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Instantaneous Gradient Change Rate Based Dual Mode Fractionally Spaced Blind equalization for Underwater Acoustic Channel
<span title="2013-05-31">2013</span>
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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xuvs3okcyvh63d3dstrikqq3t4" style="color: black;">Advances in Information Sciences and Service Sciences</a>
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To further improve the performance of wavelet neural network blind equalization based on constant modulus algorithm (CMA) cost function, an instantaneous gradient based dual mode between modified constant modulus algorithm (MCMA) and decision directed (DD) algorithm was proposed. The wavelet neural network weights change quantity of the adjacent iterative process is defined as instantaneous gradient. After the network attains convergence, the weights of wavelet neural network achieve a stable
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... ergy state and the instantaneous gradient would be zero. Therefore dual mode algorithm can be realized by criterion which set according to the instantaneous gradient. Computer simulation results show that the dual mode wavelet neural network blind equalization algorithm proposed in this paper improves the convergence rate and convergence precision effectively, and it has good tracking ability for underwater acoustic channel.
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