To guarantee rational decision-making and safety of intelligent transportation systems and autonomous driving, existing vehicle trajectory prediction (VTP) methods extract spatial and temporal features from complex traffic environments to achieve accurate forecast results. However, they generally do not use the frequency-domain information inherently embedded in vehicle trajectory data, resulting in lower prediction accuracy. To solve this problem, we propose a joint spatio-temporal-frequency-aware feature fusion (STFA-FF) method for VTP. Firstly, an intention recognition network is proposed to integrate spatial features and temporal features to infer driving intentions with high precision. Secondly, to fully utilize frequency-domain features, we present a multi-scale frequency-domain feature extraction (MSFDFE) module to map vehicle trajectory data into the frequency domain, incorporate the high-frequency attenuation mask to suppress high-frequency noise, and deeply integrate short-term variations with long-term trends. Additionally, a frequency-domain channel selection (FDCS) module is proposed to dynamically select key frequency channels related to driving modes. Furthermore, a multi-domain feature fusion prediction network is proposed to process the spatial, temporal and frequency-domain features to generate the final trajectory prediction results. Finally, experimental results demonstrate that the proposed method significantly outperforms mainstream approaches in prediction accuracy and robustness.

The L-band digital aeronautical communication system (LDACS) is a crucial advancement designed to support the increasing demand for air traffic management and ensure safety, efficiency, and reliability in aviation operations. This paper addresses a bi-objective cell planning problem for LDACS deployment in China, aiming to minimize the number of ground stations (GSs) while maximizing network coverage. A Modified Pareto Strength Ant Colony Optimization (MPSACO) method is proposed to derive the nondominated Pareto front, facilitating better decision-making in the deployment process. The incorporation of an adaptive heuristic information operator and a local search strategy enhance both the exploration and exploitation of the search, supporting MPSACO to generate a diverse set of Pareto-optimal solutions. Simulations based on real-world candidate locations and air-route data demonstrate that MPSACO consistently outperforms the state-of-the-art multi-objective optimization methods across key metrics such as hypervolume, uniformity and dominance. The obtained Pareto front provides a clear trade-off between coverage and cost, enabling decision-makers to select an efficient deployment according to specific operational priorities. This work not only delivers a practical planning tool for LDACS, but also establishes a generalizable optimization framework for large-scale, safety-critical Internet of Things (IoT) infrastructure deployment.

A novel near-field integrated sensing and communications framework for secure unmanned aerial vehicle (UAV) networks with high time efficiency is proposed. A ground base station (GBS) with large aperture size communicates with one communication UAV (C-UAV) under the existence of one eavesdropping UAV (E-UAV), where the artificial noise (AN) is employed for both jamming and sensing purpose. Given that the E-UAV’s motion model is unknown at the GBS, we first propose a near-field localization and trajectory tracking scheme. Specifically, exploiting the variant Doppler shift observations over the spatial domain in the near field, the E-UAV’s three-dimensional (3D) velocities are estimated from echo signals. To provide the timely correction of location prediction errors, the extended Kalman filter (EKF) is adopted to fuse the predicted states and the measured ones. Subsequently, based on the real-time predicated location of the E-UAV, we further propose a joint GBS beamforming and C-UAV trajectory design scheme for maximizing the instantaneous secrecy rate, while guaranteeing the sensing accuracy constraint. To solve the resultant non-convex problem, an alternating optimization approach is developed, where the near-field GBS beamforming and the C-UAV trajectory design subproblems are iteratively solved by exploiting the successive convex approximation method. Finally, our numerical results unveil that: 1) the E-UAV’s 3D velocities and location can be accurately estimated in real time with our proposed framework by exploiting the near-field spherical wave propagation; and 2) the proposed framework achieves superior secrecy rate compared to benchmark schemes and closely approaches the performance when the E-UAV trajectory is perfectly known.

