# Digital Twin-Assisted Data-Driven Optimization for Reliable Edge Caching in Wireless Networks

arXiv: https://arxiv.org/abs/2407.00286 · HTML: https://mlsanity.com/paper/2407.00286/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 18 of its 61 references that are in the map.
- First posted on arXiv 29 Jun 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 18 of 18)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Safe Reinforcement Learning via Shielding](https://mlsanity.com/paper/1708.08611/) (2017)
- [Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning](https://mlsanity.com/paper/2108.06266/) (2021)
- [A Theoretical Analysis of Deep Q-Learning](https://mlsanity.com/paper/1901.00137/) (2019)
- [A Review of Safe Reinforcement Learning: Methods, Theory and Applications](https://mlsanity.com/paper/2205.10330/) (2022)
- [Safe Reinforcement Learning via Curriculum Induction](https://mlsanity.com/paper/2006.12136/) (2020)
- [Digital-Twin-Enabled 6G: Vision, Architectural Trends, and Future Directions](https://mlsanity.com/paper/2102.12169/) (2021)
- [6G Digital Twin Networks: From Theory to Practice](https://mlsanity.com/paper/2212.02032/) (2022)
- [Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-time Popularities](https://mlsanity.com/paper/1708.06698/) (2017)
- [SafeLight: A Reinforcement Learning Method toward Collision-free Traffic Signal Control](https://mlsanity.com/paper/2211.10871/) (2022)
- [Networked Twins and Twins of Networks: an Overview on the Relationship Between Digital Twins and 6G](https://mlsanity.com/paper/2108.05781/) (2021)
- [User Dynamics-Aware Edge Caching and Computing for Mobile Virtual Reality](https://mlsanity.com/paper/2311.10645/) (2023)
- [Mapping Wireless Networks into Digital Reality through Joint Vertical and Horizontal Learning](https://mlsanity.com/paper/2404.14497/) (2024)
- [A Joint Communication and Computation Framework for Digital Twin over Wireless Networks](https://mlsanity.com/paper/2402.00381/) (2024)
- [Ultra-Dense Edge Caching under Spatio-Temporal Demand and Network Dynamics](https://mlsanity.com/paper/1703.01038/) (2017)
- [Learning Automata Based Q-learning for Content Placement in Cooperative Caching](https://mlsanity.com/paper/1903.06235/) (2019)
- [Echo State Networks for Proactive Caching in Cloud-Based Radio Access Networks with Mobile Users](https://mlsanity.com/paper/1607.00773/) (2016)
- [Multi-agent Learning for Cooperative Large-scale Caching Networks](https://mlsanity.com/paper/1807.00207/) (2018)

## Built on by (most cited first, 8 of 8)

- [INSIGHT: Enhancing Autonomous Driving Safety through Vision-Language Models on Context-Aware Hazard Detection and Edge Case Evaluation](https://mlsanity.com/paper/2502.00262/) (2025)
- [Digital Twin in Industries: A Comprehensive Survey](https://mlsanity.com/paper/2412.00209/) (2024)
- [Synergizing AI and Digital Twins for Next-Generation Network Optimization, Forecasting, and Security](https://mlsanity.com/paper/2503.06302/) (2025)
- [CENTILE: A Telemetry Foundation Model Evaluated by the Decisions It Drives](https://mlsanity.com/paper/2608.01725/) (2026)
- [Network Digital Untwinning: Towards Backward Optimization of Digital Twins](https://mlsanity.com/paper/2605.00169/) (2026)
- [On Transferring, Merging, and Splitting Task-Oriented Network Digital Twins](https://mlsanity.com/paper/2509.02551/) (2025)
- [Customized Generative AI Agent for Transportation Engineering Practice: A Development and Continued Pre-training Guideline](https://mlsanity.com/paper/2606.29014/) (2026)
- [Securing Autonomous Vehicle Systems via Twin-Aware Federated Reinforcement Learning](https://mlsanity.com/paper/2607.08137/) (2026)

## Abstract (arXiv)

Optimizing edge caching is crucial for the advancement of next-generation (nextG) wireless networks, ensuring high-speed and low-latency services for mobile users. Existing data-driven optimization approaches often lack awareness of the distribution of random data variables and focus solely on optimizing cache hit rates, neglecting potential reliability concerns, such as base station overload and unbalanced cache issues. This oversight can result in system crashes and degraded user experience. To bridge this gap, we introduce a novel digital twin-assisted optimization framework, called D-REC, which integrates reinforcement learning (RL) with diverse intervention modules to ensure reliable caching in nextG wireless networks. We first develop a joint vertical and horizontal twinning approach to efficiently create network digital twins, which are then employed by D-REC as RL optimizers and safeguards, providing ample datasets for training and predictive evaluation of our cache replacement policy. By incorporating reliability modules into a constrained Markov decision process, D-REC can adaptively adjust actions, rewards, and states to comply with advantageous constraints, minimizing the risk of network failures. Theoretical analysis demonstrates comparable convergence rates between D-REC and vanilla data-driven methods without compromising caching performance. Extensive experiments validate that D-REC outperforms conventional approaches in cache hit rate and load balancing while effectively enforcing predetermined reliability intervention modules.