Adaptive joint maximum‐likelihood detection and minimum‐mean‐square error with successive interference canceler over spatially correlated multiple‐input multiple‐output channels

Fan, Lisheng; Zhang, Yangyang; Jiang, Yongquan; Fukawa, Kazuhiko; Suzuki, Hiroshi; Wong, Kai-Kit

doi:10.1002/wcm.1172

Cited by 4 publications

(1 citation statement)

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“…Adaptive system identification includes many applications such as interference cancelation , adaptive beamforming , and channel estimation in different systems . One of the classical algorithms is called least mean square (LMS), which is first proposed by Widrow and Hoff .…”

Section: Introductionmentioning

confidence: 99%

Sparse LMS/F algorithms with application to adaptive system identification

Gui

Mehbodniya

Adachi

2013

Wireless Communications

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Standard least mean square/fourth (LMS/F) is a classical adaptive algorithm that combined the advantages of both least mean square (LMS) and least mean fourth (LMF). The advantage of LMS is fast convergence speed while its shortcoming is suboptimal solution in low signal-to-noise ratio (SNR) environment. On the contrary, the advantage of LMF algorithm is robust in low SNR while its drawback is slow convergence speed in high SNR case. Many finite impulse response systems are modeled as sparse rather than traditionally dense. To take advantage of system sparsity, different sparse LMS algorithms with l p -LMS and l 0 -LMS have been proposed to improve adaptive identification performance. However, sparse LMS algorithms have the same drawback as standard LMS. Different from LMS filter, standard LMS/F filter can achieve better performance. Hence, the aim of this paper is to introduce sparse penalties to the LMS/F algorithm so that it can further improve identification performance. We propose two sparse LMS/F algorithms using two sparse constraints to improve adaptive identification performance. Two experiments are performed to show the effectiveness of the proposed algorithms by computer simulation. In the first experiment, the number of nonzero coefficients is changing, and the proposed algorithms can achieve better mean square deviation performance than sparse LMS algorithms. In the second experiment, the number of nonzero coefficient is fixed, and mean square deviation performance of sparse LMS/F algorithms is still better than that of sparse LMS algorithms.

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Section: Introductionmentioning

confidence: 99%