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 inIEEE wireless communications letters Vol. 11; no. 7; p. 1
Main Authors Sirojuddin, Ahmad, Putra, Dony Darmawan, Huang, Wan-Jen
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.07.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Communications systems
Complexity
Convergence
Convex functions
dimension-wise sinusoidal maximization
Intelligent reflecting surface
low-complexity
Manganese
Maximization
MIMO
MIMO communication
Optimization
Optimized production technology
Reconfigurable intelligent surfaces
Resource management
Sine waves
sum capacity
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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 maximizes 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 near maximal sum-rate and faster convergence speed than two other benchmark methods.
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ISSN:2162-2337
2162-2345
DOI:10.1109/LWC.2022.3167731