# Deep Reinforcement Learning Based Mode Selection and Resource Management for Green Fog Radio Access Networks

arXiv: https://arxiv.org/abs/1809.05629 · HTML: https://mlsanity.com/paper/1809.05629/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2018.

## Built on by (most cited first, 9 of 9)

- [Transfer Learning for Future Wireless Networks: A Comprehensive Survey](https://mlsanity.com/paper/2102.07572/) (2021)
- [Holistic Network Virtualization and Pervasive Network Intelligence for 6G](https://mlsanity.com/paper/2301.00519/) (2023)
- [Joint Deep Reinforcement Learning and Unfolding: Beam Selection and Precoding for mmWave Multiuser MIMO with Lens Arrays](https://mlsanity.com/paper/2101.01336/) (2021)
- [A Plug-in Tiny AI Module for Intelligent and Selective Sensor Data Transmission](https://mlsanity.com/paper/2402.02043/) (2024)
- [Can You Fix My Neural Network? Real-Time Adaptive Waveform Synthesis for Resilient Wireless Signal Classification](https://mlsanity.com/paper/2103.03745/) (2021)
- [Ensemble Consensus-based Representation Deep Reinforcement Learning for Hybrid FSO/RF Communication Systems](https://mlsanity.com/paper/2108.02551/) (2021)
- [Lifelong Learning for Fog Load Balancing: A Transfer Learning Approach](https://mlsanity.com/paper/2310.05187/) (2023)
- [To Trust or Not to Trust: On Calibration in ML-based Resource Allocation for Wireless Networks](https://mlsanity.com/paper/2507.17494/) (2025)
- [RIS-Assisted Downlink Pinching-Antenna Systems: GNN-Enabled Optimization Approaches](https://mlsanity.com/paper/2511.20305/) (2025)

## Abstract (arXiv)

Fog radio access networks (F-RANs) are seen as potential architectures to support services of internet of things by leveraging edge caching and edge computing. However, current works studying resource management in F-RANs mainly consider a static system with only one communication mode. Given network dynamics, resource diversity, and the coupling of resource management with mode selection, resource management in F-RANs becomes very challenging. Motivated by the recent development of artificial intelligence, a deep reinforcement learning (DRL) based joint mode selection and resource management approach is proposed. Each user equipment (UE) can operate either in cloud RAN (C-RAN) mode or in device-to-device mode, and the resource managed includes both radio resource and computing resource. The core idea is that the network controller makes intelligent decisions on UE communication modes and processors' on-off states with precoding for UEs in C-RAN mode optimized subsequently, aiming at minimizing long-term system power consumption under the dynamics of edge cache states. By simulations, the impacts of several parameters, such as learning rate and edge caching service capability, on system performance are demonstrated, and meanwhile the proposal is compared with other different schemes to show its effectiveness. Moreover, transfer learning is integrated with DRL to accelerate learning process.