Advertisement

The Journal of Supercomputing

, Volume 75, Issue 3, pp 1256–1267 | Cite as

Parallel SUMIS soft detector for large MIMO systems on multicore and GPU

  • Carla Ramiro
  • M. Ángeles SimarroEmail author
  • Alberto Gonzalez
  • Antonio M. Vidal
Article
  • 55 Downloads

Abstract

The number of transmit and receiver antennas is an important factor that affects the performance and complexity of a MIMO system. A MIMO system with very large number of antennas is a promising candidate technology for next generations of wireless systems. However, the vast majority of the methods proposed for conventional MIMO system are not suitable for large dimensions. In this context, the use of high-performance computing systems, such us multicore CPUs and graphics processing units has become attractive for efficient implementation of parallel signal processing algorithms with high computational requirements. In the present work, two practical parallel approaches of the Subspace Marginalization with Interference Suppression detector for large MIMO systems have been proposed. Both approaches have been evaluated and compared in terms of performance and complexity with other detectors for different system parameters.

Keywords

Large MIMO systems SUMIS High-order constellation GPU Low-complexity detection 

Notes

Acknowledgements

This work has been partially supported by the Spanish MINECO Grant RACHEL TEC2013-47141-C4-4-R, the PROMETEO FASE II 2014/003 Project and FPU AP-2012/71274.

References

  1. 1.
    Rusek F, Persson D, Lau BK, Larsson EG, Marzetta TL, Edfors O, Tufvesson F (2013) Scaling up MIMO: opportunities and challenges with very large arrays. IEEE Signal Proc Mag 30(1):40–60CrossRefGoogle Scholar
  2. 2.
    Studer C, Burg A, Bölcskei H (2008) Soft-output sphere decoding: algorithms and VLSI implementation. IEEE J Sel Areas Commun 26(2):290–300CrossRefGoogle Scholar
  3. 3.
    Wang R, Giannakis GB (2004) Approaching MIMO channel capacity with reduced-complexity soft sphere decoding. In: Wireless Communications and Networking Conference, 2004. WCNC. 2004 IEEE vol 3, pp 1620–1625Google Scholar
  4. 4.
    Persson D, Larsson EG (2011) Partial marginalization soft MIMO detection with higher order constellations. IEEE Trans Signal Procces 59(1):453–458MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Cîrkić M, Larsson EG (2014) SUMIS: near-optimal soft-in soft-out MIMO detection with low and fixed complexity. IEEE Trans Signal Process 62(12):3084–3097MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Alberto Gonzalez C, Ramiro, M, Ángeles Simarro, Antonio M Vidal (2017) Parallel SUMIS soft detector for MIMO systems on multicore. In: Proceedings of the 17th International Conference on Computational and Mathematical Methods in Science and Engineering, pp 1729–1736Google Scholar
  7. 7.
    Hochwald BM, ten Brink S (2003) Achieving near-capacity on a multiple-antenna channel. IEEE Trans Commun 51:389–399CrossRefGoogle Scholar
  8. 8.
    Kaipeng L, Bei Y, Michael W, Joseph RC, Christoph S (2015) Accelerating massive MIMO uplink detection on GPU for SDR systems. In: 2015 IEEE dallas circuits and systems conference (DCAS), pp 1–4Google Scholar
  9. 9.
    Di W, Eilert J, Liu D (2011) Implementation of a high-speed MIMO soft-output symbol detector for software defined radio. J Signal Process Syst 63(1):27–37CrossRefGoogle Scholar
  10. 10.
    Anderson E, Bai Z, Bischof C, Blackford LS, Demmel J, Dongarra J, Du Croz J, Greenbaum A, Hammarling S, McKenney A, Sorensen D (1999) LAPACK users’ guide. SIAM, LondonCrossRefzbMATHGoogle Scholar
  11. 11.
  12. 12.
    cuBLAS Documentation (2015) http://docs.nvidia.com/cuda/cublas
  13. 13.
    Dagum L, Enon R (1998) OpenMP: an industry standard API for shared-memory programming. IEEE Comput Sci Eng 5(1):46–55CrossRefGoogle Scholar
  14. 14.
    CUDA Toolkit Documentation, Version 7.5 (2015) https://developer.nvidia.com/cuda-toolkit
  15. 15.
    Roger S, Ramiro C, Gonzalez A, Almenar V, Vidal AM (2012) Fully parallel GPU implementation of a fixed-complexity soft-output MIMO detector. IEEE Trans Veh Technol 61(8):3796–3800CrossRefGoogle Scholar
  16. 16.
    Senst M, Ascheid G, Lüders H (2010) Performance evaluation of the markov chain monte carlo MIMO detector based on mutual information. 2010 IEEE International Conference on Communications (ICC), pp 1–6Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Institute on Robotics and Information and Communication TechnologiesUniversitat de ValènciaValènciaSpain
  2. 2.Institute of Telecommunications and Multimedia ApplicationsUniversitat Politècnica de ValènciaValènciaSpain
  3. 3.Department of Information Systems and ComputationUniversitat Politècnica de ValènciaValènciaSpain

Personalised recommendations