Open Access Open Access  Restricted Access Subscription Access

A New Adaptive OMP-MAP Algorithm-Based Iterative Sparse Channel Estimation for OFDM Underwater Communication


Affiliations
1 Department of Electronics and Communication Engineering, National Institute of Technology Jamshedpur, Jharkhand, 831 014, India
 

A sparse channel estimation approach based on doubly spread underwater acoustic (UWA) channels is widely used to detect coherent acoustic orthogonal frequency division multiplexing (OFDM) signals. A new time-domain channel estimation (CE) technique for OFDM based UWA communication with Rician fading is used to exploit the channel sparsity. First, to improve the estimation accuracy in high noise conditions, we have exploited the channel sparsity to generate a closed-form equation for the termination condition. Then, in low-level noise instances, the additional criterion to balance estimation accuracy and computing costs has been established. By incorporating these two requirements within the orthogonal-matching-pursuit (OMP) structure, an adaptive-OMP (AOMP) algorithm has been proposed. The AOMP and maximum a posteriori probability (MAP) techniques are combined to provide a computationally efficient, and a new AOMP-MAP scheme for estimating the sparse complex channel path gain has been proposed. Further, The minimum variance unbiased estimator is used to improve the proposed CE technique. Exploiting the experimental channel data, computer simulations reveal that the proposed CE technique obtains the outstanding outcomes.

Keywords

Underwater Acoustic Communication, Orthogonal Frequency Division Multiplexing, Channel Estimation, Sparsity, Adaptive Orthogonal Matching Pursuit, MAP Estimation.
User
Notifications
Font Size

  • Chen Y, Clemente C & Soraghan J, J Infect, 12 (2021) 469.
  • Javaid N, Ahmad Z, Sher A, Wadud Z, Khan Z A & Ahmed S H, J Ambient Intell Humaniz. Comput,10 (2019) 4225.
  • Chen P, Rong Y, Nordholm S & He Z IEEE Trans Veh Technol, 66 (2017) 10567.
  • Taubock G, Hlawatsch F, Eiwen D & Rauhut H, IEEE J Sel Top Signal Process, 4 (2010) 255.
  • Abdzadeh-Ziabari H, Zhu W P & Swamy M N S, IEEE Trans Veh Technol, 67 (2018) 2787.
  • Zhou Y H, Tong F & Zhang G Q, Appl Acoust, 117 (2017) 160.
  • Yan Z, Yang X, Sun L & Wang, J China Commun, 18 (2021) 216.
  • Wan L, Jia H, Zhou F, Muzzammil M, Li T & Huang Y, Signal Processing,170 (2020) 107439.
  • Wan L, Qiang X, Ma L, Song Q & Qiao G, IEEE Wirel Commun Lett, 8 (2019) 117.
  • Peng B, Rossi P S, Dong H & Kansanen K, IEEE Commun Lett, 19 (2015) 1081.
  • Ahmed S, Ocean 2015 - MTS/IEEE Washingt, (2016).
  • Sifferlen J F, Song H C, Hodgkiss W S, Kuperman W A & Stevenson J M, IEEE J Ocean Eng, 33 (2008) 182.
  • Huang J, Zhou S, Huang J, Berger C R & Willett P, IEEE J Sel Top Signal Process, 5 (2011) 1524.
  • Berger C R, Zhou S, Preisig J C & Willett P, IEEE Trans. Signal Process, 58 (2010) 1708.
  • Yerramalli S, Stojanovic M & Mitra U, IEEE Trans Signal Process, 60 (2012) 5906.
  • Li Y, Sha X & Wang K, IEEE Commun Lett,17 (2013) 2260.
  • Tu K, Fertonani D, Duman T M, Stojanovic M, Proakis J G & Hursky P, IEEE J Ocean Eng, 36 (2011) 156.
  • Han J, Zhang L & Leus G, IEEE Signal Process Lett, 23 (2016) 282.
  • Yang Z & Zheng Y R, IEEE J Ocean Eng, 41 (2016) 232.
  • Chen Z, Wang J & Zheng Y R, IEEE J Ocean Eng, 42 (2017) 711.
  • Qin X, Qu F & Zheng Y R, IEEE J Ocean Eng, 46 (2021) 326.
  • Tao J, Zheng Y R, Xiao C & Yang T C, IEEE J Ocean Eng, 35 (2010) 948.
  • Rafati A, Lou H & Xiao C, IEEE J Ocean Eng, 39 (2014) 90.
  • Arunkumar K P & Murthy C R, IEEE Trans Signal Process, 66 (2018) 5041.
  • Mason S F, Berger C R, Member S, Zhou S & Willett P, IEEE J Sel Areas Commun, 26 (2008) 1638.
  • Kumar A & Kumar P, 5th Int Conf Comput, Commun Security, (ICCCS) (2020).
  • Panayirci E, Senol H, Uysal M & Poor H V, IEEE Trans Signal Process,64 (2016) 214.
  • Yin J, Ge W, Han X, Liu B & Guo L, IEEE Commun Lett, 23 (2019) 2086.
  • Panayirci E, Altabbaa M T, Uysal M & Poor H V, IEEE Trans Signal Process,67 (2019) 1550.
  • Qarabaqi P & Stojanovic M, IEEE J Ocean Eng, 38 (2013) 701.
  • Liu C, Zakharov Y V & Chen T, IEEE Trans Veh Technol, 61 (2012) 938.
  • Walree P A V, Socheleau F X, Otnes R & Jenserud T, IEEE J Ocean Eng, 42 (2017) 1007.
  • Fang K, Rugini L & Leus G, IEEE Trans Signal Process, 56 (2008) 5555.
  • Wang Z, Li Y, Wang C, Ouyang D & Huang Y, IEEE Wirel Commun Lett, 10 (2021) 1761.
  • Kay S M & Englewood C N J, Prentice-Hall, (1988).
  • Berger C R, Wang Z, Huang J & Zhou S, IEEE Commun Mag, 48 (2010) 164.
  • Li C, Song K & Yang L, IET Commun,11 (2017) 1143.
  • Huang Y, Wan L, Zhou S, Wang Z & Huang J, Phys Commun,13 (2014) 156.
  • Kumar A & Kumar P, ETRI Journal,(2022) 1.

