Efficient Deep Learning Approach for Computational Offloading in Mobile Edge Computing Networks

Abstract

The fifth-generation mobile communication technology is broadly characterised by extremely high data rate, low latency, massive network capacity, and ultrahigh reliability. However, owing to the explosive increase in mobile devices and data, it faces challenges, such as data traffic, high energy consumption, and communication delays. In this study, multiaccess edge computing (previously known as mobile edge computing) is investigated to reduce energy consumption and delay. The mathematical model of multidimensional variable programming is established by combining the offloading scheme and bandwidth allocation to ensure that the computing task of wireless devices (WDs) can be reasonably offloaded to an edge server. However, traditional analysis tools are limited by computational dimensions, which make it difficult to solve the problem efficiently, especially for large-scale WDs. In this study, a novel offloading algorithm known as energy-efficient deep learning-based offloading is proposed. The proposed algorithm uses a new type of deep learning model: multiple-parallel deep neural network. The generated offloading schemes are stored in shared memory, and the optimal scheme is generated by continuous training. Experiments show that the proposed algorithm can generate near-optimal offloading schemes efficiently and accurately.