Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin

TL;DR

This paper presents a deep learning-based approach utilizing a digital twin to optimize energy efficiency in hybrid 5G mobile edge computing systems, effectively managing user association and resource allocation in dynamic environments.

Contribution

It introduces a novel digital twin-assisted deep learning framework for real-time optimization of energy consumption in 5G MEC systems, adapting to network variations.

Findings

Achieves lower normalized energy consumption compared to existing methods.

Reduces computational complexity in network optimization.

Approaches the performance of the global optimal solution.

Abstract

In this work, we consider a mobile edge computing system with both ultra-reliable and low-latency communications services and delay tolerant services. We aim to minimize the normalized energy consumption, defined as the energy consumption per bit, by optimizing user association, resource allocation, and offloading probabilities subject to the quality-of-service requirements. The user association is managed by the mobility management entity (MME), while resource allocation and offloading probabilities are determined by each access point (AP). We propose a deep learning (DL) architecture, where a digital twin of the real network environment is used to train the DL algorithm off-line at a central server. From the pre-trained deep neural network (DNN), the MME can obtain user association scheme in a real-time manner. Considering that real networks are not static, the digital twin monitors the variation of real networks and updates the DNN accordingly. For a given user association scheme, we propose an optimization algorithm to find the optimal resource allocation and offloading probabilities at each AP. Simulation results show that our method can achieve lower normalized energy consumption with less computation complexity compared with an existing method and approach to the performance of the global optimal solution.