Joint Phase Noise Estimation and Iterative Detection of Faster-than-Nyquist Signaling Based on Factor Graph

Modern wireless communication raise the demand for higher spectral efficiency, faster-than-Nyquist (FTN) signaling is able to increase transmission rate without expanding signaling bandwidth. In this paper, we develop a graph-based iterative FTN detector in the presence of phase noise (PHN). Wiener process is employed to model the time evolution of nonstationary channel phase. The colored noise imposed by sampling of FTN signaling is approximated by autoregressive model. Based on the factor graph constructed, messages are derived on the two subgraphs, i.e., PHN estimation subgraph, and the FTN symbol detection subgraph. We propose a combined sum-product and variational message passing (SP-VMP) method to update the messages between subgraphs, which enables low- complexity parametric message passing and provides closed-form expressions for parameters updating. Simulation results show the superior performance of the proposed algorithm compared with the existing methods and verify the advantage of FTN signaling compared with the Nyquist counterpart.