5 research outputs found
Radio Channel Prediction Based on Parametric Modeling
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 range
channel 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 amplitudes
of 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 this
un-modeled residual signal, an adjusted conditional LMMSE
predictor and a Joint LS predictor are proposed.
Motivated by the analysis of measured channels and recently
published 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 are
modeled 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 Instantaneous
LS (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 measured
urban and suburban channels
Kanalprädiktion in Mehrantennensystemen
Viele moderne Übertragungsverfahren, die eine hohe Leistungsfähigkeit besitzen, benötigen senderseitige Kanalkenntnis. Typischerweise ist es jedoch schwierig, senderseitige Kanalkenntnis zu erlangen. Empfängerseitige Kanalkenntnis lässt sich klassischerweise basierend auf der Übermittlung von a priori bekannten Trainingssignalen erlangen. Es wurden verschiedene trainingssignalbasierte Verfahren untersucht, um die empfängerseitige Kanalkenntnis zu schätzen. Basierend auf der geschätzten empfängerseitigen Kanalkenntnis wurden Verfahren vorgeschlagen und untersucht, um die senderseitige Kanalkenntnis zu erlangen
Channel Prediction for Mobile MIMO Wireless Communication Systems
Temporal variation and frequency selectivity of wireless channels constitute
a major drawback to the attainment of high gains in capacity
and reliability offered by multiple antennas at the transmitter and receiver
of a mobile communication system. Limited feedback and adaptive transmission
schemes such as adaptive modulation and coding, antenna selection,
power allocation and scheduling have the potential to provide the platform
of attaining the high transmission rate, capacity and QoS requirements in
current and future wireless communication systems. Theses schemes require
both the transmitter and receiver to have accurate knowledge of Channel
State Information (CSI). In Time Division Duplex (TDD) systems, CSI at
the transmitter can be obtained using channel reciprocity. In Frequency Division
Duplex (FDD) systems, however, CSI is typically estimated at the
receiver and fed back to the transmitter via a low-rate feedback link. Due to
the inherent time delays in estimation, processing and feedback, the CSI obtained
from the receiver may become outdated before its actual usage at the
transmitter. This results in significant performance loss, especially in high
mobility environments. There is therefore a need to extrapolate the varying
channel into the future, far enough to account for the delay and mitigate the
performance degradation.
The research in this thesis investigates parametric modeling and prediction
of mobile MIMO channels for both narrowband and wideband systems.
The focus is on schemes that utilize the additional spatial information offered
by multiple sampling of the wave-field in multi-antenna systems to
aid channel prediction. The research has led to the development of several
algorithms which can be used for long range extrapolation of time-varyingchannels. Based on spatial channel modeling approaches, simple and efficient
methods for the extrapolation of narrowband MIMO channels are proposed.
Various extensions were also developed. These include methods for wideband
channels, transmission using polarized antenna arrays, and mobile-to-mobile
systems.
Performance bounds on the estimation and prediction error are vital when
evaluating channel estimation and prediction schemes. For this purpose, analytical
expressions for bound on the estimation and prediction of polarized
and non-polarized MIMO channels are derived. Using the vector formulation
of the Cramer Rao bound for function of parameters, readily interpretable
closed-form expressions for the prediction error bounds were found for cases
with Uniform Linear Array (ULA) and Uniform Planar Array (UPA). The
derived performance bounds are very simple and so provide insight into system
design.
The performance of the proposed algorithms was evaluated using standardized
channel models. The effects of the temporal variation of multipath
parameters on prediction is studied and methods for jointly tracking the
channel parameters are developed. The algorithms presented can be utilized
to enhance the performance of limited feedback and adaptive MIMO
transmission schemes
New Approaches for Channel Prediction Based on Sinusoidal Modeling
<p/> <p>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.</p
New Approaches for Channel Prediction Based on Sinusoidal Modeling
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