Widely Linear Filtering for Multi-Impairment Compensation in Coherent Optical Systems

We propose a blind joint equalization algorithm for M-QAM signals based on a widely linear filtering approach. The proposed scheme jointly compensates receiver IQ imbalance and polarization mixing, along with carrier recovery, followed by transmitter IQ imbalance compensation. We first investigate the proposed scheme's tolerance to transceiver IQ Imbalance, polarization mixing, phase noise and frequency offset through numerical simulations for 32 GBd PM-16QAM and PM-64QAM signals and compare its performance with the conventional digital processing algorithms. Further, with the proposed algorithm, we experimentally demonstrate the improvement in Q² value to up to ~ 1.22 dB for a 32 GBd PM-16QAM and ~ 3.72 dB for a 16 GBd PM-64QAM signal with a phase imbalance of 9^o. We show that the MSE convergence of the proposed joint equalizer is much faster than conventional DSP algorithms. Deployment of such an equalizer in optical communication systems is beneficial due to its improved tolerance to multiple impairments, albeit with increased complexity.

Widely Linear Filtering for Multi-Impairment Compensation in Coherent Optical Systems

We propose a blind joint equalization algorithm for M-QAM signals based on a widely linear filtering approach. The proposed scheme jointly compensates receiver IQ imbalance and polarization mixing, along with carrier recovery, followed by transmitter IQ imbalance compensation. We first investigate the proposed scheme's tolerance to transceiver IQ Imbalance, polarization mixing, phase noise and frequency offset through numerical simulations for 32 GBd PM-16QAM and PM-64QAM signals and compare its performance with the conventional digital processing algorithms. Further, with the proposed algorithm, we experimentally demonstrate the improvement in Q² value to up to ~ 1.22 dB for a 32 GBd PM-16QAM and ~ 3.72 dB for a 16 GBd PM-64QAM signal with a phase imbalance of 9^o. We show that the MSE convergence of the proposed joint equalizer is much faster than conventional DSP algorithms. Deployment of such an equalizer in optical communication systems is beneficial due to its improved tolerance to multiple impairments, albeit with increased complexity.

Machine learning aided carrier recovery in continuous-variable quantum key distribution

The secret key rate of a continuous-variable quantum key distribution (CV-QKD) system is limited by excess noise. A key issue typical to all modern CV-QKD systems implemented with a reference or pilot signal and an independent local oscillator is controlling the excess noise generated from the frequency and phase noise accrued by the transmitter and receiver. Therefore accurate phase estimation and compensation, so-called carrier recovery, is a critical subsystem of CV-QKD. Here, we explore the implementation of a machine learning framework based on Bayesian inference, namely an unscented Kalman filter (UKF), for estimation of phase noise and compare it to a standard reference method and a previously demonstrated machine learning method. Experimental results obtained over a 20-km fibre-optic link indicate that the UKF can ensure very low excess noise even at low pilot powers. The measurements exhibited low variance and high stability in excess noise over a wide range of pilot signal to noise ratios. This may enable CV-QKD systems with low hardware implementation complexity which can seamlessly work on diverse transmission lines.