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

arXiv: https://arxiv.org/abs/1907.01523 · HTML: https://mlsanity.com/paper/1907.01523/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2019.

## Built on by (most cited first, 17 of 17)

- [A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems](https://mlsanity.com/paper/2212.01351/) (2022)
- [Holistic Network Virtualization and Pervasive Network Intelligence for 6G](https://mlsanity.com/paper/2301.00519/) (2023)
- [AI Simulation by Digital Twins: Systematic Survey, Reference Framework, and Mapping to a Standardized Architecture](https://mlsanity.com/paper/2506.06580/) (2025)
- [Multiverse at the Edge: Interacting Real World and Digital Twins for Wireless Beamforming](https://mlsanity.com/paper/2305.10350/) (2023)
- [Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights](https://mlsanity.com/paper/2312.04688/) (2023)
- [Leveraging Digital Cousins for Ensemble Q-Learning in Large-Scale Wireless Networks](https://mlsanity.com/paper/2402.08022/) (2024)
- [Multiple Access in the Era of Distributed Computing and Edge Intelligence](https://mlsanity.com/paper/2403.07903/) (2024)
- [Digital Twin in Industries: A Comprehensive Survey](https://mlsanity.com/paper/2412.00209/) (2024)
- [Optimizing Wireless Resource Management and Synchronization in Digital Twin Networks](https://mlsanity.com/paper/2502.05116/) (2025)
- [A Fast Task Offloading Optimization Framework for IRS-Assisted Multi-Access Edge Computing System](https://mlsanity.com/paper/2307.08474/) (2023)
- [Secure Deep Reinforcement Learning for Dynamic Resource Allocation in Wireless MEC Networks](https://mlsanity.com/paper/2312.08016/) (2023)
- [State-of-the-Art Review: The Use of Digital Twins to Support Artificial Intelligence-Guided Predictive Maintenance](https://mlsanity.com/paper/2406.13117/) (2024)
- [Graph Neural Network-Based Bandwidth Allocation for Secure Wireless Communications](https://mlsanity.com/paper/2312.14958/) (2023)
- [FIRE: A Failure-Adaptive Reinforcement Learning Framework for Edge Computing Migrations](https://mlsanity.com/paper/2209.14399/) (2022)
- [Efficient Beam Selection for ISAC in Cell-Free Massive MIMO via Digital Twin-Assisted Deep Reinforcement Learning](https://mlsanity.com/paper/2506.18560/) (2025)
- [Developing AI Agents with Simulated Data: Why, what, and how?](https://mlsanity.com/paper/2602.15816/) (2026)
- [From Passive Mirrors to Active Agents: Holonic Digital Twins for Physical AI over Networks](https://mlsanity.com/paper/2608.06227/) (2026)

## Abstract (arXiv)

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.