| 11. |
- Chen, Ming, 1972, et al.
(författare)
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New Approaches for Channel Prediction Based on Sinusoidal Modeling
- 2007
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Ingår i: Eurasip Journal on Applied Signal Processing. - : Springer Science and Business Media LLC. - 1110-8657 .- 1687-0433. ; 2007
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Tidskriftsartikel (refereegranskat)abstract
- Long-range channel prediction is considered to be one of the most important enabling technologies to future wireless communication systems. The prediction of Rayleigh fading channels is studied in the frame of sinusoidal modeling in this paper. A stochastic sinusoidal model to represent a Rayleigh fading channel is proposed. Three different predictors based on the statistical sinusoidal model are proposed. These methods outperform the standard linear predictor (LP) in Monte Carlo simulations, but underperform with real measurement data, probably due to nonstationary model parameters. To mitigate these modeling errors, a joint moving average and sinusoidal (JMAS) prediction model and the associated joint least-squares (LS) predictor are proposed. It combines the sinusoidal model with an LP to handle unmodeled dynamics in the signal. The joint LS predictor outperforms all the other sinusoidal LMMSE predictors in suburban environments, but still performs slightly worse than the standard LP in urban environments.
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| 12. |
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| 13. |
- Chung, P. J., et al.
(författare)
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Broadband ML estimation under model order uncertainty
- 2010
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Ingår i: Signal Processing. - : Elsevier BV. - 0165-1684. ; 90:5, s. 1350-1356
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Tidskriftsartikel (refereegranskat)abstract
- The number of signals hidden in data plays a crucial role in array processing. When this information is not available, conventional approaches apply information theoretic criteria or multiple hypothesis tests to simultaneously estimate model order and parameter. These methods are usually computationally intensive, since ML estimates are required for a hierarchy of nested models. In this contribution, we propose a computationally efficient solution to avoid this full search procedure and address issues unique to the broadband case. Our max-search approach computes ML estimates only for the maximally hypothesized number of signals, and selects relevant components through hypothesis testing. Furthermore, we introduce a criterion based on the rank of the steering matrix to reduce indistinguishable components caused by overparameterization. Numerical experiments show that despite model order uncertainty, the proposed method achieves comparable estimation and detection accuracy as standard methods, but at much lower computational expense. (C) 2009 Elsevier B.V. All rights reserved.
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| 14. |
- Chung, Pei-Jung, et al.
(författare)
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Broadband ML estimation under model order uncertainty
- 2009
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Ingår i: Proceedings of ICASSP 2009. ; , s. 2121 - 2124
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Konferensbidrag (refereegranskat)abstract
- The number of signals plays a crucial role in array processing. The performance of most direction finding algorithms relies strongly on a correctly specified number of signals. When this information is not available, conventional approaches apply information theoretic criteria or multiple hypothesis tests to simultaneously estimate model order and parameter. These methods are usually computationally intensive, since ML estimates are required for a hierarchy of nested models. In the previous work, we proposed a computationally efficient solution to avoid this full search procedure and demonstrated its feasibility by extensive simulations. Here we extend to broadband data, and address issues unique to the broadband case. Our max-search approach computes ML estimates only for the maximally hypothesized number of signals, and selects relevant components through hypothesis testing. Another novelty of this work is the reduction of indistinguishable components caused by overparameterization. Our approach is based on the rank of the estimated steering matrix. Numerical experiments show that despite an unknown number of signals, the proposed method achieves comparable estimation and detection accuracy as standard methods, but at much lower computational expense.
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