Caching and Update Strategy Based on Content Popularity and Information Freshness for Fog Radio Access Networks
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摘要: 将边缘缓存技术引入雾无线接入网，可以有效减少内容传输的冗余。然而，现有缓存策略很少考虑已缓存内容的动态特性。该文提出一种基于内容流行度和信息新鲜度的缓存更新算法，该算法充分考虑用户的移动性以及内容流行度的时空动态性，并引入信息年龄(AoI)实现内容的动态更新。首先，所提出算法根据用户的历史位置信息，使用双向长短期记忆网络(Bi-LSTM)预测下一时间段用户位置；其次，根据预测得到的用户位置，结合用户的偏好模型得到各位置区的内容流行度，进而在雾接入点进行内容缓存。然后，针对已缓存内容的信息年龄要求，结合内容流行度分布，通过动态设置缓存更新窗口以实现高时效、低时延的内容缓存。仿真结果表明，所提算法可以有效地提高内容缓存命中率，在保障信息的时效性的同时最大限度地减小缓存内容的平均服务时延。Abstract: Introducing edge caching into fog radio access networks can effectively reduce the redundancy of content transmission. However, the existing content caching strategies consider rarely the dynamic nature of already cached content. A caching update algorithm based on content popularity and information freshness is proposed. The proposed algorithm considers fully the mobility of users and the temporal and spatial dynamics of content popularity. Furthermore, the Age of Information (AoI) is introduced to achieve a dynamic content update procedure. More specifically, the proposed algorithm adopts initially a Bidirectional Long Short-Term Memory network (Bi-LSTM) to predict the user's location in the next period according to the user's historical location information. Secondly, according to the acquired user location, combined with the user's preference model, the content popularity of each location area is obtained accordingly, and the most popular content will be cached at the Fog Access Points(F-APs). Finally, concerning AoI requirements of the already cached content, the caching update window can be dynamically adjusted to achieve a high-efficient and low-latency caching process. Simulation results demonstrate that the proposed algorithm improves effectively the content cache hit rate, and also minimizes the average delay of content transmission while ensuring the timeliness of the information.
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表 1 仿真参数
参数 值 覆盖半径$R$ 100 m 系统带宽$B$ 10 MHz 文件大小$\varphi $ 1 M 无线传输路径损耗指数$\alpha $ 4 加性高斯白噪声${\sigma ^2}$ –95 dBm F-APs发射功率${P_{{\rm{FAP}}} }$ 1 W 信源发射功率$P{\rm{s}}$ 0.1 W 请求到达率$\lambda $ 2000 请求/s 
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