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.

The detection of sparse signals against background noise is difficult since the information in the signal is only carried by a small portion of it. Prior information is usually assumed to ease detection. This study considers the general unknown and arbitrary sparse signal detection problem when no prior information is available. Under a Neyman–Pearson hypothesis‐testing problem model, a new detection scheme referred to as the likelihood ratio test with sparse estimation (LRT‐SE) is proposed. The SE technique from the compressive sensing theory is incorporated into the LRT‐SE to achieve the detection of sparse signals with unknown support sets and arbitrary non‐zero entries. An analysis of the effectiveness of LRT‐SE is first given in terms of the characterisation of the conditions for the Chernoff‐consistent detection. A large deviation analysis is then given to characterise the error exponent of LRT‐SE with respect to the signal‐to‐noise ratio and the angle between the sparse signal and its estimate. Numerical results demonstrate superior detection performance of the proposed scheme over existing asymptotically optimal sparse detectors for finite signal dimensions. In addition, the simulation shows that the error probability of the proposed scheme decays exponentially with the number of observations.

Relay selection has been considered as an effective method to improve the performance of cooperative communication. However, the channel state information (CSI) used in relay selection can be outdated, yielding severe performance degradation of cooperative communication systems. In this study, the authors investigate how to select relays under outdated CSI in an amplify‐and‐forward cooperative communication system to improve its outage performance. The authors adopt maximum a posteriori (MAP) estimation to predict the actual signal‐to‐noise ratio (SNR) of each relay during data transmission and propose a single‐relay selection (SRS) strategy based on the MAP estimation (SRS‐MAP). To reduce the computational complexity, we approximate the a posteriori probability density of SNR and obtain a closed form of the predicted SNR. Simulation shows that SRS‐MAP outperforms the relay selection strategies given in the literature. In order to further improve the outage performance, a new multiple‐relay selection (MRS) method for outdated CSI is proposed, and by applying it to existing SRS strategies the corresponding MRS strategies are obtained. The authors find that the MRS strategies perform much better than their corresponding SRS ones and the MRS method improves the outage performance of cooperative communication system more effectively under outdated CSI than under non‐outdated CSI.

In this paper, we investigate the hop distance optimization problem in ad hoc networks where cooperative multi-input-single-output (MISO) is adopted to improve the energy efficiency of the network. We first establish the energy model of multihop cooperative MISO transmission. Based on the model, the energy consumption per bit of the network with high node density is minimized numerically by finding an optimal hop distance, and, to get the global minimum energy consumption, both hop distance and the number of cooperating nodes around each relay node for multihop transmission are jointly optimized. We also compare the performance between multihop cooperative MISO transmission and single-input-single-output (SISO) transmission, under the same network condition (high node density). We show that cooperative MISO transmission could be energy-inefficient compared with SISO transmission when the path-loss exponent becomes high. We then extend our investigation to the networks with varied node densities and show the effectiveness of the joint optimization method in this scenario using simulation results. It is shown that the optimal results depend on network conditions such as node density and path-loss exponent, and the simulation results are closely matched to those obtained using the numerical models for high node density cases.

In wireless sensor networks where nodes are powered by batteries, it is critical to prolong the network lifetime by minimizing the energy consumption of each node. In this paper, the cooperative multi-input-multi-output (MIMO) and data aggregation techniques are jointly adopted to improve energy efficiency in a cluster based wireless sensor network. Using the energy model derived, the average energy consumption per node required to send a given number of bits is minimized through the optimization of the cluster size. As a result, energy efficiency can be enhanced significantly in cooperative MIMO systems with data aggregation, compared to either cooperative MIMO systems without data aggregation or data aggregation systems without cooperative MIMO.

The multiple-input multiple-output (MIMO) technique can be used to improve the performance of ad hoc networks. Various medium access control (MAC) protocols with multiple contention slots have been proposed to exploit spatial multiplexing for increasing the transport throughput of MIMO ad hoc networks. However, the existence of multiple request-to-send/clear-to-send (RTS/CTS) contention slots represents a severe overhead that limits the improvement on transport throughput achieved by spatial multiplexing. In addition, when the number of contention slots is fixed, the efficiency of RTS/CTS contention is affected by the transmitting power of network nodes. In this study, a joint optimisation scheme on both transmitting power and contention slots number for maximising the transport throughput is presented. This includes the establishment of an analytical model of a simplified MAC protocol with multiple contention slots, the derivation of transport throughput as a function of both transmitting power and the number of contention slots, and the optimisation process based on the transport throughput formula derived. The analytical results obtained, verified by simulation, show that much higher transport throughput can be achieved using the joint optimisation scheme proposed, compared with the non-optimised cases and the results previously reported.