Pinching-antenna systems (PASS) represent an innovative advancement in flexible-antenna technologies, aimed at significantly improving wireless communications by ensuring reliable line-of-sight connections and dynamic antenna array reconfigurations. To employ multi-waveguide PASS in multi-user communications, three practical transmission structures are proposed, namely waveguide multiplexing (WM), waveguide division (WD), and waveguide switching (WS). Based on the proposed structures, the joint baseband signal processing and pinching beamforming design is studied for a general multi-group multicast communication system, with the unicast communication encompassed as a special case. A max-min fairness (MMF) problem is formulated for each proposed transmission structure, subject to the maximum transmit power constraint. For WM, to solve the highly-coupled and non-convex MMF problem with complex exponential and fractional expressions, a penalty dual decomposition (PDD)-based algorithm is invoked for obtaining locally optimal solutions. Specifically, the augmented Lagrangian relaxation is first applied to alleviate the stringent coupling constraints, which is followed by the block decomposition over the resulting augmented Lagrangian function. Then, the proposed PDD-based algorithm is extended to solve the MMF problem for both WD and WS. Furthermore, a low-complexity algorithm is proposed for the unicast case employing the WS structure, by simultaneously aligning the signal phases and minimizing the large-scale path loss at each user. Finally, numerical results reveal that: 1) the MMF performance is significantly improved by employing the PASS compared to conventional fixed-position antenna systems; 2) WS and WM are suitable for unicast and multicast communications, respectively; 3) the performance gap between WD and WM can be significantly alleviated when the users are geographically isolated.

A novel movable-element enabled simultaneously transmitting and reflecting surface (ME-STARS) communication system is proposed, where ME-STARS elements positions can be adjusted to enhance the degress-of-freedom for transmission and reflection. For each ME-STARS operating protocols, namely energy-splitting (ES), mode switching (MS), and time switching (TS), a weighted sum rate (WSR) maximization problem is formulated to jointly optimize the active beamforming at the base station (BS) as well as the elements positions and passive beamforming at the ME-STARS. An alternative optimization (AO)-based iterative algorithm is developed to decompose the original non-convex problem into three subproblems. Specifically, the gradient descent algorithm is employed for solving the ME-STARS element position optimization subproblem, and the weighted minimum mean square error and the successive convex approximation methods are invoked for solving the active and passive beamforming subproblems, respectively. It is further demonstrated that the proposed AO algorithm for ES can be extended to solve the problems for MS and TS. Numerical results unveil that: 1) the ME-STARS can significantly improve the WSR compared to the STARS with fixed position elements and the conventional reconfigurable intelligent surface with movable elements, thanks to the extra spatial-domain diversity and the higher flexibility in beamforming; and 2) the performance gain of ME-STARS is significant in the scenarios with larger number of users or more scatterers.

The large number of antennas in extremely large aperture array (ELAA) systems shifts the propagation regime of signals in wireless communication systems towards near-field spherical wave propagation, from beamforming to beamfocusing. The design of the two-dimensional beam codebook that contains both the angular and distance domains is challenging. To address this issue, we propose a reconfigurable intelligent surface (RIS)-assisted near-field beam training based on a novel multi-attention algorithm, which provides a fine-grained codebook with enhanced spatial resolution. Specifically, we transform the beam selection task into a location detection process, enabling more effective beam search. Experimental results unveil that the proposed method achieves beam selection accuracy up to 97% at signal-to-noise ratio (SNR) of 20 dB, and improves 10% over the baseline method at different SNRs.

The real-time near-field beamforming framework with the aided of 3D velocity sensing and localization for unmanned aerial vehicle (UAV) communications is proposed. Exploiting the variant Doppler shift over the spatial domain in the near field, the three-dimensional (3D) velocities are estimated with the echo signals. To provide timely correction of the location prediction errors with the estimated velocities, the extended Kalman filter (EKF) is adopted to fuse the predicted states and the estimated ones. Subsequently, the near-field beamforming can be conducted with the predicted locations of the UAV, thereby realizing zero-pilot and low-latency transmission. Numerical results unveil that, the proposed scheme can achieve the accurate tracking of the UAV's flying route.

A novel framework of the uncrewed aerial vehicle (UAV)-mounted active simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) communications with the nonorthogonal multiple access (NOMA) is proposed for Internet of Things (IoT) networks. In particular, an active STAR-RIS is deployed onboard to enhance the communication link between the base station (BS) and the IoT devices, and NOMA is utilized for supporting the multidevice connectivity. Based on the proposed framework, a system sum rate maximization problem is formulated for the joint optimization of the active STAR-RIS beamforming, the UAV trajectory design, and the power allocation. To solve the nonconvex problem with highly coupled variables, an alternating optimization (AO) algorithm is proposed to decouple the original problem into three subproblems. Specifically, for the active STAR-RIS beamforming, the amplification coefficient, the power-splitting ratio, and the phase shift are incorporated into a combined variable to simplify the optimization process. Afterward, the penalty-based method is invoked for handling the nonconvex rank-one constraint. For the UAV trajectory design and the power allocation subproblems, the successive convex optimization method is applied for iteratively approximating the local-optimal solution. Numerical results demonstrate that: 1) the proposed algorithm achieves superior performance compared to the benchmarks in terms of the sum rate and 2) the UAV-mounted active STAR-RIS can effectively enhance the channel gain from the BS to the IoT devices by the high-quality channel construction and the power compensation.

