Journal of Communications and Networks

The Korean Institute of Commucations and Information Sciences (한국통신학회)
권호 표지
DOI QR코드

DOI QR Code

A Parallel Collaborative Sphere Decoder for a MIMO Communication System

  • Koo, Jihun (System LSI Business, Samsung Electronics) ;
  • Kim, Soo-Yong (System LSI Business, Samsung Electronics Co., Ltd.) ;
  • Kim, Jaeseok (School of Electrical and Electronic Engineering, Yonsei University)
  • Received : 2013.10.09
  • Accepted : 2014.08.30
  • Published : 2014.12.31
⟨ Previous Next ⟩

Abstract

In this paper, we propose a parallel collaborative sphere decoder with a scalable architecture promising quasi-maximum likelyhood performance with a relatively small amount of computational resources. This design offers a hardware-friendly algorithm using a modified node operation through fixing the variable complexity of the critical path caused by the sequential nature of the conventional sphere decoder (SD). It also reduces the computational complexity compared to the fixed-complexity sphere decoder (FSD) algorithm by tree pruning using collaboratively operated node operators. A Monte Carlo simulation shows that our proposed design can be implemented using only half the parallel operators compared to the approach using an ideal fully parallel scheme such as FSD, with only about a 7% increase of the normalized decoding time for MIMO dimensions of $16{\times}16$ with 16-QAM modulation.

Keywords

Acknowledgement

Supported by : MOTIE/KEIT

References

  1. I. Telatar, "Capacity of multi-antenna Gaussian channels," Eur. Trans. Telecommun., vol. 10, no. 6, pp. 585-596, Dec. 1999. https://doi.org/10.1002/ett.4460100604
  2. E. Biglieri and G. Taricco, "Large-system analysis of multiple antenna system capacities," J. Commun. Netw., vol. 5, pp. 5764, June 2003.
  3. F. Rusek et al., "Scaling up MIMO: Opportunities and challenges with very large arrays," IEEE Signal Process. Mag., vol. 30, pp. 40-60. Jan. 2013.
  4. J. Park et al., "A high performanceMIMO detection algorithm for DLMUMIMO with practical errors in IEEE 802.11ac systems," in Proc. IEEE PIMRC, Oct. 2013.
  5. U. Fincke and M. Pohst, "Improved methods for calculating vectors of short length in a lattice, including a complexity analysis," Math. Comput., vol. 44, pp. 463-471, Apr. 1985. https://doi.org/10.1090/S0025-5718-1985-0777278-8
  6. C. P. Schnorr and M. Euchner, "Lattice basis reduction: Improved practical algorithms and solving subset sum problems," Math. Programming, vol. 66, pp. 181-191, Aug. 1994. https://doi.org/10.1007/BF01581144
  7. M. O. Damen, H. E. Gamal, and G. Caire, "On maximum-likelihood detection and the search for the closest lattice point," IEEE Trans. Inform. Theory, vol. 49, no. 10, pp. 2389-2402, Oct. 2003. https://doi.org/10.1109/TIT.2003.817444
  8. L. G. Barbero and J. S. Thompson, "Fixing the complexity of the Sphere Decoder for MIMO Detector", IEEE Trans. Wireless Commun., vol. 7, pp. 2131-2142, June 2008. https://doi.org/10.1109/TWC.2008.060378
  9. C. Shen et al., "An adaptive reduced complexity k-best decoding algorithm with early termination," in Proc. IEEE CCNC, Jan. 2010.
  10. G. Li et al., "An early termination-based improved algorithm for fixedcomplexity sphere decoder," in Proc. IEEE WCNC, Apr. 2012.
  11. A. Burg et al., "VLSI implementation of MIMO detection using the sphere decoding algorithm," IEEE J. Solid-State Circuits, vol. 40, no. 7, pp. 1566-1577, July 2005. https://doi.org/10.1109/JSSC.2005.847505
  12. J. Jaldén and B. Ottersten, "On the complexity of sphere decoding in digital communications," IEEE Trans. Signal Process., vol. 53, no. 4, pp. 1474-1484, Apr. 2005. https://doi.org/10.1109/TSP.2005.843746
  13. K. Su et al., "Efficient maximum-likelihood decoding of spherical lattice codes," IEEE Trans. Commun., vol. 57, pp. 2290-2300, Aug. 2009. https://doi.org/10.1109/TCOMM.2009.08.070329
  14. B. Shim and I. Kang, "Sphere decoding with a probabilistic tree pruning," IEEE Trans. Signal Process., vol. 56, pp. 4867-4878, Oct. 2008. https://doi.org/10.1109/TSP.2008.923808