Measurement-based simulation of mobile radio ... - Semantic Scholar

Proceedings IEEE 54th Vehicular Technology Conference (VTC Fall 2001), Atlantic City, NJ, USA, October 2001

Measurement-based simulation of mobile radio channels with multiple antennas using a directional parametric data model D. Hampicke, Ch. Schneider, M. Landmann, A. Richter, G. Sommerkorn, R.S. Thomä Technical University Ilmenau, Dept. of Communication & Measurement P.O. Box 100565, 98684 Ilmenau, Germany Phone.: +49 3677 69 1157 Fax: +49 3677 69 1113 e-mail: [email protected] WWW: http://www-emt.tu-ilmenau.de Abstract- In this paper a parametric modeling approach for multiple input – multiple output (MIMO) radio channels is introduced that is based on measured data. The MIMO measurement principle can be effectively exploited to estimate the propagation direction of any significant path at both ends of the wireless link simultaneously. From the estimated parameter sets a precise reconstruction of the multidimensional wave field in the aperture domains of time, frequency and space is possible. This enables various analyses and simulations of MIMO transmission links in a very realistic way.

I. INTRODUCTION Space-time processing is a powerful technology in order to enhance the system performance of future mobile radio systems significantly. In this technology, multiple antennas are employed at the transmitter (Tx) and/or receiver (Rx) site. Especially with so-called MIMO systems (multiple-inputmultiple-output), i.e. where multiple antennas are installed at both terminals, the spatial diversity of multipath channels is optimally exploited and thus, highest data rates per user, improved coverage and link quality and also enhanced system capacity can be expected [1]. For the design, simulation, and performance evaluation of space-time adaptive processors [2], profound knowledge of the spatial radio channel impulse response (CIR) statistics is essential. For that purpose, numerous spatial channel models and propagation simulation tools have been developed in recent years (see [3] for an overview). However, in order to keep the simulations manageable, these models usually more or less simplify the complicated electromagnetic transmission processes of reflection, scattering, diffraction, shadowing, etc. Considering complicated radio environments like industrial areas or factory halls, these models seem inappropriate to reproduce the reality of wave propagation satisfactorily, especially when taking into account that due to random user mobility and possible movement of parts of the environment the radio channel must be considered as being time-variant. Consequently, the measurement of MIMO channels has gained increasing attention over the last years. Recent measurements focus on the evaluation of the system capacity [4], [5], [6], the investigation of temporal channel variations [7], and also the estimation of the propagation direction of any significant path at both ends of the wireless link [8]. In this paper we will show that the measured MIMO

data can also be used to deduce measurement-based channel models applicable for realistic system simulation and performance assessment. We will start with a short description of the MIMO measurement equipment and the explanation of the underlying multidimensional wave propagation model. Then in section III we introduce the concept of deterministic parametric channel modeling based on measured MIMO data and discuss some initial results in order to get more evidence of the potential of this modeling approach. Finally, in section IV some prospective applications of this modeling approach in terms of link-level performance simulation of MIMO transmission links are considered and an outlook is given. II. MULTIPATH PARAMETER EXTRACTION FROM MIMO CHANNEL MEASUREMENTS A. MIMO channel sounding For the measurements the wideband vector channel sounder RUSK ATM [9] has been used, which operates at 5.2 GHz and allows real-time measurements of the complex channel impulse response with a bandwidth of 120 MHz. The impulse response identification is based on broadband periodic multifrequency excitation signals and correlation processing. By fast switching of the multiple transmit antennas and receive antenna outputs real-time recording of the CIR is assured. Due to the high measurement repetition rate of the sounder hardware and its long-term recording capability both, smallscale fading of the path weights as well as long-term variations of the CIR sequence are captured. The channel sounder measurement results can be directly interpreted as a time-dependent sequence of the channel frequency response estimates H(t,f,s) with t corresponding to the time and f to the frequency. The resolved spatial domain s is represented by the antenna array output. A geometric transformation that depends on the antenna array architecture and a three-dimensional Fourier transform result in the joint Doppler/delay/angular resolved impulse response h(α,τ,Θ). For our MIMO channel sounding experiments a uniform linear array (ULA) with 8 antenna elements has been used at the Rx site. Since it seems reasonable to provide 360° coverage for the mobile station, a circular uniform beam array (CUBA) [10] was employed at the Tx site. This antenna prototype consists of a circular arrangement of directive

