Neural Sum Rate Maximization with Deep Unrolling

In this paper, we propose neural sum rate maximization, which is a neural network-based approach to tackle the nonconvex problem of maximizing the weighted sum rates with individual power constraints. Neural sum rate maximization combines both novel iterative optimization methods with data-driven models to deliver computationally efficient solution that learns the underlying statistics of the wireless network. Further-more, the solution can be refined by successive convex approximation and algorithm unrolling to accelerate the convergence of the neural sum rate maximization model training. We show that our algorithm is efficient for solving large-scale sum rate maximization problem. Numerical results validate the soundness and practicality of the proposed algorithm.