Relevant support recovery algorithm in modulated wideband converter

Abstract

Modulated wideband converter (MWC) is a compressed spectrum sensing system that randomly moves the sparse wideband signal spectrum to the baseband to realise low-speed sampling. Then, obtaining the effective support which is based on compressed sensing is the most important part for the signal recovery from samples. Aimed at the poor anti-noise performance of the support recovery, a relevant support recovery algorithm (RSRA) is proposed in modulated wideband converter for spectrum sensing. Specifically, the optimal initial support index is obtained by least squares. Then, the correlation between the measurement matrix column vector corresponding to the adjacent index and the sampling matrix is calculated, and the adjacent index with larger correlation is added to the support. The whole support is acquired by iterative updating under the thought of matching pursuit. Being different from other algorithms, RSRA takes the correlation about adjacent indexes into account, which can weaken the noise interference. Theoretical analysis and numerical simulations show that the proposed scheme can improve the recovery rate of effective support in lower SNR. While improving the anti-noise capability, other aspects of performance such as channels number and sparsity will not be sacrificed.