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Decision Feedback Equalization using Particle Swarm Optimization

机译：粒子群算法的决策反馈均衡

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摘要

It is well-known that the Decision Feedback Equalizer (DFE) outperforms the Linear Equalizer (LE) for highly dispersive channels. For time-varying channels, adaptive equalizers are commonly designed based on the Least Mean Square (LMS) algorithm which, unfortunately, has the limitation of slow convergence specially in channels having large eigenvalue spread. The eigenvalue problem becomes even more pronounced in Multiple-Input Multiple-Output (MIMO) channels. Particle Swarm Optimization (PSO) enjoys fast convergence and, therefore, its application to the DFE merits investigation. In this paper, we show that a PSO-DFE with a variable constriction factor is superior to the LMS/RLS-based DFE (LMS/RLS-DFE) and PSO-based LE (PSO-LE), especially on channels with large eigenvalue spread. We also propose a hybrid PSO-LMS-DFE algorithm, and modify it to deal with complex-valued data. The PSO-LMS-DFE not only outperforms the PSO-DFE in terms of performance but its complexity is also low. To further reduce its complexity, a fast PSO-LMS-DFE algorithm is introduced.
机译：众所周知，对于高度分散的通道，决策反馈均衡器（DFE）优于线性均衡器（LE）。对于时变信道，通常基于最小均方（LMS）算法设计自适应均衡器，该算法不幸地具有慢收敛的局限性，特别是在具有较大特征值扩展的信道中。在多输入多输出（MIMO）通道中，特征值问题变得更加明显。粒子群优化（PSO）具有快速收敛性，因此在DFE优点研究中的应用。在本文中，我们显示出具有可变压缩因子的PSO-DFE优于基于LMS / RLS的DFE（LMS / RLS-DFE）和基于PSO的LE（PSO-LE），特别是在特征值较大的通道上传播。我们还提出了一种混合PSO-LMS-DFE算法，并对其进行了修改以处理复数值数据。 PSO-LMS-DFE不仅在性能上优于PSO-DFE，而且复杂度也很低。为了进一步降低其复杂度，引入了一种快速的PSO-LMS-DFE算法。

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