Edge caching could greatly relieve the burden of the backbone network and reduce the content request latency experienced by end-user devices. This makes edge caching a promising technology for enabling data-intensive and latency-sensitive applications on the eve of the large-scale commercial operation of 5G. However, the slow-start phenomenon incurred by existing request history-based caching strategies limits the performance of wireless edge caching, especially in the dynamic scenario where both mobile devices and contents arrive and leave periodically. On the other hand, it is also a hard task for deep reinforcement learning-based methods to adapt to the dynamics of the environment. In this backdrop, a new caching algorithm, called Similarity-Aware Popularity-based Caching (SAPoC), is presented in this paper to promote the performance of wireless edge caching in dynamic scenarios through utilizing the similarity among contents. In SAPoC algorithm, a content’s popularity is determined by not only its requests history but also its similarity with existing popular ones to enable a quick-start of newly arrived contents. A series of simulation experiments are conducted to evaluate SAPoC algorithm’s performance. Results have shown that SAPoC outperforms several typical proposals in both cache hit ratio and energy consumption.

边缘缓存可以极大地减轻骨干网的负担，并减少最终用户设备经历的内容请求延迟。这使得边缘缓存成为一种有前途的技术，可以在5G大规模商业运营的前夕实现数据密集型和对延迟敏感的应用程序。但是，现有的基于请求历史记录的缓存策略引起的慢启动现象限制了无线边缘缓存的性能，尤其是在移动设备和内容都定期到达和离开的动态情况下。另一方面，基于深度强化学习的方法要适应环境的动态变化也是艰巨的任务。在这种背景下，一种新的缓存算法称为基于相似性感知的基于流行度的缓存（SAPoC），为了利用动态内容中的相似性，在动态场景中提高无线边缘缓存的性能，本文提出了一种新的方法。在SAPoC算法中，内容的受欢迎程度不仅取决于其请求历史记录，还取决于其与现有的受欢迎程度的相似性，从而可以快速启动新到达的内容。进行了一系列仿真实验以评估SAPoC算法的性能。结果表明，SAPoC在缓存命中率和能耗方面都胜过几个典型的建议。进行了一系列仿真实验以评估SAPoC算法的性能。结果表明，SAPoC在缓存命中率和能耗方面都胜过几个典型的建议。进行了一系列仿真实验以评估SAPoC算法的性能。结果表明，SAPoC在缓存命中率和能耗方面都胜过几个典型的建议。