Learned-SBL-GAMP based hybrid precoders/combiners in millimeter wave massive MIMO systems

In Millimeter-Wave (mm-Wave) massive Multiple-Input Multiple-Output (MIMO) systems, hybrid precoders/combiners must be designed to improve antenna gain and reduce hardware complexity. Sparse Bayesian learning via Expectation Maximization (SBL-EM) algorithm is not practically feasible for high signal...

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Vydáno v:PloS one Ročník 18; číslo 9; s. e0289868
Hlavní autoři: Ali K., Shoukath, Khan, Arfat Ahmad, T, Perarasi, Ur Rehman, Ateeq, Ouahada, Khmaies
Médium: Journal Article
Jazyk:angličtina
Vydáno: San Francisco Public Library of Science 08.09.2023
Public Library of Science (PLoS)
Témata:
Accuracy
Algorithms
Analysis
Antenna gain
Antennas
Antennas (Electronics)
Artificial neural networks
Bayesian analysis
Biology and Life Sciences
Communication
Complexity
Computer and Information Sciences
Computer applications
Deep learning
Efficiency
Engineering and Technology
Hybrid systems
Information management
Machine learning
Matched pursuit
Matching
Mean square errors
Medicine and Health Sciences
Message passing
Millimeter waves
MIMO communication
MIMO communications
Neural networks
Optimization
Physical Sciences
Research and Analysis Methods
Training
India
ISSN:1932-6203, 1932-6203
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Shrnutí:In Millimeter-Wave (mm-Wave) massive Multiple-Input Multiple-Output (MIMO) systems, hybrid precoders/combiners must be designed to improve antenna gain and reduce hardware complexity. Sparse Bayesian learning via Expectation Maximization (SBL-EM) algorithm is not practically feasible for high signal dimensions because estimating sparse signals and designing optimal hybrid precoders/combiners using SBL-EM still provide high computational complexity for higher signal dimensions. To overcome the issues of high computational complexity along with making it suitable for larger data sets, in this paper, we propose Learned-Sparse Bayesian Learning with Generalized Approximate Message Passing algorithm (L-SBL-GAMP) algorithm for designing optimal hybrid precoders/combiners suitable for mmWave Massive MIMO systems. The L-SBL-GAMP algorithm is an extension of the SBL-GAMP algorithm that incorporates a Deep Neural Network (DNN) to improve the system performance. Based on the nature of the training data, the L-SBL-GAMP can design the optimal Hybrid precoders/combiners, which enhances the spectral efficiency of mmWave massive MIMO systems. The proposed L-SBL-GAMP algorithm reduces the iterations, training overhead, and computational complexity compared to the SBL-EM algorithm. The simulation results unveil that the proposed L-SBL-GAMP provides higher achievable rates, better accuracy, and low computational complexity compared to the existing algorithm, such as Orthogonal Matching Pursuit (OMP), Simultaneous Orthogonal Matching Pursuit (SOMP), SBL-EM and SBL-GAMP for mmWave massive MIMO architectures.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0289868