access icon free Joint channel estimation and detection using Markov chain Monte Carlo method over sparse underwater acoustic channels

© The Institution of Engineering and Technology
Received 17/11/2016, Accepted 10/05/2017, Revised 25/04/2017, Published 12/05/2017

This study proposes a novel approach to joint channel estimation and detection of orthogonal frequency division multiplexing transmission over underwater acoustic (UWA) multipath channels exhibiting cluster sparsity. Unlike most sparse channel estimations, the authors exploit the cluster-sparsity characteristic of UWA channels without additional prior information. They adopt a modified spike-and-slab prior model in their non-parametric Bayesian learning framework. To avoid the need for a closed-form Bayesian estimate, they apply the Markov chain Monte Carlo technique to joint achieve channel estimation and signal detection. The proposed solution is amenable to being integrated with soft-input soft-output decoding to improve the performance through turbo iteration. Simulation results demonstrate improved bit error rate of the proposed algorithm over existing algorithms.

Inspec keywords: turbo codes; wireless channels; Markov processes; Monte Carlo methods; signal detection; OFDM modulation; channel estimation; underwater acoustic communication; multipath channels; iterative methods; error statistics; Bayes methods

Other keywords: Markov chain Monte Carlo method; sparse channel estimation; bit error rate; sparse underwater acoustic channel; modified spike-and-slab prior model; orthogonal frequency division multiplexing transmission; closed-form Bayesian estimation; joint channel estimation and signal detection approach; UWA multipath channels; cluster sparsity; turbo iteration; nonparametric Bayesian learning framework; soft-input soft-output decoding

Subjects: Communication channel equalisation and identification; Markov processes; Codes; Acoustic and other telecommunication systems and equipment; Signal detection; Interpolation and function approximation (numerical analysis); Monte Carlo methods