The massive number of antennas in extremely large aperture array (ELAA) systems shifts the propagation regime of signals in internet of things (IoT) communication systems towards near-field spherical wave propagation. We propose a reconfigurable intelligent surfaces (RIS)-assisted beamfocusing mechanism, where the design of the two-dimensional beam codebook that contains both the angular and distance domains is challenging. To address this issue, we introduce a novel Transformer-based two-stage beam training algorithm, which includes the coarse and fine search phases. The proposed mechanism provides a fine-grained codebook with enhanced spatial resolution, enabling precise beamfocusing. Specifically, in the first stage, the beam training is performed to estimate the approximate location of the device by using a simple codebook, determining whether it is within the beamfocusing range (BFR) or the none-beamfocusing range (NBFR). In the second stage, by using a more precise codebook, a fine-grained beam search strategy is conducted. Experimental results unveil that the precision of the RIS-assisted beamfocusing is greatly improved. The proposed method achieves beam selection accuracy up to 97% at signal-to-noise ratio (SNR) of 20 dB, and improves 10% to 50% over the baseline method at different SNRs.

A novel beam tracking approach is proposed to realize reliable air-to-ground (A2G) transmissions with reduced pilot overhead and time delay. The proposed beam tracking strategy consists of two stages, namely the model-driven channel tracking and the model-data dual driven hybrid beamforming (HBF). For the model-driven channel tracking, the angle-of-arrivals/angle-of-departures (AoAs/AoDs) are predicted by leveraging the regularity of the three-dimensional flight track and attitude, as well as the A2G geometrical information with temporal correlations. Then, the high-dimensional channel matrix estimation problem is converted to the low-dimensional multipath components parameters estimation tasks, which substantially reduces the pilot overhead. The proposed model-data dual-driven HBF module unfolds the iterative HBF algorithms and introduces a set of trainable parameters, which brings in both low complexity and high interpretability. To further improve the HBF robustness against imperfect channel state information, the denoise neural network is employed to exploit spatial-domain channel correlations for improved channel accuracy. Numerical results unveil that: 1) the proposed model-driven channel tracking scheme achieves satisfying normalized mean square error of the tracked A2G channel with significantly reduced pilot overhead; and 2) the proposed model-data dual-driven HBF algorithm is superior to the conventional counterparts in terms of reliability and robustness.

A continuous aperture array (CAPA)-based secure communication system is investigated, where a base station (BS) equipped with a CAPA transmits signals to a legitimate user under the existence of an eavesdropper. For improving the secrecy performance, the artificial noise (AN) is employed at the BS for the jamming purpose. We aim at maximizing the secrecy rate by jointly optimizing the information-bearing and AN source current patterns, subject to the maximum transmit power constraint. To solve the resultant non-convex integral-based functional programming problem, a channel subspace-based approach is first proposed via exploiting the result that the optimal current patterns always lie within the subspace spanned by all users’ channel responses. Then, the intractable CAPA continuous source current pattern design problem with an infinite number of optimization variables is equivalently transformed into the channel-subspace weighting factor optimization problem with a finite number of optimization variables. A penalty-based successive convex approximation method is developed for iteratively optimizing the finite-size weighting vectors. To further reduce the computational complexity, we propose a two-stage source current patterns design scheme. Specifically, the information-bearing and AN patterns are first designed using the maximal ration transmission (MRT) and zero-forcing (ZF) transmission, respectively. Then, the remaining power allocation is addressed via the one-dimensional search method. Numerical results unveil that 1) the CAPA brings in significant secrecy rate gain compared to the conventional discrete multiple-input multiple-output (MIMO); 2) the proposed channel subspace-based algorithm outperforms the conventional Fourier-based approach, while sustaining much lower computational complexity; and 3) the two-stage ZF-MRT approach has negligible performance loss for the large transmit power regime.

