Graph-Based Confidence Verification for Bpsk Signal Analysis Under Low Snrs

•The majorization relationships between the continuous PDFs and their discrete area sampling PDFs are analyzed, which provides a theoretical basis to compare the connectivity of the graphs converted from two different distributed random samples, and the probabilities of the graphs being complete.•A novel graph-based confidence verification algorithm to evaluate the analysis results of BPSK signals is proposed, where the block sum of the correlation spectrum is selected as an input fed to the signal-to-graph converter, and the entropy of the quantized samples’ histogram is utilized as a novel feature to check the graph's completeness, which results in better performance and lower computational cost than the conventional graph-based detector using the second largest eigenvalue of the Laplacian matrix.•Compared with existing algorithms, the proposed algorithm can obtain superior performance under lower SNRs in the presence of transmission impairments including channel fading, timing offset, Doppler frequency shift, and phase noise.