Sequential Detection and Estimation of Multipath Channel Parameters Using Belief Propagation

Xuhong Li*, Erik Leitinger, Alexander Venus, Fredrik Tufvesson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


This paper proposes a belief propagation (BP)-based algorithm for sequential detection and estimation of multipath component (MPC) parameters based on radio signals. Under dynamic channel conditions with moving transmitter/receiver, the number of MPCs, the MPC dispersion parameters, and the number of false alarm contributions are unknown and time-varying. We develop a Bayesian model for sequential detection and estimation of MPC dispersion parameters, and represent it by a factor graph enabling the use of BP for efficient computation of the marginal posterior distributions. At each time step, a snapshot-based parametric channel estimator provides parameter estimates of a set of MPCs which are used as noisy measurements by the proposed BP-based algorithm. It performs joint probabilistic data association, and estimation of the time-varying MPC parameters and the mean number of false alarm measurements, by means of the sum-product algorithm rules. The algorithm also exploits amplitude information enabling the reliable detection of “weak” MPCs with very low component signal-to-noise ratios (SNRs). The performance of the proposed algorithm compares well to state-of-the-art algorithms for high SNR MPCs, but it significantly outperforms them for medium or low SNR MPCs. Results using real radio measurements demonstrate the excellent performance of the proposed algorithm in realistic and challenging scenarios.
Original languageEnglish
Pages (from-to)8385-8402
Number of pages18
JournalIEEE Transactions on Wireless Communications
Issue number10
Early online date15 Apr 2022
Publication statusPublished - 1 Oct 2022


  • belief propagation
  • data association
  • factor graphs
  • Multipath channel
  • parametric channel estimation
  • sum-product algorithm

ASJC Scopus subject areas

  • Applied Mathematics
  • Electrical and Electronic Engineering
  • Computer Science Applications


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