Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Compressive Sensing: Performance Comparison Of Sparse Recovery
  Algorithms   Spectrum sensing is an important process in cognitive radio. A number of
sensing techniques that have been proposed suffer from high processing time,
hardware cost and computational complexity. To address these problems,
compressive sensing has been proposed to decrease the processing time and
expedite the scanning process of the radio spectrum. Selection of a suitable
sparse recovery algorithm is necessary to achieve this goal. A number of sparse
recovery algorithms have been proposed. This paper surveys the sparse recovery
algorithms, classify them into categories, and compares their performances. For
the comparison, we used several metrics such as recovery error, recovery time,
covariance, and phase transition diagram. The results show that techniques
under Greedy category are faster, techniques of Convex and Relaxation category
perform better in term of recovery error, and Bayesian based techniques are
observed to have an advantageous balance of small recovery error and a short
recovery time.

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The categories of this paper are: mtrl-th
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The categories of this paper are: comp-gas
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The categories of this paper are: dg-ga
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The categories of this paper are: patt-sol
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The categories of this paper are: alg-geom
2
The categories of this paper are: math
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The categories of this paper are: ao-sci
6
The categories of this paper are: astro-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: q-fin
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: gr-qc
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The categories of this paper are: q-alg
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The categories of this paper are: bayes-an