Wireless Personal Communications

, Volume 103, Issue 3, pp 2391–2400 | Cite as

GPU Accelerated Successive Interference Cancellation for NOMA Uplink with User Clustering

  • Talgat ManglayevEmail author
  • Refik Caglar Kizilirmak
  • Yau Hee Kho
  • Nor Asilah Wati Abdul Hamid


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.


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



Funding was provided by Nazarbayev University School of Engineering.


  1. 1.
    Tse, D., & Viswanath, P. (2005). Fundamentals of wireless communication. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  2. 2.
    Chen, Z., Ding, Z., Xuchu, D., & Zhang, R. (2016). In A mathematical proof of the superiority of NOMA compared to conventional OMA. Accessed 30 Dec 2017.
  3. 3.
    Tabassum, H., Ali, M. S., Hossain, E.,Hossain, M. & Kim, D. et al. (2016). In Non-orthogonal multiple access (NOMA) in cellular uplink and downlink: Challenges and enabling techniques. Accessed 30 Dec 2017.
  4. 4.
    Timotheou, S., & Krikidis, T. (2015). Fairness for non-orthogonal multiple access in 5G systems. IEEE Signal Processing Letters, 22(10), 1647–1651. (IEEE).CrossRefGoogle Scholar
  5. 5.
    Parvez, I., Rahmati, A., Guvenc, I., Sarwat, A., & Dai, H. (2017). In A survey on low latency towards 5G: RAN, core network and caching solutions Accessed 29 Dec 2017.
  6. 6.
    Li, K., Sharan, R., Chen, Y., Goldstein, T., et al. (2017). Decentralized baseband processing for massive MU-MIMO systems. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 7(4), 491–507. (IEEE).CrossRefGoogle Scholar
  7. 7.
    Li, K., Wu, M., Wang, G., & Cavallaro, J. (2014). A high performance GPU-based software-defined basestation. In 48th Asilomar conference on signals, systems and computers (pp. 2060–2064). IEEE.Google Scholar
  8. 8.
    Wu, M., Gupta, S., Sun, Y., & Cavallaro, J. (2009). A GPU implementation of a real-time MIMO detector. In IEEE workshop on signal processing systems (pp. 303–308). IEEE.Google Scholar
  9. 9.
    Ali, M. S., Tabassum, H., & Hossain, E. (2016). Dynamic user clustering and power allocation for uplink and downlink non-orthogonal multiple access (NOMA) systems. In IEEE access (pp. 6325–6343). IEEE.Google Scholar
  10. 10.
    Zhang, N., Wang, J., Kang, G., & Liu, Y. (2016). Uplink non-orthogonal multiple access in 5G systems. IEEE Communications Letters, 20(3), 458–461. (IEEE).CrossRefGoogle Scholar
  11. 11.
    Benjebbour, A., Saito, Y., Kishiyama, Y., & Li, A., et al. (2013). Concept and practical considerations of non-orthogonal multiple access (NOMA) for future radio access. In International symposium on intelligent signal processing and communications systems (ISPACS), (pp. 770–774). IEEE.Google Scholar
  12. 12.
    Benjebbour, A., Li, A., Saito, Y., & Kishiyama, Y. et al. (2013). System-level performance of downlink NOMA for future LTE enhancements. In Globecom Workshops (GC Wkshps), (pp. 66–70). IEEE.Google Scholar
  13. 13.
    Yakou, K., & Higuchi, K. (2015). Downlink NOMA with SIC using unified user grouping for non-orthogonal user multiplexing and decoding order. In: International symposium on intelligent signal processing and communication systems (ISPACS) (pp. 508–513). IEEE.Google Scholar
  14. 14.
    Saito, Y., Benjebbour, A., Kishiyama, Y., & Takehiro, N. (2013). System-level performance evaluation of downlink non-orthogonal multiple access (NOMA). In Personal indoor and mobile radio communications (PIMRC) (pp. 611–615). IEEE.Google Scholar
  15. 15.
    Manglayev, T., Kizilirmak, R.C., & Kho, Y. H. (2016). Optimum power allocation for non-orthogonal multiple access (NOMA). In 10th International conference on application of information and communication technologies (AICT) (pp. 1–4). IEEE.Google Scholar
  16. 16.
    Anwar, A., Seet, B-C., & Li, X. J. (2015). PIC-based receiver structure for 5G downlink NOMA. In 10th International conference on information, communications and signal processing (ICICS) (pp. 1–5). IEEE.Google Scholar
  17. 17.
    Vannithamby, R., & Talwar, S. (2017). Towards 5G: Applications. Requirements and candidate technologies. Hoboken: Wiley.Google Scholar
  18. 18.
    Ali, S. H., Hossain, E., & Kim, D. I. (2016). Non-orthogonal multiple access (NOMA) for downlink multiuser MIMO systems: User clustering, beamforming, and power allocation. In IEEE access (vol. 5, pp. 565–577). IEEE.Google Scholar
  19. 19.
    Tabassum, H., Hossain, E., & Hossain, M. J. (2017). Modeling and analysis of uplink non-orthogonal multiple access (NOMA) in large-scale cellular networks using poisson cluster processes. IEEE Transactions on Communications, 65(8), 3555–3570. (IEEE).Google Scholar
  20. 20.
    Sedaghat, M. A., Müller, R. R., & Mohammad, A. On user pairing in NOMA uplink. Accessed 29 Dec 2017.
  21. 21.
    Intel. (2018). All Intel Core Processors. Accessed 28 Dec 2017.
  22. 22.
    NVIDIA. (2017). GPU impact by domain. Accessed 28 Dec 2017.
  23. 23.
    Sanders, J., & Kandrot, E. (2010). CUDA by example: An introduction to general-purpose GPU programming. Boston: Addison-Wesley Professional.Google Scholar
  24. 24.
    Wilt, N. (2013). The CUDA handbook: A comprehensive guide to GPU programming. London: Pearson Education.Google Scholar

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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