Tracking of the temporal behaviour of path components in the radio channel - a comparison between methods
ABSTRACT An objective for future wireless communication systems is to increase capacity and transmission quality by exploiting the properties of the radio (propagation) channel. An important issue is to improve the knowledge of the temporal behavior of the radio channel through tracking and estimation algorithms. In this contribution the variation of the radio channel is described using a state-space model, where the state space consists of azimuth of arrival, delay, Doppler frequency, complex amplitude and the parameters' rate of change of propagation paths. Two nonlinear filtering algorithms are compared, i.e. a particle filter and an extended kalman filter. Simulations are conducted to evaluate the performance of these algorithms in a single-scatterer environment. Experimental investigation using measurement data is presented.
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ABSTRACT: Future wireless communication systems will exploit the rich spa- tial and temporal diversity of the radio propagation environment. This requires new advanced channel models, which need to be ver- ified by real-world channel sounding measurements. In this con- text the reliable estimation and tracking of the model parameters from measurement data is of particular interest. In this paper, we build a state-space model, and track the propagation parameters with the Extended Kalman Filter in order to capture the dynamics of the channel parameters in time. We then extend the model by considering first order derivatives of the geometrical parameters, which enhances the tracking performance due to improved predic- tion and robustness against shadowing and fading. The model also includes the effect of distributed diffuse scattering in radio chan- nels. The issue of varying state variable dimension, i.e., the number of propagation paths to track, is also addressed. The performance of the proposed algorithms is demonstrated using both simulated and measured data.
Conference Proceeding: State-space approach to propagation path parameter estimation and tracking[show abstract] [hide abstract]
ABSTRACT: In this paper we address the problem of propagation path parameter estimation in channel sounding. Propagation parameter estimation is crucial in creating realistic channel models that may be used to study the performance of multiantenna (MIMO) transceivers as well as in network planning. The proposed approach employs a nonlinear state-space model in order to capture the dynamics of the channel parameters in time. Both specular and diffuse components are considered. Extended Kalman filtering is used to estimate the state. The computational complexity is reduced by applying the matrix inversion lemma. Hence, significant savings in computation compared to conventional iterative methods is obtained. The method gives insight into the dynamic behavior of the propagation parameters, allows parameter pairing over time and facilitates analyzing the path lifetime in different measurement scenarios. The performance of the proposed technique is demonstrated using real-world channel sounding measurements.Signal Processing Advances in Wireless Communications, 2005 IEEE 6th Workshop on; 07/2005
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ABSTRACT: This study investigates the application potential of the SAGE (space-alternating generalized expectation-maximization) algorithm to jointly estimate the relative delay, incidence azimuth, Doppler frequency, and complex amplitude of impinging waves in mobile radio environments. The performance, i.e., high-resolution ability, accuracy, and convergence rate of the scheme, is assessed in synthetic and real macro- and pico-cellular channels. The results indicate that the scheme overcomes the resolution limitation inherent to classical techniques like the Fourier or beam-forming methods. In particular, it is shown that waves which exhibit an arbitrarily small difference in azimuth can be easily separated as long as their delays or Doppler frequencies differ by a fraction of the intrinsic resolution of the measurement equipment. Two waves are claimed to be separated when the mean-squared estimation errors (MSEEs) of the estimates of their parameters are close to the corresponding Cramer-Rao lower bounds (CRLBs) derived in a scenario where only a single wave is impinging. The adverb easily means that the MSEEs rapidly approach the CLRBs, i.e., within less than 20 iteration cycles. Convergence of the log-likelihood sequence is achieved after approximately ten iteration cycles when the scheme is applied in real channels. In this use, the estimated dominant waves can be related to a scatterer/reflector in the propagation environment. The investigations demonstrate that the SAGE algorithm is a powerful high-resolution tool that can be successfully applied for parameter extraction from extensive channel measurement data, especially for the purpose of channel modelingIEEE Journal on Selected Areas in Communications 04/1999; · 3.12 Impact Factor