  Recently, deep neural network (DNN) has been widely adopted in the design of
intelligent communication systems thanks to its strong learning ability and low
testing complexity. However, most current offline DNN-based methods still
suffer from unsatisfactory performance, limited generalization ability, and
poor interpretability. In this article, we propose an online DNN-based approach
to solve general optimization problems in wireless communications, where a
dedicated DNN is trained for each data sample. By treating the optimization
variables and the objective function as network parameters and loss function,
respectively, the optimization problem can be solved equivalently through
network training. Thanks to the online optimization nature and meaningful
network parameters, the proposed approach owns strong generalization ability
and interpretability, while its superior performance is demonstrated through a
practical example of joint beamforming in intelligent reflecting surface
(IRS)-aided multi-user multiple-input multiple-output (MIMO) systems.
Simulation results show that the proposed online DNN outperforms conventional
offline DNN and state-of-the-art iterative optimization algorithm, but with low
complexity.
