Low-Complexity Sum-Capacity Maximization for Intelligent Reflecting Surface-Aided MIMO Systems

Reducing computational complexity is crucial in optimizing the phase shifts of Intelligent Reflecting Surface (IRS) systems since IRS-assisted communication systems are generally deployed with a large number of reflecting elements (REs). This letter proposes a low-complexity algorithm, designated as...

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Bibliographic Details
Published inarXiv.org
Main Authors Sirojuddin, Ahmad, Dony, Darmawan Putra, Wan-Jen, Huang
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 11.04.2022
Subjects
Algorithms
Communications systems
Complexity
Maximization
Optimization
Reconfigurable intelligent surfaces
Sine waves
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Summary:Reducing computational complexity is crucial in optimizing the phase shifts of Intelligent Reflecting Surface (IRS) systems since IRS-assisted communication systems are generally deployed with a large number of reflecting elements (REs). This letter proposes a low-complexity algorithm, designated as Dimension-wise Sinusoidal Maximization (DSM), to obtain the optimal IRS phase shifts that maximize the sum capacity of a MIMO network. The algorithm exploits the fact that the objective function for the optimization problem is sinusoidal w.r.t. the phase shift of each RE. The numerical results show that DSM achieves a near-maximal sum rate and faster convergence speed than two other benchmark methods.
Bibliography:content type line 50
SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
ISSN:2331-8422