access icon free Spreading sequence estimation algorithms based on ML detector in DSSS communication systems

© The Institution of Engineering and Technology
Received 01/05/2017, Accepted 26/02/2018, Revised 01/02/2018, Published 13/03/2018

In this study, the authors address the spreading sequence estimation in direct-sequence spread-spectrum signals. At first, the maximum-likelihood (ML) estimator is derived. In order to alleviate the greater computational complexity of the ML estimator, an innovative algorithm based on the ML method is proposed. The authors’ proposed algorithm uses an initial estimation with low complexity and low estimation accuracy as a result. In the second step, the estimation accuracy increases using the ML decision rule. They analyse their proposed algorithm and they derive an analytical approximation for the error probability of the proposed suboptimal algorithm. Simulation and analytical results show great performance and acceptable complexity of the proposed method.

Inspec keywords: maximum likelihood detection; code division multiple access; maximum likelihood estimation; probability; communication complexity; spread spectrum communication

Other keywords: direct-sequence spread-spectrum signal; suboptimal algorithm; ML decision rule; spreading sequence estimation algorithm; ML detector; maximum-likelihood estimator; computational complexity; ML estimator; DSSS communication system; error probability

Subjects: Radio links and equipment; Multiple access communication; Other topics in statistics; Signal detection

References

    1. 1)
      • 15. Bin, S., Jian-xin, W.: ‘Blind estimation of the PN sequence and information sequence of a DSSS signal based on SVD’, J. Electron. Inf. Technol., 2014, 9, p. 011.
    2. 2)
      • 29. Sorensen, D.C.: ‘Implicit application of polynomial filters in a k-step Arnoldi method’, SIAM J. Matrix Anal. Appl., 1992, 13, (1), pp. 357–385.
    3. 3)
      • 22. Shen, B., Wang, J.X.: ‘Chip rate and pseudo-noise sequence estimation for direct sequence spread spectrum signals’, IET Signal Process., 2017, 11, (6), pp. 727–733.
    4. 4)
      • 21. Yu, Z., Hao, W., Yong, C.: ‘Period estimation of PN sequence for weak DSSS signals based on improved power spectrum reprocessing in non-cooperative communication systems’. 2012 Int. Conf. on Control Engineering and Communication Technology (ICCECT), Liaoning, China, 2012, pp. 924–927.
    5. 5)
      • 20. Vlok, J.D., Olivier, J.C.: ‘Blind sequence-length estimation of low-SNR cyclostationary sequences’, IET Commun., 2014, 8, (9), pp. 1578–1588.
    6. 6)
      • 6. Shuangfeng, S., Chih-Lin, I.: ‘Unified framework towards flexible multiple access schemes for 5G’, ZTE Commun., 2016, 4, p. 005.
    7. 7)
      • 24. Yu, Z., Yong, C., Hao, W.: ‘Chip rate estimation of weak DSSS signals based on improved delay-multiply in noncooperative communication systems’. 2012 IEEE 14th Int. Conf. on Communication Technology (ICCT), Chengdu, China, 2012, pp. 138–142.
    8. 8)
      • 12. Wu, L., Li, Z., Li, J., et al: ‘A blind algorithm for estimating pseudo-noise sequence of DSSS signal in lower SNR conditions’. 2010 3rd Int. Congress on Image and Signal Processing (CISP), Yantai, China, 2010, vol. 9, pp. 4286–4289.
    9. 9)
      • 1. Torrieri, D.: ‘Principles of spread-spectrum communication systems’ (Springer, Switzerland, 2015).
    10. 10)
      • 5. Masmoudi, A., Bellili, F., Affes, S., et al: ‘Maximum likelihood time delay estimation from single-and multicarrier DSSS multipath MIMO transmissions for future 5G networks’, IEEE Trans. Wirel. Commun., 2017, 16, (8), pp. 4851–4865.
    11. 11)
      • 4. Peterson, R.L., Ziemer, R.E., Borth, D.E.: ‘Introduction to spread-spectrum communications’, vol. 995 (Prentice-Hall, New Jersey, 1995).
    12. 12)
