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. Index Terms-ARIMA processes, Markov processes, stochastic processes, time series, wind power generation.

Abstract-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 graph's 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 graph's recursive structure yields both an exponential power decay and an avalanche effect.

Abstract-We propose a novel algorithm for sensor selflocalization 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 R 2 scenario. Extension to R 3 is straightforward. 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.

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.

Abstract-Binary message-passing decoders for low-density parity-check (LDPC) codes are studied by using extrinsic information transfer (EXIT) charts. The channel delivers hard or soft decisions and the variable node decoder performs all computations in the L-value domain. A hard decision channel results in the well-know Gallager B algorithm, and increasing the output alphabet from hard decisions to two bits yields a gain of more than 1.0 dB in the required signal to noise ratio when using optimized codes. The code optimization requires adapting the mixing property of EXIT functions to the case of binary message-passing decoders. Finally, it is shown that errors on cycles consisting only of degree two and three variable nodes cannot be corrected and a necessary and sufficient condition for the existence of a cycle-free subgraph is derived.