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Implementation of a High-Speed MIMO Soft-Output Symbol Detector for Software Defined Radio

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Abstract

This paper presents a programmable MMSE soft-output MIMO symbol detector that supports 600 Mbps data rate defined in 802.11n. The detector is implemented using a multi-core floating-point processor and configurable soft-bit demapper. Owing to the dynamic range supplied by the floating-point SIMD datapath, special algorithms can be adopted to reduce the computational latency of channel processing with sufficient numerical stability for large channel matrices. When compared to several existing fixed-functional solutions, the detector proposed in this paper is smaller and faster. More important, it is programmable and configurable so that it can support various MIMO transmission schemes defined by different standards.

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References

  1. IEEE (2007). IEEE WLAN 802.11n Draft Standard. Draft Std P802.11n/D2.00.

  2. Alamouti, S. M. (1998). A simple transmit diversity technique for wireless communications. IEEE Journal on Selected Areas in Communications, 16(8), 1451–1458.

    Article  Google Scholar 

  3. Raffaele, R. (2006). LDPC codes for W-LAN IEEE 802.11n: An overview. Pisa: LDPC@work.

    Google Scholar 

  4. Larsson, E. G., & Jalden, J. (2008). Soft MIMO detection at fixed complexity via partial marginalization. IEEE Transactions on Signal Processing, 56, 3397–3407.

    Article  MathSciNet  Google Scholar 

  5. Eilert, J., Wu, D., & Liu, D. (2008). Implementation of a programmable linear MMSE detector for MIMO-OFDM. In Proc. IEEE ICASSP.

  6. Golub, G. H., & Van Loan, C. F. (1996). Matrix computations, 3rd edn. Baltimore: The Johns Hopkins University Press.

    MATH  Google Scholar 

  7. Eilert, J., Wu, D., & Liu, D. (2007). Efficient complex matrix inversion for MIMO software defined radio. In Proc. IEEE ISCAS.

  8. Wu, D., Eilert, J., Liu, D., Wang, D., Al-Dhahir, N., & Minn, H. (2007). Fast complex valued matrix inversion for multi-user stbc-mimo decoding. In Proc. IEEE ISVLSI.

  9. Sayed, A. H., Younis, W. M., & Tarighat, A. (2005). An invariant matrix structure in multi-antenna communications. IEEE Signal Processing Letters, 12, 749–752.

    Article  Google Scholar 

  10. Eilert, J., Wu, D., & Liu, D. (2008). Real-time Alamouti STBC decoding on a programmable baseband processor. In Proc. IEEE ICCSC.

  11. Edman, F., & Öwall, V. (2005). A scalable pipelined complex valued matrix inversion architecture. In Proc. IEEE ISCAS.

  12. Myllylä, M., Hintikka, J., Cavallaro, J. R., Juntti, M., Limingoja, M., & Byman, A. (2005). Complexity analysis of MMSE detector architectures for MIMO OFDM systems. In Proc. 39th Asilomar conference on signals, systems and computers.

  13. Hochwald, B. H., & ten Brink, S. (2003). Achieving near-capacity on a multiple-antenna channel. IEEE Transactions on Communications, 51(3), 389–399.

    Article  Google Scholar 

  14. Collings, I. B., Butler, M. R. G., & McKay, M. (2004). Low complexity receiver design for MIMO bit-interleaved coded modulation. In Proc. IEEE ISSSTA.

  15. Strassen, V. (1969). Gaussian elimination is not optimal. Numerical Mathematics, 13, 354–356.

    Article  MATH  MathSciNet  Google Scholar 

  16. Kim, H. S., Zhu, W., Bhatia, J., Mohammed, K., Shah, A., & Daneshrad, B. (2008). A practical, hardware friendly MMSE detector for MIMO-OFDM-based system. EURASIP Journal on Advanced in Signal Processing, 2008.

  17. Burg, A., Borgmann, M., Wenk, M., Zellweger, M., Fichtner, W., & Bölcskei, H. (2005). VLSI implementation of MIMO detection using the sphere decoding algorithm. IEEE Journal of Solid State Circuits, 40(7).

  18. Li, M., Bougard, B., Xu, W., Novo, D., Perre, L. V. D., & Catthoor, F. (2008). Optimizing near-ML MIMO detector for SDR baseband on parallel programmable architectures. In Proc. IEEE DATE.

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Acknowledgements

The work of D. Wu, J. Eilert and D. Liu are supported partly by the Multi-base Project from EU-7FP. The authors would like to thank ST Microelectronics for supplying 65nm process and Prof. Erik G. Larsson for discussion on MIMO detection.

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Authors and Affiliations

  1. Department of Electrical Engineering, Linköping University, 58183, Linköping, Sweden

    Di Wu, Johan Eilert & Dake Liu

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  1. Di Wu
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  2. Johan Eilert
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  3. Dake Liu
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Correspondence to Di Wu.

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Wu, D., Eilert, J. & Liu, D. Implementation of a High-Speed MIMO Soft-Output Symbol Detector for Software Defined Radio. J Sign Process Syst 63, 27–37 (2011). https://doi.org/10.1007/s11265-009-0369-9

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  • DOI: https://doi.org/10.1007/s11265-009-0369-9

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