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A novel air-to-ground communication paradigm is conceived, where an unmanned aerial vehicle (UAV)-mounted base station (BS) equipped with multiple antennas sends information to multiple ground users (GUs) with the aid of a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). In contrast to the conventional RIS whose main function is to reflect incident signals, the STAR-RIS is capable of both transmitting and reflecting the impinging signals from either side of the surface, thereby leading to full-space 360 degree coverage. However, the transmissive and reflective capabilities of the STAR-RIS require more complex transmission/reflection coefficient design. Therefore, in this work, a sum-rate maximization problem is formulated for the joint optimization of the UAV’s trajectory, the active beamforming at the UAV, and the passive transmission/reflection beamforming at the STAR-RIS. This cutting-edge optimization problem is also subject to the UAV’s flight safety, to the maximum flight duration constraint, as well as to the GUs’ minimum data rate requirements. Given the unknown locations of obstacles prior to the UAV’s flight, we provide an online decision making framework employing reinforcement learning (RL) to simultaneously adjust both the UAV’s trajectory as well as the active and passive beamformer. To enhance the system’s robustness against the associated uncertainties caused by limited sampling of the environment, a novel “distributionally-robust” RL (DRRL) algorithm is proposed for offering an adequate worst-case performance guarantee. Our numerical results unveil that: 1) the STAR-RIS assisted UAV communications benefit from significant sum-rate gain over the conventional reflecting-only RIS; and 2) the proposed DRRL algorithm achieves both more stable and more robust performance than the state-of-the-art RL algorithms.

Unmanned Aircraft Systems (UASs) have advanced technologically and surged exponentially over recent years. Currently, due to safety concerns, most civil operations of UAS are conducted in low-level uncontrolled area or in segregated controlled airspace. As the industry progresses, both operational and technological capabilities have matured to the point where UASs are expected to gain greater freedom of access to both controlled and uncontrolled airspace. Extensive technical and regulatory surveys have been conducted to enable the expanded operations. However, most surveys are derived from the perspective of UAS own operating mechanism and barely consider interactions of their non-segregated activities with the Air Traffic Management (ATM) system. Hence, to fill the gap, this paper presents a survey conducted from the perspective of Air Navigation Service Provider (ANSP), which serves to accommodate these new entrants to the overall national airspace while continuing flight safety and efficiency. The primary objectives of this paper are to: (A) describe what typical ANSP-supplied UAS Traffic Management (UTM) architecture is required to facilitate all types of civil UAS operations; (B) identify three major ANSP considerations on how UAS can be accommodated safely in civil airspace; (C) outline future directions and challenges related with UAS operations for the ANSP.

This article aims at exploring the dynamic behaviors of signed networks under the mixed static and dynamic control protocols, which reflect the existence of two classes of communication channels. An extended leader-follower framework admitting multiple dynamic leaders is established to identify the roles of all nodes in signed networks, depending on the union of two related signed digraphs. It is shown that bipartite containment tracking is achieved for signed networks despite any topology conditions. To be specific, every leader group realizes modulus consensus and the leaders dominate the dynamic evolutions of signed networks such that all followers converge within the bounded zone spanned by the leaders' converged states and their symmetric states. Furthermore, conditions on the zero convergence of dynamic control inputs are exploited, together with those on the (interval) bipartite consensus of signed networks. Simulation examples are given to demonstrate the convergence behaviors of signed networks with respect to the mixed static and dynamic control protocols.

Estimating airspace capacity under the impact of severe weather is a crucial method to guarantee efficiency and safety of air traffic operation. It is also of vital importance to understand the spatial and temporal dependencies of adjacent airspace in the process of estimation. Therefore, in this paper, considering this spatio-temporal dependencies, a two-step airspace capacity estimation method is proposed to evaluate terminal areas’ capacity including airport and terminal sectors. The first step is to translate different types of meteorological conditions into operation features depending on the type of target airspace. Maxflow/Mincut theorem and Gradient Boosting Regression are adopted for sectors and airports, respectively. Based on the extracted features, an improved Spatio-temporal Graph Convolution Networks via Initial residual (STGCNI) is developed aiming to fit the characteristics in air traffic field. Finally, through experiments with real data of Chengdu Terminal Area (ZUUUAP), both the validity and enhanced estimation accuracy of the method are verified by comparisons with other models, which exclude spatial and temporal characteristics.

