2012 IEEE Wireless Communications and Networking Conference: PHY and Fundamentals
Integration of Heterogeneous Spectrum Sensing
Devices Towards Accurate REM Construction
Daniel Denkovski, Valentin Rakovic, Mihajlo Pavloski, Konstantin Chomu, Vladimir Atanasovski and Liljana
Gavrilovska
Faculty of Electrical Engineering and Information Technologies (FEEIT)
Ss Cyril and Methodius University in Skopje
Skopje, Macedonia
{danield, valentin, mihajlop, konstantin.chomu, vladimir, liljana}@feit.ukim.edu.mk
procedures, e.g. improved resource management through radio
field visualization, policy derivations, source localization etc.
The spectrum sensing process in practice often combines
heterogeneous devices with different capabilities (e.g.
variations in the noise floor) classified as high-, mid- and lowend devices. The high-end devices, typically represented by
signal/spectrum analyzers, offer the highest performance and
capabilities at the highest price. The mid-end devices are a
trade-off between price and performance while offering a set
of measurement capabilities. The low-end devices represent
low-price measurement solutions. They possess limited
measurement capabilities and often require custom
development of measurement software. The imeplementation
of heterogeneous devices into a single REM construction
platform is far from a trivial process and implies many
practical implementation challenges.
This paper introduces a novel REM construction
architecture capable of integrating heterogeneous spectrum
sensing devices, thus combining the spectrum sensing and the
database approach for accurate radio environmental mapping.
It elaborates on the performance of several mid- and low-end
spectrum sensing devices (i.e. USRP2s, SunSPOTs and TI
eZ430 RF2500) and discusses their implementation and
calibration into a single heterogeneous platform. All
heterogeneous devices are upgraded with custom developed
software for providing versatile spectrum sensing capabilities
based on different energy detection techniques. Finally, the
performances of the integrated heterogeneous platform are
evaluated in practice in terms of accurate REM derivation and
calculations of other spectrum occupancy peculiarities. All
presented evaluations, discussions and conclusions stem from
the authors’ own practical work in the field.
The paper is organized as follows. Section II introduces a
novel architecture for accurate REM construction with the
necessary functional blocks and interfaces. Section III provides
details on custom development spectrum sensing devices using
market available mid- and low-end devices. Moreover, the
section comments on the spectrum sensing performance of
each of the used device. Section IV elaborates on a testbed
implementation results of the entire concept. Finally, section V
concludes the paper.
Abstract— Spectrum sensing is a fundamental feature of
cognitive radio technologies facilitating detection and localization
of spectrum opportunities. Combined with centralized database
storage, it fosters reliable spatial spectrum measurements and
Radio Environmental Maps (REMs) construction that can
provide an accurate insight in the spectrum utilization over time,
space and frequency. The spectrum sensing process often
combines different devices with different capabilities. This paper
introduces novel REM construction platform that integrates
several mid- and low-end spectrum sensing devices into a single
heterogeneous testbed. The devices of interest are USRP2s, TI
RF2500 and Sun SPOTs, which are upgraded with custom
developed software for providing versatile spectrum sensing
capabilities. The paper evaluates their performances and
discusses their integration into a single heterogeneous testbed
platform. Additionally, the paper evaluates the realized
heterogeneous spectrum sensing testbed in terms of accurate
REM derivation and additional statistics gathering for indoor
environments.
Keywords: spectrum sensing, implementation, calibration,
REM.
I.
INTRODUCTION
Cognitive radios represent spectrally agile radios capable
of detecting vacant spectrum currently unused by legacy
Primary Users (PUs). They either employ sensing based
strategies [1] or database based strategies for efficient and
accurate detection of spectrum opportunities [2]. Even though
the latest developments in terms of cognitive radio regulation
strip the sensing requirement and rely exclusively on the
database approach [3] for reliable spectrum opportunities
detection, both strategies remain complementary and, if
combined, open possibilities for increased radio environmental
awareness. The spectrum sensing feature facilitates the
databases population with up-to-date and accurate radio field
information.
The spectrum sensing complemented with database storage
of sensed data triggers novel opportunities for spatial spectrum
measurements and subsequent radio field analysis and
interpretation. In this sense, the concept of Radio
Environmental Maps (REMs) [4] increasingly emerges as a
potential facilitator of subsequent network optimization
978-1-4673-0437-5/12/$26.00 ©2012 IEEE
808
II.
III.
