TY - GEN
T1 - Joint Phase Noise Estimation and Iterative Detection of Faster-than-Nyquist Signaling Based on Factor Graph
AU - Qi, Xiaotong
AU - Wu, Nan
AU - Zhou, Lei
AU - Yang, Dewei
AU - Wang, Hua
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/14
Y1 - 2017/11/14
N2 - 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.
AB - 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.
KW - Colored noise
KW - Factor graphs
KW - Faster-than-Nyquist signaling
KW - Phase noise (PHN)
KW - Sum-product algorithm (SPA)
KW - Variational message passing (VMP)
UR - http://www.scopus.com/inward/record.url?scp=85040550374&partnerID=8YFLogxK
U2 - 10.1109/VTCSpring.2017.8108439
DO - 10.1109/VTCSpring.2017.8108439
M3 - Conference contribution
AN - SCOPUS:85040550374
T3 - IEEE Vehicular Technology Conference
BT - 2017 IEEE 85th Vehicular Technology Conference, VTC Spring 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 85th IEEE Vehicular Technology Conference, VTC Spring 2017
Y2 - 4 June 2017 through 7 June 2017
ER -