      • 11. Zhang, T., Xiaokang, L., Zhengzhong, Z.: ‘Blind estimation of the PN sequence in lower SNR DS/SS signals’, IEICE Trans. Commun., 2005, 88, (7), pp. 3087–3089.
    13. 13)
      • 17. Liang, J., Wang, X., Wang, F., Huang, Z.T.: ‘Blind spreading sequence estimation algorithm for long-code DSCDMA signals in asynchronous multi-user systems’, IET Signal Process., 2017, 11, (6), pp. 704–710.
    14. 14)
      • 26. Tianqi, Z., Zhengzhong, Z.: ‘A new spectral method of periodic detection of PC sequence in lower SNR DS/SS signals’, Chin. J. Radio Sci., 2001, 16, (4), pp. 518–521.
    15. 15)
      • 27. Miao, P., Zhang, T., Zhang, W., et al: ‘Period estimation of the PN sequence for direct sequence spread spectrum in multipath environment’. 5th Int. Conf. on Wireless Communications, Networking and Mobile Computing, 2009. WiCom'09, Beijing, China, 2009, pp. 1–4.
    16. 16)
      • 23. Le Nir, V., Scheers, B.: ‘Robust blind carrier frequency synchronisation for direct sequence spread spectrum systems’, Electron. Lett., 2015, 51, (5), pp. 425–427.
    17. 17)
      • 14. Zhan, Y.-F., Cao, Z.-G., Lu, J.: ‘Spread-spectrum sequence estimation for DSSS signal in non-cooperative communication systems’, IEE Proc., Commun., 2005, 152, (4), pp. 476–480.
    18. 18)
      • 25. Tianqi, Z., Liufei, Y., Dai Shaosheng, L.X.: ‘Estimation of period and chip rate of pseudo-noise sequence for direct sequence spread spectrum signals using correlation techniques’, Aerosp. Electron. Warf., 2007, 4, p. 018.
    19. 19)
      • 3. Jamshidi, A.: ‘Direct sequence spread spectrum point-to-point communication scheme in underwater acoustic sparse channels’, IET Commun., 2011, 5, (4), pp. 456–466.
    20. 20)
      • 19. Zhang, H.G., Qing, M., Liao, H.S., et al: ‘A semidefinite relaxation approach to spreading sequence estimation for DS-SS signals’, IEICE Trans. Commun., 2011, 94, (11), pp. 3163–3167.
    21. 21)
      • 2. Flikkema, P.: ‘Spread-spectrum techniques for wireless communication’, IEEE Signal Process. Mag., 1997, 14, (3), pp. 26–36.
    22. 22)
      • 10. Bouder, C., Azou, S., Burel, G.: ‘Performance analysis of a spreading sequence estimator for spread spectrum transmissions’, J. Franklin Inst., 2004, 341, (7), pp. 595–614.
    23. 23)
      • 28. Lehoucq, R.B., Sorensen, D.C.: ‘Deflation techniques for an implicitly restarted Arnoldi iteration’, SIAM J. Matrix Anal. Appl., 1996, 17, (4), pp. 789–821.
    24. 24)
      • 16. Yongqian, C., Xianci, X.: ‘PN code sequence estimation using tabu search’. IEEE Int. Symp. on Communications and Information Technology, 2005, ISCIT 2005, Ha Long, Vietnam, 2005, vol. 2, pp. 1362–1365.
    25. 25)
      • 13. Wang, Q., Ge, Q.: ‘Blind estimation algorithm of parameters in PN sequence for DSSS-BPSK signals’. 2012 Int. Conf. on Wavelet Active Media Technology and Information Processing (ICWAMTIP), Chengdu, China, 2012, pp. 371–376.
    26. 26)
      • 8. Zhang, T., Dai, S., Yang, L., et al: ‘Estimate and track the PN sequence of weak DSSS signals’. 2007 Int. Conf. on Computational Intelligence and Security, Harbin, China, 2007, pp. 52–56.
    27. 27)
      • 9. Burel, G., Bouder, C.: ‘Blind estimation of the pseudo-random sequence of a direct sequence spread spectrum signal’. 21st Century Military Communications Conf. Proc. MILCOM 2000, Los Angeles, CA, USA, 2000, vol. 2, pp. 967–970.
    28. 28)
      • 7. Jamshidi, A., Nasiri-Kenari, M., Zeinalpour, Z., et al: ‘Space-frequency coded cooperation in OFDM multiple-access wireless networks’, IET Commun., 2007, 1, (6), pp. 1152–1160.
    29. 29)
      • 18. Peng, Y., Tang, B., Lü, M.: ‘Fast method for spreading sequence estimation of DSSS signal based on maximum likelihood function’, J. Syst. Eng. Electron., 2010, 21, (6), pp. 948–953.
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