Adaptive Edge Caching in mmWave Integrated Access and Backhaul Networks

Caching popular files at the small base stations has proved to be an effective strategy for reducing the content delivery delay in cellular networks and alleviating backhaul congestion. The challenging characteristics of radio propagation, the use of highly directional transmission in future-generation cellular networks, and the popularity of content lead to more complex and critical problems in content placement. In this paper, we propose a mathematical formulation for the centralized optimization of content placement at integrated access and backhaul (IAB) nodes to minimize the average content delivery latency in millimeter wave (mmWave) IAB cellular communications. We consider the dynamics of links and the time-varying popularity of contents as Markov decision processes (MDP) and propose a deep reinforcement learning framework to obtain a solution with low computation complexity. Simulation experiments are conducted to investigate the effectiveness of the proposed learning algorithm as well as to compare it against some schemes with different levels of adaptation to the system dynamics.