We first derive the stochastic dynamics of a Gaussian-compound model with an inverse gamma distributed texture from Jakeman's random walk model with step number fluctuations. Following a similar approach existing for the K-distribution, we show how the scattering cross-section may be inferred from the fluctuations of the scattered field intensity. By discussing the sources of discrepancy arising during this process, we derive an analytical expression for the inference error based on its asymptotic behaviours, together with a condition to minimize it. Simulated data enables verification of our proposed technique. The interest of this strategy is discussed in the context of radar applications.

Statistical analysis and modeling of wireless channels is essential to wireless communication systems. Clark's model [1] and the corresponding statistical analysis of mobile radio reception has been widely accepted in numerous wireless applications. Since the component phases in Clarke's model are assumed to be constant in time, the well-known results of statistical analysis based on this model, such as the autocorrelation and Doppler power spectrum, are not appropriate to describe real wireless channels for which the random environments (radio propagation paths) are time-varying and accordingly for which the channel is non-constant in the absence of Doppler frequency shift. In this paper, we extend the traditional Clarke's model incorporating the effect of fluctuations in the component phases, and perform the statistical analysis which results in a closed-form expression of the autocorrelation of the fading. The theoretical power spectral density function, which is the Fourier transform of the resultant autocorrelation of the fading, is shown to fit the practical measured spectra, which is in contrast to the traditional theoretical flat fading channel spectra (Jake's spectrum in [2]). The proposed model and statistical results should have important implications for detailed spectral analysis and channel simulations for real wireless communications systems in random fluctuating electromagnetic propagation environments.

Clarke's model and Jakes' spectrum have been traditionally accepted in wireless channel modeling. In comparison with measured spectra, Jake's spectrum has limitations - it is unbounded and does not incorporate the effect of temporal phase fluctuations. Previous work extended Clarke's model to yield a theoretical power spectrum that is consistent with measured data. The modified spectrum, which includes the effects of phase fluctuations explicitly, is more appropriate as a theoretical basis for channel spectrum analysis and simulations. We develop here a state-space model that represents a wireless channel with these modified spectral characteristics. This is achieved by developing the relationship between a continuous-time state-space model and the theory of the rational transfer function. A novel method for the design of a rational transfer function of a linear system is proposed. The system input is a Gaussian white noise process, which generates a wireless channel with a desired arbitrary power spectrum. We represent the rational transfer function via the observable canonical form (OCF) to obtain the continuous-time state-space model. A discrete-time version of the state-space model is then provided to represent and simulate a discrete-time flat fading wireless channel.

Modeling wireless channels is essential to wireless communication systems. An autoregressive (AR) process of order one for wireless channel has long been assumed, but without a rigorous mathematical/physical basis. In this paper, we derive a first-order stochastic AR model for a flat stationary wireless channel, which comes from stochastic differential equation (SDE) theory concerning the nature of multipath fading channels. The resulting AR model describes more of the origin of multipath fading channels than previous AR models, and it can efficiently model and generate Rayleigh-distributed stationary fading channels. The Markovian property of the AR model is inherited through the SDE approach.

In this paper a coding theorem for multiterminal estimation is presented. The theorem is a generalization of the distributed coding theorem first proved by Slepian and Wolf (1973), where the goal is to estimate the joint probability distribution of correlated sources, rather than to reconstruct them at the receiver. For this, it is shown first that the joint-type of the received sequences is a sufficient statistic for estimation. Then, it is proved that for sufficiently large sequences, only a sum-rate lower bounded by the mutual information of the correlated sources is "sufficient" to perfectly reconstruct the sufficient statistic at the receiver. Simulation results for the special case of estimation with side information at the receiver is provided