计算机科学 ›› 2022, Vol. 49 ›› Issue (9): 260-267.doi: 10.11896/jsjkx.210800019
刘鑫, 王珺, 宋巧凤, 刘家豪
LIU Xin, WANG Jun, SONG Qiao-feng, LIU Jia-hao
摘要： 随着用户终端数量的激增和5G技术的发展,形成了宏基站和小基站并存的网络。同时超高清视频、云VR/AR等应用对时延提出了更高的要求。为了缩短5G网络中的时延,文中结合小基站协同、多播和用户行为可预测的特性,提出了一种基于对抗自动编码(Adversarial Autoencoders,AAE)的协同多播主动缓存方案(Collaborative Multicast Proactive Caching Scheme Based on Adversarial Autoencoders,CMPCAAE)。该方案首先根据用户的特征信息将用户划分成偏好不同的用户组,然后通过AAE预测每个用户组可能请求的内容。为了减少缓存内容的冗余,采用蚁群算法(Ant Colony,ACO)将预测的内容预先部署到各小基站以实现小基站间的协同。在内容分发阶段,若分组中用户请求的是流行度高的内容,则以多播的方式将该内容主动缓存到分组中其他未发送请求的用户,否则以正常的方式进行分发。仿真结果表明,CMPCAAE方案在系统的平均请求时延和丢失率方面均优于经典的缓存方案。
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