Enhanced Compressive Wideband Frequency Spectrum Sensing for Dynamic Spectrum Access

TL;DR

This paper proposes an enhanced compressive sensing method for wideband spectrum sensing in dynamic spectrum access, utilizing a priori spectrum information and iterative optimization to improve accuracy and denoising.

Contribution

It introduces a novel L0/L2 optimization model incorporating spectrum allocation policies and an iterative re-weighted approach for better spectrum sensing performance.

Findings

Outperforms existing methods in accuracy

Demonstrates superior denoising capabilities

Effective in sparse spectrum environments

Abstract

Wideband spectrum sensing detects the unused spectrum holes for dynamic spectrum access (DSA). Too high sampling rate is the main problem. Compressive sensing (CS) can reconstruct sparse signal with much fewer randomized samples than Nyquist sampling with high probability. Since survey shows that the monitored signal is sparse in frequency domain, CS can deal with the sampling burden. Random samples can be obtained by the analog-to-information converter. Signal recovery can be formulated as an L0 norm minimization and a linear measurement fitting constraint. In DSA, the static spectrum allocation of primary radios means the bounds between different types of primary radios are known in advance. To incorporate this a priori information, we divide the whole spectrum into subsections according to the spectrum allocation policy. In the new optimization model, the minimization of the L2 norm of each subsection is used to encourage the cluster distribution locally, while the L0 norm of the L2 norms is minimized to give sparse distribution globally. Because the L0/L2 optimization is not convex, an iteratively re-weighted L1/L2 optimization is proposed to approximate it. Simulations demonstrate the proposed method outperforms others in accuracy, denoising ability, etc.