Variational Inference-Based Frequency-Domain Equalization for Faster-Than-Nyquist Signaling in Doubly Selective Channels

Weijie YUAN, Nan WU*, Hua WANG, Jingming KUANG

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

35 Citations (Scopus)

Abstract

This work deals with frequency-domain equalization for faster-than-Nyquist (FTN) signaling in doubly selective channels (DSCs). To handle the interference of frequency-domain symbols, the minimum mean square error (MMSE) equalizer involves high complexity in DSCs. To overcome the problem, we propose low-complexity receivers based on two variational methods, i.e., mean field (MF) and Bethe approximations. Compared with the MF method, the Bethe approximation takes into account the conditional dependencies of pairwise symbols. By only considering a small set of the frequency-domain symbols that have strong interference to each other, the complexity of the proposed algorithms increases linearly with the block length. Simulation results demonstrate that the proposed algorithms for FTN signaling are able to perform close to the MMSE equalizer in DSCs while with significantly reduced computational complexity.
Original languageEnglish
Article number7516666
Pages (from-to)1270-1274
Number of pages5
JournalIEEE Signal Processing Letters
Volume23
Issue number9
Early online date19 Jul 2016
DOIs
Publication statusPublished - Sept 2016
Externally publishedYes

Funding

This work was supported by National High Technology Research and Development Program of China (Grant No. 2015AA01A709) and National Science Foundation of China (NSFC) (Grant 61201181 and Grant 61471037).

Keywords

  • Doubly selective channel (DSC)
  • faster-than-Nyquist (FTN) signaling
  • frequency-domain equalization (FDE)
  • Variational inference

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