Analysis of SINR for UMTS Rake Receiver-Smart Antenna Processing Using Two Different Modelling Approaches M. Jevrosimovi´ c ∗ , D. Mati´ c † , L. Jorguˇ seski † , M.H.A.J. Herben ∗ and G. Brussaard ∗ ∗ Eindhoven University of Technology, PO Box 513, 5600MB Eindhoven, The Netherlands, ∗ e-mails: {m.jevrosimovic, m.h.a.j.herben, g.brussaard}@tue.nl † TNO Telecom, PO Box 421, 2260AK Leidschendam, The Netherlands, † e-mails: {d.matic, l.jorguseski}@telecom.tno.nl Abstract— In 3G mobile communication systems, smart an- tenna processing is seen as a powerful tool to combat directional interference. In this paper we derive the statistics of the signal-to- interference+noise ratio (SINR) achieved by the combined Rake receiver-smart antenna processing for the case where the thermal noise is the only source of interference, and for the case of another interfering user. The results are based on the output of two channel models with disjoint approaches: deterministic ray- tracing model μFipre and the stochastic Wideband Directional Channel Model (WDCM). I. I NTRODUCTION The UMTS system level simulator will be an important platform for testing smart antenna adaptive algorithms in the power controlled environment. The dynamical power control is performed on the basis of the estimated instantaneous SINR at the receiver side, which is very much dependent on the signal processing technique, as well as the channel itself. The combined processing of the Rake receiver and smart antenna is common to receiver algorithms for uplink Wideband Code Division Multiple Access (WCDMA) channels, where multipath propagation and interference from other users is very pronounced [1], [2]. The effect of these algorithms on the instantaneous SINR on the link level has to be incorporated into the system level simulator using reliable channel models. The choice of the channel model is a difficult task. Most models accepted within 3GPP are still more based on the channel statistics than on the concrete site-specific information due to difficulties in including more deterministic data in a flexible way. In this paper we compare the performance achieved by the above mentioned processing scheme for the deterministic ray-tracing model µFipre, developed at the Eind- hoven University of Technology (TU/e) [3], and the stochastic WDCM for UMTS, developed at the Technical University of Lisbon [4]. The comparison has been made on the basis of SINR spatial changes with the goal to put a basis of study to incorporate more deterministic data in the stochastic model. Explanations will be given for any possible mismatch. The ray-tracing model µFipre was developed for the micro- cell environment. It makes use of detailed information about buildings and vegetation in the given environment and ac- counts for transmission through buildings and scattering from trees in addition to standard propagation mechanisms like reflections and diffractions, included in earlier ray-tracing versions. The model neglects over rooftop propagation, which is valid for a micro-cell environment where the height of the base station antenna is much below the average rooftop level. The output is the complete composition of waves in terms of amplitude, delay, phase, angles of arrival at both the side of the mobile and the base station, for the given position of the mobile and that of the base station. The elements composing the wave will be referred to as the ray parameters further in the text. The WDCM model is based on the well-known Geo- metrically Based Single-Bounce Elliptical Model (GBSBEM) for micro-cells [5], which assumes a uniform distribution of scatterers within an ellipse with the mobile and the base station placed in the foci of the ellipse. The WDCM model additionally assumes the grouping of scatterers into clusters to account for more realistic scenarios where the dispersion of waves is not continuous in time and space. The output of the WDCM is also given in terms of the ray parameters. The model only accounts for a single scattering. In order to model the effect of increasing delay spread due to multiple reflections in real scenarios, a parameter called the effective street width is introduced [6]. The paper is organized as follows. The signal model is given in Section II. The combined Rake receiver-smart antenna processing is presented in Section III. Simulation results based on the two models are given in Section IV, and the conclusions are drawn in Section V. II. SIGNAL MODEL The transmitted signal for the desired user is defined as: u s (t)= b s (t) · s s (t) (1) where b s (t) represents a bipolar information bearing signal (data bit, with T s symbol duration), and s s (t) is the spreading waveform (pseudorandom sequence with T c chip duration). The transmitted signal of the interfering user is similarly defined as: u q (t)= b q (t) · s q (t) (2) If we designate all signal and propagation characteristics of the desired user with the index s, and those of the interfering one as q the received signal at the base station is defined as 0-7803-7955-1/03/$17.00 (C) 2003 IEEE