  Radio Access Networks virtualization (vRAN) is on its way becoming a reality
driven by the new requirements in mobile networks, such as scalability and cost
reduction. Unfortunately, there is no free lunch but a high price to be paid in
terms of computing overhead introduced by noisy neighbors problem when multiple
virtualized base station instances share computing platforms. In this paper,
first, we thoroughly dissect the multiple sources of computing overhead in a
vRAN, quantifying their different contributions to the overall performance
degradation. Second, we design an AI-driven Radio Intelligent Controller
(AIRIC) to orchestrate vRAN computing resources. AIRIC relies upon a hybrid
neural network architecture combining a relation network (RN) and a deep
Q-Network (DQN) such that: (i) the demand of concurrent virtual base stations
is satisfied considering the overhead posed by the noisy neighbors problem
while the operating costs of the vRAN infrastructure is minimized; and (ii)
dynamically changing contexts in terms of network demand, signal-to-noise ratio
(SNR) and the number of base station instances are efficiently supported. Our
results show that AIRIC performs very closely to an offline optimal oracle,
attaining up to 30% resource savings, and substantially outperforms existing
benchmarks in service guarantees.
