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 $Q$ -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.

中文翻译：

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D2D 通信网络中基于深度强化学习的社会感知合作缓存方案

设备到设备 (D2D) 缓存在缓解网络拥塞方面正变得普遍。然而，由于用户需求的多样性，探索有效的 D2D 缓存策略仍然存在挑战。在本文中，我们提出了一种社交感知 D2D 缓存方案，该方案将社交激励和推荐的概念与 D2D 缓存决策相结合。首先，我们研究了基于联邦学习 (FL) 的预测方法，以保护隐私的方式实现社交感知。然后，预测的社会关系为深度强化学习 (DRL) 提供先验知识，以做出最佳的 D2D 缓存决策。本文的优化问题是最大化数据卸载概率，可以表述为马尔可夫决策过程。为了解决这个问题，我们提出了一个双深 $Q$ - 基于学习网络（DDQN）的D2D缓存算法。最后，仿真结果验证了所提方案的预测和收敛性能。此外，该方案在降低平均延迟和提高整体卸载概率方面也表现出优越的缓存性能。