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首页> 外文期刊>IEEE Transactions on Circuits and Systems. I, Regular Papers >Enhanced Detection-Guided NLMS Estimation of Sparse FIR-Modeled Signal Channels
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Enhanced Detection-Guided NLMS Estimation of Sparse FIR-Modeled Signal Channels

机译：稀疏FIR建模信号通道的增强型检测引导NLMS估计

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摘要

In various signal-channel-estimation problems, the channel being estimated may be well approximated by a discrete finite impulse response (FIR) model with sparsely separated active or nonzero taps. A common approach to estimating such channels involves a discrete normalized least-mean-square (NLMS) adaptive FIR filter, every tap of which is adapted at each sample interval. Such an approach suffers from slow convergence rates and poor tracking when the required FIR filter is "long." Recently, NLMS-based algorithms have been proposed that employ least-squares-based structural detection techniques to exploit possible sparse channel structure and subsequently provide improved estimation performance. However, these algorithms perform poorly when there is a large dynamic range amongst the active taps. In this paper, we propose two modifications to the previous algorithms, which essentially remove this limitation. The modifications also significantly improve the applicability of the detection technique to structurally time varying channels. Importantly, for sparse channels, the computational cost of the newly proposed detection-guided NLMS estimator is only marginally greater than that of the standard NLMS estimator. Simulations demonstrate the favourable performance of the newly proposed algorithm.
机译：在各种信号信道估计问题中，被估计的信道可以通过离散的有限冲激响应（FIR）模型（具有稀疏分离的有源或非零抽头）很好地近似。估计此类信道的常用方法涉及离散的归一化最小均方（NLMS）自适应FIR滤波器，其每个抽头都在每个采样间隔进行调整。当所需的FIR滤波器很长时，这种方法会遇到收敛速度慢和跟踪效果差的问题。最近，已经提出了基于NLMS的算法，该算法采用基于最小二乘的结构检测技术来利用可能的稀疏信道结构，并随后提供改进的估计性能。但是，当活动抽头之间有较大的动态范围时，这些算法的性能会很差。在本文中，我们提出了对先前算法的两种修改，这些修改实质上消除了此限制。修改还显着提高了检测技术在结构上随时间变化的通道上的适用性。重要的是，对于稀疏信道，新提出的以检测为导向的NLMS估计器的计算成本仅略高于标准NLMS估计器的计算成本。仿真结果表明了该算法的良好性能。

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