With the advancement of Underwater Internet of Things (U-IoT) networks, underwater object detection has become crucial. However, detection performance is severely hindered by underwater image degradation, small object scales and weak textures, as well as the stringent resource limitations of deployment platforms. Meanwhile, existing detectors rely on heavy backbones and standard downsampling, which not only increases computation but also discards fine-grained spatial information, resulting in low precision for small objects and suboptimal deployability. To address these challenges, a lightweight multi-scale feature fusion network named LMFF-Net is proposed. Utilizing RepGhostNet as the feature extraction network and incorporating an enhanced bidirectional feature pyramid structure, the proposed LMFF-Net effectively reduces the number of parameters and computational cost while maintaining detection accuracy. In the feature pyramid, a multi-scale object enhancement (MSOE) module is designed, which synergistically combines spatial multi-scale convolutions with spatial-frequency attention mechanisms. This synergy enhances feature discriminability and improves small object representation. Furthermore, a channelized spatial fusion (CSF) module is constructed to achieve lossless downsampling of feature maps by leveraging the spatial-to-depth transformation principle, thereby maximizing the retention of fine-grained spatial information in deep networks. Experimental results on the DUO dataset show that LMFF-Net achieves an [email protected] of 84.7% with only 1.73 M parameters, significantly outperforming other classic object detection models. Additionally, generalization experiments on the URPC2020 and RUOD datasets also demonstrate the excellent generalization ability of the proposed model.

Reconfigurable intelligent surfaces (RIS) have attracted significant attention due to their capability of establishing virtual line-of-sight (VLoS) links. This paper proposes a channel model for RIS-assisted millimeter wave (mmWave) communication systems that incorporate the effective aperture (EA) of RIS elements, the horizontal and vertical rotation angles of the RIS, the servomechanism limitations associated with these rotation angles and the activation criteria to constrain the feasible range of these rotation angles. To enhance the system performance, we jointly optimize the horizontal and vertical rotation angles of the RIS with the objective of maximizing the signal-to-noise ratio (SNR) based on the proposed model. An alternating optimization (AO) algorithm is developed to solve this problem efficiently. Specifically, the original optimization problem is decomposed into two subproblems corresponding to independent optimization of the horizontal and vertical angles, and closed-form optimal solutions are derived for each subproblem. Updating iteratively these closed-form solutions yields suboptimal horizontal and vertical rotation angles for the RIS. Moreover, a global optimal solution of closed-form to the original optimization problem is derived for the special case where the base station (BS) is positioned directly in front of the RIS. Numerical results demonstrate that the suboptimal rotation angles obtained by the AO algorithm closely approximate the optimal solutions. Furthermore, the proposed AO algorithm, which jointly optimizes both rotation angles, significantly outperforms the methods that individually optimize either the horizontal or vertical angle.

Intelligent reflecting surface technology (IRS) is emerging as a major innovation in wireless communications due to its unique advantages. It takes advantage of a large number of low-cost passive elements with adjustable phase-shift capabilities, which can reflect incident signals independently. When these elements work together, IRSs can achieve three-dimensional passive beamforming without the use of any transmit RF link. This mechanism not only enhances spectrum efficiency but also reduces the energy consumption of communication systems. Based on this advantage of IRSs, the paper explores IRS-assisted multiuser wireless systems, where IRSs are cleverly deployed between a multi-antenna access point (AP) and multiple single-antenna users. By jointly optimizing the transmission beamforming of active antenna array of the AP and the passive phase-shift beamforming of the IRSs, the objective is to minimize the total transmit power of APs, while ensuring that each user’s signal-to-interference-plus-noise ratio (SINR) requirement is met. The optimization problem is challenging to solve, as it is a nonconvex quadratically constrained quadratic programming problem, and the optimization variables are highly coupled with each other. To address this challenge, a low-complexity and efficient optimization algorithm, known as the linearized alternating direction multiplier method (LADMM) algorithm is proposed to address the transmit power minimization problem. The simulation results indicate that the LADMM algorithm provides superior system performance and significantly lower complexity compared to other existing methods.

Intelligent reflecting surface (IRS) via using massive low-cost passive elements that can reflect the signals by adjusting phase shifts provides a cost-effective and energy-efficient solution to enhance the wireless communication system’s performance. In the article, we consider an IRS-aided multiuser multi-input–single-output (MISO) downlink system. We tackle the weighted sum-rate (WSR) maximization by jointly optimizing the active beamforming at the base station (BS) and the passive beamforming at the IRS. We first decouple the nonconvex optimization problem by the Lagrangian dual transform, then resort to fractional programming to address the active and passive beamforming optimizations. We develop the mirror descent (MD) method and the accelerated projected gradient (APG) method to solve subproblems. The simulation results show that the MD and APG algorithm get the comparable WSR gain and convergence speeds as existing methods, but with a significantly lower computational complexity.

