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.
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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
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