Open AccessAI based Computation for Hybrid Precoding/Combining in Millimeter-Wave Massive MIMO Systems
Author(s) -
Nazeerunnisa,
Madhavi Tatineni
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1817/1/012013
Subject(s) - mimo , extremely high frequency , precoding , particle swarm optimization , computation , computer science , spectral efficiency , key (lock) , wireless , electronic engineering , radio frequency , computer engineering , channel (broadcasting) , algorithm , telecommunications , engineering , computer security
Millimeter-wave (mm-Wave) have emerged as a potential leading technology for the 5G cellular systems due to the enormous availability of high radio-frequency spectrum, which can deliver extreme data speed and enhance spectral efficiency (SE). An economical architecture of hybrid precoder (HP) is widely used in mm-Wave massive MIMO systems (mm-WmM) to recompense for the severe propagation loss of the mm-Waves. This paper examines the design of the hybrid precoder and combiner (HPC) in mm-WmM by integrating Artificial Intelligence (AI) based optimization algorithm. AI is going to be a key component to enhance the performance of 5G wireless communications and beyond. The emerging AI based computation using Hierarchical Particle Swarm Optimization technique (HPSO) is proposed to design a HPC to maximize the SE in mm-Wave massive MIMO systems. Results obtained from simulations demonstrate the improved performance of the HPSO algorithm in contrast to the existing algorithms and can accomplish close to the optimal performance.