Artificial Intelligence for Wireless Caching: Schemes, Performance, and Challenges
Muhammad Sheraz, Manzoor Ahmed, Xueshi Hou, Yong Li, Depeng Jin, Zhu Han, Tao Jiang
Abstract
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.