DOI QR코드

DOI QR Code

Zero forcing based sphere decoder for generalized spatial modulation systems

  • Jafarpoor, Sara (Department of Electrical Engineering, College of Technical and Engineering, Saveh Branch, Islamic Azad University) ;
  • Fouladian, Majid (Department of Electrical Engineering, College of Technical and Engineering, Saveh Branch, Islamic Azad University) ;
  • Neinavaie, Mohammad (Department of Electrical Engineering, College of Technical and Engineering, Tehran Branch, Islamic Azad University)
  • Received : 2018.02.03
  • Accepted : 2018.08.01
  • Published : 2019.04.07

Abstract

To reduce the number of radio frequency (RF) chains in multiple input multiple output (MIMO) systems, generalized spatial modulation (GSM) techniques have been proposed in the literature. In this paper, we propose a zero-forcing (ZF)-based detector, which performs an initial pruning of the search tree that will be considered as the initial condition in a sphere decoding (SD) algorithm. The proposed method significantly reduces the computational complexity of GSM systems while achieving a near maximum likelihood (ML) performance. We analyze the performance of the proposed method and provide an analytic performance difference between the proposed method and the ML detector. Simulation results show that the performance of the proposed method is very close to that of the ML detector, while achieving a significant computational complexity reduction in comparison with the conventional SD method, in terms of the number of visited nodes. We also present some simulations to assess the accuracy of our theoretical results.

