2012 IEEE International Symposium on Dynamic Spectrum Access Networks
Measurements of Spectrum Use in London:
Exploratory Data Analysis and Study of Temporal,
Spatial and Frequency-Domain Dynamics
Alexandros Palaios⋆ , Janne Riihijärvi⋆ , Oliver Holland† , Andreas Achtzehn⋆ , Petri Mähönen⋆
⋆
Institute for Networked Systems, RWTH Aachen University, Aachen, Germany
† Centre for Telecommunications Research, King’s College London, UK
email: [email protected]⋆ , [email protected]⋆ , [email protected]† ,
[email protected]⋆ , [email protected]⋆
Abstract—In this paper we present results from a week
long measurement campaign on spectrum use in London (UK).
The measurements were conducted in order to understand the
characteristics and especially the variability in spectrum use over
different types of areas in a major metropolitan area. Three
spectrum analyzers were used in the measurement campaign,
one used for long-term measurements at a single location in a
given area, with the other two used to sample spectrum use
around the stationary measurement point. This measurement
approach yields much more detailed information about spectrum
use than the typical single-location campaigns reported in the
literature. We give a detailed description of the measurement
campaign, including the equipment setup and rationale for the
choice of areas in which the measurements were conducted. We
also present results from the first exploratory data analysis of
the obtained data, and study in detail the correlation structures
and dynamics in spectrum use in temporal, spatial and frequency
domains.
I. I NTRODUCTION
Characterizing the use of radio spectrum through measurements has become an intensively studied topic during
recent years. This is in part due to perceived shortage of
spectrum resulting from inflexible licensing policy, and the
resulting interest in better understanding how frequency bands
are actually used. Measurement campaigns carried out until
now have mainly focused either on characterizing long-term
spectrum use at a single location [1]–[6], country-wide surveys
[7], or, to a smaller extent, spatial measurements within a small
homogeneous area. There is comparatively little information
in the literature about how spectrum usage varies within urban
areas, for example between commercial and residential areas
in major cities. Understanding better the perceived spectrum
usage in urban, high population density areas is important not
only for dynamic spectrum access scenarios in the context of
white spaces, but also to evaluate and develop spectrally and
energy efficient reuse scenarios for future femto-cell and LTE
deployments.
In this paper we report on extensive spectrum use measurements we have carried out in London in order to fill this gap
in the literature. The measurements were carried out over the
course of seven days, covering widely different areas within
978-1-4673-4448-7/12/$31.00 ©2012 IEEE
London, ranging from busy tourist and shopping areas to residential and suburban areas. Within each area a large number of
measurement points was also covered, using a combination of
one stationary and two mobile measurement platforms. This
allows highly detailed characterization of how spectrum use
varies over time, space and frequency. Also unlike previous
campaigns, our measurement approach allows to characterize
spectrum use at the level of typical users, as opposed to high
antenna placements commonly used in long-term measurement
campaigns. We give in the following sections a detailed
description of the measurement campaign, including choice
of measurement areas, design of measurement platforms, and
the configuration of the spectrum analyzers used. We also
present results from a first exploratory data analysis, offering
a broad but detailed overview of how spectrum use varies
within London. For selected measurement locations we also
carry out a more detailed quantification of temporal, spatial
and frequency domain dynamics and correlations, using tools
from time series analysis [8] and spatial statistics [9]. We plan
is to release this extensive data set for public use by the end
of 2012, enabling other research groups to verify our results
and study the data set further.
