  New-generation wireless networks are designed to support a wide range of
services with diverse key performance indicators (KPIs) requirements. A
fundamental component of such networks, and a pivotal factor to the fulfillment
of the target KPIs, is the virtual radio access network (vRAN), which allows
high flexibility on the control of the radio link. However, to fully exploit
the potentiality of vRANs, an efficient mapping of the rapidly varying context
to radio control decisions is not only essential, but also challenging owing to
the interdependence of user traffic demand, channel conditions, and resource
allocation. Here, we propose CAREM, a reinforcement learning framework for
dynamic radio resource allocation in heterogeneous vRANs, which selects the
best available link and transmission parameters for packet transfer, so as to
meet the KPI requirements. To show its effectiveness, we develop a testbed for
proof-of-concept. Experimental results demonstrate that CAREM enables an
efficient radio resource allocation under different settings and traffic
demand. Also, compared to the closest existing scheme based on neural network
and the standard LTE, CAREM exhibits an improvement of one order of magnitude
in packet loss and latency, while it provides a 65% latency improvement
relatively to the contextual bandit approach.
