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Decorrelation Spectrum Sensing Model with Structural Information Learning
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AI
DOI：10.1016/j.dsp.2025.105759.png)
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Abstract
En 中文
In cognitive radio, spectrum sensing is promising to detect the spectrum holes. To recover the spectrum from sub-Nyquist samples efficiently, this paper proposes a novel spectrum reconstruction model with structural information matrix. In particular, we first design a sparsity bound learning algorithm to degrade the influence of correlated information. Then, we employ a spark information to learn the sparse structure of spectrum signals efficiently. Last, we develop an adaptively-regularized iterative reweighted least squares (AR-IRLS) algorithm to solve the large-scale optimization problem. Extensive simulation results demonstrate the effectiveness of the proposed algorithm and evaluate its performance in practical spectrum sensing cases.
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D
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3
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