The rest of this paper is structured as follows. In Section II
we discuss the measurement configurations and location in
more detail, especially focusing on key lessons learned from
this campaign. We then give an overview of selected results
first focusing on overall spectrum use over the different
measurement locations and bands studied in Section III. We
then continue with more detailed characterization of time,
frequency and spatial domain dynamics in Sections IV and
V. Finally, we draw conclusions in Section V.
II. M EASUREMENT S ETTINGS AND L OCATIONS
London is a metropolis with 8 Million inhabitants. Is also
the largest urban zone in the European Union. Moreover, London’s Heathrow airport is the 3rd busiest airport in the world in
terms of total passenger traffic. This measurement campaign
captured different aspects of the city by focusing each day
154
TABLE I
M EASUREMENT L OCATIONS , D ESCRIPTIONS AND D ETAILS
Date
Location
Description
Mon, July 04, 2011
Oxford Street
Trafalgar Square
All-England Lawn Tennis Club, Wimbledon
Residential Area
Tue, July 05, 2011
Business Area
Wed, July 06, 2011
Thu, July 07, 2011
Suburban Area
Heathrow Airport
London’s main Shopping Area
Touristic and Night-life Area
Gentlemen’s Singles - Finals
(Djokovic vs. Nadal)
District of London on the border of the
London Borough of Hackney and the
London Borough of Islington
Finsbury Circus and surrounding area
of the “City of London” financial district, including Liverpool Street national train terminus
Woodford, North East London suburb
Busiest airport in UK, third in world
Sat, July 02, 2011
Sat, July 02, 2011
Sun, July 03, 2011
(a) Oxford Circus Station
Fig. 1.
Measurement
locations
35
22
16
Measurement duration [h]
28
10
13
7
26
6
4
3.5
(b) Trafalgar Square
6
3
6
(c) Wimbledon
Examples of measurement locations together with the “blue box” platforms used for the measurements.
on a different area within London. This included 6 days
of dedicated measurements from the 2nd of July (Saturday)
until the 7th of July (Thursday). The weekend measurements
focused on capturing the weekend’s life of the city, starting
by the Oxford Street Shopping area in the morning and in
the evening moving to Trafalgar Square, which is a major
touristic attraction and meeting place. On Sunday a major sport
event was captured, the Wimbledon Men’s Singles Final. On
the weekdays we focused on a residential area of London,
a main Business Area, including also the Liverpool Street
train station, a sub urban area and finally the heathrow airport.
These measurement locations are summarized in Table I.
The measurements were conducted using the “blue box”
spectrum measurement platforms developed at RWTH Aachen
University (see Figure 1). These are movable weatherproof
encasings for spectrum analyzers and laptops, equipped with
a large enough battery to sustain a full day of measurements.
The encasing also provides electromagnetic protection against
interference. The hardware configuration that was used to
support the needs of this particular measurement campaign
is summarized in Table II. The antenna height was 1.75 m ,
close to the of mobile device usage for a typical. In each
measurement area three spectrum analyzers were deployed.
TABLE II
H ARDWARE AND C ONFIGURATION U SED
Device property
Spectrum Analyzer
Antenna
Antenna Frequency Range
Details
R&S FSL 6
AOR DA735G
75 MHz - 3 GHz
Resolution Bandwidth
100 kHz
Spec. Anal. Sensit@3 GHz
-108 dBm
Cable Losses@3 GHz
1 dB
Achieved
Sensitivity@3 GHz
Detectors
-107 dBm
/100 kHz
RMS, Average, Auto Peak
For each measurement location one spectrum analyzer was
placed at a specific place and remained stationary at that
location during the day’s measurement. This is called the
stationary box. Two more spectrum analyzers, called the
mobile boxes, were covering multiple different locations
around the stationary box. The GPS locations of all the
measurement locations, the date and time stamps of all the
155
(a) Measurement Locations in Oxford Street
(b) Measurement Locations in Trafalgar Square
Fig. 2. Measurement locations in Oxford Street and Trafalgar Square. The triangle corresponds to the location of the stationary measurement point, and the
diamonds to the locations at which measurements were carried out using the movable blue boxes.
completed spectrum sweeps were also stored. Two example
maps of the covered areas including the measurement
locations can be found in Figure 2. The spectrum analyzer
model, antenna model, cable types, and cable lengths
were exactly the same for the three setups, to allow direct
comparison of the gathered data.The stationary box was
doing spans from 75 MHz-3 GHz, while the other two were
focusing on the GSM900, GSM1800, UMTS, ISM bands
achieving slightly lower sweep times by focusing on these
dedicated frequency bands. Typical values of sweep times for
the stationary box were in the order of 6–10 s while for the
other two values around 5 s were achieved, a sufficient time
resolution to understand the dynamics inside the measured
bands. Finally, the reported cable losses were measured
before and after the measurement campaign to ensure that the
reported sensitivity was indeed achieved.