Dynamic air traffic flow management (DATFM) aims at flexibly balancing air traffic demand with limited airspace by scheduling aircraft, particularly during unforeseen events, to maintain efficiency in aviation operations. Genetic programming (GP) has shown success in evolving effective heuristics across various domains. However, directly adopting GP to DATFM may be less effective due to the computationally intense simulations required for large-scale aircraft decision-making over broad airspace. To address the issue, we develop a novel multifidelity surrogate-assisted GP. The core idea is that if a computationally efficient low-fidelity surrogate provides enough information to guide the population toward promising areas effectively, then employing more accurate but resource-intensive evaluations would only increase computational effort without enhancing the direction of evolution. A key innovation in our method is a surrogate management strategy that automatically determines when and which surrogate model to use, based on collective information from the evolving population. This approach allows for more effective management of computational resources during the evolutionary process, enabling exploration of a broader range of the heuristic space and increasing the likelihood of identifying promising solutions. The proposed method has been tested on various benchmark instances derived from actual air traffic data. Extensive experimental results demonstrate that the proposed algorithm significantly outperforms current state-of-the-art methods in both effectiveness and efficiency.

Onboard global positioning system (GPS) observations and downlink navigation signals from low earth orbit (LEO) navigation constellations are expected to enhance the ionospheric grid model of the BeiDou satellite-based augmentation system (BDSBAS). However, LEO ionospheric observations traverse only a limited portion of the ionosphere, rendering them unsuitable for direct integration with GPS observations. To address this limitation, this study proposes a two-step Kriging method to construct the ionospheric grid by integrating GPS and LEO data. In the first step, onboard LEO observations are utilized to develop a topside ionospheric grid model by leveraging Kriging, compensating for the lack of topside ionospheric information in LEO downlink data. In the second step, the entire ionospheric grid is constructed using Kriging, integrating both GPS observations and the compensated LEO data. Additionally, a spatial threat model is developed for integrity monitoring based on a data deprivation scheme. A simulation environment covering GPS/LEO constellations and the ionosphere was established using data from 2014 to 2016, spanning periods of high, moderate, and low solar activity for parameter development and performance evaluation. Experimental results indicate that an increase in the orbital altitude of LEO satellites contributes to the enhancement of ionospheric grid performance. At the 1000 km setting, LEO observations reduce the root mean square (RMS) of estimation errors by approximately 20% compared to GPS-only data at both system and user levels. Moreover, LEO observations also stabilize grid ionospheric vertical error (GIVE) with solar activity, reducing extreme values and preventing integrity events.

Probabilistic aircraft trajectory models in the terminal area that reflect real-world distributions can significantly facilitate conflict detection, performance analyses, and risk assessments. However, due to the inherent uncertainties in air traffic, complex spatiotemporal correlations within terminal area, and the influence of diverse weather conditions, modeling these distributions and generating realistic trajectories pose substantial challenges. In this paper, we present a diffusion model-based method to learn the distribution of aircraft trajectories in the terminal area, enabling the generation of high-quality trajectories that resemble real data distributions. This is achieved by effectively combining the generative capability of diffusion models with the ability of extracting spatiotemporal features embedded in real trajectories. Specifically, we propose an airplane trajectory diffusion network structure, which integrates a UNet deep neural network to capture multi-level noise estimation and accurately model the uncertainty inherent in trajectory data. Additionally, we employ a conditional generation module that incorporates meteorological information, allowing the model to learn the correlation between weather conditions and trajectory distributions. Experiments with real-world terminal-area datasets demonstrate that our model can generate realistic trajectories that closely resemble the actual distribution of terminal area flight paths. Comparative results present improvements over existing methods across various evaluation metrics.

