Deep learning-based optimal placement of a mobile HAP for common throughput maximization in wireless powered communication networks

Abstract

Hybrid access point (HAP) is a node in wireless powered communication networks (WPCN) that can distribute energy to each wireless device and also can receive information from these devices. Recently, mobile HAPs have emerged for efficient network use, and the throughput of the network depends on their location. There are two kinds of metrics for throughput, that is, sum throughput and common throughput; each is the sum and minimum value of throughput between a HAP and each wireless device, respectively. Likewise, two types of throughput maximization problems can be considered, sum throughput maximization and common throughput maximization. In this paper, we focus on the latter to propose a deep learning-based methodology for common throughput maximization by optimally placing a mobile HAP for WPCN. Our study implies that deep learning can be applied to optimize a complex function of common throughput maximization, which is a convex function or a combination of a few convex functions. The experimental results show that our approach provides better performance than mathematical methods for smaller maps.

The data flow risk monitoring system of the expressway networking system based on deep learning

“…The overall structure of the deep convolution neural network model consists of feature extraction and classification 23 . The feature extraction part consists of four hidden layers, and the classification part uses SoftMax as the classifier.…”

“…The overall structure of the deep convolution neural network model consists of feature extraction and classification. 23 The feature extraction part consists of four hidden layers, and the classification part uses SoftMax as the classifier. The following content describes in detail the deep convolution neural network model processing the data flow risk monitoring indexes of the expressway networking system.…”