III. E XPLORATORY DATA A NALYSIS
In this and the following section we give an overview of
the results obtained from the measurement campaigns. We
start by presenting an exploratory data analysis of the gathered
measurements. Our main objectives in this section are to provide an overview of the behavior of spectrum use in London
on the different bands measured across several measurement
sites, and highlight some of the similarities and differences
across frequency bands and measurement locations. These
observations will then be used to select smaller parts of the
data set for more detailed quantitative studies in the following
sections.
We begin by studying the distribution of the measured
power spectral density values for the different frequency bands
and measurement locations, starting with the 900 MHz GSM
downlink band. Figure 3 depicts these distributions for the
Trafalgar Square, Oxford Circus, and Suburban locations. The
sites at downtown London clearly have the highest degree of
variability, presumably a compound effect of smaller cell sizes
as well as higher attenuation from the building infrastructure.
For these sites we see that the shape of the distribution stays
relatively constant from one location to another, while there are
approximately 10 dB changes in the mean and median values
from one location to another. The variability at individual fixed
locations is much higher than the systematic changes between
locations. This variability has multiple sources, ranging from
the inherent differences in the received powers over different
bands due to differences in distances to base stations, the
used transmit powers, to fast fading caused especially by
reflections from cars and other obstacles. There is also an
element of heteroscedasticity in the data, meaning that the
intra-site variability changes from one location to another,
occasionally rather drastically (for example from location
number 10 to location number 11 for the Oxford Circus data
set). Converesely, the suburban case depicted in Figure 3(c)
shows a very small amount of activity, and little variation
between locations.
Figure 4 shows the distribution of the measured power
spectral density values for the UMTS downlink band in
two of the measurement locations. The variability both at
individual locations as well as between the locations is even
higher compared with the typical results seen for the GSM900
downlink band above. This might appear somewhat counterintuitive since UMTS is a WCDMA-based technology, and
due to the presence of the broadcast channel does not exhibit
similar temporal dynamics as the TDMA based GSM networks
do. Our resolution is such that practically all the intra-site
156
−20
−40
−60
−80
Power spectral density [ dBm / 100 kHz ]
−100
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
Measurement location
−40
−60
−80
−100
Power spectral density [ dBm / 100 kHz ]
(a) Trafalgar Square
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
Measurement location
−70
−80
−90
−100
−110
Power spectral density [ dBm / 100 kHz ]
−60
(b) Oxford Street
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
Measurement location
(c) Suburban Area
Fig. 3. Box plots of the measured PSD values for the GSM downlink band for the measurement locations at Trafalgar Square, Oxford Street, and the suburban
area. Top, middle and bottom box lines respectively represent upper quartile, median, and lower quartile respectively. Whiskers minimum and maximum values
within 1.5 interquartile range of box edges.
variability seen in the plots originates from the frequencydomain dynamics as we shall see below. The UMTS band is
almost completely in use in London as can be expected, but
the distances to the serving Node-Bs for different operators
are highly variable, leading to substantial differences between
average received signal powers between the frequency bands
assigned for different operators within the UMTS downlink
band. These considerations also highlight the importance of
careful study of time and frequency domain behaviors of
spectrum use at a given measurement location in addition
to the marginal or spatial view which can be seen from the
present plots. We shall return to these issues at the end of this
section.
157
Finally, Figure 5 gives the results for the 2.4 GHz ISM
−50
−60
−70
−80
−90
Power spectral density [ dBm / 100 kHz ]
−100
−110
1
2
3
4
5
6
7
8
9
10
11
12
13
Measurement location
−70
−80
−90
−100
−110
Power spectral density [ dBm / 100 kHz ]
−60
−50
(a) Business Area
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
Measurement location
(b) Suburban Area
Fig. 4. Box plots of the measured PSD values for the UMTS downlink band for the measurement locations at the chosen Business and Suburban areas.
Boxplot conventions at Figure 3
band for two highly different locations, namely the Business
Area, where at any measurement location more than 20 Wi-Fi
access points were typically visible, against the suburban case
where almost no ISM band activity could be observed. The
results for the suburban area are indeed extremely “quiet”, and
feature almost no discernible differences between the different
measurement locations in the area. Also the measured power
values are at the level of the noise floor of the setup, as can be
expected. The power spectral density values for the business
area are also very low, but there clearly is activity on the band,
especially around measurement locations 4, 6 and 11.
the time-frequency plot or spectrogram for this band at our
chosen measurement location. We observe expected behaviour
with basically constant power received over each of the 5 MHz
UMTS channels. Small variations within the band are due
to combined effects of fast fading and changes in transmit
power due to power control. We can also see that the 2.1
GHz UMTS downlink band is much more heavily occupied
in London compared to many of the sites at which the two
days measurements [10] took place. This is, of course, very
expected due to the high population density and large overall
customer base in the area for cellular operators.
