# Learning Decentralized Power Control in Cell-Free Massive MIMO Networks

arXiv: https://arxiv.org/abs/2303.02573 · HTML: https://mlsanity.com/paper/2303.02573/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 5 Mar 2023.

## Builds on (most cited first, 4 of 4)

- [Scalability Aspects of Cell-Free Massive MIMO](https://mlsanity.com/paper/1902.11275/) (2019)
- [Learning Optimal Fronthauling and Decentralized Edge Computation in Fog Radio Access Networks](https://mlsanity.com/paper/2103.11284/) (2021)
- [Learning-Based Downlink Power Allocation in Cell-Free Massive MIMO Systems](https://mlsanity.com/paper/2109.03128/) (2021)
- [Deep Learning Methods for Joint Optimization of Beamforming and Fronthaul Quantization in Cloud Radio Access Networks](https://mlsanity.com/paper/2107.02520/) (2021)

## Abstract (arXiv)

This paper studies learning-based decentralized power control methods for cell-free massive multiple-input multiple-output (MIMO) systems where a central processor (CP) controls access points (APs) through fronthaul coordination. To determine the transmission policy of distributed APs, it is essential to develop a network-wide collaborative optimization mechanism. To address this challenge, we design a cooperative learning (CL) framework which manages computation and coordination strategies of the CP and APs using dedicated deep neural network (DNN) modules. To build a versatile learning structure, the proposed CL is carefully designed such that its forward pass calculations are independent of the number of APs. To this end, we adopt a parameter reuse concept which installs an identical DNN module at all APs. Consequently, the proposed CL trained at a particular configuration can be readily applied to arbitrary AP populations. Numerical results validate the advantages of the proposed CL over conventional non-cooperative approaches.