The non-nominal troposphere induced by climate extremes poses a threat to the precision approach and landing of the civil aircraft in ground-based augmentation system (GBAS). However, there is currently no effective non-nominal troposphere monitoring method at both GBAS stations and aircrafts, which increases the integrity risk. This study proposes a novel approach for simultaneously modelling and monitoring ground station and aircraft non-nominal troposphere using precision point positioning (PPP) technology based on a regional reference network. The global navigation satellite system (GNSS) and meteorological data from the Hong Kong satellite positioning reference station network and numerical weather models from the European centre for medium-range weather forecasts are processed in all the year of 2017. A tropospheric correction (TC) model is used to correct station height-induced biases in zenith total delay (ZTD) obtained from PPP. By comparing the performance of 6 typical spatial interpolation algorithms in tropospheric delay interpolating, the universal Kriging algorithm is found to be the most suitable one which achieves a minimum mean absolute error (MAE) of 8.24 mm. The non-nominal troposphere is monitored using the constructed corrected ZTD map using universal Kriging method. A non-nominal troposphere up to 50 mm is observed, which can affect the integrity of the air navigation. Furthermore, the relationship between non-nominal troposphere and the rainfall is investigated. The study integrates widely distributed GNSS and meteorological data into a seamless non-nominal tropospheric monitoring service, enhancing the integrity of GBAS in air transportation.

In urban environments, frequent multipath (MP) and non-line-of-sight (NLOS) occurrences introduce significant measurement outliers, posing challenges for accurate and robust positioning via global navigation satellite system (GNSS). This paper proposes a robust position estimator that capitalizes on the fact that MP/NLOS typically affect only a subset of satellites, leading to sparsity of the unknown MP/NLOS bias vector. The method employs real-time estimation and correction of the MP/NLOS bias vector from a Bayesian perspective, leveraging a kinematic model, measurement quality, and sparsity. Initially, the augmented linear representation of the extended Kalman filter (EKF) is used to transform the mixed dense-sparse problem into a standard Bayesian sparse regression form through a subspace projection technique. Subsequently, a reweighting scheme is developed to assess the likelihood of individual elements in the bias vector being zero, and a horseshoe prior is applied to represent their sparsity. Finally, using Bayes’ rule, the maximum a posteriori (MAP) estimation for the bias vector is formulated, and non-convex optimization is solved via local linear approximation (LLA) to quickly obtain the sparse solution for bias correcting and robust positioning. Experimental results on a typical urban environmental dataset demonstrate that the proposed method effectively mitigates the impact of MP/NLOS outliers, reducing root mean square (RMS) value of positioning errors by 78.14%, 75.46%, and 85.09% in the east, north, and vertical directions, respectively, compared to the classical EKF positioning method.

A novel time-efficient framework is proposed for improving the robustness of a broadband multiple-input multiple-output (MIMO) system against unknown interference under rapidly-varying channels. A mean-squared error (MSE) minimization problem is formulated by optimizing the beamformers employed. Since the unknown interference statistics are the premise for solving the formulated problem, an interference statistics tracking (IST) module is first designed. The IST module exploits both the time- and spatial-domain correlations of the interference-plus-noise (IPN) covariance for the future predictions with data training. Compared to the conventional signal-free space sampling approach, the IST module can realize zero-pilot and low-latency estimation. Subsequently, an interference-resistant hybrid beamforming (IR-HBF) module is presented, which incorporates both the prior knowledge of the theoretical optimization method as well as the data-fed training. Taking advantage of the interpretable network structure, the IR-HBF module enables the simplified mapping from the interference statistics to the beamforming weights. The simulations are executed in high-mobility scenarios, where the numerical results unveil that: 1) the proposed IST module attains promising prediction accuracy compared to the conventional counterparts under different snapshot sampling errors; and 2) the proposed IR-HBF module achieves lower MSE with significantly reduced computational complexity.

Due to the high-mobility of aeronautical communications, real-time hybrid beamforming (HBF) is indispensable. In this paper, a novel HBF scheme in the aeronautical multiple-input multiple-output orthogonal frequency division multiplexing systems is proposed with model-driven deep learning. In particular, we formulate a mean square error minimization problem with manifold constraints. As the conventional iterative optimization algorithm has high computational complexity, we propose a deep-unfolding HBF network which unfolds the iterations and introduces a set of trainable parameters. The proposed algorithm produces hybrid beamformer with several layers, which avoids cumbersome iteration procedures. Moreover, the deep-unfolding algorithm preserves the operation of the iterative optimizer, which brings in high interpretability. Numerical results show that the proposed HBF algorithm is superior to the conventional model-based counterparts in terms of reliability.

