Extending the 3GPP Spatial Channel Model (SCM) - Communication ...

An Interim Channel Model for Beyond-3G Systems Extending the 3GPP Spatial Channel Model (SCM) Daniel S. Baum and Jan Hansen

Jari Salo

ETH Zürich, Zürich, Switzerland {dsbaum,hansen}@nari.ee.ethz.ch

Helsinki University of Technology, Espoo, Finland [email protected]

Giovanni Del Galdo and Marko Milojevic

Pekka Kyösti

Ilmenau University of Technology, Ilmenau, Germany {giovanni.delgaldo,marko.milojevic}@tu-ilmenau.de

Elektrobit Ltd., Oulu, Finland [email protected]

Abstract— This paper reports on the interim beyond-3G (B3G) channel model developed by and used within the European WINNER project. The model is a comprehensive spatial channel model for 2 and 5 GHz frequency bands and supports bandwidths up to 100 MHz in three different outdoor environments. It further features time-evolution of system-level parameters for challenging advanced communication algorithms, as well as a reduced-variability tapped delay-line model for improved usability in calibration and comparison simulations.

While the WINNER project only started recently, there is an immediate demand for models suitable for initial usage. This document presents the result of our studies in form of a model that is used for initial evaluation of B3G technologies in outdoor scenarios within the WINNER project. Contributions. Our specific contributions are as follows: •

We analyze shortcomings of a selected spatial channel model standard with respect to the identified requirements from other WINNER Work Packages.

•

We evaluated results found from literature search and derived from our own measurement data to devise missing parameters.

•

We propose a set of backward compatible extension to the 3GPP Spatial Channel Model (SCM).

Keywords- channel model, beyond-3G, MIMO, SCM, 3GPP

I.

INTRODUCTION

In recent years Multiple-Input Multiple-Output (MIMO) wireless communication techniques have attracted strong attention in research and development due to their potential benefits in spectral efficiency, throughput and quality of service. Only recently, however, has this technology been considered to be included in wireless communication system standards, such as IEEE 802.11n for wireless LANs (WLAN), IEEE 802.16 for broadband fixed wireless access (FWA), and 3GPP high-speed downlink packet access (HSDPA) for cellular mobile communications. Any wireless communication system needs to specify a propagation channel model that can act as a basis for performance evaluation and comparison. With advancing communication technologies, these models need to be refined as further characteristics of the channel can be exploited and thus need to be modeled. To enable MIMO, the standardization groups 802.11 and 3GPP thus first defined spatial channel models suitable for their applications [1], [2]. Upcoming communication systems will be based on a new set of system parameters (e.g. extended bandwidth and new frequency bands), a broader range of and additional scenarios (e.g. mobile to mobile, mobile hotspot), and new communication techniques (e.g., tracking algorithms). This triggers new requirements on the underlying channel models. The European WINNER project [3], which is part of the Framework 6 effort, is currently researching the outline of a system design of such a B3G system. In WINNER, it is the goal of Work Package 5 to come up with channel models that suit the needs in the project. This work has been performed in the framework of the IST project IST2003-507581 WINNER, which is partly funded by the European Union. The authors would like to acknowledge the contributions of their colleagues.

0-7803-8887-9/05/$20.00 (c)2005 IEEE

This paper summarizes the results reported in [4]. II.

3GPP SCM

We have identified two publications ([1], [2]) defining spatial / MIMO radio channel models that are commonly accepted and used. Other publications focus mainly on aspects and certain effects of the radio channel. As the 802.11n model is targeted towards indoor applications, we have selected the 3GPP SCM as a basis for outdoor channel model extensions. A. Properties The SCM is a so-called geometric or ray-based model based on stochastic modeling of scatterers. It defines three environments (Suburban Macro, Urban Macro, and Urban Micro) where Urban Micro is differentiated in line-of-sight (LOS) and non-LOS (NLOS) propagation. There is a fixed number of 6 “paths” in every scenario, each representing a Dirac function in delay domain, but made up of 20 spatially separated “sub-paths” according to the sum-of-sinusoids method [5]. Path powers, path delays, and angular properties for both sides of the link are modeled as random variables defined through probability density functions (PDFs) and cross-correlations. All parameters, except for fast-fading, are drawn independently in time, in what is termed “drops”.

