Compressive sampling-based CFO-estimation with exploited features

Based on the compressed sensing (CS) technique, the carrier frequency offset (CFO) is estimated in compressive sampling scenarios. We firstly confirm the compressibility of estimation metric vector (EMV) of conventional maximum likelihood (ML)-based CFO estimation, and thus conduct the compressive sampling at receiver. By exploiting the EMV features, introducing a circle cluster, and proposing a novel coherence-pattern, we then form a feature-aided weight coherence (FAWC) optimization to optimize measurement-matrix. Besides the proposed FAWC optimization, by referencing compressive sampling matching pursuit (CoSaMP) algorithm and exploiting EMV features, a metric-feature based CoSaMP (MFB-CoSaMP) algorithm is proposed to improve the EMV-reconstruction accuracy, and to reduce computational complexity of classic CoSaMP. With reconstructed EMV, we finally develop a CFO estimation method to estimate the coarse CFO and fine CFO. Relative to weighted coherence minimization (WCM) and classic CoSaMP, the elaborate performance evaluations show that the FAWC and MFB-CoSaMP can independently or jointly improve accuracy of the CFO-estimation (including coarse CFO-estimation and fine CFO-estimation), and the improvement is robust to system parameters, e.g., sparsity level, number of measurements, etc. Furthermore, the mean squared error (MSE) of proposed CFO estimation method can almost reach to its Cramé$\acute {\texttt {e}}$r-Rao lower Bound (CRLB) when a relative large number of measurements, a relative high carrier-to-noise ratio (CNR), and a reasonable length of observed signals can be obtained.