Abstract
Underwater acoustic channel (UAC) is one of the most challenging communication channels in the world, owing to its complex multi-path and absorption as well as variable ambient noise. Although adaptive equalization could effectively eliminate the inter-symbol interference (ISI) with the help of training sequences, the convergence rate of equalization in sparse UAC decreased remarkably. Besides, channel estimation algorithms could roughly figure out channel impulse response and other channel parameters through several specific mathematical criterions. In this paper, a typical channel estimation method, least square (LS) algorithm, is applied in adaptive equalization to obtain the initial tap weights of least mean square (LMS) algorithm. Simulation results show that the proposed method significantly enhances the convergence rate of the LMS algorithm.
Highlights
With further exploration of ocean resources, underwater communication is playing a more critical role in both military and civilian aspects
This paper aims to exploit the typical channel estimation algorithm—least square (LS) [8] to obtain the initial coefficients of equalizer tap weights
The results show that our proposed algorithm has 0.5 dB better bit error rate (BER) performance than least mean square (LMS) algorithm in a low SNR environment
Summary
With further exploration of ocean resources, underwater communication is playing a more critical role in both military and civilian aspects. Information frame needs to carry longer training sequences to guarantee that iterations could reach the steady state of convergence during training mode It would occupy more bandwidth and reduce communication effectiveness, and this would be a deadly drawback for the fact that underwater acoustic channel is badly band-limited due to low-frequency ship noise and absorption of high-frequency energy [2]. Li et al EURASIP Journal on Wireless Communications and Networking (2017) 2017:169 methods, which are critical to the whole iterations and convergence rate Channel estimation is another way to impede and compensate channel fading, which obtains an approximate channel response through a series of mathematical analysis and calculations. Simulation results under UAC reveal that our proposed algorithm improves the convergence rate and BER compared with the traditional LMS adaptive equalizer, especially in a low SNR region
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