This work investigates a downlink nonorthogonal multiple access (NOMA) scheme with unmanned aerial vehicle (UAV) aided wireless communication, where a single UAV was regarded as an air base station (ABS) to communicate with multiple ground users. Considering the constraints of velocity and maneuverability, a UAV energy efficiency (EE) model was proposed via collaborative design resource allocation and trajectory optimization. Based on this, an EE maximization problem was formulated to jointly optimize the transmit power of ground users and the trajectory of the UAV. To obtain the optimal solutions, this nonconvex problem was transformed into an equivalent convex optimization problem on the basis of three user clustering algorithms. After several alternating iterations, our proposed algorithms converged quickly. The simulation results show an enhancement in EE with NOMA because our proposed algorithm is nearly 99.6% superior to other OMA schemes.

Aiming to address the issue of low efficiency and a high false-detection rate in artificial defect detection in nitrile medical gloves, CCA-YOLO was proposed on the basis of YOLOv5 to realize the detection of tear and scratch defects. CCA-YOLO added a small-target detection layer to the YOLOv5 network backbone and further proposed an innovative channel coordinate attention mechanism. According to the different characteristics of tears and scratches, focal and efficient IoU loss and α-IoU loss functions were introduced to further improve the positioning accuracy. The data enhancement method was used to generate a dataset of nitrile gloves, which was divided into datasets for horizontal angular tear detection, vertical angular tear detection, and scratch detection. The problem of class imbalance with few defect samples was solved. Our experiments show that CCA-YOLO can effectively identify tear and scratch defects in nitrile medical gloves in the self-made datasets. Compared with YOLOv5, the mean average precision (mAP) of the three models for horizontal angular tear detection, vertical angular tear detection, and scratch detection can reach 99.3%, 99.8%, and 99.6%, showing increments of 4.2%, 5.3%, and 12.4%, respectively, thereby meeting the performance requirements of glove defect detection.

To support a vast number of devices with less energy consumption, we propose a new user association and power control scheme for machine to machine enabled heterogeneous networks with non-orthogonal multiple access (NOMA), where a mobile user (MU) acting as a machine-type communication gateway can decode and forward both the information of machine-type communication devices and its own data to the base station (BS) directly. MU association and power control are jointly considered in the formulated as optimization problem for energy efficiency (EE) maximization under the constraints of minimum data rate requirements of MUs. A many-to-one MU association matching algorithm is firstly proposed based on the theory of matching game. By taking swap matching operations among MUs, BSs, and sub-channels, the original problem can be solved by dealing with the EE maximization for each sub-channel. Then, two power control algorithms are proposed, where the tools of sequential optimization, fractional programming, and exhaustive search have been employed. Simulation results are provided to demonstrate the optimality properties of our algorithms under different parameter settings.

Mobile edge computing (MEC) allows ground vehicles (GVs) to offload computationally intensive tasks to edge servers, offering advantages over centralized cloud computing by reducing the energy consumption and network congestion. However, the dynamic nature of communication links often results in suboptimal offloading performance due to signal occlusion and interference. Reconfigurable intelligent surface (RIS) technology, a promising component of sixth-generation (6G) communication networks, can enhance wireless network capabilities by modifying the phase and amplitude of reflective components. In this paper, we propose a RIS-assisted MEC strategy to provide a distributed edge intelligence (DEI) solution for systems providing fast wireless connectivity and low latency to ground vehicles in dynamic environments. The RIS-assisted vehicular networks model has recently showed promising results when the delay was minimized by jointly optimizing the computation offloading strategy and the RIS phase shift. The delay optimization problem was modeled as a markov decision process (MDP), and a delay minimization algorithm (DDPG-DM) based on deep reinforcement learning (DRL) was proposed. Simulation results demonstrate that the proposed algorithm significantly outperforms existing non-RIS learning algorithms and classical methods, achieving superior performance in reducing delay. The findings suggest that integrating RIS with MEC can substantially improve the efficiency of computation offloading in dynamic vehicular environments.