Learning to Optimize with Unsupervised Learning: Training Deep Neural Networks for URLLC

Learning the optimized solution as a function of environmental parameters by deep neural networks (DNN) is effective in solving numerical optimization in real time for time-sensitive resource allocation in wireless systems. Existing works of learning to optimize train the DNN with labels, which are generated by solving the optimization problems. The learned solution are often inaccurate and hence cannot be employed to ensure the stringent quality of service. In this paper, we propose a framework to learn the latent function with unsupervised deep learning, where the property that the optimal solution should satisfy is used as the "supervision signal" implicitly. The framework is applicable to both variable and functional optimization problems with constraints, which are respectively formulated to optimize variables and functions of concern. We take a variable optimization problem in ultra-reliable and low-latency communications as an example, which demonstrates that the ultra-high reliability can be supported by the DNN without supervision labels.