Troels Pedersen

ARIMA-Based Time Series Model of Stochastic Wind Power Generation

This paper proposes a stochastic wind power model based on an autoregressive integrated moving average (ARIMA) process. The model takes into account the nonstationarity and physical limits of stochastic wind power generation. The model is constructed based on wind power measurement of one year from the Nysted offshore wind farm in Denmark. The proposed limited-ARIMA (LARIMA) model introduces a limiter and characterizes the stochastic wind power generation by mean level, temporal correlation and driving noise. The model is validated against the measurement in terms of temporal correlation and probability distribution. The LARIMA model outperforms a first-order transition matrix based discrete Markov model in terms of temporal correlation, probability distribution and model parameter number. The proposed LARIMA model is further extended to include the monthly variation of the stochastic wind power generation.

A variational message passing algorithm for sensor self-localization in wireless networks

We propose a novel algorithm for sensor self-localization in cooperative wireless networks where observations of relative sensor distances are available. The variational message passing (VMP) algorithm is used to implement a mean field solution to the estimation of the posterior probabilities of the sensor positions in an ∝2 scenario. Extension to ∝3 is straight-forward. Compared to non-parametric methods based on belief propagation, the VMP algorithm features significantly lower communication overhead between sensors. This is supported by performance simulations which show that the estimated mean localization error of the algorithm stabilizes after approximately 30 iterations.

Modeling of Reverberant Radio Channels Using Propagation Graphs

In measurements of in-room radio channel responses, an avalanche effect can be observed: earliest signal components, which appear well separated in delay, are followed by an avalanche of components arriving with increasing rate of occurrence, gradually merging into a diffuse tail with exponentially decaying power. We model the channel as a propagation graph in which vertices represent transmitters, receivers, and scatterers, while edges represent propagation conditions between vertices. The recursive structure of the graph accounts for the exponential power decay and the avalanche effect. We derive a closed-form expression for the graphs transfer matrix. This expression is valid for any number of interactions and is straightforward to use in numerical simulations. We discuss an example where time dispersion occurs only due to propagation in between vertices. Numerical experiments reveal that the graphs recursive structure yields both an exponential power decay and an avalanche effect.

Tracking of Time-Variant Radio Propagation Paths Using Particle Filtering

In this contribution a low-complexity particle filtering algorithm is proposed to track the parameters of time-variant propagation paths in multiple-input multiple-output (MIMO) radio channels. A state-space model is used to describe the path evolution in delay, azimuth of arrival, azimuth of departure, Doppler frequency and complex amplitude dimensions. The proposed particle filter (PF) has an additional resampling step specifically designed for wideband MIMO channel sounding, where the posterior probability density functions of the path states is usually highly concentrated in the multi-dimensional state space. Preliminary investigations using measurement data show that the proposed PF can track paths stably with a small number of particles, e.g. 5 per path, even in the case where the paths are undetected by the conventional SAGE algorithm.

Distance Dependent Model for the Delay Power Spectrum of In-room Radio Channels

A model based on experimental observations of the delay power spectrum in closed rooms is proposed. The model includes the distance between the transmitter and the receiver as a parameter which makes it suitable for range based radio localization. The experimental observations motivate the proposed model of the delay power spectrum with a primary (early) component and a reverberant component (tail). The primary component is modeled as a Dirac delta function weighted according to an inverse distance power law (d-n). The reverberant component is an exponentially decaying function with onset equal to the propagation time between transmitter and receiver. Its power decays exponentially with distance. The proposed model allows for the prediction of, e.g., the path loss, mean delay, root mean squared (rms) delay spread, and kurtosis versus the distance. The model predictions are validated by measurements: they show good agreement with respect to distance dependent trends.

Radio Channel Modelling Using Stochastic Propagation Graphs

In this contribution the radio channel model proposed in [1] is extended to include multiple transmitters and receivers. The propagation environment is modelled using random graphs where vertices of a graph represent scatterers and edges model the wave propagation between scatterers. Furthermore, we develop a closed form analytical expression for the transfer matrix of the propagation graph. It is shown by simulation that impulse response and the delay-power spectrum of the graph exhibit exponentially decaying power as a result of the recursive scattering structure of the graph. The impulse response exhibits a transition from specular to diffuse signal contributions as observed in measurements.

Parametric characterization and estimation of Bi-Azimuth and delay dispersion Of individual path components

In this contribution, we derive a distribution that is suitable for characterizing biazimuth (azimuth of arrival and azimuth of departure) and delay dispersion of individual path components in the response of the radio channel. This distribution maximizes the entropy under the constraint that its first and second moments are specified. We propose to use the density function of the derived distribution to characterize the shape of the biazimuth-delay power spectrum of individual path components. The applicability of this characterization in real conditions is assessed using measurement data.

Joint estimation of Doppler frequency and directions in channel sounding using switched Tx and Rx arrays

To save hardware and reduce the effort to calibrate the system, channel sounding with Tx and Rx antenna arrays is commonly performed in TDM mode where the array elements are successively switched. We refer to this technique as TDM-MIMO channel sounding. The ISI-SAGE algorithm (Fleury, B.H. et al., 2003; Yin, X. et al., 2003), applied in combination with TDM-MIMO channel sounding, makes it possible to extend the Doppler frequency estimation range (DFER) (Yin, X. et al., 2003). The extension is significant when arrays with large element numbers are employed. We derive the signal model for TDM-MIMO channel sounding and report analytical investigations showing that this DFER extension requires selection of switching modes (SMs) tailored to the array characteristics. The SM of a switched array is the temporal order in which the array elements are switched. In fact, the traditionally used SMs of uniform linear and planar arrays, where the elements are switched according to their natural spatial ordering, prove to be inappropriate as they lead to an ambiguity in the joint estimation of DF and directions. We also introduce the concept of normalized sidelobe level (NSL) associated to the SM of a switched array. We show that minimizing the NSL is a sensible criterion for the identification of SM, leading to DF and direction estimates with nearly optimum performance in terms of root mean square estimation error. Finally experimental investigations illustrate the impact of the SM of a uniform planar array on the behaviour of the DF and direction of arrival estimates computed with the ISI-SAGE algorithm.

EXIT Chart Analysis of Binary Message-Passing Decoders

Binary message-passing decoders for LDPC codes are analyzed using EXIT charts. For the analysis, the variable node decoder performs all computations in the L-value domain. For the special case of a hard decision channel, this leads to the well-know Gallager B algorithm, while the analysis can be extended to channels with larger output alphabets. By increasing the output alphabet from hard decisions to four symbols, a gain of more than 1.0 dB is achieved using optimized codes. For this code optimization, the mixing property of EXIT functions has to be modified to the case of binary message-passing decoders.

Parametric Characterization and Estimation of Bi-Azimuth Dispersion Path Components

In this contribution, we derive a probability distribution suitable for characterizing bi-azimuth (azimuth of arrival and azimuth of departure) direction dispersion of individual path components in the response of the propagation channel. This distribution belongs to the family of generalized von-Mises-Fisher distributions. The elements in this family maximize the entropy under the constraint that the expectations and correlation matrix of the directions are known. The probability density function (pdf) of the proposed distribution is used to describe the bi-azimuth power spectrum of individual path components. An estimator of the parameters of the pdf is derived and applied to characterize the spreads in both azimuth of departure and azimuth of arrival, as well as the correlation between both azimuths of individual path components. Preliminary results from an experimental investigation demonstrate the applicability of the proposed characterization in real environments