  With the growing demand for latency-critical and computation-intensive
Internet of Things (IoT) services, mobile edge computing (MEC) has emerged as a
promising technique to reinforce the computation capability of the
resource-constrained mobile devices. To exploit the cloud-like functions at the
network edge, service caching has been implemented to (partially) reuse the
computation tasks, thus effectively reducing the delay incurred by data
retransmissions and/or the computation burden due to repeated execution of the
same task. In a multiuser cache-assisted MEC system, designs for service
caching depend on users' preference for different types of services, which is
at times highly correlated to the locations where the requests are made. In
this paper, we exploit users' location-dependent service preference profiles to
formulate a cache placement optimization problem in a multiuser MEC system.
Specifically, we consider multiple representative locations, where users at the
same location share the same preference profile for a given set of services. In
a frequency-division multiple access (FDMA) setup, we jointly optimize the
binary cache placement, edge computation resources and bandwidth allocation to
minimize the expected weighted-sum energy of the edge server and the users with
respect to the users' preference profile, subject to the bandwidth and the
computation limitations, and the latency constraints. To effectively solve the
mixed-integer non-convex problem, we propose a deep learning based offline
cache placement scheme using a novel stochastic quantization based
discrete-action generation method. In special cases, we also attain suboptimal
caching decisions with low complexity leveraging the structure of the optimal
solution. The simulations verify the performance of the proposed scheme and the
effectiveness of service caching in general.
