  With the growing demand for latency-critical and computation-intensive
Internet of Things (IoT) services, the IoT-oriented network architecture,
mobile edge computing (MEC), has emerged as a promising technique to reinforce
the computation capability of the resource-constrained IoT devices. To exploit
the cloud-like functions at the network edge, service caching has been
implemented to reuse the computation task input/output data, thus effectively
reducing the delay incurred by data retransmissions and repeated execution of
the same task. In a multi-user cache-assisted MEC system, users' preferences
for different types of services, possibly dependent on their locations, play an
important role in joint design of communication, computation and service
caching. In this paper, we consider multiple representative locations, where
users at the same location share the same preference profile for a given set of
services. Specifically, by exploiting the location-aware users' preference
profiles, we propose joint optimization of the binary cache placement, the edge
computation resource and the bandwidth allocation to minimize the expected
sum-energy consumption, subject to the bandwidth and the computation
limitations as well as the service latency constraints. To effectively solve
the mixed-integer non-convex problem, we propose a deep learning (DL)-based
offline cache placement scheme using a novel stochastic quantization based
discrete-action generation method. The proposed hybrid learning framework
advocates both benefits from the model-free DL approach and the model-based
optimization. The simulations verify that the proposed DL-based scheme saves
roughly 33% and 6.69% of energy consumption compared with the greedy caching
and the popular caching, respectively, while achieving up to 99.01% of the
optimal performance.
