Abstract
For reducing the complexity of equalization, linear equalization can be adopted for generalized spatial modulation (GSM) which is a special case of multiple-input-and-multiple-output (MIMO). However, because of its inferior performance, linear equalization may be infeasible for practical GSM systems which has large number of antennas and constellation. On the other hand, lattice-reduction (LR) is an effective method to improve the performance of linear equalization. The lattice reduction can’t be utilized by GSM directly, because signals on some antennas don’t exist. For tackling this problem, we propose a compatible 8-QAM constellation scheme integrating LR-aided linear equalization with GSM effectively. Next, we prove that LR-aided linear equalizers collect the same diversity order as that exploited by the ML detector under Rayleigh fading channels, and implement some simulations. Simulation results show the superior of the proposed 8-QAM over traditional 4-QAM and 8-QAM under Rayleigh fading channel. Moreover, our scheme obtains the full receive diversity under correlated channel.
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References
Mesleh, R.Y., Haas, H., Sinanovic, S., Ahn, C.W., Yun, S.: Spatial modulation. IEEE Trans. Veh. Technol. 57(4), 2228–2241 (2008)
Younis, A., Serafimovski, N., Mesleh, R., Haas, H.: Generalised spatial modulation. In: Proceedings of 2010 Asilomar Conference on Signals, Systems and Computers, pp. 1498–1502 (2010)
Fu, J., Hou, C., Wei, X., Yan, L., Hou, Y.: Generalised spatial modulation with multiple active transmit antennas. In: Proceedings of 2010 IEEE Globecom Workshop on Broadband Wireless Access, pp. 839–844 (2010)
Wang, J., Jia, S., Song, J.: Generalised spatial modulation system with multiple active transmit antennas and low complexity detection scheme. IEEE Trans. Wirel. Commun. 11(4), 1605–1615 (2012)
Yao, H., Wornell, G.W.: Lattice-reduction-aided detectors for MIMO communication systems. In: Proceedings of IEEE Global Communications Conference (GLOBECOM), Taipei, Taiwan (2002)
Gan, Y.H., Ling, C., Mow, W.H.: Complex lattice reduction algorithm for low-complexity full-diversity MIMO detection. IEEE Trans. Signal Process. 57(7), 2701–2710 (2009)
Mow, H.W.: Universal lattice decoding: a review and some recent results. In: Proceedings of IEEE International Conference on Communications (ICC), Paris, France, vol. 5, pp. 2842–2846 (2004)
Lenstra, A.K., Lenstra, H.W., Lovász, L.: Factoring polynomials with rational coefficients. Math. Ann. 261(4), 515–534 (1982)
Ma, X., Zhang, W.: Performance analysis for MIMO systems with lattice-reduction aided linear equalization. IEEE Trans. Commun. 56(2), 309–318 (2008)
Wübben, D., Böhnke, R., Kühn, V., Kammeyer, K.D.: MMSE-based lattice reduction for near-ML detection of MIMO systems. In: Proceedings of ITG Workshop Smart Antennas (WSA), Munich, Germany, March 2004
Bai, L., Choi, J.: Low Complexity MIMO Detection. Springer Science+Business Media, New York (2012). https://doi.org/10.1007/978-1-4419-8583-5
Taherzadeh, M., Mobasher, A., Khandani, A.K.: Lattice-basis reduction achieves the precoding diversity in MIMO broadcast systems. In: Proceedings of 39th Conference on Information Sciences and Systems. Johns Hopkins University, Baltimore, 15–18 March 2005
Tse, D., Viswanath, P.: Fundamentals of Wireless Communication. Cambridge University Press, Cambridge (2004)
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Liu, C., Wang, C., Zhang, W. (2018). Lattice Reduction Aided Linear Detection for Generalized Spatial Modulation. In: Gu, X., Liu, G., Li, B. (eds) Machine Learning and Intelligent Communications. MLICOM 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 226. Springer, Cham. https://doi.org/10.1007/978-3-319-73564-1_18
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DOI: https://doi.org/10.1007/978-3-319-73564-1_18
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