  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. We develop the extended
multi-armed bandit (Extended MAB), which combines the generalized global bandit
(GGB) and Standard Multi-armed bandit (MAB). For the second problem, a
multi-agent Extended MAB-based solution is presented to avoid the
mis-estimation of parameters and achieve the decentralized cache placement
policy. The proposed solution determines the primary time slot and secondary
time slot for each edge server. The proposed strategies are proven to achieve
the bounded regret according to the mathematical analysis. Extensive
simulations verify the optimality of the proposed strategies when comparing
with baselines.