In order to gain more detailed understanding of the behavior
of a particular frequency band during the measurements, we
shall study here the full time-frequency characteristics for an
individual measurement location. We have chosen at random
one of the Oxford Circus sites to be used as a case, the results
for which are depicted in Figure 6. We begin our analysis with
the 2.1 GHz UMTS downlink band, since this should have the
most straightforward behavior of all cellular bands due to the
“always on” nature of the UMTS Node Bs. Figure 6(c) shows
Let us now move on to the 900 MHz GSM downlink
band, for which the spectrogram is shown in Figure 6(b).
The TDMA nature of GSM is clearly visible in most of the
spectrogram area, with individual unused time slots resulting
in dark red points interleaved between time-frequency areas
with high received powers corresponding to ongoing transmissions. Overall the utilization of the band is clearly very
high. The possibly surprising feature in the spectrogram can
be seen in the frequency band between 930 and 935 MHz,
158
−95
−100
−105
Power spectral density [ dBm / 100 kHz ]
−110
1
2
3
4
5
6
7
8
9
10
11
12
13
Measurement location
−104
−106
−108
−110
Power spectral density [ dBm / 100 kHz ]
(a) Business Area
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
Measurement location
(b) Suburban Area
Fig. 5. Box plots of the measured PSD values for the 2.4 GHz ISM band for the measurement locations at the chosen Business and Suburban areas. Boxplot
conventions at Figure 3
where a continuous transmission is clearly taking place. This
corresponds, in fact, to one of the first deployments of UMTS
in the 900 MHz band.
The spectrogram for the uplink band of GSM900 shown
in Figure 6(a) shares some of the characteristics seen in
the downlink case. However, the level of burstiness is much
higher, since how the TDMA nature is combined with the
randomness induced by the call behavior and mobility of the
users. Interestingly the usage of UMTS in this band is also
clearly visible. The high variability of received powers can
also be clearly seen. Distributional plots used above do not
capture very well just how heavy-tailed the distribution of the
received power is in the uplink case, and how large is the
influence of randomness induced by mobility of user terminals
and their associated call dynamics. Only utilizing statistics
that capture the extreme values suffice in such situations to
reveal such behavior. We shall return to these issues in the
next section, where we outline some of the tools that can be
used to characterize such behavior in more detail.
We conclude this case study with the spectrogram of the 2.4
GHz ISM band shown in Figure 6(d). Clearly the usage of the
band is again highly bursty, with the exception of few almost
continuously transmitting narrowband sources around 2450
MHz. The three most commonly used Wi-Fi channels can also
be seen clearly. Note that due to the sweeping operation of
the spectrum analyzer and the very short duration of a typical
Wi-Fi frame, individual frames are not seen over the whole
22 MHz channel at the same time. Due to such effects, wide
band transmitters can easily be mistaken as a larger number
of narrow band ones.
We finish this section by providing a first order time domain
analysis of the data for selected measurement points. Here
we show two typical examples of the duty cycle analysis,
with a threshold of -101 dBm in Figure 7. We see that the
duty cycles remain almost constant for the entire measurement
duration, and there are no major trends or differences in the
variability over time. This indicates that the data set is fairly
stationary, making analysis of correlations and other second
order statistics meaningful. This also shows how “saturated”
and stationary a traffic can be in metropolitan areas. We shall
159
(a) Oxford Street GSM900 UpLink
(b) Oxford Street GSM900 Downlink
(c) Oxford Street UMTS
(d) Oxford Street ISM
Fig. 6. Examples of time-frequency behavior of the PSD values at a randomly selected Oxford Street measurement location for the GSM uplink and downlink,
UMTS downlink, and the 2.4 GHz ISM band. The color bar indicates the measured power spectral density in the given time-frequency bin.
proceed with such analysis in the following section.
and the semivariogram
2 1 E T (t + k) − T (t) .
