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The possible application of adaptive antennas in microcellular operational environments is studied in this paper with the help of a site-specific ray-tracing propagation model. First are examined the statistics of propagation parameters that play an important role for the performance of adaptive antennas. Particular emphasis is put on the spatial characteristics of the radio channel under line-of-sight (LOS) and non-LOS conditions. For this reason, area statistics are produced for a typical small-cell environment for parameters such as the number of multipath clusters, number of rays, angular size, and excess delay of each impulse response, as well as the number of rays, angular size and power of the multipath clusters. Results show that contrary to the common belief for such environments, the signal is not 'almost' uniformly distributed in the spatial domain, instead is contained in a few narrow clusters. Then is examined the effect of the microcellular environment on the uplink performance of some well-known techniques like conventional beamforming, maximum entropy, switched beams, maximum ratio and optimum combining, and beam-space optimum combining. Results show that for typical time-division multiple-access (TDMA) scenarios with one and two users, the beam space optimum combining (strongest eigenvalue version) and the switched beams methods, offer the best performance. Index Terms--Adaptive antennas, microcells, propagation, ray tracing, smart antennas, space division multiple access (SDMA).

In order to evaluate the performance of third-generation mobile communication systems, radio channel models are required. The models should be capable of handling nonstationary scenarios with dynamic evolution of multipaths. In this context and due to the introduction of advanced antenna systems to exploit the spatial domain, a further expansion is needed in order to include the nonstationary characteristics of the channel. In an attempt to solve these problems, this paper presents a new stochastic spatiotemporal propagation model. The model is a combination of the geometrically-based single reflection and the Gaussian wide-sense stationary uncorrelated scattering models, and is further enhanced in order to be able to handle nonstationary scenarios. The probability density functions of the number of the multipath components, the scatterers' lifetime, and the angle of arrival are calculated to support these features. The input parameters of the model are based on results from measurement campaigns published in the open literature. Index Terms--Antenna arrays, radio channel model, wireless communications.

There is still an open debate within the research community regarding the likely performance enhancement of smart antennas versus their complexity for commercial wireless applications. The goal of the study presented in this paper is to investigate the performance improvement attainable using relatively simple smart antenna techniques when applied to the third-generation W-CDMA air interface. Methods to achieve this goal include fixed multibeam architectures with different beam selection algorithms (maximum power criterion, combined beams) or adaptive solutions driven by relatively simple direction finding algorithms. After comparing these methods against each other for several representative scenarios, some issues related to the sensitivity of these methods are also studied, (e.g., robustness to environment, mismatches originating from implementation limitations, etc.). Results indicate that overall, conventional beamforming seems to be the best choice in terms of balancing the performance and complexity requirements, in particular when the problem with interfering high-bit-rate W-CDMA users is considered. Index Terms--Adaptive algorithms, smart antennas, third generation systems, wavelength code division multiple access (WCDMA).

Features a study which proposed a stochastic spatio-temporal propagation model for third generation mobile communication systems with antenna arrays. Classification of spatio-temporal models; Description of the radio channel model and its employed parameters; Numerical examples; Conclusions.