Message Passing-Aided Joint Data Detection and Estimation of Nonlinear Satellite Channels

Yikun Zhang1; Bin Li1; Nan Wu1; Yunsi Ma1; Weijie Yuan2; Lajos Hanzo3

2022

10.1109/TVT.2022.3206254

IEEE Transactions on Vehicular Technology   Impact Factor & Quartile

1939-9359

1939-9359

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.

Nonlinear satellite channel Volterra series joint channel estimation and data detection mean field approximation generalized approximate message passing

SCI

English

Others

National Natural Science Foundation of China["62001027","61971041"] ; Engineering and Physical Sciences Research Council Projects["EP/W016605/1","EP/P003990/1"] ; European Research Council's Advanced Fellow Grant QuantCom[789028]

Engineering ; Telecommunications ; Transportation

Engineering, Electrical & Electronic ; Telecommunications ; Transportation Science & Technology

WOS:000944202400030

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

IEEE

1.School of Information and Electronics, Beijing Institute of Technology, Beijing, China
2.Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China
3.School of Electronics and Computer Science, University of Southampton, Southampton, U.K.

Yikun Zhang,Bin Li,Nan Wu,et al. Message Passing-Aided Joint Data Detection and Estimation of Nonlinear Satellite Channels[J]. IEEE Transactions on Vehicular Technology,2022,PP(99):1-14.

Yikun Zhang,Bin Li,Nan Wu,Yunsi Ma,Weijie Yuan,&Lajos Hanzo.(2022).Message Passing-Aided Joint Data Detection and Estimation of Nonlinear Satellite Channels.IEEE Transactions on Vehicular Technology,PP(99),1-14.

Yikun Zhang,et al."Message Passing-Aided Joint Data Detection and Estimation of Nonlinear Satellite Channels".IEEE Transactions on Vehicular Technology PP.99(2022):1-14.