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.

翻译：为解决这一问题,我们调查了一种深层次学习方法,即将优化模块替换为训练有素的深神经网络(DNN);提出了一种有效的学习解决办法,即建立一个DNN,以产生最佳波形和量化战略的低维代表性;数字结果验证了拟议的学习解决方案的优点。