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.

Autonomous aerial vehicle (AAVs) are increasingly utilized in smart city applications, such as Artificial Intelligence of Things (AIoT) systems due to their agility, low cost, and independence from ground road conditions. However, they require substantial computing resources and electricity consumption as support, leading to significant carbon emissions and energy consumption. This article presents a novel sustainable scheme for AAV-assisted low-altitude communication enabled by radar point clouds (RPCs) and the moving interaction station (MIS) to address this challenge. Vehicles are represented as rigid shapes with the 4-D RPC, which is close to the real world, and AAVs can ride on buses defined as MIS to conserve energy. Then, a joint optimization of the AAV trajectory, vehicle association, and resource allocation problem is formulated to complete the communication task and minimize energy consumption, which is limited by the Age of Information (AoI) indicators. An advanced hierarchical deep reinforcement learning (HDRL) algorithm containing a central control module and five submodules is proposed to solve the proposed optimization problem. The five submodules optimize the AAV trajectory, vehicle association, and resource allocation to complete the communication task with the constraint of AoI indicators. The central control module manages five submodules to minimize energy consumption by scheduling time. Simulation results demonstrate that the proposed scheme is effective for smart city applications, and the HDRL algorithm achieves competitive performance compared with the benchmarks.

Vehicle trajectory prediction (VTP) is an important task that can enhance the safety and efficiency of autonomous driving. However, existing VTP methods often struggle to fully extract spatiotemporal features, resulting in inaccurate prediction results. To solve this problem, we propose a hierarchical long short-term memory and graph attention network (HLSTM-GAT) model. First, we design a hierarchical network architecture to model different spatiotemporal features. The first-layer network (FLN) focuses on short-term trajectory information and immediate vehicle interactions to generate preliminary candidate trajectories. In the FLN, long short-term memory (LSTM) encoder processes historical trajectories to extract temporal features of vehicles, while a graph attention network (GAT) handles the LSTM-encoded outputs to capture spatial features between vehicles. Second, the second-layer network (SLN) combines the candidate trajectories generated by the FLN with historical trajectories to form comprehensive trajectories for accurate prediction. Subsequently, SLN adopts a GAT to process these comprehensive trajectories to precisely model the spatial relationships between vehicles. Moreover, we propose two distance threshold-based dynamic GAT models to perfectly capture spatial features between vehicles. They construct vehicle spatial interaction relationships at two distinct levels based on real-time distances in the FLN and predicted future distances in the SLN, respectively. These dynamic GAT models effectively filter out irrelevant information and enable the proposed method to consider potential future interactions. Finally, we conduct extensive experiments on two publicly available datasets, and experimental results demonstrate that the proposed method outperforms other state-of-the-art VTP methods in terms of prediction accuracy, robustness and computational efficiency.

Traffic flow prediction (TFP) plays a crucial role in optimizing road resource allocation and alleviating traffic congestion. However, existing TFP methods have limitations in capturing the complex spatiotemporal dependencies from traffic data, resulting in low prediction accuracy. To solve this problem, we propose a hybrid long short-term memory (LSTM) and graph attention network (GAT) model based on multi-scale temporal features (MSTF-LG) to predict traffic flow. Firstly, we employ trigonometric functions (TF) to process timestamp information to extract its periodicity and continuity features. These features are then integrated with traffic data to construct a comprehensive input representation. We further extract recent traffic data as well as daily, weekly, and monthly periodic data from this representation. Secondly, we adopt the LSTM encoder to process recent traffic data to extract recent trend features, and apply LSTM encoders to handle daily, weekly and monthly periodic data to extract periodic features. Furthermore, we employ the GAT to process these LSTM-encoded multi-scale temporal features of traffic data to capture dynamic spatial characteristics. The normalized GAT outputs are fed into the LSTM decoder to effectively capture the dynamic temporal changes of traffic data, and then a linear layer transforms the output of the LSTM decoder into TFP results. Finally, experimental results demonstrate that the proposed method outperforms existing TFP methods in terms of prediction accuracy, robustness and computational efficiency.

