Fast and computationally efficient generative adversarial network algorithm for unmanned aerial vehicle–based network coverage optimization

Abstract

The challenge of dynamic traffic demand in mobile networks is tackled by moving cells based on unmanned aerial vehicles. Considering the tremendous potential of unmanned aerial vehicles in the future, we propose a new heuristic algorithm for coverage optimization. The proposed algorithm is implemented based on a conditional generative adversarial neural network, with a unique multilayer sum-pooling loss function. To assess the performance of the proposed approach, we compare it with the optimal core-set algorithm and quasi-optimal spiral algorithm. Simulation results show that the proposed approach converges to the quasi-optimal solution with a negligible difference from the global optimum while maintaining a quadratic complexity regardless of the number of users.

Mixed Attention Mechanism Generative Adversarial Network Painting Image Conversion Algorithm Based on Multi-Class Scenes

“…The coefficient is multiplied by the original feature map to generate a feature map of interest that includes channel and region information. The generation of attention feature maps is Equation (1).…”

“…Painting image conversion algorithm has a wide range of applications in the field of computer vision and image processing. It can convert images from one style or scene to another, providing powerful tools for image editing and synthesis [1]. With the continuous development of computer graphics and deep learning technology, the painting image conversion algorithm has made remarkable progress [2].…”

URL generative recognition method based on generative countermeasure network

“…After obtaining the URL data in the network using the Flume tool, the special segmentation characters are divided into word segments from the URL string. By calculating the boundary similarity of URL strings, the URL confusion degree after word segmentation is determined [6]. The boundary similarity formula of URL strings p and q is as follows:…”

A Study Of Civil Engineering Education At Singapore Nanyang Technology University And At The University Of Florida

“…While utilizing UAVs as mobile stations to provide temporary network links in dynamic wireless communications is becoming increasingly popular, optimizing the network can be complex due to factors such as varying UAV altitudes, mobility patterns, spatial-domain interference distribution and external environmental conditions, which present unique challenges. In addressing the optimization of network coverage with limited UAVs, the authors in [160] propose the use of cGAN. This framework comprises a generator for modeling and predicting optimal network configurations, a discriminator for evaluating the efficiency of these configurations against real-world scenarios, and an encoding mechanism for adaptability and scalability.…”

“…The method based on cGAN not only guarantees the best positioning of UAVs but also simplifies computational complexity, achieving O(k 2 ). In contrast, traditional methods like the core-set algorithm [165] and the spiral algorithm [166] have the time complexities of O(pk) and O(k 3 ), respectively where p represents the number of user equipment in the area, while k denotes the number of UAVs in the fleet [160]. Another solution proposed by the authors in [163] utilized the self-attention-based transformer to predict the user mobility and enhance the aerial base station placement.…”