TY - JOUR
T1 - Message Passing-Aided Joint Data Detection and Estimation of Nonlinear Satellite Channels
AU - Zhang, Yikun
AU - Li, Bin
AU - Wu, Nan
AU - Ma, Yunsi
AU - Yuan, Weijie
AU - Hanzo, Lajos
N1 - Publisher Copyright:
© 1967-2012 IEEE.
PY - 2023/2/1
Y1 - 2023/2/1
N2 - Satellite communication is capable of supporting seamless global coverage. However, owing to the reliance on limited-duration solar power, the high power amplifier (HPA) is often driven close to its saturation point, which leads to severe nonlinear distortion in satellite channels. Thus, mitigating the effect of the nonlinear distortion becomes essential for reliable communications. In this article, we propose an efficient joint channel estimation and data detection method based on message passing within the associated factor graph modelling the HPA employed in nonlinear satellite channels. Then, we develop a combined belief propagation and mean field (BP-MF) method to cope with the hard constraints and dense short loops on the factor graph. In particular, the parametric message updating expressions relying on the canonical parameters are derived in the symbol detection part. To alleviate the impact of dense loops, we reformulate the system model into a compact form within the channel estimation part and then reconstruct a loop-free subgraph associated with vector-valued nodes to guarantee convergence. Furthermore, the proposed BP-MF method is also extended to the realistic scenario of having unknown noise variance. To further reduce the computational complexity of the large-scale matrix inversion of channel estimation, the generalized approximate message passing (GAMP) algorithm is employed to decouple the vector of channel coefficient estimation into a series of scalar estimations. Simulation results show that the proposed methods outperform the state-of-the-art benchmarks both in terms of bit error rate performance and channel estimation accuracy.
AB - Satellite communication is capable of supporting seamless global coverage. However, owing to the reliance on limited-duration solar power, the high power amplifier (HPA) is often driven close to its saturation point, which leads to severe nonlinear distortion in satellite channels. Thus, mitigating the effect of the nonlinear distortion becomes essential for reliable communications. In this article, we propose an efficient joint channel estimation and data detection method based on message passing within the associated factor graph modelling the HPA employed in nonlinear satellite channels. Then, we develop a combined belief propagation and mean field (BP-MF) method to cope with the hard constraints and dense short loops on the factor graph. In particular, the parametric message updating expressions relying on the canonical parameters are derived in the symbol detection part. To alleviate the impact of dense loops, we reformulate the system model into a compact form within the channel estimation part and then reconstruct a loop-free subgraph associated with vector-valued nodes to guarantee convergence. Furthermore, the proposed BP-MF method is also extended to the realistic scenario of having unknown noise variance. To further reduce the computational complexity of the large-scale matrix inversion of channel estimation, the generalized approximate message passing (GAMP) algorithm is employed to decouple the vector of channel coefficient estimation into a series of scalar estimations. Simulation results show that the proposed methods outperform the state-of-the-art benchmarks both in terms of bit error rate performance and channel estimation accuracy.
KW - Nonlinear satellite channel
KW - Volterra series
KW - generalized approximate message passing
KW - joint channel estimation and data detection
KW - mean field approximation
UR - http://www.scopus.com/inward/record.url?scp=85139437200&partnerID=8YFLogxK
U2 - 10.1109/TVT.2022.3206254
DO - 10.1109/TVT.2022.3206254
M3 - Article
AN - SCOPUS:85139437200
SN - 0018-9545
VL - 72
SP - 1763
EP - 1774
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 2
ER -