A DRL Enhanced Caching Based on Age of Information for 6G Mobile Edge Computation

Abstract

the advancement of 6G commercial use, a large number of new applications that rely on high speed and low latency have emerged, e.g., Mixed Reality (MR). Considering the transmission of service content from the central cloud to the MR device will bring great delay and energy consumption, the Mobile Edge Computing (MEC) technology has been introduced. It can reduce latency and energy consumption by caching the user’s pre-rendered environment frames on the MEC server. With the limited cache resources on the MEC server, a content caching scheme based deep reinforcement learning (DRL) method was proposed to make caching decisions. Then, a new utility function was proposed to measure the performance of the caching scheme, and the proposed scheme was simulated and verified.