Joint Cache Allocation and Replacement for Content-Centric Network-Based Private 5G Networks: Deep Reinforcement Learning Approach

Abstract

Mobile network traffic volume is increasing significantly every year, which could become a burden on the backbone of the 5G and beyond network in the future. Network caching has been a promising solution to improve quality of service (QoS) by reducing backhaul traffic and network latency. This paper studies network caching for private networks specialized for specific use cases in 5G. We adopt the Content-Centric Network (CCN) as the core network to improve caching efficiency, and suggests a Deep Q-Network (DQN) based method that jointly addresses content allocation strategy and cache replacement policy. Unlike previous studies, the proposed method can handle content and cache storage of heterogeneous sizes by implementing two-bit channel state generator which maps different-sized environment data to a fixed-sized image format. Simulation results shows that the proposed system outperforms existing cache systems by 20% to 50% in terms of cache hit ratio and network latency.

Keywords

• Private 5G
• content-centric network
• network caching
• deep reinforcement learning