References

    1. 1)
      • 16. Lv, X., Bi, G., Wan, C.: ‘The group lasso for stable recovery of block-sparse signal representations’, IEEE Trans. Signal Process., 2011, 59, (4), pp. 1371–1382.
    2. 2)
      • 39. Schniter, P.: ‘A message-passing receiver for BICM-OFDM over unknown clustered-sparse channels’, IEEE J. Sel. Topics Signal Process., 2011, 5, (8), pp. 1462–1474.
    3. 3)
      • 5. Berger, C.R., Zhou, S., Preisig, J.C., et al: ‘Sparse channel estimation for multicarrier underwater acoustic communication: from subspace methods to compressed sensing’, IEEE Trans. Signal Process., 2010, 58, (3), pp. 1708–1721.
    4. 4)
      • 24. Fang, J., Shen, Y., Li, H., et al: ‘Pattern-coupled sparse Bayesian learning for recovery of block-sparse signals’, IEEE Trans. Signal Process., 2015, 63, (2), pp. 360–372.
    5. 5)
      • 25. Prasad, R., Murthy, C.R., Rao, B.D.: ‘Joint channel estimation and data detection in MIMO-OFDM systems: a sparse Bayesian learning approach’, IEEE Trans. Signal Process., 2015, 63, (20), pp. 5369–5382.
    6. 6)
      • 30. Rabaste, O., Chonavel, T.: ‘Estimation of multipath channels with long impulse response at low SNR via an MCMC method’, IEEE Trans. Signal Process., 2007, 55, (4), pp. 1312–1325.
    7. 7)
      • 42. Wu, Q., Zhang, Y., Amin, M.G., et al: ‘High-resolution passive SAR imaging exploiting structured Bayesian compressive sensing’, IEEE J. Sel. Topics Signal Process., 2015, 9, (8), pp. 1484–1497.
    8. 8)
      • 44. Qarabaqi, P., Stojanovic, M.: ‘Statistical characterization and computationally efficient modeling of a class of underwater acoustic communication channels’, IEEE J. Ocean. Eng., 2013, 38, (4), pp. 701–717.
    9. 9)
      • 11. Berger, C.R., Huang, J., Moura, J.M.F.: ‘Study of pilot overhead for iterative OFDM receivers on time-varying and sparse underwater acoustic channels’. Proc. IEEE OCEANS, Waikoloa, USA, September 2011, pp. 1–8.
    10. 10)
      • 40. Zhou, M., Chen, H., Paisley, J., et al: ‘Nonparametric Bayesian dictionary learning for analysis of noisy and incomplete images’, IEEE Trans. Image Process., 2012, 21, (1), pp. 130–144.
    11. 11)
      • 3. Li, W., Preisig, J.C.: ‘Estimation of rapidly time-varying sparse channels’, IEEE J. Ocean. Eng., 2007, 32, (4), pp. 927–939.
    12. 12)
      • 13. Tibshirani, R.: ‘Regression shrinkage and selection via the lasso’, J. R. Statist. Soc. B, 1994, 58, pp. 267–288.
    13. 13)
      • 29. Chen, R., Liu, J.S., Wang, X.: ‘Convergence analyses and comparisons of Markov chain Monte Carlo algorithms in digital communications’, IEEE Trans. Signal Process., 2002, 50, (2), pp. 255–270.
    14. 14)
      • 37. Laot, C., Glavieux, A., Labat, J.: ‘Turbo equalization: adaptive equalization and channel decoding jointly optimized’, IEEE J. Sel. Areas Commun., 2001, 19, (9), pp. 1744–1752.
    15. 15)
      • 19. Tipping, M.E.: ‘Sparse bayesian learning and the relevance vector machine’, J. Mach. Learn. Res., 2001, 1, pp. 211–244.
    16. 16)
      • 17. Eldar, Y.C., Kuppinger, P., Bolcskei, H.: ‘Block-sparse signals: uncertainty relations and efficient recovery’, IEEE Trans. Signal Process., 2010, 58, (6), pp. 3042–3054.
    17. 17)
      • 10. Qu, F., Nie, X., Xu, W.: ‘A two-stage approach for the estimation of doubly spread acoustic channels’, IEEE J. Ocean. Eng., 2015, 40, (1), pp. 131–143.
    18. 18)
      • 20. Wipf, D., Rao, B.: ‘Sparse bayesian learning for basis selection’, IEEE Trans. Signal Process., 2004, 52, (8), pp. 2153–2164.
    19. 19)
      • 9. Xu, X., Wang, Z., Zhou, S., et al: ‘Parameterizing both path amplitude and delay variations of underwater acoustic channels for block decoding of orthogonal frequency division multiplexing’, J. Acoust. Soc. Am., 2012, 131, (6), pp. 1662–1673.
    20. 20)
      • 8. Donoho, D.: ‘Compressed sensing’, IEEE Trans. Inf. Theory, 2006, 52, (4), pp. 1289–1306.
    21. 21)
      • 43. Ten Brink, S.: ‘Convergence behavior of iteratively decoded parallel concatenated codes’, IEEE Trans. Commun., 2001, 49, (10), pp. 1727–1737.
    22. 22)
      • 14. Kim, S.J., Koh, K., Lustig, M., et al: ‘An interior-point method for large-scale l1-regularized least squares’, IEEE J. Sel. Topics Signal Process., 2007, 1, (4), pp. 606–617.
    23. 23)
