Adaptive Cache Policy Optimization Through Deep Reinforcement Learning in Dynamic Cellular Networks

Abstract

We explore the use of caching both at the network edge and within User Equipment (UE) to alleviate traffic load of wireless networks. We develop a joint cache placement and delivery policy that maximizes the Quality of Service (QoS) while simultaneously minimizing backhaul load and UE power consumption, in the presence of an unknown time-variant file popularity. With file requests in a time slot being affected by download success in the previous slot, the caching system becomes a non-stationary Partial Observable Markov Decision Process (POMDP). We solve the problem in a deep reinforcement learning framework based on the Advantageous Actor-Critic (A2C) algorithm, comparing Feed Forward Neural Networks (FFNN) with a Long Short-Term Memory (LSTM) approach specifically designed to exploit the correlation of file popularity distribution across time slots. Simulation results show that using LSTM-based A2C outperforms FFNN-based A2C in terms of sample efficiency and optimality, demonstrating superior performance for the non-stationary POMDP problem. For caching at the UEs, we provide a distributed algorithm that reaches the objectives dictated by the agent controlling the network, with minimum energy consumption at the UEs, and minimum communication overhead.