Author(s): Liangfang Ni, Huijie Dai, Weixia Li, Kangbo Zhuo, Chengchao Zhang
An iterative soft-input soft-output (SISO) improved complex sphere detection and decoder algorithm is proposed for signal detection in Turbo-MIMO system. It forms candidate points set Θ in terms of an accumulated cost function based on a search arc constrained by the received signals. Then, the candidate points subset, the lower cost bound of which is not smaller than upper bound, is fathomed and dropped from further consideration. Meanwhile, once a new feasible candidate point is turned up, the path closest to completion is casted upon to generate the set Θ with optimal candidate vectors, aiming to determining the extrinsic information for a Turbo coded bit with most likelihood. Bridged by de-multiplexing and multiplexing, an SISO improved complex sphere detection is concatenated with a SISO Log maximum a posteriori Turbo decoder as if a principal Turbo detection is embedded with a subordinate Turbo decoder, exchanging each other’s detection and decoding soft-decision information iteratively. As a result, the proposed algorithm converges rapidly, which results in lower computational complexity. The transfer curves that relate the input mutual information to the output mutual information is achieved through simulations. Thus, an asymptotic interval of the input SNR threshold for the proposed scheme to converge has been observed. Finally, an upper bound of the diversity has been obtained based on the intuitional deduction and theoretical analyses. The simulation results also show that the proposed scheme has a strong ability of anti-multi-stream interference, and its performance is close to that of the iterative soft-input soft-output list complex sphere detection and decoder algorithm, but with a shorter time delay.
Turbo-MIMO System, Soft-Input Soft-Output, Iterative Detection And Decoder, Improved Complex Sphere Decoder, Transfer Curve
- E. Telatar, “Capacity of multi-antenna Gaussian channels,” Eur. Trans. Telecomm., vol.10, no.6, pp. 585–595, Nov.-Dec. 1999.
- G. J. Foschini, “Layered space-time architecture for wireless communication in a fading environment when using multi element antennas,” Bell Labs Technical Journal, vol.2, pp. 41–59 Oct. 1996.
- Specification Framework for TGac. IEEE 802.11-09/0992r21, IEEE P802.11ac, Jan. 2011.
- T. M. Duman, and A. Ghrayeb, Coding for MIMO communication systems, John Wiley & Sons, Chichester, 2007.
- M. Sellathurai, and S. Haykin, “Turbo-BLAST for wireless communications: theory and experiments,” IEEE Trans. Sig. Proc., vol.50, no.10, pp. 2538–46, Oct. 2002.
- S. Haykin, M. Sellathurai, Y. Jong, and T. Willink, “Turbo- MIMO for wireless communications,” IEEE Commun. Magazine., vol.42, no.10, pp. 48–53, Oct. 2004.
- G. Papa, D. Ciuonzo, G. Romano, and P. S. Rossi, “A dominance-based soft-input soft-output mimo detector with near-optimal performance,” IEEE Trans. Commun., vol.62, no.12, pp. 4320– 4335, Dec. 2014.
- M. M. Mansour, and L. M. A. Jalloul, “Optimized configurable architectures for scalable soft-input soft-output MIMO detectors with 256-QAM,” IEEE Trans. Sig. Proc., vol.63, no.18, pp. 4969–4984, Sep. 2015.
- H. Kim, H. Lee, and J. Kim, “MMSE-based lattice-reduction-aided fixed-complexity sphere decoder for low-complexity near-ML MIMO detection,” IEEE International Workshop on Local and Metropolitan Area Networks, Beijing, P. R. China, pp. 1–6, Apr. 2015.
- C. Studer, S. Fateh, and D. Seethaler, “ASIC implementation of soft-input soft-output MIMO detection using MMSE parallel interference cancellation,” IEEE J. Solid-State Circuits. vol.46, no.7, pp. 1754–1765, 2011.
- J. W. Choi, A. C. Singer, J. Lee, and N. I. Cho, “Improved linear soft-input soft-output detection via soft feedback successive interference cancellation,” IEEE Trans. Commun., vol.58, no.3, pp. 986– 996, Mar. 2010.
- B.M. Hochwald, and S.T. Brink, “Achieving near-capacity on a multiple antenna channel,” IEEE Trans. Commun., vol.51, no.3, pp. 389– 399, Mar. 2003.
- L. G. Barbero, and J. S. Thompson, “Extending a fixed-complexity sphere decoder to obtain likelihood information for Turbo-MIMO systems,” IEEE Trans. Vehicular Technology, vol.57, no.5, pp. 2804–2814, Sep. 2008.