      • 4. Li, B., Zhou, S., Stojanovic, M., et al: ‘Multicarrier communication over underwater acoustic channels with nonuniform doppler shifts’, IEEE J. Ocean. Eng., 2008, 33, (2), pp. 198–209.
    24. 24)
      • 28. Lu, B., Wang, X.: ‘Bayesian blind turbo receiver for coded OFDM systems with frequency offset and frequency-selective fading’, IEEE J. Sel. Areas Commun., 2001, 19, (12), pp. 2516–2527.
    25. 25)
      • 15. Tropp, J., Gilbert, A.: ‘Signal recovery from random measurements via orthogonal matching pursuit’, IEEE Trans. Inf. Theory, 2007, 53, (12), pp. 4655–4666.
    26. 26)
      • 38. Tuchler, M., Koetter, R., Singer, A.C.: ‘Turbo equalization: principles and new results’, IEEE Trans. Commun., 2002, 50, (5), pp. 754–767.
    27. 27)
      • 2. Stojanovic, M., Preisig, J.C.: ‘Underwater acoustic communication channels: propagation models and statistical characterization’, IEEE Commun. Mag., 2009, 47, (1), pp. 84–89.
    28. 28)
      • 12. Huang, Y., Wan, L., Zhou, S., et al: ‘Comparison of sparse recovery algorithms for channel estimation in underwater acoustic OFDM with data-driven sparsity learning’, Phys. Commun., 2014, 13, pp. 156–167.
    29. 29)
      • 36. Berrou, C., Glavieux, A.: ‘Near optimum error correcting coding and decoding: turbo-codes’, IEEE Trans. Commun., 1996, 44, (10), pp. 1261–1271.
    30. 30)
      • 6. Byun, S.H., Seong, W., Kim, S.M.: ‘Sparse underwater acoustic channel parameter estimation using a wideband receiver array’, IEEE J. Ocean. Eng., 2013, 38, (4), pp. 718–729.
    31. 31)
      • 32. Ling, J., Li, J.: ‘Gibbs-sampler-based semiblind equalizer in underwater acoustic communications’, IEEE J. Ocean. Eng., 2012, 37, (1), pp. 1–13.
    32. 32)
      • 35. Douillard, C., Jezequel, M., Berrou, C., et al: ‘Iterative correction of intersymbol interference: turbo-equalization’, Eur. Trans. Telecommun., 1995, 6, (5), pp. 507–511.
    33. 33)
      • 41. Wu, Q., Zhang, Y., Amin, M.G., et al: ‘Multi-task bayesian compressive sensing exploiting intra-task dependency’, IEEE Signal Process. Lett., 2015, 22, (4), pp. 430–434.
    34. 34)
      • 23. Zhang, Z., Rao, B.: ‘Sparse signal recovery with temporally correlated source vectors using sparse Bayesian learning’, IEEE J. Sel. Topics Signal Process., 2011, 5, (5), pp. 912–926.
    35. 35)
      • 7. Candes, E., Tao, T.: ‘Decoding by linear programming’, IEEE Trans. Inf. Theory, 2005, 51, (12), pp. 4203–4215.
    36. 36)
      • 31. Peng, R.H., Chen, R.R., Farhang-Boroujeny, B.: ‘Markov chain Monte Carlo detectors for channels with intersymbol interference’, IEEE Trans. Signal Process., 2010, 58, (4), pp. 2206–2217.
    37. 37)
      • 21. Ji, S., Xue, Y., Carin, L.: ‘Bayesian compressive sensing’, IEEE Trans. Signal Process., 2008, 56, (6), pp. 2346–2356.
    38. 38)
      • 27. Wang, X., Chen, R.: ‘Blind turbo equalization in Gaussian and impulsive noise’, IEEE Trans. Veh. Technol., 2001, 50, (4), pp. 1092–1105.
    39. 39)
      • 33. Wan, H., Chen, R.R., Choi, J.W., et al: ‘Markov chain Monte Carlo detection for frequency-selective channels using list channel estimates’, IEEE J. Sel. Topics Signal Process., 2011, 5, (8), pp. 1537–1547.
    40. 40)
      • 1. Kilfoyle, D., Baggeroer, A.: ‘The state of the art in underwater acoustic telemetry’, IEEE J. Ocean. Eng., 2000, 25, (1), pp. 4–27.
    41. 41)
      • 34. Huang, J., Zhou, S., Huang, J., et al: ‘Progressive inter-carrier interference equalization for OFDM transmission over time-varying underwater acoustic channels’, IEEE J. Sel. Topics Signal Process., 2011, 5, (8), pp. 1524–1536.
    42. 42)
      • 18. Baraniuk, R.G., Cevher, V., Duarte, M.F., et al: ‘Model-based compressive sensing’, IEEE Trans. Inf. Theory, 2010, 56, (4), pp. 1982–2001.
    43. 43)
      • 26. Ballal, T., Al-Naffouri, T.Y., Ahmed, S.F.: ‘Low-complexity Bayesian estimation of cluster-sparse channels’, IEEE Trans. Commun., 2015, 63, (11), pp. 4159–4173.
    44. 44)
      • 22. Yu, L., Sun, H., Barbot, J., et al: ‘Bayesian compressive sensing for cluster structured sparse signals’, Signal Process., 2012, 92, (1), pp. 259–269.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-com.2016.1339
Loading

Related content

content/journals/10.1049/iet-com.2016.1339
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading