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Sequential Detection and Estimation of MultipathChannel Parameters Using Belief Propagation

Li, Xuhong LU ; Leitinger, Erik LU ; Venus, Alexander and Tufvesson, Fredrik LU orcid (2022)
Abstract
This paper proposes a BP-based algorithm for sequential detection and estimation of MPC parameters based on radio signals. Under dynamic channel conditions with moving transmitter and/or receiver, the number of MPCs reflected from visible geometric features, the MPC dispersion parameters (delay, angle, Doppler frequency, etc), 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 instance, a snapshot-based channel estimator provides parameter estimates of a set of MPCs which are used as... (More)
This paper proposes a BP-based algorithm for sequential detection and estimation of MPC parameters based on radio signals. Under dynamic channel conditions with moving transmitter and/or receiver, the number of MPCs reflected from visible geometric features, the MPC dispersion parameters (delay, angle, Doppler frequency, etc), 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 instance, a snapshot-based 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, estimation of the time-varying MPC parameters, and the mean number of false alarm measurements by means of the sum-product algorithm rules. The results using synthetic measurements show that the proposed algorithm is able to cope with a high number of false alarm measurements originating from the snapshot-based channel estimator and to sequentially detect and estimate MPC parameters with very low SNR. The performance of the proposed algorithm compares well to existing algorithms for high SNR MPCs, but significantly it outperforms them for medium or low SNR MPCs. In particular, we show that our algorithm outperforms the KEST algorithm, a state-of-the-art sequential channel parameters estimation method. Furthermore, results with real radio measurements demonstrate the excellent performance of the algorithm in realistic and challenging scenarios. (Less)
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author
; ; and
organization
publishing date
type
Working paper/Preprint
publication status
published
subject
publisher
arXiv.org
DOI
10.48550/arXiv.2109.05623
language
English
LU publication?
yes
id
a49c52d0-320d-4c47-bc96-732a7f9c3d53
date added to LUP
2021-09-16 17:45:58
date last changed
2022-07-01 08:56:27
@misc{a49c52d0-320d-4c47-bc96-732a7f9c3d53,
  abstract     = {{This paper proposes a BP-based algorithm for sequential detection and estimation of MPC parameters based on radio signals. Under dynamic channel conditions with moving transmitter and/or receiver, the number of MPCs reflected from visible geometric features, the MPC dispersion parameters (delay, angle, Doppler frequency, etc), 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 instance, a snapshot-based 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, estimation of the time-varying MPC parameters, and the mean number of false alarm measurements by means of the sum-product algorithm rules. The results using synthetic measurements show that the proposed algorithm is able to cope with a high number of false alarm measurements originating from the snapshot-based channel estimator and to sequentially detect and estimate MPC parameters with very low SNR. The performance of the proposed algorithm compares well to existing algorithms for high SNR MPCs, but significantly it outperforms them for medium or low SNR MPCs. In particular, we show that our algorithm outperforms the KEST algorithm, a state-of-the-art sequential channel parameters estimation method. Furthermore, results with real radio measurements demonstrate the excellent performance of the algorithm in realistic and challenging scenarios.}},
  author       = {{Li, Xuhong and Leitinger, Erik and Venus, Alexander and Tufvesson, Fredrik}},
  language     = {{eng}},
  month        = {{03}},
  note         = {{Preprint}},
  publisher    = {{arXiv.org}},
  title        = {{Sequential Detection and Estimation of MultipathChannel Parameters Using Belief Propagation}},
  url          = {{http://dx.doi.org/10.48550/arXiv.2109.05623}},
  doi          = {{10.48550/arXiv.2109.05623}},
  year         = {{2022}},
}