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

• URL: http://arxiv.org/abs/2212.12744v1
• Date: Sat, 24 Dec 2022 14:58:15 GMT
• Title: Energy Efficiency Maximization in IRS-Aided Cell-Free Massive MIMO System
• Authors: Si-Nian Jin, Dian-Wu Yue, Yi-Ling Chen, Qing Hu
• Abstract summary: 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 problem, we propose an iterative optimization algorithm by using quadratic transform and Lagrangian dual transform to find the optimum beamforming and phase shifts. 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
• Score: 2.9081408997650375
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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