Deep Learning Methods for Joint Optimization of Beamforming and Fronthaul Quantization in Cloud Radio Access Networks
This work addresses efficiency challenges in C-RAN systems for telecommunications, but it is incremental as it applies existing deep learning methods to a known optimization problem.
The paper tackles the high computational complexity of joint beamforming and fronthaul quantization optimization in cloud radio access networks by replacing iterative algorithms with a deep neural network, achieving validated advantages in numerical results.
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.