ARCHITECTURE FOR ACCURATE REM CONSTRUCTION
The heterogeneity of the market available devices, in terms
of their sensing and communication capabiilities, yields the
need for an integrated REM construction arcchitecture able to
incorporate various sensing devices. Thiss general REM
architecture should enable storage and prrocessing of the
sensed data taking the capabilities and limitatiions of each type
of sensing device into consideration. The proccessed REM data
can be afterwards utilized for evaluation of thee spatio-temporal
usage of the frequency spectrum, various raddio resource and
policy management purposes etc. This secction presents a
general framework for REM constructionn describing the
architectural components and the correspondinng interfaces.
IMPLEMENTATION OF MEASUREMENT DEVICES
As stated before different devices possess different
sensing, processing and hardwaree capabilities, in terms of
sensitivity, data resolution, gains, sweeping time, processing
power, antennas etc. Their integrration into a single REM
construction platform yields variou
us practical implementation
challenges like, code compatibility
y, processing limitations, as
well as calibration challenges. This section targets the
integration and performance of mid
d- and low-end SDs, i.e. the
Universal Software Radio Peripheeral 2 (USRP2) [6], Texas
Instruments eZ430 [7] and Sun SPO
OTs [8]. They are calibrated
using a benchmark high-end spectrum analyzer. The
calibration process is performed by
y generation and reception
of various signals with different power levels and known
constant amplitudes by both the speectrum analyzer and the SD.
The difference in the received signaal levels by both devices is
called offset which is used to map the input/output
characteristic of the SD in a closee analytical form f(y) = x,
where x is the output value and y is the input value. The
inverse function of f(y) gives the output
o
function g(x) = y of
the actual displayed values by the SD and is referred as the
calibrating curve. Each SD hass a calibrating curve for
different values of frequency range and resolution bandwidth.
The callibration process with
hin the REM construction
architecture from section II can be either
e
centralized, where all
calibrating curves are stored in thee SDS and downloaded by
the RPC when a given SD performss the sensing, or distributed,
where every SD downloads the calibrating curves from the
SDS and sets its output values accorrdingly.
Following subsections providee insight into the spectrum
sensing performances of the targeeted SDs, based on noise
statistics evaluation, as well as a discussion
d
on their mutual
comparisons towards transparent integration into a general
REM construction architecture.
A. Architecture components
Considering the above mentioned requireements, a general
REM construction architecture (Fig. 1) shoulld comprise four
main functional entities [4], i.e. heteroggeneous Sensing
Devices (SDs) to perform spectrum sensing and
measurements, a Spectrum Data Server (SD
DS) to store and
manage the sensing and processed data, a R
REM Processing
Center (RPC) to process the raw sensing ddata and create a
REM and REM Data Users (RDUs) to utilizze the REM data
for various purposes, e.g. a Graphical User Interface (GUI)
that plots the REM data and visualizes the sspectral usage of
different frequency bands. Another instantiattion of the RDU
can be a Radio Resource Manager (RRM) or a Policy Manager
(PM) [5], which can perform spectrum managgement based on
the processed REM data.
A. USRP2 measurement device
The USRP2 is an open sourcee Software Defined Radio
(SDR) hardware platform from the
t
USRP product family
developed by Ettus Research [6]. Itts hardware, composed of a
motherboard and a daughterboard, enables flexible tuning of
Tx/Rx characteristics of the radio. According to its spectrum
measurement properties, this devicce belongs to the mid-end
group of devices.
Fig. 2 depicts the general USRP2 energy detection
architecture [9]. It contains six GN
NU Radio and one custom
made processing blocks written in C++:
C
1. Block no.1: creates the USRP
P2 source and controls the
hardware (sets up sampling ratee and tuning frequencies);
2. Block no.2: converts a stream
m of complex samples to
vector of complex samples;
t
on input complex
3. Block no.3: calculates a FFT transform
samples;
4. Block no.4: calculates squareed magnitude (power) on
complex samples;
p
block - selects
5. Block no.5: custom made processing
between different detector ty
ypes (Energy, FAR, HoS
Detector) and sensing mo
odes, initiates frequency
switching;
Figure 1. General REM construction archiitecture
B. Architecture interfaces
The REM construction architecture compprises three main
interfaces [4]. The Sensing-Server (SS) interf
rface enables the
communication between the heterogeneous SDs and the SDS,
the Server-Fusion (SF) interface covers thee communication
between the SDS and the RPC and the Fussion-Presentation
(FP) interface handles the communication bbetween the RPC
and the RDUs. All interfaces possess a speccific and custom
protocol format messages for proper operationn [4]. In terms of
integration of heterogeneous SDs in the archhitecture, the SS
interface is the focal one.
The following section gives extensive ddetails, from the
authors’ own experience, on the performancce as well as the
process of integration of various SDs in the general
architecture.
809
6.
