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arXiv · 1903.08163

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

Trinh Van Chien·Emil Björnson·Erik G. Larsson
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Abstract

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.

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Trinh Van Chien, Emil Björnson, Erik G. Larsson. 2019-03-19. Sum Spectral Efficiency Maximization in Massive MIMO Systems: Benefits from Deep Learning. https://arxiv.org/abs/1903.08163

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