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Expected complexity analysis of increasing radii algorithm by considering multiple radius schedules

Expected complexity analysis of increasing radii algorithm by considering multiple radius schedules

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In this study, the authors investigate the expected complexity of increasing radii algorithm (IRA) in an independent and identified distributed Rayleigh fading multiple-input–multiple-output channel with additive Gaussian noise and then present its upper bound result. IRA employs several radii to yield significant complexity reduction over sphere decoding, whereas performing a near-maximum-likelihood detection. In contrast to the previous expected complexity presented by Gowaikar and Hassibi (2007), where the radius schedule was hypothetically fixed for analytic convenience, a new analytical result is obtained by considering the usage of multiple radius schedules. The authors analysis reflects the effect of the random variation in the radius schedule and thus provides a more reliable complexity estimation. The numerical results support their arguments, and the analytical results show good agreement with the simulation results.

References

    1. 1)
      • 1. Hassibi, B., Vikalo, H.: ‘On the sphere decoding algorithm. Part I: The expected complexity’, IEEE Trans. Signal Process., 2005, 53, (8), pp. 28062818.
    2. 2)
      • 2. Chang, X.-W., Han, Q.: ‘Solving box-constrained integer least squares problems’, IEEE Trans. Wirel. Commun., 2008, 7, (1), pp. 277287.
    3. 3)
      • 3. Damen, M.O., Gamel, H.E., Caire, G.: ‘On maximum-likelihood detection and the search for the closest lattice point’, IEEE Trans. Inf. Theory, 2003, 49, (10), pp. 23892402.
    4. 4)
      • 4. Windpassinger, C., Lampe, L., Fischer, R., Hehn, T.: ‘A performance study of MIMO detectors’, IEEE Trans. Wirel. Commun., 2006, 5, (8), pp. 20042008.
    5. 5)
      • 5. Barbero, L.G., Thompson, J.S.: ‘Performance of the complex sphere decoder in spatially correlated MIMO channels’, IET Commun., 2007, 1, (1), pp. 122130.
    6. 6)
      • 6. Brunel, L.: ‘Multiuser detection techniques using maximum likelihood sphere decoding in multicarrier CDMA systems’, IEEE Trans. Wirel. Commun., 2004, 3, (3), pp. 949957.
    7. 7)
      • 7. Mow, W.: ‘Maximum likelihood sequence estimation from the lattice viewpoint’, IEEE Trans. Inf. Theory, 1994, 40, (5), pp. 15911600.
    8. 8)
      • 8. Ajtai, M.: ‘The shortest vector problem in L2 is NP-hard for randomized reductions’. Proc. 30th Annual ACM Symp. on Theory of Computing, Dallas, TX, USA, May 1998, pp. 1019.
    9. 9)
      • 9. Agrell, E., Eriksson, T., Vardy, A., Zeger, K.: ‘Closest point search in lattices’, IEEE Trans. Inf. Theory, 2002, 48, (8), pp. 22012214.
    10. 10)
      • 10. Gowaikar, R., Hassibi, B.: ‘Statistical pruning for near-maximum likelihood decoding’, IEEE Trans. Signal Process., 2007, 55, (6), pp. 26612675.
    11. 11)
      • 11. Ghaderipoor, A., Tellambura, C.: ‘A statistical pruning strategy for Schnorr-Euchner sphere decoding’, IEEE Commun. Lett., 2008, 12, (2), pp. 121123.
    12. 12)
      • 12. Liang, Y., Ma, S., Ng, T.-S.: ‘Low complexity near-maximum likelihood decoding for MIMO systems’. Proc. IEEE PIMRC, Tokyo, Japan, September 2009, pp. 24292433.
    13. 13)
      • 13. Seethaler, D., Bölcskei, H.: ‘Performance and complexity analysis of infinity-norm sphere-decoding’, IEEE Trans. Inf. Theory, 2010, 56, (3), pp. 10851105.
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