Scenario

Suburban Macro, Urban Macro

No. mid-paths per path Mid-path power and delay relative to paths

Urban Micro

3

4

1

10/20

0 ns

6/20

0 ns

2

6/20

7 ns

6/20

5.8 ns

3

4/20

26.5 ns

4/20

13.5 ns

4

-

-

4/20

27.6 ns

B. Shortcomings The SCM was defined for a 5 MHz bandwidth CDMA system in the 2 GHz band, whereas the currently defined WINNER system parameters are 100 MHz bandwidth in both 2 and 5 GHz frequency range [6]. Other issues are the drop based concept, i.e., no short-term system-level time-variability in the model, the lack of Ricean K-factor models (LOS support) for macro scenarios, and the lack of a wider range of scenarios. III.

INTERIM BEYOND-3G CHANNEL MODEL

Our main goal for the extension was to keep it simple, backward-compatible, and within the conceptual approach of the SCM. This approach provides consistency and comparability. In the following we discuss the underlying concepts and the reasoning behind the proposed extensions. A. Bandwidth To extend the model in a way such that its characteristics remain unchanged if compared at the original 5 MHz resolution bandwidth, we add intra-path delay-spread (DS), which is zero in the SCM. A possible power-delay profile (PDP) is a one-sided exponential function. This approach of so-called intra-cluster DS was originally proposed by Saleh and Valenzuela for indoor propagation modeling [7]. The intracluster DS model has also been adopted for outdoor scenarios in the COST 259 [8] model. Following the SCM philosophy, which is partly based on COST 259, we use this as our guideline. The path DS was chosen under the following considerations •

•

•

In SCM, all paths within a scenario have the same path azimuth-spread (AS). Equivalently, we set the path DS to be constant. The path AS and DS then define the minimum observable total (over all paths) AS and DS. Both from measurements and intuition it follows that this minimum total spread lies somewhere between zero and a fraction of the mean total spread. The error in power between an exponential PDP and the SCM definition (no DS) is illustrated in Figure 1. For a path DS of 10 ns, this error is slightly below -20 dB and can be considered reasonably small. We set it equivalent to this value for all paths.

We split the 20 sub-paths into subsets, denoted “midpaths”, which we then move to different delays relative to the original path. Even though a mid-path consists of multiple subpaths, it remains a single tap (delay-resolvable component). This approach limits the diversity increase to reasonable

values, and avoids that single sub-paths become delayresolvable. Furthermore, lumping together a number of subpaths keeps the fading distribution of that tap close to Rayleigh and thus aids a potential implementation with a classic Gaussian-distributed number generator. We found that 4 is the absolute minimum number of sinusoids to yield a reasonable Rayleigh distribution. The number of mid-paths, and the power and delay parameters chosen for each mid-path are tabulated in Table 1. The mid-path powers, i.e. number of sub-paths, were chosen by considering the decreasing power with delay while staying above the minimum number of sub-paths. The delays for the mid-paths were then derived by employing the method from [9] with the DS set to the predetermined value of 10 ns and the predetermined set of powers given for the mid-paths. In SCM, each sub-path has an angle relative to the path mean angle assigned to it. By perturbing the set of sub-paths assigned to a mid-path, the AS of that mid-path can be varied. It has been reported, e.g. [10], that the intra-cluster AS conditioned on the intra-cluster delay is approximately independent of the delay. Hence, the mid-path ASs (ASi, where i is the mid-path index) were optimized such that the deviation from the path AS (ASn, where n is the path index), i.e. the AS of all mid-paths combined, is minimized. The result is tabulated in Table 2. B. Frequency Range 1) Path-Loss Model The SCM path-loss model is based on the COST-HataModel [11] for Suburban and Urban Macro and the COSTWalfish-Ikegami-Model (COST-WI) [11] for Urban Micro. Some relevant references on path-loss were found ([12]-[18]), however only few of them allow direct comparison between equivalent measurements at 2 and 5 GHz. These few however indicate that the most significant difference can be attributed to different gains in free-space path-loss, which is 8 dB higher at 5 GHz compared to 2 GHz. Thus, for comparability reasons, we propose a 5 GHz path-loss model that has an offset of 8 dB 0 -5 error power below original signal in dB

TABLE 1. MIDPATH POWER-DELAY PARAMETERS

3 mid-paths 4 mid-paths exponential PDP

-10 -15 -20 -25 -30 -35 -40 -45

0

10

1

10 delay-spread in ns

Figure 1. Relative power of channel impulse response difference when pathDS is added, compared at 5 MHz bandwidth