Abstract Views: 218

PDF Views: 110




  • A New Adaptive OMP-MAP Algorithm-Based Iterative Sparse Channel Estimation for OFDM Underwater Communication

Abstract Views: 218  |  PDF Views: 110

Authors

Anand Kumar
Department of Electronics and Communication Engineering, National Institute of Technology Jamshedpur, Jharkhand, 831 014, India
Prashant Kumar
Department of Electronics and Communication Engineering, National Institute of Technology Jamshedpur, Jharkhand, 831 014, India

Abstract


A sparse channel estimation approach based on doubly spread underwater acoustic (UWA) channels is widely used to detect coherent acoustic orthogonal frequency division multiplexing (OFDM) signals. A new time-domain channel estimation (CE) technique for OFDM based UWA communication with Rician fading is used to exploit the channel sparsity. First, to improve the estimation accuracy in high noise conditions, we have exploited the channel sparsity to generate a closed-form equation for the termination condition. Then, in low-level noise instances, the additional criterion to balance estimation accuracy and computing costs has been established. By incorporating these two requirements within the orthogonal-matching-pursuit (OMP) structure, an adaptive-OMP (AOMP) algorithm has been proposed. The AOMP and maximum a posteriori probability (MAP) techniques are combined to provide a computationally efficient, and a new AOMP-MAP scheme for estimating the sparse complex channel path gain has been proposed. Further, The minimum variance unbiased estimator is used to improve the proposed CE technique. Exploiting the experimental channel data, computer simulations reveal that the proposed CE technique obtains the outstanding outcomes.

Keywords


Underwater Acoustic Communication, Orthogonal Frequency Division Multiplexing, Channel Estimation, Sparsity, Adaptive Orthogonal Matching Pursuit, MAP Estimation.

References