Wireless data traffic is growing unprecedentedly and it may impede network performance by consuming an ever-greater amount of bandwidth. With the advancement in technology there exist profound techniques having potentials to improve performance of wireless networks. Artificial Intelligence (AI) is one such evolving technology that enables systems to take intelligent decisions. AI can be incorporated in wireless networks for performing an optimal data caching based on accurate predictions of users’ data requests and data popularity profile. AI-based data caching is a promising candidate to effectively harness the issues of rising backhaul data traffic of future wireless networks such as duplicate data transmission and data access delay. In this paper, we provide a systematic survey of state-of-the-art intelligent data caching approaches based on learning mechanism to optimize data caching. First we give an overview of traditional caching approaches and their limitations. Then, after rendering brief introduction of several AI techniques, we introduce state-of-the-art learning approaches in cache-enabled wireless networks. We unfold significant research efforts utilizing AI for efficient data placement for optimizing network performance in terms of cache hit rate, throughput, and offloading etc. Finally, we highlight existing challenges and research directions of AI-based data caching.

中文翻译：

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用于无线缓存的人工智能：方案、性能和挑战


无线数据流量正在空前增长，并且可能会消耗越来越多的带宽，从而影响网络性能。随着技术的进步，存在有潜力提高无线网络性能的深刻技术。人工智能 (AI) 就是这样一种不断发展的技术，它使系统能够做出智能决策。人工智能可以集成到无线网络中，以便根据对用户数据请求和数据流行度概况的准确预测来执行最佳数据缓存。基于人工智能的数据缓存是一种很有前景的候选方案，可以有效地解决未来无线网络回程数据流量不断增加的问题，例如重复数据传输和数据访问延迟。在本文中，我们对基于学习机制来优化数据缓存的最先进的智能数据缓存方法进行了系统的调查。首先，我们概述传统的缓存方法及其局限性。然后，在简要介绍了几种人工智能技术之后，我们介绍了支持缓存的无线网络中最先进的学习方法。我们开展了大量研究工作，利用人工智能进行有效的数据放置，以优化缓存命中率、吞吐量和卸载等方面的网络性能。最后，我们强调了基于人工智能的数据缓存的现有挑战和研究方向。