TABLE 2. SUB-PATHS TO MID-PATHS ASSIGNMENT AND RESULTING NORMALIZED MID-PATH ANGLE-SPREADS 3 mid-path configuration

4 mid-path configuration

Midpath

Pwr

Sub-paths

ASi / ASn

Pwr

Subpaths

ASi / ASn

1

10/20

1, 2, 3, 4, 5, 6, 7, 8, 19, 20

0.9865

6/20

1, 2, 3, 4, 19, 20

1.2471

2

6/20

9, 10, 11, 12, 17, 18

1.0056

6/20

5, 6, 7, 8, 17, 18

0.9145

3

4/20

13, 14, 15, 16

1.0247

4/20

9, 10, 15, 16

0.8891

4

-

-

-

4/20

11, 12, 13, 14

0.7887

to the current 2 GHz model. There are some issues here though. The COST-231-HataModel was derived for the purpose of GSM coverage prediction and has a distance range of 1-20 km. The 5 GHz band on the other hand is likely going to be used for shortrange high-throughput services. In this case, a path-loss based on the COST-WI model with a distance range of 0.02-5 km is much more suitable. Note that this model has also been accepted by the ITU-R and was selected as Urban / Alternative Flat Suburban path-loss model in the IEEE 802.16 standard for fixed wireless access [19]. Furthermore, the model distinguishes between LOS and NLOS situations. In conclusion, we propose to use the COST-WI model as an alternative path-loss model for all scenarios with the following parameters: base station (BS) antenna height: Macro – 32 m, Micro – 10 m; building height: Urban – 12 m, Suburban – 9 m; building to building distance: 50 m, street width: 25 m, mobile station (MS) antenna height: 1.5 m, orientation: 30° for all paths, and selection of: Macro – medium sized city / suburban centres, Micro – metropolitan. The results are summarized in Table 3. 2) Delay-Spread, Angle-Spread and Ricean K-factor Preliminary measurement analysis and literature findings [20] indicate that DS and AS statistics do not significantly deviate with doubling of the channel frequency and we thus leave both parameter definitions unchanged in a first approximation. Similarly, we propose using the 2 GHz K-factor for 5 GHz range. We apply the same argument in the case of TABLE 3. PATH-LOSS MODEL Scenario

Suburban Macro

Urban Macro

Urban Micro

SCM pathloss (dB), d is in m

NLOS

31.5 + 35.0 log10(d)

34.5 + 35.0 log10(d)

34.53 + 38.0 log10(d)

LOS

-

-

30.18 + 26.0 log10(d)

SCM shad. std. dev. (dB)

NLOS

8

8

10

LOS

-

-

4

Alternative. short-range path-loss (dB)

NLOS

7.17 + 38.0 log10(d)

11.14 + 38.0 log10(d)

31.81 + 40.5 log10(d)

LOS

30.18 + 26.0 log10(d)

30.18 + 26.0 log10(d)

30.18 + 26.0 log10(d)

Alt. shad. std. Dev. (dB)

