A detector for space-time block coding is proposed to combat time-selective fading. To suppress both noise and interference, a minimum mean square error (MMSE) based detector is introduced for space-time block coding. It is shown by simulations that the proposed detector outperforms the conventional detectors when the channel is time-selective fading.

This paper introduces some modelling methods of time-varying stochastic process and its linear/nonlinear adaptive identification. Time-varying models are often identified by using a least square criterion. However the criterion should assume a time invariant stochastic model and infinite observed data. In order to adjust these serious different assumptions, some windowing techniques are introduced. Although the windows are usually applied to a batch processing of parameter estimates, all adaptive methods should also consider them at difference point of view. In this paper, two typical windowing techniques are explained into adaptive processing. In addition to the use of windows, time-varying stochastic ARMA models are built with these criterions and windows. By using these criterions and models, this paper explains nonlinear parameter estimation and the property of estimation convergence. On these discussions, some approaches are introduced, i.e., sophisticated stochastic modelling and multi-rate processing.

A theoretical and experimental study of a thin card-sized antenna is presented. The method of moment with a wire-grid model is used to analyze this antenna. In order to validate numerical efficiency, measurements using Wheeler method are preformed on this antenna and its wire-grid models. The experimental and theoretical results are in good agreement if the wire conductivity is well chosen. And the noise reduction of measured Wheeler efficiency using least mean square method is also examined.