Cache Placement Optimization in Mobile Edge Computing Networks With Unaware Environment—An Extended Multi-Armed Bandit Approach

作者：Yuqi Han, Lihua Ai, Rui Wang, Jun Wu, Dian Liu, Haoqi Ren · 发表于：IEEE Transactions on Wireless Communications · 年份：2021 · DOI：10.1109/twc.2021.3090440 · 被引用次数：30 · 研究领域：Caching and Content Delivery、Advanced Bandit Algorithms Research、Optimization and Search Problems

Caching high-frequency reuse contents at the edge servers in the mobile edge computing (MEC) network omits the part of backhaul transmission and further releases the pressure of data traffic. However, how to efficiently decide the caching contents for edge servers is still an open problem, which refers to the cache capacity of edge servers, the popularity of each content, and the wireless channel quality during transmission. In this paper, we discuss the influence of unknown user density and popularity of content on the cache placement solution at the edge server. Specifically, towards the implementation of the cache placement solution in the practical network, there are two problems needing to be solved. First, the estimation of unknown users’ preference needs a huge amount of records of users’ previous requests. Second, the overlapping serving regions among edge servers cause the wrong estimation of users’ preference, which hinders the individual decision of caching placement. To address the first issue, we propose a learning-based solution to adaptively optimize the cache placement policy without any previous knowledge of the user density and the popularity of the contents. We develop the extended multi-armed bandit (Extended MAB), which combines the generalized global bandit (GGB) and Standard Multi-armed bandit (MAB), to iteratively estimate both a global parameter, i.e., the user density, and individual parameters, i.e., the popularity of each content. For the second pr...