NLOS

10

10

10

LOS

4

4

4

(N)LOS

+ 8 dB

+ 8 dB

+ 8 dB

5 vs. 2 GHz path-loss

shadowing and make no differentiation for frequency range. C. Other Extensions 1) LOS for All Scenarios In the SCM, the LOS model, consisting of path-loss and Ricean K-factor definition, is a switch selectable option for Urban Micro only. We extend the K-factor option to cover also Urban and Suburban Macro scenarios as follows. Urban and Suburban Macro are assigned the same parameters. The probability of having LOS is calculated as [21] PLOS = (1 - hB/hBS)(1 - d/dco), dco < 300, hBS > hB and is zero otherwise. Here, hBS is the BS height, hB the average height of the rooftops, and dco is the cut-off distance. Values for these parameters are proposed in [8]. We use the empirical K-factor model presented in [22] for typical (American) suburban environments and BS heights of approximately 20 m. In [23], an excellent agreement with this model was reported based on independent measurements under similar conditions. We propose the following parameters: MS antenna height 1.5 m, MS beam-width: 360°, and selection of season: summer. The resulting model is K = 15.4 - 5.0 log10(d), where d is the BS-MS distance in m, and K is in dB. 2) Time-Evolution The literature on dynamic / non-stationary channel models, that is, channel models with time-varying channel parameters, is relatively scarce. Initial references of dynamic channel models appear to be [24]-[26]. Dynamic channel models for indoor environments are developed in [27]-[29], of which the first reference focuses on dynamic delay-domain characterization and the latter two also incorporate spatial dynamics of the indoor channel. The standard [30] defines a simple model for varying tap delays and tap birth-death. However, this model is intended for receiver testing and does not represent a realistic channel. The concept of drops in SCM can be seen as relatively short channel observation periods that are significantly separated from each other in time or space such that the channel parameters become constant and independent during these periods. Our approach is to virtually extend the lengths of these periods by adding short-term time-variability of some channel parameters within the drops. All channel parameters remain independent between drops. The three effects we model are discussed in the following. a) Drifting of Path Delays and Angles The paths, sub-paths, and spatial sampling instants (within a drop) are indexed by n, m, and k, respectively. We assume that the positions of scatterers are fixed during a drop. As a consequence, the scatter angles as seen from the BS (angles-ofdeparture, AoDs) do not change, with the exception of the LOS AoD in LOS scenarios. This assumption is valid in many cases of practical interest. Based on the fixed-geometry assumption, the scatter angles as seen from the MS (angles-of-arrival, AoAs, θnm,AoA,k) as well as the sub-path delays change during a drop due to the MS movement. Similarly, the LOS directions

from BS and MS (θBS,k and θMS,k, respectively) vary in time. In the following equations λ is the wavelength, c is the speed of light, v is the velocity of the MS, θv is the direction of MS movement, DS is the sample density per half wavelength, dMS-BS is the MS-BS distance (LOS path length), and dnm is the distance between MS and the last-bounce scatter (LBS) of the mth sub-path of the nth path. All angles are defined with respect to the normal of the antenna broadside with positive angles in counter-clockwise direction. For illustration of the geometry, see Fig. 5.2 of [2]. The update equations for the AoA / AoD of the LOS sub-path (when present) are θMS,k+1 = θv – γk for dMS-BS,k+1 < dMS-BS,k, θMS,k+1 = θv – 180° + γk otherwise, and θBS,k+1 = θBS,k – (θMS,k+1 – θMS,k), with 2 2 , d MS − BS ,k +1 = d MS − BS , k + l − 2d MS − BS , k l cos(ϕ MS − BS , k )

 d MS − BS ,k sin(ϕ MS − BS ,k )  , λ ,  l = 2 DS d MS − BS , k +1  

γ k = arcsin 

and ϕMS-BS,k = θv – θMS,k. The update equations for the AoAs of the scattered (non-LOS) sub-paths are θnm,AoA,k+1 = θv – ξk for dnm,k+1 < dnm,k, θnm,AoA, k+1 = θv – 180° + ξk otherwise, where  d nm ,k sin(ϕ nm ,k )  ,  d nm ,k +1  

ξ k = arcsin 

2 2 , d nm,k +1 = d nm , k + l − 2 d nm , k l cos(ϕ nm ,k )

and ϕnm, k = θv – θnm,AoA,k. The sub-path delays are updated according to τnm,k+1 – τnm,k = cos(Φk) l/c, where Φk = ϕMS-BS,k for the LOS sub-path, and Φk = ϕnm,k for other sub-paths. Initial values for k=0 are generated according to [2]. For calculating the AoA drift, the initial distance between MS and LBS is required. This distance is unknown since SCM is not a single-bounce geometrical model and hence cannot simply be inferred from the geometry. Instead, we propose a simple stochastic model where the initial distance, dn,0, to all LBSs of the nth path is a random variable, independent for all n=1...6. As a plausible PDF for dn,0, we select a log-normal distribution with a constant, small offset and parameters given in Table 4. In general, the angle Φk is a nonlinear function in time. For l much smaller than the MS-LBS (or MS-BS) distance, a linearization of this function yields a good approximation for observation periods of several meters. One can then use a constant change of AoA, AoD and delay over the drop. This yields significant savings in computation while resulting in a usable model for simulation of dynamic MIMO channels. b) Drifting of Shadow Fading The time-evolution of shadow fading is determined by its spatial autocorrelation function. References show that an exponential function fits well and the drifting can thus be modeled by a first order autoregressive process. Derived from publications on measurement data around 2 GHz ([31]-[37]), we propose using correlation distances (50% correlation point) listed in Table 4. 3) Tapped Delay-Line Model In the SCM, most parameters are defined by their PDFs. While this provides richness in variability, it can turn out to be

TABLE 4. DISTRIBUTION PARAMETERS FOR dn,0 = dmin + X AND CORRELATION DISTANCES FOR SHADOW FADING Parameters of log(X)

Scenario

dmin (m)

Suburban Macro

10

2+2

Urban Macro

10

2+2

Urban Micro

10

var.