- K. Nikitopoulos, D. Zhang, I. W. Lai, G. Ascheid, “Complexity- efficient enumeration techniques for soft-input, soft-output sphere decoding,” IEEE Commun. Lett., vol.14, no.4, pp. 312–314, Apr. 2010.
- L. Zheng, and D. N. C. Tse, “Diversity and multiplexing: a fundamental tradeoff in multiple-antenna channels,” IEEE Trans. Inf. Theory, vol. 49, no. 5, pp. 1073–1096, May 2003.
- K. Nikitopoulos, A. Karachalios, and D. Reisis, “Exact max-log MAP soft-output sphere decoding via approximate Schnorr- Euchner enumeration,” IEEE Trans. Vehicular Technology, vol. 64, no. 6, pp. 2749–2753, Jun. 2015.
- Z. Guo, and P. Nilsson, “Algorithm and implementation of the k-best sphere decoding for MIMO detection,” IEEE JSAC, vol. 24, no.3, pp. 491–503, Mar. 2006.
- S. Han, T. Cui, and C. Tellambura, “Improved k-best sphere detection for uncoded and coded MIMO systems,” IEEE Wireless Commun. Lett., vol. 1, no. 5, pp. 472–475, Oct. 2012.
- K. C. Lai, C. C. Huang, and J. J. Jia, “Variation of the fixed- complexity sphere decoder,” IEEE Commun. Lett., vol. 15, no. 9, pp. 1001–1003, Sep. 2011.
- S. Barik, and H. Vikalo, “Sparsity-aware sphere decoding: algorithms and complexity analysis,” IEEE Trans. Sig. Proc. vol. 62, no. 9, pp. 2212–2225, May 2014.
- C. Studer, and H. Bölcskei, “Soft–input soft–output single tree-search sphere decoding," IEEE Trans. Inf. Theory, vol. 56, no. 10, pp. 4827–4842, Oct. 2010.
- L. Liu, “Energy-efficient soft-input soft-output signal detector for iterative MIMO receivers,” IEEE Trans. Circuits and Systems—I: Regular Papers, vol. 61, no. 8, pp. 2422–2432, Aug. 2014.
- C. Xu, X. Zuo, S. X. Ng, R. G. Maunder, and L. Hanzo, “Reduced-complexity soft-decision multiple-symbol differential sphere detection,” IEEE Trans. Commun., vol. 63, no. 9, pp. 3275–3289, Sep. 2015.
- D. Pham, K. R. Pattipati, P. K. Willett, and J. Luo, “An improved complex sphere decoder for V-BLAST systems,” IEEE Signal Process. Lett., vol. 11, no. 9, pp. 748–751, Sep. 2004.
- N. Merhav, “List decoding–random coding exponents and expurgated exponents,” IEEE Trans. Inf. Theory, vol. 60, no. 11, pp. 6749– 6759, Nov. 2014.
- D. Divsalar, S. Dolinar, and F. Pollara, “Iterative Turbo decoder analysis based on density evolution,” IEEE JSAC., vol. 19, no. 5, pp. 891– 907, May 2001.
- H. E. Gamal, and A. R. H. Jr., “Analyzing the Turbo decoder using the gaussian approximation,” IEEE Trans. Inf. Theory, vol. 47, no. 2, pp. 671–686, Feb. 2001
- T. Richardson, “The geometry of turbo-decoding dynamics," IEEE Trans. Inf. Theory, vol. 46, no. 1, pp. 9–23, Jan. 2000.
- S. Brink, “Convergence of iterative decoding,” Electronics Lett. vol. 35, no. 10, pp. 806–808, May 1999.
- J. G.. Proakis, Digital Communications, 5th ed., Publishing House of Electronics Industry, Beijing, 2009.
- T. M. Cover, and J. A. Thomas, Elements Of Information Theory, 2nd ed., John Wiley & Sons, Inc., Hoboken, New Jersey, 2006.
- S. Lin, and D.J. Costello, Error Control Coding, 2nd ed., Prentice Hall, New Jersey, 2004.
- C. Berrou, A. Glavieux, and P. Thitimajshima, “Near Shannon limit error-correcting coding and decoding: turbo-codes,” in Proc. Int. Conf. Communications, Geneva, Switzerland, pp. 1064–1070, May 1993.
- Andrej Stefanov, and Tolga M. Duman, “Performance bounds for turbo-coded multiple antenna systems,” IEEE JSAC., vol. 21, no. 3, pp. 374-381, Apr. 2003.
- H. Su and E. Geraniotis, “Space–time turbo codes with full antenna diversity,” IEEE Trans. Commun., vol. 49, pp. 47–57, Jan. 2001.
International Journal of Sciences is Open Access Journal.
This article is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License.
Author(s) retain the copyrights of this article, though, publication rights are with Alkhaer Publications.