Intelligent reflecting surface (IRS) composed of a large number of low-cost passive reflecting elements has attracted significant attention from communication communities because of its ability to substantially improve the communication performance. In this paper, the aperture area and radiation pattern of the IRS reflecting elements are considered, and a novel geometry-based three-dimensional (3-D) wideband channel model is proposed for IRS-assisted unmanned aerial vehicle (UAV) communication systems. In the proposed model, the reflection phase is designed by jointly considering the aperture area of the IRS reflecting element and the propagation phases among UAV, IRS, and receiver (Rx), each IRS reflecting element is modeled as an anomalous reflector instead of a specular reflector, and large-scale IRS reflecting elements can jointly beamform the signal in a desired direction. Based on the proposed model, the effects of arbitrary trajectory of UAV and the number and the size of passive reflecting elements on channel statistical characteristics are considered, and the average received signal power and the ergodic sum capacity, which consider the impacts of the number and the size of passive reflecting elements, are also investigated. Furthermore, the path loss of the IRS-assisted link, which is in inverse proportion to the square of aperture area of IRS reflecting elements, is derived, and it coincides with the measured results in real outdoor scenarios. Analysis shows that the communication performance can be enhanced by increasing the number and the size of IRS reflecting elements, and it is validated by numerical results and Monte-Carlo simulation results.

Knowledge Graph Embedding (KGE) is to learn continuous vectors of entities and relations in the Knowledge Graph (KG). Inspired by the R-GCN model, we propose a novel embedding learning model named RotatGAT, which combines the RotatE model and the GAT model. The goal is to overcome the shortcomings of R-GCN, that has a relatively high computing complexity and cannot distinguish the importance of neighbors. We introduce the RotatE model into RotatGAT to represent the embeddings of heterogeneous entities and relations in KG. Considering RotatE cannot use the structure information to learn entities' embeddings, we introduce the GAT model to learn the importance of neighbors of an entity and aggregate the feature information of neighbors for graph embedding learning. The link prediction experiments show the overall performance of RotatGAT on four benchmark datasets outperforms existing state-of-the-art models.

In this article, a nonstationary 3-D wideband geometry-based stochastic model (GBSM) is proposed for low-altitude unmanned aerial vehicle (UAV) multiple-input–multiple-output (MIMO) communication systems. The proposed GBSM is a combination of Line-of-Sight (LoS) components, local multipath components (MPCs) scattering from the scatterers around the receiver (Rx), named as local scatterers, and far MPCs scattering from far scatterers, defined as not local scatterers, and uses 2-D one-ring and 3-D cylinder to mimic local scatterers as well as 3-D multiple confocal elliptic cylinders to mimic far scatterers. In this article, two-state continuous-time Markov chains (CTMCs) are introduced to model appearances or disappearances of the LoS components, local MPCs at the transmitter (Tx) installed on UAV, and far MPCs at the Tx and Rx, and the evolution process of the far MPCs is also investigated. The concept of the visibility region (VR) is introduced to model the birth and death processes of the local MPCs at the Rx, and the effect of the size of the VR on channel statistics is also considered. In the proposed GBSM, the inherited nature of the LoS components, the local MPCs and far MPCs, is considered, and the corresponding statistical properties are derived. The proposed nonstationary 3-D GBSM is validated by the measured results in terms of temporal correlation, and the numerical results show that the proposed 3-D GBSM is suitable for describing nonstationarity of the 3-D UAV-MIMO channel.

Abstract Because of extreme sensitivity to time and energy consumption, many computation‐ and data‐intensive tasks are difficult to implement on mobile terminals and cannot meet the needs of the rapid development of mobile networks. To solve this problem, mobile edge computing (MEC) appears to be a promising solution. In this study, we propose two offloading schemes in the multiple unmanned aerial vehicles (UAVs) enabled MEC network. Their optimisation goals are to minimise the global computing time and energy consumption of all UAVs, respectively. Different from previous research, the UAV can perform tasks locally or offload an appropriate percentage to the desired MEC server in the two proposed schemes. In order to get the minimum global computing time, we prove the existence condition and obtain the optimal offloading proportion. In addition, in order to minimise global energy consumption, we also obtain the optimal offloading proportion and present the optimal transmission power through solving Karush–Kuhn–Tucker conditions. Finally, because UAVs are selfish, we adopt the game theory to get optimal solutions of the proposed offloading strategies. Numerical results verify that the proposed schemes can effectively decrease the global computing time and energy consumption, especially for a large number of UAVs.