50% correlation point (m)

τ n −τ1 τ N −τ1

1

200

τ n − τ1 τ N − τ1

1

50

2

1

5

Mean

a headache for accurate simulations as the simulation time grows exponentially with the number of random parameters. As a practical add-on, we have thus defined a set of fixed values for the power, delays, and angular parameters of the paths tabulated in Table 5. This is similar to the SCM link-level model. However, while the latter targets 3GPP comparability, our solution is close to the SCM system-level model and furthermore optimized for small frequency autocorrelation. The parameters were derived as follows. The fixed delays of the 6 paths were fitted to the PDP of the SCM system-level model using the method from [9]. These delays were then perturbed until a satisfactory frequency decorrelation was achieved. Mean angles were randomized until the total ASs roughly equalled the expected values for AS. IV.

IMPLEMENTATION

The original SCM has been implemented in MATLAB1 and is available [38] under a public license. Please check the referenced website for updated information about any extensions of the implementation. REFERENCES [1] [2] [3]

[4]

[5]

[6] [7]

[8]

[9]

1

V. Erceg, L. Schumacher, P. Kyritsi, A. Molisch, D. S. Baum, et al., “TGn channel models”, IEEE 802.11-03/940r2, Jan. 2004. 3GPP, “Spatial channel model for MIMO simulations”, TR 25.996 V6.1.0, Sep. 2003. [Online]. Available: http://www.3gpp.org/ 6th Framework Programme, Information Society Technologies, Wireless World Initiative New Radio (WINNER), IST-2003-507591, [online] Available: https://www.ist-winner.org/ D. S. Baum, G. Del Galdo, J. Salo, P. Kyösti, T. Rautiainen, M. Milojevic, and J. Hansen, “SCM extensions,” WINNER WP5.5 – Internal, Jan. 2005. M. F. Pop, and N. C. Beaulieu, “Limitations of sum-of-sinusoids fading channel simulators,” IEEE Trans. Commun., vol. 49, no. 4, Apr. 2001, pp. 699–708. J. von Häfen, U. Schwark, G. Mange, M. Litzenburger, D. C. Schultz, et al., “System Requirements,“ WINNER D7.1 – Public, Jul. 2004. A. Saleh, and R. A. Valenzuela, “A statistical model for indoor multipath propagation,” IEEE J. Select. Areas Commun., vol. SAC-5, no. 2, Feb. 1987, pp. 128–137. L. M. Correia, Wireless Flexible Personalized Communications, COST 259: European Cooperation in Mobile Radio Research, John Wiley & Sons, 2001. Section 3.2 by M. Steinbauer and A. F. Molisch, “Directional channel models”. J. Kivinen, X. Zhao, and P. Vainikainen, “Empirical characterization of wideband indoor radio channel at 5.3 GHz,” IEEE Trans. Ant. Prop., Aug. 2001, pp. 1192–1203.

MATLAB is a registered trademark of The MathWorks, Inc.

TABLE 5. TAPPED DELAY-LINE PARAMETERS Scenario Power-delay parameters: relative path power (dB) / delay (µs)

Suburban Macro

Urban Micro

1

0

0

0

0

0

0

2

-2.6682

0.1408

-2.2204

0.3600

-1.2661

0.2840

3

-6.2147

0.0626

-1.7184

0.2527

-2.7201

0.2047

4

-10.4132

0.4015

-5.1896

1.0387

-4.2973

0.6623

5

-16.4735

1.3820

-9.0516

2.7300

-6.0140

0.8066

6

-22.1898

2.8280

-12.5013

4.5977

-8.4306

Resulting total DS (µs)

0.231

Path AS at BS, MS (deg) Angular parameters: AoA (deg) / AoD (deg)

