuracy of the analysis is verified by Monte Carlo simulations. The results indicate that in the presence of random beam misalignment, the optimal number of UAV antennas needs to be adjusted to be relatively larger when the density of UAVs increases or the altitude of UAVs becomes higher.

Unlike terrestrial communications, unmanned aerial vehicle (UAV) communications have some advantages such as line-of-sight (LoS) environment and flexible mobility. However, the interference will be still inevitable. In this paper, we analyze the effect of the interference on the UAV communications by considering the LoS probability and different channel fadings for LoS and non-line-of-sight (NLoS) links, which are affected by the elevation angle of the communication link. We then derive a closed-form outage probability in the presence of an interfering node for all the possible scenarios and environments of main and interference links. After discussing the impacts of transmitting and interfering node parameters on the outage probability, we show the existence of the optimal height of the UAV that minimizes the outage probability. We also show the NLoS environment can be better than the LoS environment if the average received power of the interference is more dominant than that of the transmitting signal in UAV communications.

Data-centric approaches are increasingly shaping wireless communication research, where the availability and quality of datasets directly influence the reliability of learning-based and model-driven methods. In this context, unmanned aerial vehicle (UAV) communication poses unique challenges, as it requires datasets that jointly capture geometric information, propagation conditions, and diverse link configurations. This work introduces a geometry-aware UAV communication dataset designed to support research on controlled UAV communication link directions and propagation scenarios. The dataset is generated using standardized 3GPP and ITU-R channel models across multiple urban, suburban, and rural regions, accounting for variations in altitude, carrier frequency, and node distribution. The dataset provides spatially resolved channel parameters along with geometry-rich files containing environmental features, which can be used to extract relevant parameters for UAV communication studies. These data support reproducible research in geometry-aware channel modelling, path-loss prediction, LOS/NLOS analysis, delay-related modelling, and trajectory-conditioned link-quality analysis.