基于线性预测的盲最小均方误差均衡器
Blind MMSE Equalizer Based on Linear Prediction
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摘要: 盲过采样均衡器仅用二阶统计量便可减小码间干扰，该文采用线性预测方法，提出了一种盲最小均方误差(MMSE)均衡器。该方法不需要先估计信道，可直接利用过采样的接收信号均衡信道。此外，该均衡器可采用递推最小二乘算法自适应地实现，具有较高的计算效率。仿真结果表明，该均衡器比基于线性预测的盲置零均衡器有更小的符号估计均方误差。Abstract: Blind fractionally spaced equalizers can reduce intersymbol interference using only second-order statistics. A blind MMSE equalizer based on linear prediction is presented in this paper. It can directly equalize the channel from the fractionally sampled observations without performing channel identification. In addition, it can be implemented efficiently using the RLS algorithm. Simulation results show that the blind MMSE equalizer has smaller mean-square error of symbol estimation than the corresponding zero-forcing equalizer.
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