Deep Reinforcement Learning Based Mode Selection and Resource Management for Green Fog Radio Access Networks (1809.05629v1)

Abstract: 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.

• The paper proposes a deep reinforcement learning (DRL) approach for dynamic mode selection and resource management in fog radio access networks (F-RANs) to minimize long-term system power consumption.
• Using a deep Q-network (DQN), the DRL method learns real-time control policies from network dynamics, effectively managing communication modes and processor states based on edge cache status.
• Numerical results demonstrate that the DRL approach significantly reduces power consumption compared to benchmarks, and transfer learning enables faster adaptation to changing network conditions.

Deep Reinforcement Learning for Resource Management in Fog Radio Access Networks

This paper presents a deep reinforcement learning (DRL) based approach to mode selection and resource management in fog radio access networks (F-RANs), focusing on minimizing long-term system power consumption. The authors recognize that traditional resource management models, which are generally static and limited to a single communication mode, are insufficient for managing the complexities and dynamics of F-RANs. These complexities arise from fluctuating network conditions and the integration of diverse resources, such as radio and computing resources at the edge.

System Model and Problem Framework

The paper examines a downlink F-RAN model comprising multiple remote radio heads (RRHs), user equipment (UEs), and cloud-based processors with heterogenous computational capabilities. Each UE can operate either in a cloud-based radio access network (C-RAN) mode or in a device-to-device (D2D) mode. The central controller aims to minimize energy consumption through intelligent mode selection and the dynamic management of processing resources. This is achieved by utilizing DRL to leverage the dynamics of edge cache states and intelligently control the processors' on-off states along with the UEs' communication modes.

DRL Approach and Practical Methodologies

The core advancement detailed in the paper is the application of DRL—a robust integration of deep learning with reinforcement learning—enabling real-time decision-making and adaptation to varying network conditions. DRL facilitates learning from high-dimensional input data, allowing the system to effectively manage complex and dynamic states without requiring explicit transition and reward models.

The methodology involves training a deep Q-network (DQN) to generate control policies based on historical interaction data stored in replay memory. By employing iterative updates to the DQN, the system learns to minimize power consumption and optimize resource allocation under diverse communication scenarios. This DRL-based approach contrasts traditional Q-learning and optimization techniques, such as particle swarm optimization or evolutionary game theory, offering substantial benefits in terms of scalability and computational efficiency given large state-action spaces.

Numerical Results and Observations

The simulation results underscore the efficacy of the proposed DRL approach, highlighting substantial reductions in system power consumption compared to other benchmarks, including Q-learning and static mode strategies. Notably, the sensitivity analysis explores the impact of key learning parameters, such as the learning rate and batch size, providing critical insights into optimizing DRL performance. Furthermore, experiments integrating transfer learning demonstrate the model's adaptability to changes in caching environments, reducing the training time required to achieve optimal performance in updated scenarios.

Practical Implications and Future Directions

The paper suggests significant practical implications for future implementations of F-RANs, advocating for enhanced energy efficiency and optimized resource utilization across varying network states. The proposed DRL framework offers potential for broader applications within edge computing paradigms, particularly in domains requiring real-time, adaptive resource management solutions.

Future research could explore extensions of this framework to incorporate additional factors, such as power control for D2D communications, more complex fronthaul resource allocation, and the integration of subchannel assignments—all to further improve system efficiency within dynamically heterogeneous network conditions. As DRL techniques continue to evolve, they will likely offer richer opportunities for enhancing the operational capabilities and efficiency of next-generation wireless networks.