Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Extra Gain:Improved Sparse Channel Estimation Using Reweighted l_1-norm
  Penalized LMS/F Algorithm   The channel estimation is one of important techniques to ensure reliable
broadband signal transmission. Broadband channels are often modeled as a sparse
channel. Comparing with traditional dense-assumption based linear channel
estimation methods, e.g., least mean square/fourth (LMS/F) algorithm,
exploiting sparse structure information can get extra performance gain. By
introducing l_1-norm penalty, two sparse LMS/F algorithms, (zero-attracting
LMSF, ZA-LMS/F and reweighted ZA-LMSF, RZA-LMSF), have been proposed [1].
Motivated by existing reweighted l_1-norm (RL1) sparse algorithm in compressive
sensing [2], we propose an improved channel estimation method using RL1 sparse
penalized LMS/F (RL1-LMS/F) algorithm to exploit more efficient sparse
structure information. First, updating equation of RL1-LMS/F is derived.
Second, we compare their sparse penalize strength via figure example. Finally,
computer simulation results are given to validate the superiority of proposed
method over than conventional two methods.

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The categories of this paper are: econ
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The categories of this paper are: q-alg
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The categories of this paper are: supr-con
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The categories of this paper are: plasm-ph
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The categories of this paper are: math-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: chem-ph
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The categories of this paper are: physics
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-ex
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The categories of this paper are: dg-ga
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The categories of this paper are: adap-org
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The categories of this paper are: eess
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The categories of this paper are: stat
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The categories of this paper are: comp-gas
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The categories of this paper are: patt-sol