# Power Control in Cellular Massive MIMO with Varying User Activity: A Deep Learning Solution

## TL;DR

This paper introduces PowerNet, a deep learning approach for real-time power control in multi-cell Massive MIMO systems with varying active users, achieving near-optimal spectral efficiency with minimal runtime.

## Contribution

The paper presents a novel neural network that handles dynamic user activity and predicts power control settings using only large-scale fading data, advancing practical Massive MIMO solutions.

## Key findings

- PowerNet achieves only 2% loss in sum spectral efficiency compared to iterative algorithms.
- PowerNet predicts power settings in 0.03 ms on GPU, enabling real-time implementation.
- It outperforms traditional optimization with good training labels in spectral efficiency.

## Abstract

This paper considers the sum spectral efficiency (SE) optimization problem in multi-cell Massive MIMO systems with a varying number of active users. This is formulated as a joint pilot and data power control problem. Since the problem is non-convex, we first derive a novel iterative algorithm that obtains a stationary point in polynomial time. To enable real-time implementation, we also develop a deep learning solution. The proposed neural network, PowerNet, only uses the large-scale fading information to predict both the pilot and data powers. The main novelty is that we exploit the problem structure to design a single neural network that can handle a dynamically varying number of active users; hence, PowerNet is simultaneously approximating many different power control functions with varying number inputs and outputs. This is not the case in prior works and thus makes PowerNet an important step towards a practically useful solution. Numerical results demonstrate that PowerNet only loses $2\%$ in sum SE, compared to the iterative algorithm, in a nine-cell system with up to $90$ active users per in each coherence interval, and the runtime was only $0.03$ ms on a graphics processing unit (GPU). When good data labels are selected for the training phase, PowerNet can yield better sum SE than by solving the optimization problem with one initial point.