# Energy Efficiency Maximization in IRS-Aided Cell-Free Massive MIMO System

arXiv: https://arxiv.org/abs/2212.12744 · HTML: https://mlsanity.com/paper/2212.12744/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 20 references that are in the map.
- First posted on arXiv 24 Dec 2022.

## Builds on (most cited first, 10 of 10)

- [Convolutional Neural Networks at Constrained Time Cost](https://mlsanity.com/paper/1412.1710/) (2014)
- [Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming](https://mlsanity.com/paper/1810.03961/) (2018)
- [Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning](https://mlsanity.com/paper/2002.10072/) (2020)
- [Intelligent Reflecting Surface Enhanced Wireless Network: Joint Active and Passive Beamforming Design](https://mlsanity.com/paper/1809.01423/) (2018)
- [A joint precoding framework for wideband reconfigurable intelligent surface-aided cell-free network](https://mlsanity.com/paper/2002.03744/) (2020)
- [Energy Efficiency Maximization in RIS-Aided Cell-Free Network with Limited Backhaul](https://mlsanity.com/paper/2012.11736/) (2020)
- [Channel Estimation for Intelligent Reflecting Surface Assisted Multiuser Communications: Framework, Algorithms, and Analysis](https://mlsanity.com/paper/1912.11783/) (2019)
- [Cascaded Channel Estimation for Intelligent Reflecting Surface Assisted Multiuser MISO Systems](https://mlsanity.com/paper/2108.09002/) (2021)
- [Beyond Cell-free MIMO: Energy Efficient Reconfigurable Intelligent Surface Aided Cell-free MIMO Communications](https://mlsanity.com/paper/2011.08473/) (2020)
- [RIS-Aided Cell-Free Massive MIMO Systems: Joint Design of Transmit Beamforming and Phase Shifts](https://mlsanity.com/paper/2112.06593/) (2021)

## Abstract (arXiv)

In this paper, we consider an intelligent reflecting surface (IRS)-aided cell-free massive multiple-input multiple-output system, where the beamforming at access points and the phase shifts at IRSs are jointly optimized to maximize energy efficiency (EE). To solve EE maximization problem, we propose an iterative optimization algorithm by using quadratic transform and Lagrangian dual transform to find the optimum beamforming and phase shifts. However, the proposed algorithm suffers from high computational complexity, which hinders its application in some practical scenarios. Responding to this, we further propose a deep learning based approach for joint beamforming and phase shifts design. Specifically, a two-stage deep neural network is trained offline using the unsupervised learning manner, which is then deployed online for the predictions of beamforming and phase shifts. Simulation results show that compared with the iterative optimization algorithm and the genetic algorithm, the unsupervised learning based approach has higher EE performance and lower running time.