Deep learning for hybrid 5G services in mobile edge computing systems: learn from a digital twin
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In this paper, 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 the 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. The 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.
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Advanced ML techniques such as reinforcement learning and predictive analytics can optimize synchronization processes by predicting updates and reducing computational loads.
“Advanced ML techniques, such as reinforcement learning and predictive analytics, can optimize synchronization processes by predicting updates and reducing computational loads [130].”
From Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0 (2025) · cites this paper as [130]
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“Advanced ML techniques, such as reinforcement learning and predictive analytics, can optimize synchronization processes by predicting updates and reducing computational loads [130].”
- Authors
- Dong, R., She, C., Hardjawana, W., Li, Y., & Vucetic, B.
- Affiliations
- The University of Sydney (AU)
- Venue
- IEEE Transactions on Wireless Communications
- Published
- 2019-10
- Volume
- 18
- Issue
- 10
- Pages
- 4692-4707
- ISSN
- 1536-1276 (ISSN-L)
- 1558-2248
- OpenAlex
- W2962737505
- Cited by
- 295
- References
- 37
- Citations by year
- 2026: 15 · 2025: 41 · 2024: 56 · 2023: 66 · 2022: 58 · 2021: 41 · 2020: 17 · 2019: 1
- FWCI
- 16.02
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Wireless Communications (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Australian Research Council Discovery Early Career Research Award (DE150101704)
- Australian Research Council (DP150104019, DP190101988)
- Australian Research Council Laureate Fellowship (FL160100032)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Network Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced MIMO Systems Optimization
- Keywords
- mobile edge computing, 5G services, digital twin, deep learning, user association, resource allocation, energy consumption per bit, ultra-reliable low-latency communication, delay-tolerant services, quality of service
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