TY - GEN
T1 - A graphical model based frequency domain equalization for FTN signaling in doubly selective channels
AU - Yuan, Weijie
AU - Wu, Nan
AU - Qi, Xiaotong
AU - Wang, Hua
AU - Kuang, Jingming
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/12/21
Y1 - 2016/12/21
N2 - Modern mobile communication applications raise the requirement of high quality support for high mobility users. In this paper, we present a Bayesian graphical model based frequency domain equalization method for faster-than-Nyquist (FTN) signaling in doubly selective channels. The conventional frequency domain minimum mean squared error (FD-MMSE) equalizer suffers high complexity due to the interferences induced by adjacent frequency symbols. To tackle this problem, a low complexity iterative message passing method namely, belief propagation is employed on the Bayesian graphical model to detect the FTN symbols. Compared to the low complexity variational inference method, the proposed algorithm considers the conditional dependencies between symbols and therefore can improve the performance. Simulation results show that the proposed equalization method has similar performance of the MMSE equalizer and outperforms the variational inference method.
AB - Modern mobile communication applications raise the requirement of high quality support for high mobility users. In this paper, we present a Bayesian graphical model based frequency domain equalization method for faster-than-Nyquist (FTN) signaling in doubly selective channels. The conventional frequency domain minimum mean squared error (FD-MMSE) equalizer suffers high complexity due to the interferences induced by adjacent frequency symbols. To tackle this problem, a low complexity iterative message passing method namely, belief propagation is employed on the Bayesian graphical model to detect the FTN symbols. Compared to the low complexity variational inference method, the proposed algorithm considers the conditional dependencies between symbols and therefore can improve the performance. Simulation results show that the proposed equalization method has similar performance of the MMSE equalizer and outperforms the variational inference method.
KW - Bayesian graphical model
KW - Faster-than-Nyquist signaling
KW - belief propagation
KW - frequency domain equalization
UR - http://www.scopus.com/inward/record.url?scp=85010023472&partnerID=8YFLogxK
U2 - 10.1109/PIMRC.2016.7794782
DO - 10.1109/PIMRC.2016.7794782
M3 - Conference contribution
AN - SCOPUS:85010023472
T3 - IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC
BT - 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, PIMRC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 27th IEEE Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, PIMRC 2016
Y2 - 4 September 2016 through 8 September 2016
ER -