In 2019, China's air traffic control service handled approximately 10.766 million flight movements, with an increase of 7.60% compared to 2018. However, this rapid growth has resulted in flight delays due to airspace capacity limitations, and air traffic control complications. Faced with the realities of airspace capacity limitations, Air Traffic Flow Management (ATFM) plays a central role in alleviating demand-capacity imbalances and subsequently reducing airspace congestion and flight delays. Yet, in the existence of airspace capacity uncertainty, the ATFM operations may be non-practical or ineffective when the deterministic models are adopted since the latter presumes that airspace capacity is known. In this paper, the Air Traffic Sector Network Flow Optimization (ATSNFO) problem is formulated into a Chance-Constrained Optimization Program (C-COP), and a scenario approach optimization method for airspace sector capacity in the presence of uncertainties is proposed to approximately solve the C-COP with a predetermined probabilistic confidence. Then, a mixed-integer programming model is developed based on the obtained deterministic sector capacity. Computation experiment results showed that our model is more efficient and reliable in dealing with uncertainty in ATFM problems. Our findings also highlight the capability of the scenario approach method for solving these problems.

The Air Traffic Flow Management (ATFM) is considered as an effective method for air safety and efficiency guarantee. However, as one of the vital part of flight efficiency, fuel consumption is easily affected by external factors induced by various uncertainties such as severe weather conditions, which impact decision making process. Therefore, a distributionally robust mixed integer programming model for ATFM problem (DR-ATFM) is introduced in this paper to handle the uncertainty of fuel consumption while minimizing the total cost of fuel consumption, flight cancellation and flight delays. By exploiting the moment information of the fuel consumption data, a moment ambiguity set is constructed which characterizes the actual distribution of fuel consumption influctation. Based on this, an equivalent reformulation of DR-ATFM is derived to transform the problem into a mathematically solvable one, which is further solved through a cutting plane-based decomposition algorithm proposed in this paper. Finally, the effectiveness and robustness of the method are verified based on computational results of small-sized instances.

A multi-agent reinforcement learning (MARL) based conflict resolution method is proposed. The motivation is to reduce the workloads of air traffic controllers (ATCOs) and pilots in operation over the dense airspace. First, a intermediate waypoints generation method is presented to avoid the frequent fine-tuning in the resolution process. This method enables the controllers and pilots to resolve conflicts in one-step decision making. Next, the multi-agent reinforcement learning method is used to search for the optimal intermediate waypoints. Several numerical examples are presented to illustrate the proposed methodology. A detailed discussion of the sample efficiency with respect to various number of agents is given. Both the benchmark and practical examples are used for validation. The proposed method is able to handle the mulit-conflict scenarios without recourse to frequent disturbance of the pilots and controllers.

Analysis of the flight delay recovery is crucial in the airports network operation. However, the analysis is very complex, as the effect of delays can be propagated through the network variously such that the recovery process depends on inherent characteristics of the network. In this paper, analyses of operation data are employed, and a simulation model is conducted to study the delay process with consideration of flight itineraries. Both the airports departure punctuality and the delay recovery indicator are employed to assess the influence of original delays considering different durations and start times. Our experiments are based on the Chinese historical flight data from July to September 2019. Results provide a comprehensive understanding of the delay recovery process with respect to flight itineraries. Surprisingly, the departure punctuality is non-monotonic process with multiple troughs in the considered recovery period, which implies that air traffic transport has the self-influence phenomenon. Sensitivity analysis implies that the phenomenon is highly correlated with characteristics of interruptions, which is an inherent quality of the structure of the flight schedule. Therefore, a well-organised flight schedule should be implemented in case delays relapse. This work can provide a broad prospect for the future flight schedule optimisation and improvement of airports network.

In this paper, an aircraft trajectory modeling method is presented to extract and identify flight status on the basis of performance learning. The aim is to facilitate understand of the intrinsic mechanism of flight operations and improve the quality of trajectory analysis and waypoint identification, as it is of vital significance for the implementation of TBO in the future. In the first step, the spatial and status data of real trajectory for different aircraft types and flight phases is categorized to generate a subset of performance data. Next, Gaussian mixture clustering algorithm and a comprehensive evaluation index are utilized to extract flight status features from the subset data. The KD-Tree method is used to quickly and effectively classify the input trajectory points and identify corresponding flight status with the extracted features as evaluation criteria. Finally, the K-fold cross-validation method is applied to assess the feasibility. Experimental results based on real data show the method could achieve a satisfying accuracy in trajectory waypoint categorization.