A simultaneously transmitting and reflecting surface (STARS) enabled edge caching system is proposed for reducing backhaul traffic and ensuring the quality of service. A novel Caching-at-STARS structure, where a dedicated smart controller and cache memory are installed at the STARS, is proposed to satisfy user demands with fewer hops and desired channel conditions. Then, a joint caching replacement and information-centric hybrid beamforming optimization problem is formulated for minimizing the network power consumption. As long-term decision processes, the optimization problems based on independent and coupled phase-shift models of Caching-at-STARS contain both continuous and discrete decision variables, and are suitable for solving with deep reinforcement learning (DRL) algorithm. For the independent phase-shift Caching-at-STARS model, we develop a frequency-aware based twin delayed deep deterministic policy gradient (FA-TD3) algorithm that leverages user historical request information to serialize high-dimensional caching replacement decision variables. For the coupled phase-shift Caching-at-STARS model, we conceive a cooperative TD3 \& deep-Q network (TD3-DQN) algorithm comprised of FA-TD3 and DQN agents to decide on continuous and discrete variables respectively by observing the network external and internal environment. The numerical results demonstrate that: 1) The Caching-at-STARS-enabled edge caching system has advantages over traditional edge caching, especially in scenarios where Zipf skewness factors or cache capacity is large; 2) Caching-at-STARS outperforms the RIS-assisted edge caching systems; 3) The proposed FA-TD3 and cooperative TD3-DQN algorithms are superior in reducing network power consumption than conventional TD3.

翻译：提出了一种基于同步透射与反射表面（STARS）的边缘缓存系统，用于降低回程流量并保障服务质量。通过在STARS处部署专用智能控制器与缓存存储器，本文提出了一种新颖的Caching-at-STARS结构，以更少的跳数和理想的信道条件满足用户需求。随后，构建了联合缓存替换与信息中心混合波束成形的优化问题，旨在最小化网络功耗。作为长期决策过程，基于Caching-at-STARS独立及耦合相移模型的优化问题同时包含连续与离散决策变量，适用于深度强化学习算法求解。针对独立相移Caching-at-STARS模型，本文开发了一种基于频率感知的双延迟深度确定性策略梯度（FA-TD3）算法，利用用户历史请求信息对高维缓存替换决策变量进行序列化处理。针对耦合相移Caching-at-STARS模型，本文设计了一种由FA-TD3与深度Q网络（DQN）智能体协作的联合TD3-DQN算法，分别通过观测网络外部与内部环境对连续与离散变量进行决策。数值结果表明：1）基于Caching-at-STARS的边缘缓存系统在齐普夫偏斜因子或缓存容量较大时，性能优于传统边缘缓存；2）Caching-at-STARS的性能优于RIS辅助的边缘缓存系统；3）所提出的FA-TD3与联合TD3-DQN算法在降低网络功耗方面优于传统TD3算法。