Proceedings IEEE 54th Vehicular Technology Conference (VTC Fall 2001), Atlantic City, NJ, USA, October 2001 antennas and has been designed for 5.2 GHz. The large measurement bandwidth together with the properly antenna array dimensions gives the potential to accurately resolve the multiple dimensions of time delay of arrival (TDOA), direction of arrival (DOA) at the receiver site, direction of departure (DOD) at the transmitter site, and Doppler shift. B. Extraction of multipath parameters Let us consider a finite sum of discrete, locally planar waves, i.e. the wave fronts along the aperture of the receiving and transmitting antennas are presumed to be planar. It is further assumed that the relative bandwidth is small enough so that the time delay of the impinging waves simply transforms to a phase shift between individual antennas of the arrays, and the array aperture is small enough that there is no observable magnitude variation of any single wave received at different array elements. Furthermore, the TDOA τ p of the wave-front p , its DOA at the receiver ψ Rp , ϑ Rp , DOD at the transmitter ψ Tp , ϑTp (both in terms of azimuth and elevation), and Doppler shift α p are presumed to be timeinvariant during a measurement snapshot time interval, which is used to estimate one set of channel parameters. The measurement time interval is given by the number of channel response vector snapshots. In complex envelope notation, we can define the basic signal model as

∑ γ δ (α − α )δ (τ − τ ) ) δ (ϑ − ϑ )δ (ψ − ψ ) δ (ϑ − ϑ )

h (α , τ ,ψ R , ϑ R ,ψ T , ϑT ) =

P

p

p

p

p =1

(

⋅ δ ψ R −ψ R

P

R

RP

T

TP

T

(1)

TP

giving the complex multipath channel impulse response described by the P dominant paths and resolved in 6 dimensions for both directions seen from Tx and Rx, delay, and Doppler frequency shift. Expression γp represents the 2x2 path weight matrix describing the two orthogonal polarization responses of the Rx and Tx antennas, respectively, and the cross polarization coupling. Presupposing appropriate antenna arrays at Tx and Rx site, the transformation of the data model from (1) to the aperture space leads to [11] P

H(t , f , n R , m R , nT , mT ) = ∑ C HR γ p C T ⋅ e ⋅e

⋅e

− j 2πn Rϕ R p

e

− j 2πmRθ R p

⋅e

− j 2πnT ϕ T p

III. MEASUREMENT-BASED PARAMETRIC CHANNEL MODELING Modeling of the mobile radio channel is essential for the development of new mobile radio systems, since this allows to assess the benefits of different multiple accessing and signal processing techniques in order to improve the performance of those systems. Statistical channel models attempt to reproduce certain channel characteristics observed from propagation measurements by statistical means and have been widely studied in recent years. In contrast, deterministic models (e.g. ray tracing models) try to give a more or less detailed reproduction of the actual physical wave propagation process for a given environment. They are based on geometric optics and attempt to model the wave propagation phenomena in a continuum of reflecting, diffracting and scattering objects by a superposition of plane waves. The accuracy of the ray tracing method is controlled by the number of rays used. However, from a practical point of view the high computational burden, the necessity of detailed site-specific geometric information, and the required knowledge about the reflection coefficients of the scattering or reflecting objects make ray tracing models difficult to use. Mobile station (MS) + local scattering

Base station (BS)

− j 2πα p t

(2)

p =1

− j 2πτ p f

vector snapshot time length, the measurement bandwidth, and the finite array aperture, a satisfactory resolution can only be obtained if a superresolution parameter estimation procedure is applied. Because of its computational efficiency, the Unitary ESPRIT algorithm [12] has been chosen for joint multipath channel parameter estimation.

e

− j 2πmT θ T p

with the matrices CT and CR defining the radiation patterns at Tx and Rx. Variables m and n represent the corresponding spatial aperture domains at receiver and transmitter antenna. This harmonic retrieval problem can be solved using a Kdimensional parameter estimation procedure based on the ESPRIT algorithm thus identifying the parameters set {αP, τP, θRP, ϕRP, θTP, ϕTP} (details on the calculation procedure can be found e.g. in [11]). Since the aperture size in the time, frequency, and Tx/Rx spatial domains is limited by the maximum channel response

Fig. 1. Mobile station surrounded with local scatterers as seen from the base station. For our investigations we now adapt the underlying deterministic plane wave model from ray tracing and apply it to the measurement data. Considering at first the case of measurements with a limited receiver resolution we will realize that non-resolved paths superimpose with different (time-varying) phase shifts, thus causing fast fading. It is well known that a wide measurement bandwidth essentially reduces fading. Additionally, directional resolution yields even more potential to resolve multipath. Fig. 1 indicates that

Proceedings IEEE 54th Vehicular Technology Conference (VTC Fall 2001), Atlantic City, NJ, USA, October 2001 especially in the case of double directional resolution local scattering around the MS (mobile station) position might be resolved, which usually is not resolved from the BS (base station) position. Therefore we can state that with increasing resolution in terms of the individual parameters as well as with respect to the number of resolved parameters, the individual path weights γp can be considered to be timeinvariant for a short-time measurement sequence. Note that the deterministic time variation of the path phases is contained in the Doppler shift parameter. This reveals that with a high multidimensional resolution, the measurement data model approaches the deterministic data model of ray tracing. From a practical point of view it is clear that for this modeling approach the application of appropriate MIMO measurement techniques together with proper antenna architectures plays a key role. The antennas should be arranged in such a way that channel model parameters describing the K-dimensional wave propagation model from (1) can be resolved as accurately and generally as possible. In this context the application of a circular arrangement of directive antennas, for example the CUBA mentioned above is of interest since this antenna offers the inherent advantage of 360° coverage and thus gives the potential to resolve multipath scattering around the MS. Using the modified

