Mobile networks are experiencing tremendous increase in data volume and user density. An efficient technique to alleviate this issue is to bring the data closer to the users by exploiting the caches of edge network nodes, such as fixed or mobile access points and even user devices. Meanwhile, the fusion of machine learning and wireless networks offers a viable way for network optimization as opposed to traditional optimization approaches which incur high complexity, or fail to provide optimal solutions. Among the various machine learning categories, reinforcement learning operates in an online and autonomous manner without relying on large sets of historical data for training. In this survey, reinforcement learning-aided mobile edge caching is presented, aiming at highlighting the achieved network gains over conventional caching approaches. Taking into account the heterogeneity of sixth generation (6G) networks in various wireless settings, such as fixed, vehicular and flying networks, learning-aided edge caching is presented, departing from traditional architectures. Furthermore, a categorization according to the desirable performance metric, such as spectral, energy and caching efficiency, average delay, and backhaul and fronthaul offloading is provided. Finally, several open issues are discussed, targeting to stimulate further interest in this important research field.
翻译:移动网络在数据量和用户密度方面正在经历巨大的增长。缓解这一问题的一个有效技术是通过利用边缘网络节点的缓存点,例如固定或移动接入点,甚至用户装置,使数据更接近用户。与此同时,机器学习和无线网络的融合提供了一种可行的网络优化方法,而不是产生高度复杂性或未能提供最佳解决办法的传统优化方法。在各种机器学习类别中,强化学习以在线和自主的方式运作,而不必依赖大量历史数据进行培训。在这次调查中,介绍了强化学习辅助的移动边缘缓存,目的是突出在传统缓存方法上取得的网络收益。考虑到第六代(6G)网络在各种无线环境中的异质性,例如固定、车辆和飞行网络,学习辅助边缘缓存是脱离传统结构的。此外,还根据光谱、能源和缓存效率、平均延迟、背向和前向前倾载等理想的业绩计量进行了分类。最后,讨论了几个未决问题,目的是进一步激发对重要领域的兴趣。