With the rapid popularity of autonomous aerial vehicles (AAV)-Internet of Things (IoT) networks, timely and energy-efficient data collection is a critical issue. The strong Line-of-Sight (LoS) communication of UAVs makes them more susceptible to jamming attacks, resulting in increased communication latency and energy consumption (EC). In this article, the problem of jointly minimizing Age of Information (AoI) and EC in data collection under malicious jamming attacks is formulated by optimizing the diagonal phase shift matrix of the intelligent reflective surface (IRS), UAV trajectory, and the transmit powers of IoT devices. The formulated problem is solved by an alternating optimization (AO) scheme. Specifically, we first obtain the closed-form solution of the IRS diagonal phase shift matrix by using the quantitative passive beamforming method. Then, the UAV trajectory is optimized by the variable parameter particle swarm annealing (VPPSA) algorithm, and the suboptimal solution of the transmit powers of IoT devices is obtained by the successive convex approximation (SCA) algorithm. Extensive simulation results demonstrate that the proposed algorithm outperforms the benchmark schemes in terms of AoI and EC.

Traditional beam prediction approaches are suitable for dealing with point-like targets rather than extended targets, which are commonly detected by the millimeter-wave (mmWave) radar. To solve this problem, a novel adaptive beam prediction approach for complex real-world mmWave vehicle-to-infrastructure (V2I) communications is proposed in this paper. By adopting a vehicle kinematic model based on the axle load point and combining it with an adaptive extended Kalman filter (AEKF) algorithm, the proposed approach can accurately predict and track vehicle motion states and beam directions in time division duplexing (TDD) mode. The AEKF algorithm is employed to dynamically update the process and measurement noise covariance matrices to reduce the impact of various noises in real-world scenarios, which can enhance the accuracy of beam prediction, and then improve the received signal-to-noise ratio (SNR) and communication rate. During the downlink phase, the roadside unit (RSU) formulates transmit beam by using predicted beam direction, and then transmits integrated sensing and communication (ISAC) signals to the vehicle. After receiving the echo point clouds reflected by the vehicle body, the RSU can estimate its relative direction and motion states through the AEKF algorithm. In the uplink phase, the RSU uses the above estimated states to further predict the direction and vehicle states, and then estimate and predict them through the AEKF algorithm by using the received uplink signal. Simulation results and DeepSense 6G dataset-based test results show that the proposed approach offers significant improvements in both sensing and communication performance compared with traditional approaches.

This paper investigates movable antenna (MA) empowered secure transmission in near-field multiple-input multiple-output (MIMO) communication systems, where the base station (BS) equipped with an MA array transmits confidential information to a legitimate user under the threat of a potential eavesdropper. To enhance physical layer security (PLS) of the considered system, we aim to maximize the secrecy rate by jointly designing the hybrid digital and analog beamformers, as well as the positions of MAs at the BS. To solve the formulated non-convex problem with highly coupled variables, an alternating optimization (AO)-based algorithm is introduced by decoupling the original problem into two separate subproblems. Specifically, for the subproblem of designing hybrid beamformers, a semi-closed-form solution for the fully-digital beamformer is first derived by a weighted minimum mean-square error (WMMSE)-based algorithm. Subsequently, the digital and analog beamformers are determined by approximating the fully-digital beamformer through the manifold optimization (MO) technique. For the MA positions design subproblem, we utilize the majorization-minimization (MM) algorithm to iteratively optimize each MA’s position while keeping others fixed. Extensive simulation results validate the considerable benefits of the proposed MA-aided near-field beam focusing approach in enhancing security performance compared to the traditional far-field and/or the fixed position antenna (FPA)-based systems. In addition, the proposed scheme can realize secure transmission even if the eavesdropper is located in the same direction as the user and closer to the BS.

The integrated sensing and communication-enhanced (ISAC-enhanced) intelligent transportation system (ITS) has great development potential and market value in future urban vehicle-to-infrastructure (V2I) scenarios. With limited total power consumption, joint design of power distribution and beam steering of sensing and communication subsystems is required to achieve optimal system performance. This paper introduces an intelligent power allocation and beam-steering control system, where both the system actions are determined based on the radar sensing data cube. The adaptive power allocation module employs the Actor-Critic reinforcement learning agents to optimize power distribution based on vehicle GPS data, communication channel capacity, and historical power allocation, balancing the overall system performance including the sensing and communication functions. The automatic beam control module, utilizing the convolutional neural network (CNN) for dynamic beam control, intelligently adapts communication beams for vehicles navigating complex roadways, enhancing the beam tracking accuracy and quality of service. Validation on real-world datasets showcases the effectiveness of these modules, highlighting the potential to enhance vehicular communication performance.