Keywords

References

  1. L. Zheng and D. N. C. Tse, Diversity and multiplexing: a fundamental tradeoff in multiple‐antenna channels, IEEE Trans. Inform. Theory 49 (2013), no. 5, 1073-1096. https://doi.org/10.1109/TIT.2003.810646
  2. D. Tse and P. Viswanath, Fundamentals of wireless communications, Cambridge University Press, Cambridge, UK, 2005.
  3. G. T. Foschini and M. J. Gans, On limits of wireless communication in a fading environment when using multiple antennas, Wireless Pers. Commun. 6 (1998), no. 3, 311-335. https://doi.org/10.1023/A:1008889222784
  4. E. Telatar, Capacity of multi‐antenna Gaussian channels, Eur. Trans. Telecommun. 10 (1999), no. 6, 585-596. https://doi.org/10.1002/ett.4460100604
  5. A. Paularj, An introduction to space-time wireless communication systems, Cambridge University Press, Cambridge, UK, 2003.
  6. R. Meseleh et al., Spatial modulation a new low Complexity spectral efficiency enhancing technique, In Int. Conf. Commun. Netw., China, Oct. 25-27, 2006, pp. 1-5.
  7. A. Younis et al., Generalised spatial modulation, In Proc. Asilomar Conf. Signals, Syst. Comput., Pacific Grove, CA, USA, Nov. 7-10, 2010, pp. 1498-1502.
  8. J. Fu et al., Generalised spatial modulation with multiple active transmit antennas, In Proc. IEEE GLOBECOM, Miami, FL, USA, Dec. 8-10, 2010, pp. 839-844.
  9. U. Fincke and M. Pohst, Improved methods for calculating vectors of short length in a lattice, including a complexity analysis, Math. Comput. 44 (1985), no. 170, 463-471. https://doi.org/10.1090/S0025-5718-1985-0777278-8
  10. H. Vikalo and B. Hassibi, On the sphere‐decoding algorithm. II. Generalization, second‐order statistics, and applications to communications, IEEE Trans. Signal Process. 53 (2005), no. 8, 2819-2834. https://doi.org/10.1109/TSP.2005.850350
  11. L. G. Barbero and J. S. Thompson, Fixing the complexity of the sphere decoder or MIMO detection, IEEE Trans. Wireless Commun. 7 (2008), no. 6, 2131-2142. https://doi.org/10.1109/TWC.2008.060378
  12. J. W. Choi et al., Low‐complexity decoding via reduced dimension maximum‐likelihood search, IEEE Trans. Signal Process. 58 (2010), no. 3, 1780-1793. https://doi.org/10.1109/TSP.2009.2036482
  13. J. Jalden et al., The error probability of the fixed‐complexity sphere decoder, IEEE Trans. Signal Process. 57 (2009), no. 7, 2711-2720. https://doi.org/10.1109/TSP.2009.2017574
  14. R. H. Chen and W. H. Chung, Reduced complexity MIMO detection scheme using statistical search space reduction, IEEE Commun. Lett. 16 (2012), no. 3, 292-295. https://doi.org/10.1109/LCOMM.2012.021612.120017
  15. B. Shim and I. Kang, Sphere decoding with a probabilistic tree pruning, IEEE Trans. Signal Process. 56 (2008), no. 10, 4867-4878. https://doi.org/10.1109/TSP.2008.923808
  16. M. Neinavaie and M. Derakhtian, ML performance achieving algorithm with the zero‐forcing complexity at high SNR regime, IEEE Trans. Wireless Commun. 15 (2016), no. 7, 4651-4659. https://doi.org/10.1109/TWC.2016.2543217
  17. Y. Jiang et al., Performance analysis of ZF and MMSE equalizers for MIMO systems: a in‐depth study of the high SNR regime, IEEE Trans. Inform. Theory 57 (2011), no. 4, 2008-2026. https://doi.org/10.1109/TIT.2011.2112070
  18. A. K. Lenstra et al., Factoring polynomials with rational coefficient, Math. Annu. 261 (1982), no. 4, 515-534. https://doi.org/10.1007/BF01457454
  19. M. Taherzadeh, A. Mobasher, and A. K. Khandani, LLL reduction achieves the receive diversity in MIMO decoding, IEEE Trans. Inform. Theory 53 (2007), no. 12, 4801-4805. https://doi.org/10.1109/TIT.2007.909169
  20. M. Di Renzo et al., Spatial modulation for generalized MIMO: challenges, opportunities, and implementation, Proc. IEEE 102 (2014), no. 1, 56-103. https://doi.org/10.1109/JPROC.2013.2287851
  21. P. Yang et al., Design guidelines for spatial modulation, IEEE Commun. Surveys Tutorials 17 (2015), no. 1, 6-26. https://doi.org/10.1109/COMST.2014.2327066
  22. G. H. Lee and T. H. Kim, Implementation of a near‐optimal detector for spatial modulation MIMO systems, IEEE Trans. Circuits Syst. II, 63 (2016), no. 10, 954-958. https://doi.org/10.1109/TCSII.2016.2536239
  23. Y. Xiao et al., Low‐complexity signal detection for generalized spatial modulation, IEEE Commun. Lett. 18 (2014), no. 3, 403-406. https://doi.org/10.1109/LCOMM.2013.123113.132586
  24. C. E. Chen, C. H. Li, and Y. H. Huang, An improved ordered-block MMSE detector for generalized spatial modulation, IEEE Commun. Lett. 19 (2015), no. 5, 707-710. https://doi.org/10.1109/LCOMM.2015.2402251
  25. A. Younis et al., Generalised sphere decoding for spatial modulation, IEEE Trans. Commun. 61 (2013), no. 7, 2805-2815. https://doi.org/10.1109/TCOMM.2013.061013.120547
  26. A. Younis et al., Sphere decoding for spatial modulation, In Proc. Intell. Conf. Commun. IEEE, Kyoto, Japan, June 5-9, 2011, pp. 1-6.
  27. A. Younis et al., Reduced complexity sphere decoder for spatial modulation detection receivers, In Proc. Global Telecommun. Conf. IEEE, Miami, FL, USA, Dec. 6-10, 2010, pp. 1-5.
  28. J. A. Cal-Braz and R. Sampaio-Neto, Low‐complexity sphere decoding detector for generalized spatial modulation systems, IEEE Commun. Lett. 18 (2014), no. 6, 949-952. https://doi.org/10.1109/LCOMM.2014.2320936
  29. B. Hassibi and H. Vikalo, On the sphere decoding algorithm I. Expected complexity, IEEE Trans. Signal Process. 53 (2005), no. 8, 2806-2818. https://doi.org/10.1109/TSP.2005.850352
  30. M. Abramowitz and I. A. Stegun, Handbook of mathematical functions with formulas, graphs, and mathematical tables, U.S. Department of Commerce, Boulder, CO, USA, 1972.
  31. C. Tang et al., High precision low complexity matrix inversion based on Newton iteration for data detection in the massive MIMO, IEEE Commun. Lett. 20 (2016), no. 3, 490-493. https://doi.org/10.1109/LCOMM.2015.2514281
  32. I. S. Gradshteyn and I. M. Ryzhik, Table of integrals, series and products, Academic, New York, NY, USA, 1980.