(2)
2
For second-order stationary stochastic processes these are
related by γT (k) = CT (0) − CT (k). We shall mainly apply
the semivariogram in the following, due to its slightly higher
degree of generality (requiring only intrinsic stationarity1 in
order to be defined), and superior estimation robustness.
Typical shape of the semivariogram is depicted in Figure 8.
The nugget denotes discontinuity at origin, or, equivalently,
the right limit limh→0+ γ(h). For any suitably regular random
γT (k) ≡
IV. T IME -F REQUENCY C ORRELATIONS
In this section we will present a more detailed, quantitative
look at the time-frequency dynamics occurring in the measurement data sets. Figure 6 already gives an indication that traffic
dynamics are highly different between the analyzed frequency
bands. We have seen early indications of how different these
are expected to be across different bands in, and we shall now
deepen that understanding by studying the correlations in the
measurement data sets in these two domains. For a time series
T (t) the two most commonly used measures of correlation
over time are the autocovariance function
CT (k) ≡ E T (t) − µT T (t + k) − µT ) ,
(1)
1 A process T (t) is intrinsically stationary if it has constant mean, and the
variance of T (t+k)−T (t) only depends on k, and not t. From the stationarity
analysis carried out in the previous section both of these properties hold well
to our data sets.
160
1.0
1.0
0.8
0.6
Duty Cycle
0.2
0.0
0
1000
2000
3000
4000
5000
6000
0
Sweep Number
100
200
300
400
500
600
Sweep Number
(a) Trafalgar Stationary Location
Fig. 7.
GSM900
GSM1800
WLAN
UMTS
0.4
0.6
0.4
0.0
0.2
Duty Cycle
0.8
GSM900
GSM1800
ISM
UMTS
(b) Liverpool Train Station Measurement Location
Duty Cycles for a threshold of -101 dBm for Trafalgar Stationary Location and Liverpool Train Station. The plots were slightly smoothed.
γ(h)
where N is the normalizing factor needed for the averaging.
The interpretation of the one-dimensional and image semivariograms is very similar. Small values of γ indicate high degree
of similarity, whereas large values indicate independence of
the corresponding random variables. As the value of k in the
one-dimensional case or the values of i and j in the image
semivariogram cases become large, the values of γ tend to the
marginal variance of the data set.
Range
1.0
Sill
0.5
Nugget
h
0.0
Fig. 8.
0.5
1.0
1.5
Typical structure of the semivariogram [11].
field this limit is zero (counterexamples can be obtained
by superimposing noise), so typically nonzero values arise
from measurement errors and random effects with short range
correlations in the system. The range of the semivariogram is
roughly the distance at which values of Z become uncorrelated
(for a white noise process range would be zero). Finally, the
sill corresponds to the residual variance of the process as
discussed above.
Given that our data is given in both time and frequency
domains, the semivariogram definition for time series data
cannot be applied directly. Instead, we can treat measurement
results at a single location as a matrix or image Z(i, j), and
characterize its two-dimensional correlation properties by the
empirical image semivariogram
γZ (i, j) ≡
1 X
{Z(k + i, l + j) − Z(k, l)}2 ,
(k,l)
2N
(3)
Figure 9 shows the image semivariograms for selected data
sets, with the horizontal direction corresponding to offset or
lag in the frequency domain, and vertical direction to the
temporal one. The results for the ISM band differ clearly from
all the rest. Due to the small amount of activity and bursty
nature of the transmitters operating on the ISM band there is
very little correlation either in time or frequency, except for the
case of purely time-like separation between the measurement
points. This seems to be a combination of persistent narrowband signals seen the spectrogram plot above, together with
packet-based transmissions by Wi-Fi devices with frequency
selective fading reducing the frequency-domain correlations.
