To improve the algorithm convergence, EM-based joint channel parameters estimation and data detection algorithm is proposed.

为进一步提高算法的收敛速度，提出联合信道参数估计和数据检测算法。

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The algorithm is deduced and difference equation of parameter error given by which the algorithm convergence is proved theoretically.

对该算法进行了推导，并给出了参数误差的差分方程，在理论上证明了算法的收敛性。

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For accelerating the algorithm convergence and avoiding the local optimization, an individual learning mechanism is often applied to generic algorithm to improve algorithm performance.

在遗传算法中引入个体学习机制能够提高算法的性能，避免算法收敛过慢或陷入局部最优。

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For accelerating the algorithm convergence and avoiding the local optimization, an individual learning mechanism is often applied to generic algorithm to improve algorithm performance.

在遗传算法中引入个体学习机制能够提高算法的性能，避免算法收敛过慢或陷入局部最优。

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