Likelihood-based blind separation of QAM signals in time-varying dual-polarized channels

This paper presents a new method for separating QAM signals in time-varying dual-polarized channels. The system applies an adaptive blind source separation (BSS) method based on the likelihood functions of the amplitude of the transmitted signals to recover the input signals and to track the time-varying polarization coefficients. The results demonstrate that the likelihood-based adaptive BSS method is able to recover the source signals of different modulation types for a wide range of input SNRs. The symbol error rate (SER) of estimated signals is close to the theoretical SER of different modulation types at lower SNRs. At high SNRs, the SERs are dominated by the source separation errors. The results also show that this algorithm tracks the time-varying polarization channels coefficients with small errors.