Integrated sensing and communication (ISAC) systems have the issue of secrecy leakage when using the ISAC waveforms for sensing, thus posing a potential risk for eavesdropping. To address this problem, we propose to employ movable antennas (MAs) and reconfigurable intelligent surface (RIS) to enhance the physical layer security (PLS) performance of ISAC systems, where an eavesdropping target potentially wiretaps the signals transmitted by the base station (BS). To evaluate the synergistic performance gain provided by MAs and RIS, we formulate an optimization problem for maximizing the sum-rate of the users by jointly optimizing the transmit/receive beamformers of the BS, the reflection coefficients of the RIS, and the positions of MAs at communication users, subject to a minimum communication rate requirement for each user, a minimum radar sensing requirement, and a maximum secrecy leakage to the eavesdropping target. To solve this non-convex problem with highly coupled variables, a two-layer penalty-based algorithm is developed by updating the penalty parameter in the outer-layer iterations to achieve a trade-off between the optimality and feasibility of the solution. In the inner-layer iterations, the auxiliary variables are first obtained with semi-closed-form solutions using Lagrange duality. Then, the receive beamformer filter at the BS is optimized by solving a Rayleigh-quotient subproblem. Subsequently, the transmit beamformer matrix is obtained by solving a convex subproblem. Finally, the majorization-minimization (MM) algorithm is employed to optimize the RIS reflection coefficients and the positions of MAs. Extensive simulation results validate the considerable benefits of the proposed MAs-aided RIS-ISAC systems in enhancing security performance compared to traditional fixed position antenna (FPA)-based systems.

Due to the inherent characteristics of system extensibility and implementation flexibility, unmanned aerial vehicle (UAV)-assisted data aggregations will play an essential role in Internet of Things (IoT) networks, where both communication security and timeliness are of high priority. In this paper, we study uplink data aggregation in cooperative jamming-aided large-scale UAV-IoT networks under the threat of eavesdroppers. By employing stochastic geometry, we derive performance metrics related to the age of information (AoI) and secrecy outage probability (SOP) in a system-level manner, and formulate the combat between legitimate entities (i.e., IoT devices and cooperative jammers) and eavesdroppers as a two-stage Stackelberg game. To solve the formulated game, the backward induction method is utilized to obtain the Stackelberg equilibrium (SE) iteratively. Specifically, we first obtain the minimum detection error probability for the eavesdropper by optimizing its detection threshold using the successive convex approximation (SCA) technique. Subsequently, the minimization of AoI violation probability and SOP for the legitimate entity is achieved using the proposed tighter α branch and bound (T-αBB) method by jointly optimizing the transmit powers of the typical IoT device and cooperative jammers as well as the deployment altitude of the typical UAV. Extensive numerical results demonstrate that the proposed solution converges rapidly, with the timeliness and secrecy metrics decreasing by 25.1%, 33.1%, 35.9%, and 37.6% compared to the benchmark scheme in suburban, urban, dense urban, and high-rise urban environments, respectively.

Radar target detection on the sea surface is challenging due to the influence of sea clutter. Traditional radar target detection methods cannot model the sea clutter distributions precisely, resulting in poor target detection performance. In this letter, we propose a novel PointNet-based method to parallelly detect multiple targets. We extract the global features to solve a classification problem, i.e., detecting whether there exist the targets in a radar echo frame, and extract the local features to solve a segmentation problem, i.e., detecting whether it has a target in each range cell. In addition, to implement constant false alarm rate (CFAR) detection, we apply a statistical method by precisely adjusting the detection threshold to keep a desired probability of false alarm (PFA). Simulation results show that the proposed method can realize the target classification with 95.985% total accuracy rate when PFA is 0.1, and achieve a larger detection probability under a desired PFA based on the IPIX radar dataset compared with the baselines.

