Deep Learning Based Joint Resource Scheduling Algorithms for Hybrid MEC Networks

TL;DR

This paper introduces a deep learning framework for real-time resource scheduling in hybrid MEC networks, optimizing energy efficiency by jointly managing UAV and GVs positions, user association, and resource allocation.

Contribution

It proposes a novel hybrid deep learning-based online offloading framework combining LSFCM, U-PSO, and DNN to adaptively optimize resource management in dynamic H-MEC environments.

Findings

Effective prediction of UAV and GV positions using LSFCM.

Optimized resource allocation with reduced energy consumption.

Framework adapts to varying user numbers without dimensionality issues.

Abstract

In this paper, we consider a hybrid mobile edge computing (H-MEC) platform, which includes ground stations (GSs), ground vehicles (GVs) and unmanned aerial vehicle (UAVs), all with mobile edge cloud installed to enable user equipments (UEs) or Internet of thing (IoT) devices with intensive computing tasks to offload. Our objective is to obtain an online offloading algorithm to minimize the energy consumption of all the UEs, by jointly optimizing the positions of GVs and UAVs, user association and resource allocation in real-time, while considering the dynamic environment. To this end, we propose a hybrid deep learning based online offloading (H2O) framework where a large-scale path-loss fuzzy c-means (LSFCM) algorithm is first proposed and used to predict the optimal positions of GVs and UAVs. Secondly, a fuzzy membership matrix U-based particle swarm optimization (U-PSO) algorithm is applied to solve the mixed integer nonlinear programming (MINLP) problems and generate the sample datasets for the deep neural network (DNN) where the fuzzy membership matrix can capture the small-scale fading effects and the information of mutual interference. Thirdly, a DNN with the scheduling layer is introduced to provide user association and computing resource allocation under the practical latency requirement of the tasks and limited available computing resource of H-MEC. In addition, different from traditional DNN predictor, we only input one UE information to the DNN at one time, which will be suitable for the scenarios where the number of UE is varying and avoid the curse of dimensionality in DNN.