Beamforming Optimization for Intelligent Reflecting Surface Assisted MISO: A Deep Transfer Learning Approach
Yimeng Ge, Jiancun Fan
Abstract
This article studies the beamforming optimization for intelligent reflecting surface (IRS) assisted multiple-input single-output (MISO) wireless communication system. We establish a deep transfer learning (DTL)-based framework to learn how to optimize the phase shifts at the IRS side. Based on it, we also design a loss function to implement unsupervised training without a large number of labeled data samples. Finally, we extend the optimization problem to discrete phase shift constraint to solve the hardware limitation. The simulation verifies that the proposed DTL-based approach can achieve similar performance compared with upper bound while substantially reducing the computational complexity.
Topics & Concepts
BeamformingWireless power transferWirelessComputer scienceConstraint (computer-aided design)Transfer of learningComputational complexity theoryTransfer functionOptimization problemPhase (matter)Deep learningSurface (topology)Constrained optimization problemUpper and lower boundsElectronic engineeringArtificial intelligenceAlgorithmEngineeringTelecommunicationsMathematicsElectrical engineeringOrganic chemistryMechanical engineeringMathematical analysisChemistryGeometryAdvanced Wireless Communication TechnologiesIndoor and Outdoor Localization TechnologiesUnderwater Vehicles and Communication Systems