A rate maximization method for mobile edge computing based on semi-supervised learning

Abstract

A semi-supervised learning-based method for maximizing the rate of mobile edge computing, including the following steps: 1) each wireless device needs to establish contact with the base station; 2) using two non-overlapping sets M 0 and M 1 respectively represent all wireless devices in local computing mode and offloading mode; 3) in set M 0 The wireless devices in can harvest energy and process local tasks at the same time, while in the set M 1 The wireless devices in can only offload the task to the base station after collecting energy; 4) The mode selection of all wireless devices will be through their channel gain h i decision, the role of semi-supervised learning is to take their channel gains as input, and then generate an optimal mode selection that maximizes the sum calculation rate of all wireless devices, that is, decide which wireless devices' tasks are processed locally and which are offloaded to the base station for processing. The present invention maximizes the total calculation rate of all wireless devices under the premise of ensuring user experience.

Description

technical field [0001] The invention belongs to the field of communication, and in particular relates to a communication system for mobile edge computing and a method for maximizing the rate of mobile edge computing based on semi-supervised learning. Background technique [0002] Recent developments in IoT technology are a critical step towards truly intelligent and autonomous control, especially in many important industrial and commercial systems. In an IoT network, a large number of wireless devices (WDs) capable of communication and computing are deployed. Due to device size constraints and production cost considerations, IoT devices (such as sensors) often carry batteries with limited capacity and energy-saving low Performance processors, therefore, limited device lifetime and low computing power cannot support the growing number of sustainable new applications requiring high-performance computing, such as autonomous driving and augmented reality. The deployment of wire...

AI Technical Summary

Problems solved by technology

In an IoT network, a large number of wireless devices (WDs) capable of communication and computing are deployed. Due to device size constraints and production cost considerations, IoT devices (such as sensors) often carry batteries with limited capacity and energy-saving low Performance processors, therefore, limited device lifetime and low computing power cannot support the growing number of sustainable new applications requiring high-performance computing, such as autonomous driving and augmented reality

The deployment of wireless power transfer systems (WPT) can solve the two aforementioned performance problems, but frequent device battery failures not only disrupt the normal operation of individual wireless devices but also significantly degrade the overall network performance, for example, in wireless sensor networks Sensing accuracy

Traditional wireless systems require frequent manual battery replacement, which is expensive and inconvenient. Due to strict battery capacity constraints, in battery-powered wireless systems, minimizing energy consumption and prolonging the operating life cycle of wireless devices is a key design

Images