  Cooperative beamforming across access points (APs) and fronthaul quantization
strategies are essential for cloud radio access network (C-RAN) systems. The
nonconvexity of the C-RAN optimization problems, which is stemmed from per-AP
power and fronthaul capacity constraints, requires high computational
complexity for executing iterative algorithms. To resolve this issue, we
investigate a deep learning approach where the optimization module is replaced
with a well-trained deep neural network (DNN). An efficient learning solution
is proposed which constructs a DNN to produce a low-dimensional representation
of optimal beamforming and quantization strategies. Numerical results validate
the advantages of the proposed learning solution.
