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.
@article{arxiv.1903.08163,
title = {Sum Spectral Efficiency Maximization in Massive MIMO Systems: Benefits from Deep Learning},
author = {Trinh Van Chien and Emil Björnson and Erik G. Larsson},
journal= {arXiv preprint arXiv:1903.08163},
year = {2019}
}
Comments
4 figures, 1 table. Accepted by ICC 2019. arXiv admin note: text overlap with arXiv:1901.03620