  Network adaptation is essential for the efficient operation of Cloud-RANs.
Unfortunately, it leads to highly intractable mixed-integer nonlinear
programming problems. Existing solutions typically rely on convex relaxation,
which yield performance gaps that are difficult to quantify. Meanwhile, global
optimization algorithms such as branch-and-bound can find optimal solutions but
with prohibitive computational complexity. In this paper, to obtain
near-optimal solutions at affordable complexity, we propose to approximate the
branch-and-bound algorithm via machine learning. Specifically, the pruning
procedure in branch-and-bound is formulated as a sequential decision problem,
followed by learning the oracle's action via imitation learning. A unique
advantage of this framework is that the training process only requires a small
dataset, and it is scalable to problem instances with larger dimensions than
the training setting. This is achieved by identifying and leveraging the
problem-size independent features. Numerical simulations demonstrate that the
learning based framework significantly outperforms competing methods, with
computational complexity much lower than the traditional branch-and-bound
algorithm.