Tx

ESPRIT algorithm for CUBA arrangements introduced in [13], the DOD/DOA/TDOA parameters are jointly estimated, and from these now the multidimensional wave field in the aperture domains of time, frequency and space can be reconstructed. This way we get a replica of the wave propagation in the measured scenario without facing the restrictions of ray tracing modeling. Superresolution in this context means that the approximated aperture area is larger than the measured aperture. This extrapolation gain, however, depends on the inherent resolution gain. Considering for example the spatial aperture, this area is restricted to the local environment at the Tx and Rx antennas, respectively. For demonstration a measurement example is given in Fig. 2. The measurements were performed in an open courtyard at the Ilmenau Technical University using the RUSK channel sounder. The scenario was about 27m × 26m in dimension with several trees inside and can be characterized as typically microcell. At the Rx site the 8element uniform linear array has been employed while at the Tx site the CUBA was used. For a measurement with fixed Tx and Rx position all relevant different propagation paths have been estimated using the CUBA ESPRIT [13].

3 2 1 0 -1 -2

Rx

-3 -3

-2

-1

0

1

2

3

Fig. 2: Joint DOA/DOD/TDOA estimation result for the courtyard measurements (left) and example for the electromagnetic field reconstruction from estimated parameters around the CUBA-Tx based on the plane wave assumption (right). Antennaindependent investigation based on the reconstructed wave field is illustrated in the right figure for a 16-element uniform circular array (represented by “ o “) and for an 8×8 uniform rectangular array (represented by “ + “).

Proceedings IEEE 54th Vehicular Technology Conference (VTC Fall 2001), Atlantic City, NJ, USA, October 2001 As shown in the left part of Fig. 2 several propagation paths as seen from Tx and Rx appear. The DOD/DOA angles are indicated by the path line orientation and the line length (equally split up to Rx and Tx) represents the TDOA. The line width corresponds to the path magnitude relative to lineof-sight. Some lines clearly indicate the presence of double bounce reflections, identified by path lines that cannot be triangulated to a single reflection point. From the estimated path parameters we are now able to reconstruct the electromagnetic field in a distinct surrounding of Tx or Rx, based on the plane wave assumption. The example in the right part of Fig. 2 illustrates an interference pattern obtained for a reconstructed wave field around the Tx position employing a CUBA (Note that the CUBA is located in the center of the interference pattern since all beams of this antenna have a common phase center). The scaling of the axes hereby corresponds to the number of wavelengths. This method of spatial electromagnetic field modeling turns out to be more or less independent of the antennas used during the measurement. Consequently, during the off-line simulation practically arbitrary antenna architectures can be created artificially. These arrays may even be bigger in size than the array used during the measurement. This is also indicated in the right part of Fig. 2, where two other possible array structures were placed in the interference pattern: a uniform rectangular array with 8×8 elements separated by half a wavelength (represented by “ + ”) and a uniform circular array with 16 elements on a diameter of two wavelengths (represented by “ ο “). Besides the extrapolation in spatial direction it seems also reasonable to apply this principle of extrapolation to the other aperture domains of frequency and time (presupposing stationarity in the respective domain). This way statistic ensembles of channel response vector snapshots can be created artificially that might be used for statistical evaluation and link-level performance simulation of MIMO transmission links. (Note that a somewhat different proposal for creation of a whole ensemble of channel snapshots from a single measured snapshot was given in [5]. This, however, was achieved by assigning some random phase factor to the estimated multipath components). Compared to other measurement-based simulation approaches where no parameter step is involved (e.g. [14]) this offers the advantage that measurements must not be repeated many times under well-defined conditions and thus the simulations can be much more flexible. Moreover, considering that the carrier wavelength is much smaller than the extrapolated array dimension, some "virtual movement” of the MS can be introduced that is superimposed on the MS trajectory covered during recording the measured data. Another advantage of this measurement-based modeling method is given by the fact that due to the measurementbased data possible fading correlation between the antenna elements at Tx and Rx is inherently included. In many of the existing investigations on MIMO channels complete decorrelation was assumed, or the mutual correlation was