Understanding surface congestion is essential for improving taxiing efficiency and reducing carbon emissions in busy airports. Diverging from the traditional input-output and dynamics analysis, we propose a novel end-to-end framework to study the complete dynamic process of airport surface congestion and recovery. This framework employs a stochastic hybrid system to model multi-timescale dynamics, integrating continuous states and discrete modes of surface operations under uncertainty. First, the probabilistic reachable set for the input-output state is computed via a chance-constrained optimization program to represent the relationship between the number of aircraft taxiing out and the departure throughput. Next, the discrete modes are divided based on taxiing efficiency and traffic load, utilizing tailored congestion contour regression and density-based clustering, respectively. Finally, the transition trajectory incorporating mode information is constructed to depict the complete process from congestion formation to subsequent recovery, followed by employing an unsupervised algorithm to identify representative patterns. The proposed framework is verified using two years of real-world datasets from Chengdu Shuangliu International Airport, China. Experimental results demonstrate the superiority of our approach compared with the baselines. Moreover, this work also reveals some intriguing findings, such as the diverse multi-timescale dynamical phenomena, and their implications for practical airport surface operations.

The single-frequency (SF) BeiDou satellite-based augmentation system (BDSBAS) has been broadcasting small ionospheric residual uncertainties and grid ionospheric vertical errors (GIVEs), which may cause events of ionospheric integrity or system integrity. The system availability at high latitudes over Chinese territory is also constrained by the conventional interpolation algorithm within the ionosphere map surrounded by ionospheric grid points (IGPs). In this article, we proposed an airborne ionospheric correction approach based on the combination of a residual uncertainty model, a GIVE inflation strategy and a global interpolation algorithm, developed and verified with data from January 2021 to June 2022. The uncertainty model was rebuilt using the broadcast information from BDSBAS and the global ionosphere map (GIM) from the Center for Orbit Determination in Europe (CODE). From this model and historical residual data, we obtained the inflated GIVE which increased the average envelope probability of ionospheric residual errors from 94.08% to 99.99% for 117 BDSBAS IGPs. The biharmonic spline method (BSM) based on Green functions was introduced to extend the original ionosphere map to the north by 10° to enable more augmented signals. BSM increased the interpolation accuracy of the user ionospheric vertical delay (UIVD) by about 63%. The results from the regional test proved that our proposed approach could obtain an average minimum ionospheric safety index of 6.27 within the extended ionosphere map and increase the average coverage rate in Chinese mainland from 95.15% to 99.07%. In the static station tests and the dynamic flight tests, positioning accuracies increased or remained the same while misleading information (MI) events could be avoided.

A reconfigurable intelligent surface (RIS) aided air-to-ground uplink non-orthogonal transmission framework is investigated for next generation multiple access. Occupying the same spectrum resource, unmanned aerial vehicle (UAV) users and ground users (GUs) are connected to terrestrial cellular networks via the uplink non-orthogonal multiple access (NOMA) protocol. As the flight safety is important for employing UAVs in civil airspace, the collision avoidance mechanism has to be considered during the flight. Therefore, a joint optimization problem of the UAV trajectory design, RIS configuration, and uploading power control is formulated for maximizing the network sum rate, while ensuring the UAV’s fight safety and satisfying the minimum data rate requirements of both the UAV and GU. The resultant problem is a sequential decision making one across multiple coherent time slots. Besides, the unknown locations of obstacles bring uncertainties into the decision making process. To tackle this challenging problem, a sample-efficient deep reinforcement learning (DRL) algorithm is proposed to optimize the UAV trajectory, RIS configuration, and power control simultaneously. Moreover, considering the ambiguous uncertainties in the environment, a distributionally robust DRL algorithm is further proposed to provide the worst-case performance guarantee. Numerical results demonstrate that the two proposed DRL algorithms outperform the conventional ones in terms of learning efficiency and robustness. It is also shown that the network sum rate is significantly improved by the proposed RIS-NOMA scheme compared to the conventional RIS-orthogonal multiple access (OMA) scheme and the case where no RIS is deployed.

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.

