  Low latency communication is one of the fundamental requirements for 5G
wireless networks and beyond. In this paper, a novel approach for joint
caching, user scheduling and resource allocation is proposed for minimizing the
queuing latency in serving user's requests in cloud-aided wireless networks.
Due to the slow temporal variations in user requests, a time-scale separation
technique is used to decouple the joint caching problem from user scheduling
and radio resource allocation problems. To serve the spatio-temporal user
requests under storage limitations, a Reinforcement Learning (RL) approach is
used to optimize the caching strategy at the small cell base stations by
minimizing the content fetching cost. A spectral clustering algorithm is
proposed to speed-up the convergence of the RL algorithm for a large content
caching problem by clustering contents based on user requests. Meanwhile, a
dynamic mechanism is proposed to locally group coupled base stations based on
user requests to collaboratively optimize the caching strategies. To further
improve the latency in fetching and serving user requests, a dynamic matching
algorithm is proposed to schedule users and to allocate users to radio
resources based on user requests and queue lengths under probabilistic latency
constraints. Simulation results show the proposed approach significantly
reduces the average delay from 21% to 90% compared to random caching strategy,
random resource allocation and random scheduling baselines.
