Milan Narandzic

Comparison of SCM, SCME, and WINNER Channel Models

This paper is summarizing and comparing properties of channel models used for Beyond-3G (B3G) MIMO simulations: 3GPP spatial channel model (SCM), its extension (SCME), and models developed by WINNER. Compared models are offering complete channel model description in a sense of large-scale as well as small-scale effects in MIMO radio-channel. WINNER targeted model was supposed to provide reliable tool for estimation of system performance, covering frequencies up to 5 GHz and bandwidths of 100 MHz in different types of environment. Since SCM was originally proposed for 2 GHz range and 5 MHz bandwidth, certain extensions (SCME) were necessary. However, SCME performance was restricted since it has been design as backward compatible with SCM. That was the motivation to start using the new WINNER generic channel model, where model parameters are extracted from channel-sounding measurements covering targeted frequency range and bandwidth. This paper describes all important differences and compares features and performances of the models.

3D-Antenna Array Model for IST-WINNER Channel Simulations

The IST-WINNER channel model (WIM) offers a complete channel model description in a sense of large-scale as well as small-scale effects in MIMO radio-channel for B3G system designs. This paper proposes the extension of 1D regular array structures being currently supported within WIM (uniform linear array - ULA) to arbitrary 3D antenna arrays. Arbitrary antenna structures are necessary for both modelling and validation purposes since antenna arrays define the spatial dimension of the radio-channel. General 3D antenna array description also embeds polarization concepts that are becoming important for modelling of 3D rotation of antenna array. In this paper two different strategies of using 3D antenna arrays in channel model simulations are proposed and discussed. Both methods are compared in terms of the computational complexity and their suitability for the current WIM channel model implementation.

Large Scale Parameter for the WINNER II Channel Model at 2.53 GHz in Urban Macro Cell

This paper presents results of wide band channel measurements at 2.53 GHz for a representative urban macro cell environment in Ilmenau, Germany. The extensive channel sounding campaign covered the MIMO radio links from 22 mobile tracks to 3 different base stations and 1 relay station. The results presented in this paper provide insight into the large scale parameter analysis of the power, delay domain, including the transmission loss and the statistical distributions of the shadow fading, narrowband k-factor and delay spread. Large scale parameters from angle domain (azimuth and elevation) are derived based on the high resolution multipath parameter estimation (RIMAX). Furthermore the cross correlation of these parameters are investigated and compared to state-of-the-art channel models as from the IST-WINNER.

Clustering of MIMO Channel Parameters - Performance Comparison

Novel channel models as from COST 273 and IST- WINNER projects are models to evaluate the performance of multi-antenna concepts under link-level and system-level. For consistent performance evaluation the channel models needed to be parameterized by multipath parameters based on measurements. It seems these parameters can be grouped into geometrically co-located paths, so called clusters. The reliability and reproducibility of the estimated parameter groups, depend inter alia on the decision criterions, initialization and the chosen cluster algorithm itself. In this paper the focus is to analyse the performance of different clustering algorithms and initialization stages. Furthermore an improved initialization approach is presented. Index Terms—Multipath clustering, spatial channel modelling, high resolution multipath parameter estimation, geometry-based stochastic channel model, generic multi-path cluster MIMO channel

Estimation of the Correlation Properties of Large Scale Parameters from Measurement Data

Interdependences of radio-channel model parameters, being observed in some measurement data, should be reproduced by the model. For that purpose the statistical correlation is often used. From the same wideband multiple-input and multiple- output (MIMO) channel data, estimated correlation between large-scale propagation parameters (e.g. delay spread and shadow fading) could be different due to non-uniqueness of post-processing procedure. In this paper, through examining a set of measurement data, we studied the impacts of differently parameterized post-processing procedures of measurement data on the auto- and cross-correlation properties of large scale parameters. We focus on the single parameter in postprocessing procedures: different margins of noise cutting level. The measurement data are gathered in a public hotspot bridge-to-car highway LOS scenario in Ulm, Germany.

Influence of Extraction Procedures on Estimated Wideband MIMO Channel Parameters

Properties of radio-channels were quantified through appropriate measures to enable comparison between different realizations or to get suitable model representation. In past such measures are established for wideband SISO channels as well as for narrowband MIMO channels. Due to lack of joint measures, radio-channels being both wideband and MIMO are still characterized separately with wideband and MIMO measures. Since original (wideband or MIMO) measures do not account for additional dimensions of measured data, they become ambiguous. As a consequence, obtained parameters could not be compared without complete knowledge about employed processing sequences. This paper illustrates non-uniqueness of subspace-defined measures in context of WINNER wideband MIMO channel model parameterization.

Estimation of the Radio Channel Parameters from a Circular Array with Directional Antennas

Channel sounding, channel modeling and their parameter extraction in particular considering MIMO configurations, plays an important role for todays realistic wireless system design and evaluation. The analysis of the gathered channel sounding data has to match the specifics in the measurement system configuration. Within this contribution directional antenna arrays are used at both sides of the measured link. Furthermore, the measurement system applies automatic gain control between subsequent MIMO snapshots and additionally within the MIMO matrix. Both features lead to improved measurement accuracy. A new extension of existing power and delay domain analysis procedures considering the automatic gain control within the MIMO matrix is introduced and evaluated. The results show that delay spread and K-factor are more affected, while shadow fading and cross polarization ratios are almost not.

Computational Complexity of Drop Based Radio Channel Simulation

Radio channel models intended for system performance assessment need to balance between accuracy and complexity. Computational complexity of channel realisation generation tend to grow due to increasing bandwidth, number of antennas, number of users, as well as with advanced network topologies. This paper investigates complexity related to radio channel parameterisation of a drop based MIMO simulation, where a number of user terminals are dropped into a network topology and short segments of the radio channel are simulated. The focus is on pre-processing associated with antenna embedding, e.g. interpolation of antenna field patterns, and stochastic propagations parameter determination, e.g. random delays, powers and azimuth angles. Complexities are calculated based on WINNER II channel model. Two modelling principles are compared, namely correlation matrix based and sum of rays based methods. Also variation of the bit error ratio (BER) performance on different drops was measured with a 2times2 MIMO hardware demonstrator. This study show that drop based simulation does not necessarily increase the complexity compared to the traditional fixed models. It also shows that geometry based simulation has lower pre-processing complexity than correlation based simulation.