A Deep-Reinforcement-Learning-Based Social-Aware Cooperative Caching Scheme in D2D Communication Networks
Yalu Bai, Dan Wang, Gang Huang, Bin Song
Abstract
Device-to-device (D2D) caching is becoming prevalent in relieving network congestion. However, there remain challenges in exploring efficient D2D caching strategies due to the diverse user requirements. In this article, we propose a social-aware D2D caching scheme that integrates the concept of social incentive and recommendation with D2D caching decision making. First, we investigate federated learning (FL)-based prediction method to achieve the social-aware in a privacy-preserving manner. Then, the predicted social relationship provides prior knowledge for deep reinforcement learning (DRL) to make optimal D2D caching decisions. The optimization problem of this article is to maximize the data offloading probability, which can be formulated as a Markov decision process. To solve it, we propose a double deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning network (DDQN)-based D2D caching algorithm. Finally, simulation results validate the prediction and convergence performance of the proposed scheme. Besides, the scheme also shows superior caching performance in reducing the average delay and improving overall offloading probability.