One should be careful when interpreting the ISM band results,
as already was mentioned there might be correlations in time
and frequency scales smaller than can be determined by our
measurement setup. The results for the cellular systems are
rather similar, with the UMTS case featuring slowest decay
in the correlation values or, equivalently, the semivariogram
reaching the sill at the slowest rate. This is, of course, due to
the wide bandwidth of UMTS together with the CDMA nature
of the technology. For GSM the time slot structure combined
with the narrow channel bandwidths makes the spectrum use
around a randomly selected time-frequency point look much
more random than in the case of UMTS, which is readily
visible in the image semivariograms. The usage of GSM900
161
20
20
100
15
15
160
140
80
100
10
Sweeps
10
Sweeps
120
60
5
5
80
60
40
0
0
40
−10
0
10
20
−10
Frequency Bins
0
10
20
Frequency Bins
20
(b) Oxford Street GSM 1800
20
(a) Oxford Street GSM
18
140
15
15
16
120
14
80
10
Sweeps
10
Sweeps
100
12
10
5
5
60
40
8
20
0
0
6
−10
0
10
20
−10
Frequency Bins
0
10
20
Frequency Bins
(c) Oxford Street UMTS
(d) Oxford Street ISM
Fig. 9. Time-frequency image variograms for GSM900, GSM1800, and UMTS downlink bands as well as for the 2.4 GHz ISM band for measurement
locations in Oxford Street.
downlink has somewhat stronger correlations than GSM1800,
which is presumably due to the overall higher utilization of
the band.
In order to complement the image semivariograms, we give
the (one-dimensional) time and frequency domain variograms
for selected data sets in Figure 10. From these the large differences in the correlation structures between time and frequency
separations are even more clearly visible. It is also worth
noting how especially the time domain variogram has a very
smooth structure, following closely the classical exponential
correlation structure we saw in Figure 8. This indicates that
the time-domain structure on these highly utilized cellular
bands should be relatively straightforward to model (since
many common Markov and Semi-Markov models lead to
exponential semivariogram forms). Our earlier work in [5] has
showed that this is the case for smaller cities, and the present
measurements indicate that this might hold for the London
data set as well.
V. S PATIAL C ORRELATION A NALYSIS
We shall now move from the time and frequency domain
analysis to the spatial domain. Throughout this section we
treat different statistics of the time-frequency data gathered
across the different locations as samples from a random field
Z, that is, a spatial stochastic process defined around our
162
40
Semivariance
20
25
30
35
100
80
60
Semivariance
15
40
20
5
10
15
Frequency
Time
10
Frequency
Time
20
5
Lag
10
15
20
Lag
(b) Oxford Street GSM 1800
12
10
Semivariance
6
20
8
40
Semivariance
60
14
16
80
18
(a) Oxford Street GSM
Frequency
Time
0
4
Frequency
Time
5
10
15
20
5
Lag
10
15
20
Lag
(c) Oxford Street UMTS
(d) Oxford Street WLAN
Fig. 10. One-dimensional time and frequency domain semivariograms for GSM900, GSM1800, and UMTS downlink bands as well as for the 2.4 GHz ISM
band for measurement locations in Oxford Street.
measurement locations. Such random fields can be characterized in several ways. The most common one is to use the
semivariograms introduced above, but letting the parameter
of the semivariogram be the distance between measurement
locations considered, instead of a time or frequency separation.
In this case the natural estimator for the spatial semivariogram
becomes the empirical variogram of Matheron [12] given by
γ̂(h) ≡
1
2|N (h)|
X
{Z(si ) − Z(sj )}2 ,
(4)
(si ,sj )∈N (h)
where N (h) ≡ { (si , sj ) | si − sj = h }.
The main challenge in applying this semivariogram def-
inition to our measurement data set is that the number of
individual measurement locations within a measurement area
is not very large, resulting in a high estimation variance
at an individual bin of this histogram style estimator. This
phenomenon is illustrated in Figure 11, showing the spatial
empirical semivariograms for the GSM1800 downlink band
for the Oxford Street and Wimbledon measurement areas.
Clearly while in some cases the shape of the semivariogram
is as expected, using this approach for characterizing spatial
correlations in our data sets is not very effective.
Because of these estimation difficulties with the spatial
empirical semivariogram, we shall forego the attempt to
163
50
100
20
10
0
100
200
300
400
500
600
0
distance
200
400
600
800
distance
(a) Trafalgar GSM 1800
Fig. 11.
30
semivariance
40
80
60
semivariance
40
20
0
0
(b) Wimbledon GSM 1800
Spatial variograms for GSM1800 downlink band for measurement locations in Trafalgar Square and Wimbledon Stadium.
characterize spatial correlations as function of distance, and
instead compute “global” measures of spatial autocorrelation,
resulting in a single number for the whole measurement site.
