Deep reinforcement learning based computation offloading and resource allocation for low-latency fog radio access networks

Abstract

Fog Radio Access Networks (F-RANs) have been considered a groundbreaking technique to support the services of Internet of Things by leveraging edge caching and edge computing. However, the current contributions in computation offloading and resource allocation are inefficient; moreover, they merely consider the static communication mode, and the increasing demand for low latency services and high throughput poses tremendous challenges in F-RANs. A joint problem of mode selection, resource allocation, and power allocation is formulated to minimize latency under various constraints. We propose a Deep Reinforcement Learning (DRL) based joint computation offloading and resource allocation scheme that achieves a suboptimal solution in F-RANs. The core idea of the proposal is that the DRL controller intelligently decides whether to process the generated computation task locally at the device level or offload the task to a fog access point or cloud server and allocates an optimal amount of computation and power resources on the basis of the serving tier. Simulation results show that the proposed approach significantly minimizes latency and increases throughput in the system.