Joint channel estimation and decoding in the presence of phase noise over time-selective flat-fading channels

Oscillator phase noise (PHN) can result in significant performance loss in coherent communication systems if not compensated appropriately. Most existing studies focus on either PHN estimation over additive white Gaussian noise channels or channel impulse response (CIR) estimation in the absence of PHN. In this study, joint CIR estimation and decoding over time-selective flat-fading channels impacted by PHN is studied. Both the time evolutions of CIR and PHN are approximated by autoregressive models. Building on this, factor graph of the joint a posteriori probability function is constructed and the sum-product algorithm is applied to derive messages on factor graph. Due to the non-linearity of PHN, no closed-form expressions of the messages can be obtained. To this end, the authors use canonical distribution approach, which approximates the messages by Gaussian and Tikhonov probability density functions on the sub-graphs of CIR and PHN, respectively. Accordingly, the messages can be calculated by updating the parameters of the canonical distributions. A mixed serial-parallel message passing schedule is presented to implement the algorithm, which enables the compromise between the bit error rate performance and the processing throughput. Simulation results show that the proposed joint estimation and decoding algorithm significantly outperforms the existing methods in fading channels impacted by PHN.