This increases the available data the correlation measure is
computed for, and will significantly increase the reliability of
our estimates. As the measure of spatial autocorrelation we
adopt Moran’s I, defined by
P
n i,j Wij (Zi − Z̄)(Zj − Z̄)
P
,
(5)
I≡
W i (Zi − Z̄)2
where Wij is a matrix of weights,
P Z̄ denotes the usual estimate
for the mean of Z, and W = i,j Wij . The value of Moran’s
I lies between −1 and +1, former indicating strong negative
autocorrelation and latter strong positive autocorrelation. For
independent Zi we would have I ≈ 0.
For the assignment of weights, we apply the kth nearest
neighbor approach. That is, for a given location i, we set
Wij = 1 if location j is among the k nearest neighboring
measurement locations to i, and zero otherwise. We shall use
k = 3 in the following, but small changes in this value do not
influence the results significantly.
Table III gives the values of Moran’s I for different statistics used to construct the local values of Z based on the
measurements on the GSM900 downlink band. Clearly the
spatial structure of the data sets varies significantly across the
measurement areas. In the residential and suburban locations
high spatial autocorrelations can be observed, whereas in
Heathrow area and more central downtown locations correlations become much lower or even insignificant. This is in part
due to the intrinsic effects of network structure and deployment
as discussed in Section III above, and also in the case of
Heathrow due to measurement locations being rather far apart
from each other. It is also interesting to note that the choice
of statistic used also has a strong impact on the correlation
coefficient. Typically the analysis in the literature has focused
on the random field corresponding to the (linear) mean power
spectral density, given in the second column of the table.
However, the results show that also the tail behavior of the
mean PSD distribution can feature high spatial correlations.
VI. C ONCLUSIONS
In this paper we have presented results of the first exploratory data analysis from the spectrum measurement campaign done in London. There has been earlier work to characterize cellular band usage also in urban areas, most notably by
[13] and [14]. Our results are not limited to cellular bands, and
unlike [14] we do not use traffic data collected by an operator
from base stations but actual in situ spectrum measurements.
Nevertheless, some comparison is possible with these previous
works, and in general we see similar effects although our wide
coverage of environments also provide new insights. We observe an element of heterosedasticity in our measurement data.
Overall we are able to show that the variance on measured
power spectral density is strongly affected by location type
and even within the same location type moving around can
cause larger variance than expected earlier. This means that
some of the statistical estimates not only for spectrum sensing
accuracy, but also for accuracy of the propagation models
with the white space databases, should be readdressed and
rather conservative estimates for protection mechanism (such
as no talk-zones) need to be used. We are able to confirm
that the use of cellular bands is very high, although the spatial
analysis shows that correlation structure between locations and
times can be quite complex. The ISM bands have substantially
lower duty cycles, although this also depends on location and
detection accuracy.
164
TABLE III
VALUES OF M ORAN ’ S I FOR DIFFERENT STATISTICS OF THE MEASUREMENTS FOR GSM900 DOWNLINK BAND .
Tail behavior / percentiles
Location
Mean
Mean (dB)
Median
0.05
0.25
0.75
0.95
Oxford Street
Trafalgar Square
Wimbledon Tennis Court
Residential Area
Business Area
Suburban Area
Heathrow Airport
0.33
0.05
0.31
0.53
0.20
0.60
-0.17
-0.04
0.14
0.33
0.49
0.13
0.18
-0.01
0.30
0.03
0.12
0.59
0.12
0.42
0.02
0.62
0.35
0.57
0.25
-0.04
0.81
-0.04
0.42
0.08
0.37
0.42
0.10
0.73
-0.01
0.27
-0.16
0.04
0.65
0.21
0.52
-0.25
0.21
0.24
0.59
0.27
0.18
0.56
-0.24
Generally, the exploratory data analysis shows that the
metropolitan area data is quite complex and rich with phenomena. It can exhibit, depending on location, many different
usage and correlation patterns – thus being a microcosmos to
study many phenomena that are seen also in non-metropolitan
areas. Moreover, the very high-population density areas show
already in the preliminary analysis unique patterns and very
high spectrum use for cellular systems. However, in the latter
case it is also clear that a simplistic duty cycle analysis is
not sufficient analysis method instead full spatial- and timedomain analysis is required to understand situation better. In
fact, one challenge would be to correlate spectrum measurements with some ground truth data from the operator networks,
i.e. monitoring the base station information like the authors in
[15], [16] and combining that information with spectrum data
like what we have generated.
