SPARLS: The Sparse RLS Algorithm
SPARLS: The Sparse RLS Algorithm
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DOI：
10.1109/tsp2010.2048103
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发表时间：
2010-08
影响因子：
5.4
通讯作者：
B. Babadi;N. Kalouptsidis;V. Tarokh
中科院分区：
文献类型：
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作者：
B. Babadi;N. Kalouptsidis;V. Tarokh
我们开发了一个递归的L1-正则化最小二乘（SPARLS）算法估计的稀疏抽头权重向量的自适应滤波设置。SPARLS算法利用抽头权重向量输出流的噪声观测，并使用期望最大化型算法产生其估计。我们证明了收敛的SPARLS算法在一个平稳的环境中的一个接近最优的估计，并提出了分析结果的稳态误差。在信道估计的背景下，采用多径无线信道的仿真研究表明，SPARLS算法有显着的改善，比传统的广泛使用的递归最小二乘（RLS）算法的均方误差（MSE）。此外，这些模拟研究表明，SPARLS算法（略有修改）可以操作较低的计算要求比RLS算法，当应用于抽头权重向量与固定的支持。
We develop a recursive L1-regularized least squares (SPARLS) algorithm for the estimation of a sparse tap-weight vector in the adaptive filtering setting. The SPARLS algorithm exploits noisy observations of the tap-weight vector output stream and produces its estimate using an expectation-maximization type algorithm. We prove the convergence of the SPARLS algorithm to a near-optimal estimate in a stationary environment and present analytical results for the steady state error. Simulation studies in the context of channel estimation, employing multipath wireless channels, show that the SPARLS algorithm has significant improvement over the conventional widely used recursive least squares (RLS) algorithm in terms of mean squared error (MSE). Moreover, these simulation studies suggest that the SPARLS algorithm (with slight modifications) can operate with lower computational requirements than the RLS algorithm, when applied to tap-weight vectors with fixed support.