Sparse channel estimation with lp-norm and reweighted l1-norm penalized least mean squares
Sparse channel estimation with lp-norm and reweighted l1-norm penalized least mean squares
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DOI：
10.1109/icassp.2011.5947082
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发表时间：
2011-05
期刊：
影响因子：
--
通讯作者：
O. Taheri;S. Vorobyov
中科院分区：
文献类型：
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作者：
O. Taheri;S. Vorobyov
最小均方（LMS）算法是最常用的参数递推估计方法之一。在其标准形式中，它不考虑参数化模型可能具有的任何特殊特性。假设这样的模型在某些领域是稀疏的（例如，它具有稀疏的脉冲或频率响应），我们的目标是开发这样的LMS算法，可以适应底层的稀疏性，并实现更好的参数估计。特别地，考虑了具有稀疏信道冲激响应的信道估计的示例。提出的LMS的修改是lp-范数和重新加权的l1-范数惩罚LMS算法。我们的仿真结果证实了所提出的算法的优越性，在标准的LMS以及其他稀疏意识的修改LMS在文献中。
The least mean squares (LMS) algorithm is one of the most popular recursive parameter estimation methods. In its standard form it does not take into account any special characteristics that the parameterized model may have. Assuming that such model is sparse in some domain (for example, it has sparse impulse or frequency response), we aim at developing such LMS algorithms that can adapt to the underlying sparsity and achieve better parameter estimates. Particularly, the example of channel estimation with sparse channel impulse response is considered. The proposed modifications of LMS are the lp-norm and reweighted l1-norm penalized LMS algorithms. Our simulation results confirm the superiority of the proposed algorithms over the standard LMS as well as other sparsity-aware modifications of LMS available in the literature.