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Signal Detection in MIMO-OFDM Systems Based on SSDE Algorithm

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Abstract

Device-to-device communication enables to improve the application performance of multi-input multi-output (MIMO) technology. In a multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system, its performance is largely reflected by the signal detection algorithm used in receiver. As a sub-optimal maximum likelihood (ML) detection method, the Selective Spanning with Fast Enumeration algorithm can be successfully applied in MIMO-OFDM systems with high-order modulation. However, its Fast Enumeration scheme calculates constellation points based on fixed formula, which tends to yield pseudo constellation points outside of constellation maps, and consequently cannot work well in low-order modulation. To address this, a selective spanning with direct enumeration (SSDE) algorithm is proposed in this paper. Simulation results proved that the SSDE can achieve similar detection performance at a much lower computational cost in comparison with the ML method. The performance in terms of bit error rate (BER) obtained by SSDE method is also superior to those from the Minimum mean square error and Zero forcing detection algorithms with a huge savings in computational load. By adjusting the parameters used in the SSDE, the tradeoff between BER and computation complexity can be flexibly changed to satisfy specific design requirements in different applications.

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References

  1. 3GPP (2012). Feasibility study for proximity services (prose), In 3GPP, Technical Report; TR 22.803 V0.2.0.

  2. Song, L., Han, Z., Zhang, Z., & Jiao, B. (2012). Non-cooperative feedback-rate control game for channel state information in wireless networks. Selected Areas in Communications, IEEE Journal on, 30(1), 188–197.

    Article  Google Scholar 

  3. Liu, D. N., & Fitz, M. P. (2008). Low complexity affine MMSE detector for iterative detection-decoding MIMO OFDM systems. IEEE Transactions on Communications, 56(1), 150–158.

    Article  Google Scholar 

  4. Zhang, R., & Cioffi, J. M. (2008). Appraoching MIMO-OFDM capacity with zero-forcing V-BLAST decoding and optimized power, rate, and antenna-mapping feedback. IEEE Transactions on Signal Processing, 56(10), 5191–5203.

    Article  MathSciNet  Google Scholar 

  5. Maddah-Ali, M., & Sadrabadi, M. (2008). Broadcast in MIMO systems based on a generalized QR decomposition: Signaling and performance analysis. IEEE Transactions on Information Theory, 54(3), 1124–1138.

    Article  MathSciNet  Google Scholar 

  6. Wubben, D., & Rohnke, J. (2001). Efficient algorithm for decoding layered space-time codes. IEEE Electronic Letters, 37(22), 1348–1350.

    Article  Google Scholar 

  7. Wubben D., Rohnke J., Kuhn V., & Kammeyer, K. D. (2003). MMSE extension of V-BLAST based on sorted QR decomposition. In IEEE 58th Vehicular Technology Conference (Vol. 1 pp. 508–512).

  8. Viterbo, E., & Boutros, J. (1999). Universal lattice decoder for fading channel. IEEE Transactions on Information Theory, 54(5), 1639–1642.

    Article  MathSciNet  Google Scholar 

  9. Zhao, W. L., & Giannakis, G. B. (2005). Sphere decoding algorithms with improved radius search. IEEE Transactions on Communications, 54(7), 1104–1109.

    Article  Google Scholar 

  10. Pham, D., Pattipati, K., Willett, P., & Luo, J. (2004). An improved complex sphere decoder for V-BLAST systems. IEEE Signal Processing Letters, 11(9), 748–751.

    Article  Google Scholar 

  11. Hesham, E. G., Giuseppe, C., & Mohamed, O. D. (2004). Lattice coding and decoding achieve the optimal diversity-multiplexing tradeoff of MIMO channels. IEEE Transactions on Information Theory, 50(6), 968–985.

    Article  MATH  Google Scholar 

  12. Kyeong, J. K., Jiang, Y., Ronald, A. I., & Jerry, D. G. (2005). A QRD-M/Kalman filter-based detection and channel estimation algorithm for MIMO-OFDM systems channels. IEEE Transactions on Wireless Communications, 4(2), 710–720.

    Article  Google Scholar 

  13. Zhan, G., & Peter, N. (2006). Algorithm and implementation of the K-best sphere decoding for MIMO detection. IEEE Journal on Selected Areas in Communications, 24(3), 491–503.

    Article  Google Scholar 

  14. Chen, S. Z., Zhang, T., & Xin, Y. (2007). Relaxed K-best MIMO signal detector design and VLSI implementation. IEEE Transactions on Very Large Scale Integation (VLSI) Systems, 15(3), 328–337.

    Article  Google Scholar 

  15. Shen, C. A., & Eltawil, A. M. (2010). A radius adaptive K-best decoder with early termination: Algorithm and VLSI architecture. IEEE Transactions on Circuits and Systems I, 57(9), 2476–2486.

    Article  MathSciNet  Google Scholar 

  16. Tae, H. I., Park, I., Kim, J., & Yi, J. (2009). A new signal detection method for spatially multiplexed MIMO systems and its VLSI implementation. IEEE Transactions on Circuits and Systems II, 56(5), 399–403.

    Article  Google Scholar 

  17. Li, M., & Bougard, B., et al. (2008). Selective spanning with fast enumeration: A near maximum-likelihood MIMO detector designed for parallel programmable baseband architectures. In IEEE International Conference on Communications (pp. 737–741).

  18. Fasthuber, R., Li, M., Novo, D., & Raghavan, P., et al. (2009). Novel energy-efficient scalable soft-output SSFE MIMO detector architectures. In International Symposium on Systems, Architectures, Modeling, and Simulation (pp. 165–171).

  19. Fasthuber, R., Li, M., Novo, D., Raghavan, P., et al. (2011). Exploration of soft-output MIMO detector implementations on massive parallel processors. Journal of Signal Processing Systems, 64(1), 75–92.

    Article  Google Scholar 

  20. Berenguer, I., & Wang, X. D. (2004). MIMO antenna selection with lattice reduction aided linear receivers. IEEE Transactions on Vehicular Techology, 53(5), 1298–1302.

    Google Scholar 

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Correspondence to Yu Du.

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Hu, F., Du, Y., Cen, L. et al. Signal Detection in MIMO-OFDM Systems Based on SSDE Algorithm. Wireless Pers Commun 82, 2709–2725 (2015). https://doi.org/10.1007/s11277-015-2374-6

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  • DOI: https://doi.org/10.1007/s11277-015-2374-6

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