Abstract

In this paper, the estimation of doubly spread underwater acoustic (UWA) channels is investigated. The UWA channels are characterized by severe delay spread and significant Doppler effects, and can be well modeled as a multi-scale multi-lag (MSML) channel. Furthermore, exploiting the sparsity of UWA channels, MSML channel estimation can be transformed into the estimation of parameter sets (amplitude, Doppler scale factor, time delay). Based on this, the orthogonal matching pursuit (OMP) algorithm has been widely used. However, the estimation accuracy of OMP depends on the size of the dictionary, which is related with both delay spread and Doppler spread. Thus it requires high computational complexity. This paper proposes a new method, called wavelet transform (WT) based algorithm, for the UWA channel estimation. Different from OMP algorithm which needs to search in both time domain and Doppler domain, WT-based algorithm only needs to search in time domain by using the Doppler invariant characteristic of hyperbolic frequency modulation (HFM) signal. The performance of the proposed algorithm is evaluated by computer simulations based on BELLHOP. The simulation results show that WT-based algorithm performs slightly better than OMP algorithm in low signal to noise ratio (SNR) while can greatly reduce computational complexity.

Highlights

  • Underwater acoustic (UWA) channels pose grand challenges for reliable high data-rate communications, due to significant Doppler effects and severe delay spread [1,2,3]

  • We use computer simulations to evaluate the performance of the proposed wavelet transform (WT)-based algorithm, and comparisons with orthogonal matching pursuit (OMP) algorithm will be included

  • A reference which uses the true channel information is included. It shows that the performance of WT-based algorithm approaches to the lower bound while OMP algorithm has some performance losses at low signal to noise ratio (SNR)

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Summary

A Novel Approach for the Estimation of Doubly

School of Electronic and Information Engineering, Qingdao University, Qingdao 266071, China School of Automation and Electrical Engineering, Qingdao University, Qingdao 266071, China Received: 29 October 2017; Accepted: 20 December 2017; Published: 1 January 2018

Introduction
Channel Model
HFM Signal
WT-Based Algorithm
Performances
Complexity Analysis
Conclusions

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