Journal of Signal Processing Systems

, Volume 68, Issue 2, pp 171–182 | Cite as

Area-Efficient Antenna-Scalable MIMO Detector for K-best Sphere Decoding

  • Nariman Moezzi-Madani
  • Thorlindur Thorolfsson
  • Patrick Chiang
  • William Rhett Davis
Article
First Online:
Received:
Revised:
Accepted:

Abstract

K-best sphere decoding is one of the most popular MIMO (Multi-Input Multi-Output) detection algorithms because of its low complexity and close to Maximum Likelihood (ML) Bit Error Rate (BER) performance. Unfortunately, conventional multi-stage sphere decoders suffer from the inability to adapt to varying antenna configurations, requiring implementation redesign for each specific array structure. In this paper, we propose a reconfigurable in-place architecture that is scalable to an arbitrary number of antennas at run-time, while reducing area significantly compared with other sphere decoders. To improve the throughput of the in-place architecture without any degradation in BER performance, we propose partial-sort-bypass and symbol interleaving techniques, and also exploit multi-core design. Implementation results for a 16-QAM MIMO decoder in a 130 nm CMOS technology show a 41% reduction in area compared to the smallest sphere decoder while maintaining antenna reconfigurability, and better throughput. When implemented for the 802.11n standard, our architecture results in 42% reduction in area compared to the multi-stage architecture.

Keywords

MIMO K-best Sphere decoder VLSI 
This is a preview of subscription content, log in to check access.

