  The paper presents a reinforcement learning solution to dynamic resource
allocation for 5G radio access network slicing. Available communication
resources (frequency-time blocks and transmit powers) and computational
resources (processor usage) are allocated to stochastic arrivals of network
slice requests. Each request arrives with priority (weight), throughput,
computational resource, and latency (deadline) requirements, and if feasible,
it is served with available communication and computational resources allocated
over its requested duration. As each decision of resource allocation makes some
of the resources temporarily unavailable for future, the myopic solution that
can optimize only the current resource allocation becomes ineffective for
network slicing. Therefore, a Q-learning solution is presented to maximize the
network utility in terms of the total weight of granted network slicing
requests over a time horizon subject to communication and computational
constraints. Results show that reinforcement learning provides major
improvements in the 5G network utility relative to myopic, random, and first
come first served solutions. While reinforcement learning sustains scalable
performance as the number of served users increases, it can also be effectively
used to assign resources to network slices when 5G needs to share the spectrum
with incumbent users that may dynamically occupy some of the frequency-time
blocks.