Urban Macro

0.841

2, 35

0.9227 0.294

2, 35

5, 35

1

156.1507

-101.3376

65.7489

81.9720

76.4750

-127.2788

0.6966

6.6100

2

-137.2020

-100.8629

45.6454

80.5354

-11.8704

-129.9678

-13.2268

14.1360

3

39.3383

-110.9587

143.1863

79.6210

-14.5707

-136.8071

146.0669

50.8297

4

115.1626

-112.9888

32.5131

98.6319

17.7089

-96.2155

-30.5485

38.3972

5

91.1897

-115.5088

-91.0551

102.1308

167.6567

-159.5999

-11.4412

6.6690

6

4.6769

-118.0681

-19.1657

107.0643

139.0774

173.1860

-1.0587

40.2849

Resulting total AS at BS,MS (deg)

4.70, 64.78

[10] K. I. Pedersen, P. E. Mogensen, and B. H. Fleury, “A stochastic model of the temporal and azimuthal dispersion seen at the base station in outdoor propagation environments,” IEEE Trans. Veh. Technol., vol. 49, no. 2, Mar. 2000, pp. 437-447. [11] COST 231, “Urban transmission loss models for mobile radio in the 900- and 1800 MHz bands,” TD(90)119 Rev. 2, Sep. 1991. [12] A. J. Rustako, Jr., V. Erceg, R. S. Roman, T. M. Willis, and J. Ling, “Measurements of microcellular propagation loss at 6 GHz and 2 GHz over NLOS paths in the city of Boston,” in Proc. IEEE GLOBECOM’95, vol. 1, Nov. 1995, pp. 758–763. [13] A. Karlsson, R. E. Schuh, C. Bergljung, P. Karlsson, and N. Lowendahl, “The influence of trees on radio channels at frequencies of 3 and 5 GHz,” in Proc. IEEE VTC’01, vol. 4, Oct. 2001, pp. 2008–2012. [14] N. Kita, A. Sato, and M. Umehira, “A path loss model with height variation in residential area based on experimental and theoretical studies using a 5G/2G dual band antenna,” in Proc. IEEE VTC’00, vol. 2, Sep. 2000, pp. 840–844. [15] X. Zhao, J. Kivinen, P. Vainikainen, and K. Skog, “Propagation characteristics for wideband outdoor mobile communications at 5.3 GHz,” IEEE J. Select. Areas Commun., vol. 20, Apr. 2002, pp. 507–514. [16] K. Yonezawa, T. Maeyama, H. Iwai, and H. Harada, “Path loss measurement in 5 GHz macro cellular systems and consideration of extending existing path loss prediction methods,” in Proc. IEEE WCNC’04, vol. 1, Mar. 2004, pp. 279–283. [17] C.-C. Chong, C.-M. Tan, D. I. Laurenson, S. McLaughlin, M. A. Beach, and A. R. Nix, “A new statistical wideband spatio-temporal channel model for 5 GHz band WLAN systems,” IEEE J. Select. Areas Commun., vol. 21, no. 2, Feb. 2003, pp. 139–150. [18] X. Zhao, T. Rautiainen, K. Kalliola, and P. Vainikainen, “Path loss models for urban microcells at 5.3 GHz,” COST 273 TD(04)207, Duisburg, Germany, Sep. 2004. [19] V. Erceg, K. V. S. Hari, M. S. Smith, D. S. Baum, et al., “Channel models for fixed wireless applications,” IEEE Broadband Wireless Access Working Group, Tech. Rep., IEEE 802.16.3c-01/29r4, Jul. 2001. [20] J. A. Wepman, J. R. Hoffman, et al., “Impulse response measurements in the 902-928 and 1850-1990 MHz bands in macrocellular environments,” in Proc. IEEE ICUPC’93, vol. 2, Oct. 1993, pp. 590 – 594. [21] H. Asplund and J.-E. Berg, “Parameter distributions for the COST 259 directional channel model,” COST 259 TD(99)108, Sep. 1999. [22] L. J. Greenstein, S. Ghassemzadeh, V. Erceg, and D. G. Michelson, “Ricean K-factors in narrowband fixed wireless channels,” in Proc. WPMC’99, Amsterdam, The Netherlands, Sep. 1999. [23] P. Soma, D. S. Baum, V. Erceg, et al., “Analysis and modeling of MIMO radio channel based on outdoor measurements conducted at 2.5

7.87, 62.35

[24] [25]

[26] [27] [28]

[29]

[30] [31]

[32]

[33]

[34] [35]

[36] [37]

[38]

15.76, 62.19

18.21, 67.80

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