References

  1. 1.
    Sun, Y., Cavallaro, J. R. (2009). High Throughput VLSI Architecture for Soft-Output MIMO Detection Based on a Greedy Graph Algorithm. ACM GLSVLSI'09, Boston, Massachusetts, USA.Google Scholar
  2. 2.
    Wu, D., Eilert, J., Asghar, R., & Liu, D. (2010) VLSI Implementation of a Fixed-Complexity Soft-Output MIMO Detector for High-Speed Wireless. EURASIP Journal on Wireless Communications and Networking, vol. 2010.Google Scholar
  3. 3.
    Studer Test, C., et al. (2008). Soft-output sphere decoding: algorithms and VLSI implementation. IEEE Journal on Selected Areas in Communications, 26(2), 290–300.CrossRefGoogle Scholar
  4. 4.
    Guo, Z., & Nilsson, P. (2006). Algorithm and implementation of the K-best sphere decoding for MIMO detection. IEEE Journal on Selected Areas in Communications, 24(3), 491–503.CrossRefGoogle Scholar
  5. 5.
    Barbero, L., & Thompson, J. (2006). A fixed-complexity MIMO detector based on the complex sphere decoder. Cannes: IEEE Workshop on Signal Processing Advances for Wireless Communications.Google Scholar
  6. 6.
    Jenkal, R. S., & Davis, W. R. (2007). An architecture for energy efficient sphere decoding. In Proc. of ISLPED’07, pp. 244–249, Portland.Google Scholar
  7. 7.
    Barbero, L. G., Ratnarajah, T., & Cowan, C. (2008). A low-complexity soft mimo detector based on the fixed-complexity sphere decoder. in IEEE International Conference on acoustics, speech and signal processing (ICASSP ’08), Las Vegas.Google Scholar
  8. 8.
    Burg, A., et al. (2005). VLSI implementation of MIMO detection using the sphere decoding algorithm. IEEE Journal of Solid-State Circuits, 40, 1566–1577.CrossRefGoogle Scholar
  9. 9.
    Hess, C., Wenk, M., Burg, A., Luethi, P., Studer, C., Felber, N., & Fichtner, W. (2007). Reduced-complexity MIMO detector with close-to ML error rate performance. ACM GLSVLSI'07, Italy. pp. 200–203.Google Scholar
  10. 10.
    Amiri, K., Dick, C., Rao, R., & Cavallaro J. R. (2008) Novel Sort-free Detector with Modified Real-valued Decomposition (M-RVD) Ordering in MIMO Systems. IEEE GLOBECOM Conference, New Orleans, LA.Google Scholar
  11. 11.
    Wenk, M., et al. (2006). K-Best MIMO Detection VLSI architectures achieving up to 424 Mbps. In proc. IEEE ISCAS, 3, 1151–1154.Google Scholar
  12. 12.
    Chen, S., Zhang, T., & Xin, Y. (2007). Relaxed K-best MIMO signal detector design and VLSI implementation. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 15(3), 328–337.CrossRefGoogle Scholar
  13. 13.
    Mondal, S., Eltawil, A. M, & Salama, K. N. (2009). Architectural Optimizations for Low-Power K-Best MIMO Decoders. Accepted to IEEE Transactions on Vehicular Technologies Google Scholar
  14. 14.
    Moezzi-Madani, N., & Davis, W. R. (2009). High-throughput low-complexity MIMO detector based on K-best algorithm,” GLSVLSI’09, pp. 451–456, BostonGoogle Scholar
  15. 15.
    Kim, H., Lee, D., & Villasenor, J. D. (2008). Design tradeoffs and hardware architecture for real-time iterative MIMO detection using sphere decoding and LDPC coding. IEEE Journal on Selected Areas in Communications, 26(6), 1003–1014.CrossRefGoogle Scholar
  16. 16.
    Moezzi-Madani, N., & Davis, W. R. (2009). Parallel merge algorithm for high-throughput signal processing applications. Electronics Letters, 45(3), 188–189.CrossRefGoogle Scholar
  17. 17.
    Yang, C., & Markovic, D. (2008). A Multi-Core Sphere Decoder VLSI Architecture for MIMO Communications. IEEE GLOBECOM’08, pp. 3297–3301Google Scholar
  18. 18.
    Radosavljevic, P., Guo, Y., & Cavallaro, J. R. (2009). Probabilistically bounded soft sphere detection for MIMO-OFDM receivers: algorithm and system architecture. IEEE Journal on Selected Areas in Communications, 27(8), 1318–1330.CrossRefGoogle Scholar
  19. 19.
    Wu, D., & Eilert, J, & Liu, D. (2009). Implementation of a high-speed MIMO soft-output symbol detector for software defined radio. Journal of Signal Processing Systems, Springer.Google Scholar
  20. 20.
    Moezzi-Madani, N., Thorolfsson, T., & Davis, W. R. (2010). A low-area flexible MIMO detector for WiMAX/WiFi standards. Proceedings of Design, Automation and Test in Europe (DATE’10), pp. 1637–1640Google Scholar
  21. 21.
    Amiri, K., Dick, C., Rao, R., & Cavallaro, J. R. (2009). A High Throughput Configurable SDR Detector for Multi-user MIMO Wireless Systems. Journal of Signal Processing Systems, Springer.Google Scholar
  22. 22.
    Wubben, D., Bohnke, R., Kuhn, V., & Kammeyer, K. D. (2003). MMSE Extension of V-BLAST based on Sorted QR Decomposition. in IEEE Proc. VTCl, Orlando.Google Scholar
  23. 23.
    Batcher, K. E. (1968). Sorting networks and their applications. AFIPS Proceedings on Spring Joint Computer Conference, pp. 304–314.Google Scholar
  24. 24.
    Davis, W. R., Eun Chu, Oh, Sule, A., & Franzon, P. D. (2009). Application exploration for 3D integrated circuits: TCAM, FIFO, and FFT case studies. IEEE Transactions on VLSI Systems, 17(4), 496–506.CrossRefGoogle Scholar
  25. 25.
    Zyren, J., & McCoy, W. (2007). Overview of the 3GPP Long Term Evolution Physical Layer, Online available.Google Scholar
  26. 26.
    Airmagnet, ‘802.11n Primer’, whitepaper, http://www.airmagnet.com/assets/whitepaper/WP-802.11nPrimer.pdf
  27. 27.
  28. 28.
    Luethi, P., Burg, A., Haene, S., Perels, D., Felber, N., & Fichtner, W. (2007). VLSI implementation of a high-speed iterative sorted MMSE QR decomposition. in ISCAS, New Orleans, pp. 1421–1424Google Scholar
  29. 29.
    Fasthuber, R., Li, M., Novo, D., Raghavan, P., & Perre, L. V., et al. (2010). Exploration of Soft-Output MIMO Detector Implementations on Massive Parallel Processors. Journal of Signal Processing Systems, Springer.Google Scholar
  30. 30.
    Shamshiri, S., Lisherness, P., Pan, S.-J., & Tim Cheng K.-T. (2008). A cost analysis framework for multi-core systems with spares. IEEE International Test Conference (ITC), pp. 1–8.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Nariman Moezzi-Madani
    • 1
  • Thorlindur Thorolfsson
    • 1
  • Patrick Chiang
    • 2
  • William Rhett Davis
    • 1
  1. 1.North Carolina State UniversityRaleighUSA
  2. 2.Oregon State UniversityCorvallisUSA

Personalised recommendations