ACKNOWLEDGMENT
We would like to thank the European Union for providing
partial funding of this work through the FARAMIR project
(grant number ICT-248351) and also acknowledge a partial
funding support from EU ACROPOLIS Network of Excellence
project. One of us (PM) ackowledges also a support from DFG
through UMIC Research Center funding.
R EFERENCES
[6] M. Wellens and P. Mahönen, “Lessons learned from an extensive
spectrum occupancy measurement campaign and a stochastic duty cycle
model,” in Testbeds and Research Infrastructures for the Development
of Networks Communities and Workshops, 2009. TridentCom 2009. 5th
International Conference on, April 2009, pp. 1 –9.
[7] OFCOM, “Mobile ”Not-Spot” Measurement Campaign, 7 February
2011, OFCOM,” Feb. 2011.
[8] P. Brockwell and R. Davis, Time Series: Theory and Methods, 2nd
Edition. Springer, 1991.
[9] M. Wellens, J. Riihijärvi, and P. Mähönen, “Spatial statistics and models
of spectrum use,” Computer Communications, vol. 32, no. 18, pp. 1998–
2011, 2009.
[10] A. Palaios et al., “Two days of spectrum use in Europe,” June 2012,
proc. of CROWNCOM 2012, Stockholm.
[11] J. Riihijärvi, P. Mähönen, and S. Sajjad, “Influence of transmitter
configurations on spatial statistics of radio environment maps,” in Proc.
of IEEE PIMRC 2009, Tokyo, Japan, September 2009.
[12] G. Matheron, Traité de géostatistique appliquée. Éditions Technip,
1962.
[13] D. Willkomm, S. Machiraju, J. Bolot, and A. Wolisz, “Primary users in
cellular networks: A large-scale measurement study,” in New Frontiers
in Dynamic Spectrum Access Networks, 2008. DySPAN 2008. 3rd IEEE
Symposium on, Oct. 2008, pp. 1 –11.
[14] A. Subramanian, M. Al-Ayyoub, H. Gupta, S. Das, and M. Buddhikot,
“Near-optimal dynamic spectrum allocation in cellular networks,” in
New Frontiers in Dynamic Spectrum Access Networks, 2008. DySPAN
2008. 3rd IEEE Symposium on, Oct. 2008, pp. 1 –11.
[15] G. Fusco, M. Buddhikot, H. Gupta, and S. Venkatesan, “Finding green
spots and turning the spectrum dial: Novel techniques for green mobile
wireless networks,” in New Frontiers in Dynamic Spectrum Access
Networks (DySPAN), 2011 IEEE Symposium on, May 2011, pp. 613
–617.
[16] U. Paul, A. Subramanian, M. Buddhikot, and S. Das, “Understanding
traffic dynamics in cellular data networks,” in INFOCOM, 2011 Proceedings IEEE, April 2011, pp. 882 –890.
[1] M. A. McHenry, “NSF spectrum occupancy measurements project
summary,” Shared Spectrum Company, East Lansing, Michigan, Tech.
Rep., August 2005.
[2] M. A. McHenry and D. McCloskey, “Multi-band, multi-location spectrum occupancy measurements,” in Proc. of International Symposium
on Advanced Radio Technologies (ISART), Boulder, CO, USA, March
2006.
[3] T. Harrold, R. Cepeda, and M. Beach, “Long-term measurements of
spectrum occupancy characteristics,” in New Frontiers in Dynamic
Spectrum Access Networks (DySPAN), 2011 IEEE Symposium on, May
2011, pp. 83 –89.
[4] T. Taher, R. Bacchus, K. Zdunek, and D. Roberson, “Long-term spectral
occupancy findings in chicago,” in New Frontiers in Dynamic Spectrum
Access Networks (DySPAN), 2011 IEEE Symposium on, May 2011, pp.
100 –107.
[5] M. Wellens, J. Riihijärvi, and P. Mahönen, “Evaluation of adaptive
mac-layer sensing in realistic spectrum occupancy scenarios,” in New
Frontiers in Dynamic Spectrum, 2010 IEEE Symposium on, April 2010,
pp. 1 –12.
165