Sum Spectral Efficiency Maximization in Massive MIMO Systems: Benefits from Deep Learning

This paper investigates the joint data and pilot
power optimization for maximum sum spectral efficiency (SE)
in multi-cell Massive MIMO systems, which is a non-convex
problem. We first propose a new optimization algorithm, inspired
by the weighted minimum mean square error (MMSE) approach,
to obtain a stationary point in polynomial time. We then use this
algorithm together with deep learning to train a convolutional
neural network to perform the joint data and pilot power
control in sub-millisecond runtime, making it suitable for online
optimization in real multi-cell Massive MIMO systems. The
numerical result demonstrates that the solution obtained by the
neural network is 1% less than the stationary point for four-cell
systems, while the sum SE loss is 2% in a nine-cell system