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- Chen, Ming, 1972, et al.
(författare)
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Adaptive Channel Prediction Based on Polynomial Phase Signals
- 2008
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Ingår i: 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP; Las Vegas, NV; United States; 31 March 2008 through 4 April 2008. - 1520-6149. - 9781424414840 ; , s. 2881-2884
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Konferensbidrag (refereegranskat)abstract
- Motivated by recently published physics based scattering SISO and MIMO channel models, a new adaptivechannel prediction using Kalman filter based on non-stationary polynomial phase signals with time-varying amplitudes is proposed. To mitigate the influence of the time-varying amplitudes on parameter estimation, an iterative estimation using the Non-linear instantaneous LS criterion is proposed, where the number of signal components and model orders are known. The new predictoroutperforms the classical Linear Prediction and stationarysinusoidal modeling based prediction in Monte Carlo simulations.
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| 2. |
- Chen, Ming, 1972
(författare)
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Channel Prediction Based On Sinusoidal Modeling
- 2005
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Long range channel prediction is considered as 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 thesis. A stochastic sinusoidal model to represent Rayleigh fading channel is proposed. The average of the conditional power spectrum of this model is shown to be the well known Jake's model. Given Doppler frequencies to be deterministic, the Cramer-Rao Lower Bound (CRLB) for the frequency estimates is derived. An algorithm to calculate the compressed CRLB is also proposed. Using measurement data, the Jake's model is confirmed by the Normalized Mean Doppler Spectrum (NMDS) in both urban and suburban environments. The analysis of the time varying property of the model parameters shows that the model parameters are more consistent in suburban than in urban environment. A strong dominant sinusoid was observed in most suburban measurements, which might be due to the direct path in Line-Of-Sight (LOS). Based on the statistical sinusoidal modeling, three different predictors are proposed. These methods outperform the standard LP in Monte Carlo simulations, but underperform with real measurement data. A subjective study of the LMMSE prediction methods to nearby tones are performed by simulations. The Unconditioned LMMSE predictor is found to be more suitable for the prediction of closely separated sinusoids. Later, a Joint Moving Average and Sinusoidal (JMAS) model is proposed for channel prediction, which predicts the channel by LP and sinusoidal predictor jointly, together with a simple SVD based the ith biggest gradient model selection method. This method is termed Joint LMMSE predictor. It outperforms all the other predictors in suburban environments, but still performs slightly worse than the standard LP in urban environments.
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| 5. |
- Chen, Ming, 1972, et al.
(författare)
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Long Range Channel Prediction Based on Non-Stationary Parametric Modeling
- 2009
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Ingår i: IEEE Transactions on Signal Processing. - 1941-0476 .- 1053-587X. ; 57:2, s. 622-634
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Tidskriftsartikel (refereegranskat)abstract
- Motivated by the analysis of measured radio channels and recently published physics-based scattering SISO and MIMO channel models, a new approach of long-range channel prediction based on nonstationary multicomponent polynomial phase signals (MC-PPS) is proposed. An iterative and recursive method for detecting the number of signals and the orders of the polynomial phases is proposed. The performance of these detectors and estimators is evaluated by Monte Carlo simulations. The performance of the new channel predictors is evaluated using both synthetic signals and examples of real world channels measured in urban and suburban areas. High-order polynomial phase parameters are detected in most of the measured data sets, and the new methods outperform the classical LP in given examples for long-range prediction for the cases where the estimated model parameters are stable. For the more difficult data sets, the performance of these methods are similar, which provides alternatives for system design when other issues are concerned.
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| 7. |
- Chen, Ming, 1972, et al.
(författare)
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Models and Predictions of Scattered Radio Waves on Rough Surfaces
- 2007
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Ingår i: IEEE ICASSP 2007 (Honolulu, Hawaii, USA, 2007). ; 3, s. 785-788
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Konferensbidrag (refereegranskat)abstract
- Scattering of radio waves on rough surfaces is investigated using ray tracing techniques, which results in a sinusoidal model with time varying amplitudes. An AR(d) model with nonzero mean is proposed to characterize and predict the time variation of the amplitudes. A covariance sequence based method is proposed to estimate the autoregressive coefficients from the channel observations. An adaptive channel predictor using a Kalman filter is proposed to predict the complex amplitudes of the scattering signal. The proposed method outperforms other sinusoidal modeling based channel predictors and Linear Predictors with single scattering scenarios.
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| 8. |
- 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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| 9. |
- Chen, Ming, 1972
(författare)
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Radio Channel Prediction Based on Parametric Modeling
- 2007
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Long range channel prediction is a crucial technology for future wireless communications. The prediction of Rayleigh fading channels is studied in the frame of parametric modeling in this thesis.Suggested by the Jakes model for Rayleigh fading channels,deterministic sinusoidal models were adopted for long rangechannel prediction in early works. In this thesis, a number of new channel predictors based on stochastic sinusoidal modeling are proposed. They are termed conditional and unconditional LMMSE predictors respectively. Given frequency estimates, the amplitudesof the sinusoids are modeled as Gaussian random variables in the conditional LMMSE predictors, and both the amplitudes and frequency estimates are modeled as Gaussian random variables in the unconditional LMMSE predictors. It was observed that a part of the channels cannot be described by the periodic sinusoidal bases, both in simulations and measured channels. To pick up thisun-modeled residual signal, an adjusted conditional LMMSEpredictor and a Joint LS predictor are proposed.Motivated by the analysis of measured channels and recentlypublished physics based scattering SISO and MIMO channel models, a new approach for channel prediction based on non-stationary Multi-Component Polynomial Phase Signal (MC-PPS) is further proposed. The so-called LS MC-PPS predictor models the amplitudes of the PPS components as constants. In the case of MC-PPS with time-varying amplitudes, an adaptive channel predictor using the Kalman filter is suggested, where the time-varying amplitudes aremodeled as auto-regressive processes. An iterative detection and estimation method of the number of PPS components and the orders of polynomial phases is also proposed. The parameter estimation is based on the Nonlinear LS (NLLS) and the Nonlinear InstantaneousLS (NILS) criteria, corresponding to the cases of constant and time-varying amplitudes, respectively.The performance of the proposed channel predictors is evaluated using both synthetic signals and measured channels. High order polynomial phase parameters are observed in both urban and suburban environments. It is observed that the channel predictors based on the non-stationary MC-PPS models outperform the other predictors in Monte Carlo simulations and examples of measuredurban and suburban channels.
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