Joint Blind Estimation of Dual Synchronization Sequence in PCMA System

‍ ‍Paired-carrier multiple-access （PCMA） is an important satellite-communication multiple-access method that uses the same frequency to transmit the modulation waveforms of both communication parties. The blind separation of the PCMA signals is an important part of radio spectrum monitoring， and high-precision modulation parameter information is needed for this blind separation， while the method of modulation-parameter estimation aided by the synchronous sequence has high accuracy and good robustness. However， in the blind reception of PCMA signals， it is often impossible to predict the synchronization sequence in advance， and estimations of the two synchronous sequences must be obtained under the condition that separation and demodulation cannot be completed， making it even more difficult. This study focused on PCMA signals using amplitude-phase modulation （APM）. The singular value decomposition （SVD） results of the signal correlation matrix under different conditions were analyzed by exploiting the structural characteristics of the communication system’s data frames and the idea of subspace decomposition， and the approximate mathematical relationships between the eigenvalues， eigenvectors， and synchronous waveforms of the two signals were derived. A “whole first and then local” algorithm flow was proposed. First， overall SVD decomposition was performed on the signal segmented by frame length to obtain a rough estimation of the synchronous waveform and its position. Then， local SVD decomposition was performed on the original PCMA signal at the synchronous waveform position to further improve the estimation performance. This made it possible to solve the problem of dual synchronous sequence estimation in the blind reception of PCMA signals. Simulation results showed that the scheme could be widely applied to the blind estimation of a synchronization waveform and synchronization sequence of APM-modulated PCMA signals. Under a low signal-to-noise ratio， this method outperformed the maximum likelihood estimation method. By improving the signal-to-noise ratio and increasing the volume of data， the estimation performance could be further improved， enabling the high-precision estimation of PCMA-signal dual synchronous sequences. This would provide a prerequisite for the application of data-aided parameter estimation methods in engineering practice， and provide an important foundation for PCMA signal modulation parameter estimation and subsequent blind separation technology research.