TY - JOUR
T1 - A BP-MF-EP Based Iterative Receiver for Joint Phase Noise Estimation, Equalization, and Decoding
AU - Wang, W.
AU - Wang, Z.
AU - Zhang, C.
AU - Guo, Qinghua
AU - Sun, P.
AU - Wang, X.
PY - 2016/10
Y1 - 2016/10
N2 - © 2016 IEEE.In this letter, with combined belief propagation (BP), mean field (MF), and expectation propagation (EP), an iterative receiver is designed for joint phase noise estimation, equalization, and decoding in a coded communication system. The presence of the phase noise results in a nonlinear observation model. Conventionally, the nonlinear model is directly linearized by using the first-order Taylor approximation, e.g., in the state-of-the-art soft-input extended Kalman smoothing approach (Soft-in EKS). In this letter, MF is used to handle the factor due to the nonlinear model, and a second-order Taylor approximation is used to achieve Gaussian approximation to the MF messages, which is crucial to the low-complexity implementation of the receiver with BP and EP. It turns out that our approximation is more effective than the direct linearization in the Soft-in EKS, leading to a significant performance improvement with similar complexity as demonstrated by simulation results.
AB - © 2016 IEEE.In this letter, with combined belief propagation (BP), mean field (MF), and expectation propagation (EP), an iterative receiver is designed for joint phase noise estimation, equalization, and decoding in a coded communication system. The presence of the phase noise results in a nonlinear observation model. Conventionally, the nonlinear model is directly linearized by using the first-order Taylor approximation, e.g., in the state-of-the-art soft-input extended Kalman smoothing approach (Soft-in EKS). In this letter, MF is used to handle the factor due to the nonlinear model, and a second-order Taylor approximation is used to achieve Gaussian approximation to the MF messages, which is crucial to the low-complexity implementation of the receiver with BP and EP. It turns out that our approximation is more effective than the direct linearization in the Soft-in EKS, leading to a significant performance improvement with similar complexity as demonstrated by simulation results.
U2 - 10.1109/LSP.2016.2593917
DO - 10.1109/LSP.2016.2593917
M3 - Article
SN - 1070-9908
VL - 23
SP - 1349
EP - 1353
JO - IEEE Signal Processing Letters
JF - IEEE Signal Processing Letters
IS - 10
M1 - 7519016
ER -