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GPU Accelerated Successive Interference Cancellation for NOMA Uplink with User Clustering

  • Talgat Manglayev
  • Refik Caglar Kizilirmak
  • Yau Hee Kho
  • Nor Asilah Wati Abdul Hamid
Article
  • 54 Downloads

Abstract

Non-orthogonal multiple access (NOMA) can achieve high throughput by using the same time and frequency resources for multiple users. NOMA distinguishes multiple users in power domain by computationally-heavy successive interference cancellation (SIC) method. Recently, outsourcing baseband computations to graphics processing units (GPUs) have become an attractive solution for some wireless communication applications, particularly for the ones include parallel processing. Although SIC is a sequential operation, when user clustering is deployed, multiple SIC operations are required and GPU based computation becomes a natural solution to alleviate the high computation demand of SIC receivers. In this work, we implemented GPU based SIC implementation for uplink NOMA systems with user clustering and our results reveal a significant speedup when compared to that of using central processing unit based computations.

Keywords

Non-orthogonal multiple access (NOMA) Successive interference cancellation (SIC) Graphics processing unit (GPU) CUDA User clustering 5G 

Notes

Acknowledgements

Funding was provided by Nazarbayev University School of Engineering.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018
corrected publication August 2018

Authors and Affiliations

  1. 1.School of EngineeringNazarbayev UniversityAstanaKazakhstan
  2. 2.School of Engineering and Computer ScienceVictoria University of WellingtonWellingtonNew Zealand
  3. 3.Faculty of Computer Science and Information TechnologyUniversiti Putra MalaysiaSerdangMalaysia

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