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Sequential Detection and Estimation of Multipath Channel Parameters Using Belief Propagation

Li, Xuhong LU ; Leitinger, Erik LU ; Venus, Alexander and Tufvesson, Fredrik LU orcid (2022) In IEEE Transactions on Wireless Communications 21(10). p.8385-8402
Abstract

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... (More)

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.

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author
; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Bayes methods, Channel estimation, Dispersion, Estimation, Probabilistic logic, Signal to noise ratio, Wireless communication
in
IEEE Transactions on Wireless Communications
volume
21
issue
10
pages
8385 - 8402
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • scopus:85128623177
ISSN
1536-1276
DOI
10.1109/TWC.2022.3165856
language
English
LU publication?
yes
id
419fd6cb-8b35-4fcd-a22c-6150552aca46
date added to LUP
2022-06-30 14:25:55
date last changed
2023-01-16 10:16:58
@article{419fd6cb-8b35-4fcd-a22c-6150552aca46,
  abstract     = {{<p>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 &amp;#x201C;weak&amp;#x201D; 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.</p>}},
  author       = {{Li, Xuhong and Leitinger, Erik and Venus, Alexander and Tufvesson, Fredrik}},
  issn         = {{1536-1276}},
  keywords     = {{Bayes methods; Channel estimation; Dispersion; Estimation; Probabilistic logic; Signal to noise ratio; Wireless communication}},
  language     = {{eng}},
  number       = {{10}},
  pages        = {{8385--8402}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{IEEE Transactions on Wireless Communications}},
  title        = {{Sequential Detection and Estimation of Multipath Channel Parameters Using Belief Propagation}},
  url          = {{http://dx.doi.org/10.1109/TWC.2022.3165856}},
  doi          = {{10.1109/TWC.2022.3165856}},
  volume       = {{21}},
  year         = {{2022}},
}