Flight delay prediction has recently gained growing popularity due to the significant role it plays in efficient airline and airport operation. Most of the previous prediction works consider the single-airport scenario, which overlooks the time-varying spatial interactions hidden in airport networks. In this paper, the flight delay prediction problem is investigated from a network perspective (i.e., multi-airport scenario). To model the time-evolving and periodic graph-structured information in the airport network, a flight delay prediction approach based on the graph convolutional neural network (GCN) is developed in this paper. More specifically, regarding that GCN cannot take both delay time-series and time-evolving graph structures as inputs, a temporal convolutional block based on the Markov property is employed to mine the time-varying patterns of flight delays through a sequence of graph snapshots. Moreover, considering that unknown occasional air routes under emergency may result in incomplete graph-structured inputs for GCN, an adaptive graph convolutional block is embedded into the proposed method to expose spatial interactions hidden in airport networks. Through extensive experiments, it has been shown that the proposed approach outperforms benchmark methods with a satisfying accuracy improvement at the cost of acceptable execution time. The obtained results reveal that deep learning approach based on graph-structured inputs have great potentials in the flight delay prediction problem.

The threats and challenges of unmanned aerial vehicle (UAV) invasion defense due to rapid UAV development have attracted increased attention recently. One of the important UAV invasion defense methods is radar network detection. To form a tight and reliable radar surveillance network with limited resources, it is essential to investigate optimized radar network deployment. This optimization problem is difficult to solve due to its nonlinear features and strong coupling of multiple constraints. To address these issues, we propose an improved firefly algorithm that employs a neighborhood learning strategy with a feedback mechanism and chaotic local search by elite fireflies to obtain a trade-off between exploration and exploitation abilities. Moreover, a chaotic sequence is used to generate initial firefly positions to improve population diversity. Experiments have been conducted on 12 famous benchmark functions and in a classical radar deployment scenario. Results indicate that our approach achieves much better performance than the classical firefly algorithm (FA) and four recently proposed FA variants.

Transit agencies electronically publish transit schedules and route data to improve customer experience or as part of the open-data initiative. The release of such data poses several data management challenges because schedules for a single city can easily exceed storage requirements of several hundreds of megabytes. One way to deal with these challenges is data compression. The encoding of public transit schedules often follows the General Transit Feed Specification (GTFS), which cannot be compressed well out of the box. We propose GTFSCompress, which is an algorithm for compression of GTFS data, based on referential compression queues, which compress a column stream depending on previously seen items in this stream and other streams. Our evaluation on ten real-world data sets shows that GTFS feeds can be compressed by a factor of 100 and more. This is up to one order of magnitude better than using the best standard compressors.

The ever-growing air traffic flow has brought about great challenges to balance the airspace congestion and air traffic demands. This fact sparks numerous studies on Network-wide Flights Planning Optimization (NFPO) which aims to reconcile the contradiction between flight delay cost and airspace congestion by optimizing the pre-strategic flight plans from a network-wide point of view. In consideration of bi-objective and large-scale characteristics of the NFPO problem, this paper proposes a multi-objective Memetic Algorithm with Rerouting Meme (MARM) that incorporates an evolutionary global search framework with a problem-specific meme ulocal search operator. With the idea of heuristically reducing the interactions among flight trajectories to decongest the airspace, the Trajectories Correlation (TC) is defined as key network-wide knowledge and is applied to design the critical Rerouting Meme (RM). Additionally, to balance the ability of exploitation and exploration, the idea of simulated heating configuration setting is adopt for RM to integrate with the global search. Extensive empirical studies conducted on real large-scale traffic data of China air traffic network and flight plans support that MARM is beneficial to the NFPO problem via showing the improvement on effectiveness.

Capacitated Arc Routing Problem (CARP) has drawn much attention during the last few years because of its applications in the real world. Recently, we developed a Memetic Algorithm with Extended Neighborhood Search (MAENS), which is powerful in solving CARP. The excellent performance of MAENS is mainly due to one of its local search operators, namely the Merge-Split (MS) operator. However, the higher computational complexity of the MS operator compared to traditional local search operators remains as the major drawback of MAENS, especially when applying it to large-size instances. In this paper, we propose a heuristic candidate list strategy to sample the neighbors generated by the MS operator instead of enumerating or sampling them randomly, in order to avoid unnecessary callings of the MS operator during local search. Based on the strategy, an improved algorithm of MAENS, namely MAENS-II, is developed. Experimental results on benchmark instances showed that MAENS-II managed to obtain the same level of solution quality as MAENS with much less computational time. This should be credited to the utilization of the proposed heuristic strategy. On the other hand, in case both MAENS and MAENS-II were provided comparable computational time, MAENS-II outperformed MAENS in terms of solution quality.