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提出了一种适用于加性对称α稳定(SαS)分布噪声环境下Turbo均衡方法。通过利用分段多项式构造较为精确地逼近SαS分布的概率密度函数,并用于最大后验概率Turbo均衡的软信息迭代过程,从而更充分地利用信道非Gauss噪声的先验统计信息。仿真结果表明:该方法在不同强度噪声情况下,误码率性能均优于Gauss噪声下的标准Turbo均衡和Chuah提出的截断Turbo均衡方法;在中等脉冲噪声情况下(α=1.5),达到10-4的误码率时信噪比Eb/N0比截断Turbo均衡方法小约2.2 dB,且与AWGN信道下卷积编码误码极限的信噪比之差约为0.8 dB。仿真结果还表明该方法具有更好的迭代收敛性能。
A Turbo equalization method suitable for additive symmetrical α-stable (SαS) distribution noise is proposed. By using the piecewise polynomial construction, the probability density function that approximates the SαS distribution is constructed and used for the iterative process of the maximum a posteriori probabilistic Turbo equalization to take full advantage of the prior statistical information of the channel non-Gaussian noise. The simulation results show that the performance of the proposed method is superior to the standard Turbo equalization under Gaussian noise and the truncated Turbo equalization method proposed by Chuah under the condition of different intensity and noise. In the case of moderate impulse noise (α = 1.5) -4 bit error rate Eb / N0 than the truncated Turbo equalization method is about 2.2 dB, and AWGN channel convolutional coding error limit of the signal to noise ratio of about 0.8 dB difference. Simulation results also show that the method has better performance of iterative convergence.