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

by   Yaohua Sun, et al.

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.


Mode Selection and Resource Allocation in Sliced Fog Radio Access Networks: A Reinforcement Learning Approach

The mode selection and resource allocation in fog radio access networks ...

Deep Reinforcement Learning-Aided RAN Slicing Enforcement for B5G Latency Sensitive Services

The combination of cloud computing capabilities at the network edge and ...

Self-play Learning Strategies for Resource Assignment in Open-RAN Networks

Open Radio Access Network (ORAN) is being developed with an aim to democ...

Deep Reinforcement Learning for Network Slicing

Network slicing means an emerging business to operators and allows them ...

Learn to Schedule (LEASCH): A Deep reinforcement learning approach for radio resource scheduling in the 5G MAC layer

Network management tools are usually inherited from one generation to an...

Online Reinforcement Learning of X-Haul Content Delivery Mode in Fog Radio Access Networks

We consider a Fog Radio Access Network (F-RAN) with a Base Band Unit (BB...

Please sign up or login with your details

Forgot password? Click here to reset