Morten Lomholt Jakobsen

Parametric Modeling and Pilot-Aided Estimation of the Wireless Multipath Channel in OFDM Systems

In this paper we present a refined model of the wireless multipath channel along with a thorough analysis on the impact of spatial smoothing techniques when used for improved channel estimation. The state-of-the-art channel estimation algorithm for pilot-aided OFDM systems is robustly designed and operates without knowledge of the time-varying multipath propagation delays in the wireless channel. However, algorithms exploiting knowledge of these time-varying delay parameters can outperform the state-of-the-art solution. We demonstrate from simulations how the Unitary ESPRIT algorithm together with spatial smoothing techniques exhibit a promising potential for multipath propagation delay estimation. Furthermore, we show that the optimum smoothing parameters depend notably on the channel model assumed, specifically in terms of the dynamical behavior of the multipath delays.

Non-Stationary Propagation Model for Scattering Volumes With an Application to the Rural LMS Channel

The design of efficient positioning algorithms in navigation satellite systems, like GNSS, operating in land mobile environments demands for detailed models of the radio channel. On the one hand, the models need to accurately describe scattering and shadowing/obstruction caused by vegetation. On the other hand, they have to incorporate the steady change in the propagation constellation due to the receiver displacement. In this paper we propose a model of the non-stationary radio channel in a scenario where a mobile receiver drives past a scattering volume, such as a ball or a cuboid, while the transmitter is elevated, like in satellite positioning applications. Such a volume may represent the canopy of a single tree, the canopies of trees in a grove, or a small forest. Scattering by the volume is characterized by means of multiple point-source scatterers that are assumed to form a marked spatial point process. The system functions of the radio channel are given. An integral form of the time-frequency correlation function of the component in the system functions contributed by the scattering volume is obtained as a direct consequence of Campbells Theorem. Furthermore, a closed-form approximation of this integral form is derived for time lags corresponding to displacements along the receiver trajectory for which the plane wave assumption holds. The approximation takes into account the steady change in the propagation constellation. The proposed model is validated by means of Monte Carlo simulations and by comparing its prediction capabilities with experimental data in a scenario where a mobile receiver drives past a roadside tree. A good agreement is observed, despite the simplicity of the model.

Analysis of Stochastic Radio Channels With Temporal Birth-Death Dynamics: A Marked Spatial Point Process Perspective

We employ the theory of spatial point processes to revisit and reinterpret a particular class of time-variant stochastic radio channel models. Common for all models in this class is that individual multipath components are emerging and vanishing in a temporal birth-death like manner, with the underlying stochastic birth-death mechanism governed by two facilitating assumptions. Well-known analytical properties of this class of channel models are reestablished by simple arguments and several new results are derived. The primary tool used to obtain these results is Campbells Theorem which enables novel assessment of the autocorrelation functions of random processes used in the general channel model description. Under simplifying assumptions the channel transfer function is shown to be wide-sense stationary in both time and frequency (despite the birth-death behavior of the overall channel). The proof of this result is a consequence of the point process perspective, in particular by circumventing enumeration issues arising from the use of integer-indexed path components in traditional channel modeling approaches. The practical importance of being able to analytically characterize the birth-death channel models is clearly evidenced, e.g., by the fact that key parameters enter explicitly in measurable quantities such as the power-delay profile.

Online estimation of wind turbine blade deflection with UWB signals

In this paper we use ultra-wideband (UWB) signals for the localization of blade tips on wind turbines. Our approach is to acquire two separate distances to each tip via time-delay estimation, and each tip is then localized by triangulation. We derive an approximate maximum a posteriori (MAP) delay estimator exploiting i) contextual prior information and ii) a direct-path approximation. The resulting deflection estimation algorithm is computationally feasible for online usage. Simulation studies are conducted to assess the overall triangulation uncertainty and it is observed that negative correlation between the two distance estimates is detrimental for the tip localization accuracy. Measurement data acquired in an anechoic chamber is used to confirm that the UWB-hardware complies with the desired/relevant ranging accuracy. Finally, measurement data obtained from a static test bench is used to demonstrate that the approximate MAP-based localization algorithm is able to outperform standard methods.

Wireless indoor positioning relying on observations of received power and mean delay

This contribution introduces position estimation methods relying on observations of the received power and mean delay obtained in a wideband multi-link scenario. In particular, one- and two-step methods are introduced based on statistical models of the observed link parameters. The proposed methods are tested on data from a wideband measurement campaign. The results show that including observations of mean delay of the wideband links can notably improve positioning accuracy as compared to relying on observations of received power alone.

Analysis of Smoothing Techniques for Subspace Estimation with Application to Channel Estimation

In this paper, we present a thorough investigation on the impact of spatial smoothing and forward-backward averaging techniques for subspace-based channel estimation. The spatial smoothing technique requires the selection of a window size, which, if not selected properly, leads to dramatically performance breakdown of the subspace-based methods. We aim to provide an explanation of the performance drop for certain window sizes and subsequently an understanding of a proper window size selection. In particular, we describe the behavior of the magnitude of the least signal eigenvalue as a function of the window size employed. Through simulations we show that the magnitude of this eigenvalue is of particular importance for estimating the subspace and the entailing performance of the channel estimator.

Online estimation of context dynamics and its impact on context sensitive applications

Context management systems allow services and application easy access to remote dynamic context elements, in turns allow these to adapt to the users context. The fact that most information is dynamic and changes value over time, leads to potential risk of obtaining information mismatching the true value due to the communication delay. The probability for this we call the mismatch probability. Obtaining the mismatch probability is not trivial, as it involves knowledge of both the delay process and description of the dynamics of the information element. This paper addresses online estimation of the parameters related to the information element needed to derive the mismatch probability, and the effect that this estimation has on a context sensitive service discovery for Personal Networks.