Vehicle position prediction (VPP) is of great significance for navigation planning and traffic safety of intelligent vehicles. In general, particle filtering (PF) uses global navigation satellite system (GNSS) to implement VPP. However, it does not consider geographic layer information (GLI) and its particle weight is not combined with the real-world geographic position information, which leads to insufficient prediction preparation. To resolve this problem, we propose a novel PF-based VPP method by using three-dimensional convolutional neural network and long short-term memory (3D CNN-LSTM) network model. First, for data preprocessing, we extract kinematic information features from GNSS, and evenly divide the area around each GNSS point into multiple grids and calculate the probability of grids center belonging to each GLI type. In addition, in order to better reflect the relationship between two consecutive positions due to the factors such as the conversion angle, we construct tilted cells to represent possible positions of each vehicle at any time. Second, a novel 3D CNN-LSTM model is designed to calculate the vehicle occurrence probability (VOP) in each tilted cell by processing the GLI and GNSS data, which can optimize the PF weight of each particle, and then improve PF to make more precise position prediction. Finally, the experimental results demonstrate that the proposed VPP method can improve the cell prediction accuracy, and then significantly improve the position prediction precision.

As the commercial launch of the fifth-generation (5G) wireless communications gets near, the trend from the Internet of Things (IoT) to the Internet of Everything (IoE) is emerging. Due to the advantages of the high mobility, high Line-of-Sight (LoS) probability and low labor cost, unmanned aerial vehicles (UAVs) may play an important role in the future IoT communication networks, e.g., data collection in remote areas. In this article, we study the 3-D placement and resource allocation of multiple UAV-mounted base stations (BSs) in an uplink IoT network, where the balanced task for the UAV-BSs, the limited channel resource, and the signal interference are taken into consideration. In the considered system, the total transmission power of IoT devices is minimized, subject to a signal-to-interference-and-noise ratio (SINR) threshold for each device. First, aiming to balance the task of each UAV, we propose a clustering algorithm based on an improved$K$-means method to divide IoT devices into several groups so that the number of devices in each group is roughly the same. Then, based on matching theory, a modified-Hungarian-based dynamic many–many matching (HD4M) algorithm is designed for assigning subchannels to IoT devices, which can efficiently mitigate the interference. Finally, we jointly optimize the transmission power of IoT devices and the altitudes of UAVs via an alternating iterative method. The simulation results show that the total transmission power decreases significantly after applying the proposed algorithms.

To solve intra-cluster and inter-cluster transmission collision problems in vehicular ad-hoc networks, this paper proposes a novel cluster-based collision-free multichannel medium access control (CCFM-MAC) protocol. All nodes are grouped into different clusters based on the link expiration times with their neighbors. Adjacent clusters use different channels to avoid inter-cluster interference. A cluster head (CH) is in charge of assigning time slots to its members according to their state and relative location to achieve packet transmissions without collisions. In addition, a CH allocates time slots to its members based on their traffic demands in order to guarantee the access fairness while improving throughput. Finally, simulation results show that the proposed protocol outperforms EDCA MAC protocol in terms of average throughput, average end-to-end delay and successful transmission probability.

In order to prolong the lifetime of wireless sensor networks (WSNs), a low-duty-cycle mode is widely used to save the energy for sensor nodes. Under this mode, sensor nodes switch between active and dormant states, which incurs a high latency for traditional routing algorithms. To mitigate this, in this paper, the data collection problem in low-duty-cycle WSNs is formulated as a delay optimization problem of traffic flow with consideration of both congestion and collision, which is solved by a distributed algorithm based on network utility maximization. Our proposed distributed routing algorithm achieves a better tradeoff between latency and energy conservation than existing schemes, and our schemes can find a nearly global-optimal-path to achieve almost minimum average end-to-end (E2E) delay with less energy consumption. The computation complexity and energy consumption of the distributed algorithm are analyzed and evaluated in detail. The simulation results show that the proposed algorithm can achieve almost the same average E2E delay performance as the global optimal algorithm with less energy, and reduce the average E2E delay by about 30% than the shortest path algorithm when the data generation rate is high.