represented by some statistical approximation [15]. However, analyses given in [16] have discussed this effect and shown possible impacts on the system capacity if fading correlation is present. Moreover, due the long-term recording capability of the channel sounder long-term variations of the CIR sequence can be reproduced easily. Considering the temporal changes of a mobile radio channel it is apparent that multipath components occasionally appear and disappear. While in other modeling approaches this problem is either ignored or treated by introducing some appropriate processes (e.g. Poisson processes) to model the generation and elimination of those multipath components (e.g. [17]), with measured MIMO data it is straightforward to cover dominant propagation paths during their whole “life time”. This is especially important for simulation of tracking algorithms and also for prediction of future fading, enabling to track propagation paths during their whole life time. IV. CONCLUSION AND OUTLOOK In this work we have introduced the concept of deterministic parametric channel modeling based on measured MIMO data. Using advanced MIMO radio vector channel sounding techniques for real-time measurements together with proper antenna array architectures, the channel model parameters describing the multidimensional wave propagation model can be resolved as accurately and generally as possible. Hereby the advantage of MIMO compared to SIMO measurements is that it yields much higher resolution power to estimate the deterministic multipath model. This way even the characterization of temporal changes of the model parameters (DOA, TDOA, DOD, path weights) for typical movements in a mobile radio environment is possible. Since this method of spatial electromagnetic field modeling in principle is independent of the antennas used during the measurement, models for almost arbitrary antenna architectures can be generated. Moreover, statistic ensembles of channel impulse response vector snapshots can be created artificially in order to facilitate link level simulations of multi-user scenarios. Initial investigations have been carried out based on measured data. An extensive quantitative evaluation of the proposed deterministic modeling approach with measured data from different propagation environments is currently in progress. This includes the assessment of the validity range regarding the field reconstruction and extrapolation in any of the possible aperture dimensions. Moreover, the validity of the plane wave fronts assumption must be verified by means of measurements and simulations and where applicable, bend wave fronts must be considered in the analysis. Prospective applications of this modeling approach include the realistic link-level simulation of multi-user scenarios based on estimated parameter sets as well as evaluation of the capacity of such systems in differing locations and configurations. Simulations based on the measured channel impulse response sequences can be much more flexible and

Proceedings IEEE 54th Vehicular Technology Conference (VTC Fall 2001), Atlantic City, NJ, USA, October 2001 informative since the variety of different processor principles, the complexity of the processors, as well as timing and synchronization mechanisms can be tested. Thus, measurement-based simulations bridge the gap between the costly hardware experiment and the simplified simulation. This offers innovative potential for realistic simulation and optimization of MIMO space-time modems, which are considered very promising for capacity enhancement in future mobile radio systems. ACKNOWLEDGMENT This work was supported by the Deutsche Forschungsgemeinschaft (DFG) within the AKOM focus project. The authors wish to thank MEDAV GmbH for cooperation, R. Klukas (IRK Dresden) and Prof. Wiesbeck (University of Karlsruhe, IHE) for support in antenna design. REFERENCES [1] G.J. Foschini, M.J. Gans: “On Limits of Wireless Communications in a Fading Environment when Using Multiple Antennas,” Wireless Personal Communications, Vol. 6, pp. 311-355, 1998. [2] A. Paulraj, C.B. Papadias: “Space-time processing for wireless communications,” IEEE Signal Processing Magazine, vol.14, no.5, November 1997. [3] R.B. Ertel, P. Cardieri, K.W. Sowerby, T.S. Rappaport, J.H. Reed, „Overview of Spatial Channel Models for Antenna Array Communication Systems“ IEEE Personal Comm., Vol. 5, no.1, pp. 10-22, Feb. 1998. [4] C. Martin, N. Sollenberger, J. Winters: “Multiple-Input Multiple-Output (MIMO) Radio Channel Measurements,” First IEEE Sensor Array and Multichannel Signal Processing Workshop, Boston, MA, March 2000. [5] M. Steinbauer, A.F. Molisch, A. Burr, R. Thomä: „MIMO Channel Capacity based on Measurement Results,“ Proc. European Conference on Wireless Technology (ECWT 2000), pp.52-55, Paris, October 2000. [6] R. Stridh, B. Ottersten: „Spatial Characterization of Indoor Radio Measurements at 5 GHz,“ Proc. First IEEE Sensor Array and Multichannel Signal Proc. Workshop, Cambridge, MA, 2000. [7] D.P. McNamara, M.A. Beach, P.N. Fletcher, P. Karlsson: “Temporal Analysis of Indoor MultipleInput Multiple-Output (MIMO) Channel Measurements,” Proceedings IEE ICAP, Manchester, Vol. 2, pp. 578-582, April 2001. [8] A. Richter, D. Hampicke, G. Sommerkorn, R.S. Thomä: „MIMO Measurement and Joint M-D Parameter Estimation of Mobile Radio Channels,“ Proc. IEEE Vehicular Technology Conference VTC2001-Spring, Rhodos, Greece, 2001.

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