Work

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

Year: 2023

Type: article

Abstract: In this letter, 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 real-time prediction. 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.

Source: IEEE Wireless Communications Letters

DOI: 10.1109/lwc.2023.3276868

Authors: Si-Nian Jin, Dian-Wu Yue, Yi-Ling Chen, Qing Hu

Institutions: Dalian Maritime University, State Key Laboratory of Millimeter Waves, Southeast University, Dalian Neusoft University of Information

Language: English

Topic: Advanced Wireless Communication Technologies

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

Funders: State Key Laboratory of Millimeter Waves, National Key Research and Development Program of China, Fundamental Research Funds for the Central Universities