On Compressed Sensing Based Iterative Channel Estimator for UWA OFDM Systems

  • Sumit ChakravartyEmail author
  • Ankita Pramanik
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 70)


The ever-increasing demand for high-data-rate communication over a wireless multipath fading channel usually necessitates that at the receiver, prior knowledge about the channel is known. This is often achieved using known pilot signals that track the channel and produces at the receiver channel impulse response reconstruction obtained from the received signals. Recently, empirical studies have demonstrated that rich multipath channel assumption is violated in most physical systems and that the channel instead exhibits a sparse multipath behavior that is characterized by only a few dominant paths in propagation. In past decades, there has been a growing interest in the discussion and study of using underwater acoustic channel as the physical layer for communication systems. In this work, Compressed Sensing (CS)-based iterative channel estimators for Underwater Acoustic (UWA) OFDM systems are proposed where channel is assumed to be both sparse and time varying. The estimation of UWA channel is mainly based on Kalman filtered Compressed Sensing (KFCS) algorithms. CS with Kalman filter (KF) provides new idea about channel estimation for UWA OFDM communication systems, whose result outweigh traditional CS-UWA results.


Underwater acoustic communications Compressed sensing Kalman filtering Iterative channel estimation 


  1. 1.
    Bajwa, W.U., Sayeed, A., Nowak, R.: Compressed sensing of wireless channels in time, frequency, and space. In: 42nd Asilomar Conference on Signals, Systems and Computers, pp. 2048–2052 (2008)Google Scholar
  2. 2.
    Bajwa, W.U., Haupt, J., Sayeed, A.M., Nowak, R.: Compressed channel sensing: a new approach to estimating sparse multipath channels. Proc. IEEE 98(6), 1058–1076 (2010)CrossRefGoogle Scholar
  3. 3.
    Berger, C.R., Wang, Z., Huang, J., Zhou, S.: Application of compressive sensing to sparse channel estimation. IEEE Commun. Mag. 48(11), 164–174 (2010)CrossRefGoogle Scholar
  4. 4.
    Jakobsen, M.L., Laugesen, K., Manchn, C.N.: Parametric modeling and pilot-aided estimation of the wireless multipath channel in OFDM systems. In: Proceeding of IEEE International Conference on Communications (ICC), pp. 1–6 (2010)Google Scholar
  5. 5.
    Candes, E.J., Tao, T.: Near-optimal signal recovery from random projections: universal encoding strategies. IEEE Trans. Inf. Theory 5406–5425 (2006)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Brady, D., Preisig, J.C.: Underwater acoustic communications. Wireless Commun. Signal Process. Perspect. 8, 330–379 (1998)Google Scholar
  7. 7.
    Singer, A.C., Nelson, J.K., Kozat, S.S.: Signal processing for underwater acoustic communications. IEEE Commun. Mag. 47(1), 90–96 (2009)CrossRefGoogle Scholar
  8. 8.
    Iglesias, I., Song, A., Garcia-Frias, J., Badiey, M., Arce, G.R. Image transmission over the underwater acoustic channel via compressive sensing. In: 45th Annual Conference on Information Sciences and Systems, pp. 1–6 (2011)Google Scholar
  9. 9.
    Candes, E.J., Tao, T.: Decoding by linear programming. IEEE Trans. Inf. Theory 4203–4215 (2005)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Chen, B., Cui, Q., Yang, F., Xu, J.: A novel channel estimation method based on Kalman filter compressed sensing for time-varying OFDM system. In: 2014 Sixth International Conference on Wireless Communications and Signal Processing (WCSP), pp. 1–5. Hefei (2014)Google Scholar
  11. 11.
    Qi, C., Wu, L., Wang, X.: Underwater acoustic channel estimation via complex Homotopy. In: IEEE International Conference on Communications (ICC), pp. 3821–3825 (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Kennesaw State UniversityKennesawUSA
  2. 2.IIESTShibpurIndia

Personalised recommendations