## 1. Introduction

[2] In recent years, multiple-input multiple-output (MIMO) systems have emerged as one of the most promising approaches for maximizing capacity in wireless systems [*Winters*, 1987; *Telatar*, 1999; *Foschini and Gans*, 1998]. In principle, MIMO systems are able to provide multiple independent channels, leading to a substantial increase in channel capacity. In reality, this capacity improvement can be severely degraded by signal correlations that may arise at both the transmitter and receiver [*Shiu et al.*, 2000]. There is thus a great interest in characterizing and modeling MIMO channels for different environments, so that the channel capacity can be accurately predicted.

[3] MIMO models are usually built upon fundamental principles of conventional single-input single-output (SISO) models, while incorporating additional spatial information on the multiple antenna elements. One approach is to construct a parametric model and record a large number of typical channel realizations in order to establish the statistical distribution of the relevant parameters [*Molisch*, 2004]. Such generic models are able to reproduce certain MIMO propagation effects, but rely heavily on channel measurements. Another approach is to construct a geometrically based scattering model and assume a suitable probability density function (PDF) for the location of the scatterers. Such semideterministic models are advantageous as they can relate directly to the actual propagating waves and do not depend on expensive and time-consuming channel sounding measurements. However, many researchers have found that a considerable amount of power is contributed from multiple reflections in microcellular environments [*Steinbauer et al.*, 2001; *Gabriela Marques and Correia*, 2001; *Laurila et al.*, 2002], suggesting that single-scattering geometrical models are inadequate in terms of modeling angle of arrival and time dispersion in the channel. In this paper, a wideband semideterministic MIMO model considering multiple reflections for a microcell street scenario is presented. A typical urban main street is usually lined with irregular high-rise buildings, occasionally separated by alleys and side streets. Following *Blaunstein* [1998] and *Constantinou and Mughal* [2005], the large-scale discontinuities in building faces and/or streets in an urban environment can be modeled statistically to represent a more generic scenario. Additional scattering from such irregularities and multiple reflections along the street canyon are then evaluated analytically to construct our model.

[4] In the current model, diffractions of higher order are not considered. Furthermore, there can be instances when a diffraction is followed by a reflection and vice versa. This kind of ray component is complicated by the fact that the combined reflected and diffracted ray can have higher orders in either the reflection or diffraction part. Modeling these higher-order contributions can improve model predictions at the expense of higher complexity in the analysis as well as computational load. However, such contributions are often insignificant when compared to higher-order reflections, hence providing only a slight improvement in accuracy [*Mazar et al.*, 1998]. In order to keep the model simple and computationally efficient, all such contributions will not be taken into account.