Block no.6: calculates magnitude (amplittude) on complex
samples;
m on input real
7. Block no.7: calculates a FFT transform
samples.
The high processing capabilities of the US
SRP2 can provide
a variety of detection techniques like meann-hold, the maxhold, the min-hold, the FFT Averaging Ratioo (FAR) detector
and the Higher Order Statistics (HOS) detectoor .
spectrum measurement properties put
p the TI RF2500 into the
group of low-end devices.
GUI
TI_sense
Figure 4. Implementation exaample of TI RF2500
Fig. 4 depicts an implementatio
on example of TI RF2500.
The TI_sense represents a custo
om developed C-language
based sensing application ported on
o the device. Application
porting and RF part configuration
n are conducted via IAR
Embedded Workbench Programmiing environment. The GUI
is a set of host applications for meassured data representation.
Fig. 5 presents the ROC curvess of the TI eZ430 RF2500
device for four different cases of inp
put power. It is evident that
the TI eZ430 RF2500 device can reliably
r
detect signals up to
-110 dBm. The results correspon
nd to the most sensitive
settings i.e. 100 samples averaging
g in the dBm domain. This
proves as the most efficient mode of operation of this device
in terms of spectrum sensing.
Figure 2. USRP2 based sensing application arrchitecture
Due to the non-linear input-output charracteristic of the
RFX2400 USRP2 daughterboard, its calibratinng curve must be
empirically estimated. The authors propose thhat the USRP2’s
calibrating curve could be approximated (with sufficient
accuracy) with a second order polynomial funcction, i.e.:
p x 2 + p2 x + p3
(1)
g ( x) = 1
x 2 + q1 x + q2
Fig. 3 presents Receiver Operating Charracteristic (ROC)
curves for the USRP2 for different input siggnals. The curves
were obtained for the RFX2400 daughterbboard on center
frequency of 2.401 GHz, resolution bandwidtth of 2 MHz and
receiver gain of 40 dB. It is evident that thhis configuration
allows reliable detection of input signals oof -94 dBm and
above. The performances can be improved w
with higher gain
settings.
Figure 5. TI eZ430 RF2500 ROC curvees for five different input power
levels, and sample averaging of 100 RSSI
R
values in dBm domain
The performance evaluation of
o the TI eZ430 RF2500
shows that if appropriately used it can provide satisfying
results. This device, although sen
nsitive to low input power
levels, has a higher variability of the noise samples. Therefore,
the averaging case proves to be an efficient sensing approach.
It must be stressed that thiss device performed the
measurements with relatively low resolution bandwidth (812.5
KHz) resulting in lower noise floorr. Furthermore, the device’s
registers have been set to give the highest possible sensitivity.
Figure 3. USRP2 ROC curves for input RF power of -1102, -98, -94 and -90
dBm, resolution bandwidth of 2 MHz and 40 dB off receiving gain
C. Sun SPOT measurement device
The Sun SPOT (Sun Smaall Programmable Object
Technology) [8] represents small Java-based
J
wireless device.
The Sun SPOTs possess modest measurement
m
performances
due to the hardware fixed resolu
ution bandwidth and interfrequency step. On the other han
nd, Sun SPOTs have solid
processing power, which can increease its sensing reliability.
The device has linear input-outp
put characteristic and the
calibrating curve can be represented
d with a given offset value,
as in the case of the TI RF2500. According to its spectrum
measurement properties, this devicce belongs to the low-end
group.
B. TI RF2500 measurement device
The Texas Instruments (TI) eZ430 is an USB-based
MSP430 wireless development tool. It coverss the full 2.4GHz
ISM band and possesses modest sensing capaabilities in terms
of its sensitivity and data resolution. The devvice has a linear
input/output characteristic, thus the calibrating curve is
defined only with a given offset that reflects the current state
of the radio environment (i.e. deep fading staate). On the other
hand the TI RF2500 has limited processsing capabilities
making it suitable only for energy based detection. Its
810
It is evident that the USRP2
2 poses the best sensing
capabilities of all SDs making it the
t most agile and reliable
sensing device in this compariso
on case. Additionally the
elaborated SDs are compared based on their sensing
performance in terms of ROC currves, Fig. 8. The low cost
devices use their optimized sensing
g cases, i.e. the Sun SPOT
and the TI eZ430 RF2500 perform
m 100 samples averaging in
dBm domain.
Figure 6. Implementation example of Sun SPO
OT devices
To enable the spectrum sensing capabiilities, additional
user code is required in the Sun SPOTs’ librrary suite. Fig. 6
depicts a possible implementation of these deevices. SS_sense
is a custom developed sensing application ddeployed at each
device. The GUI visualizes a set of host appplications, e.g. an
oscilloscope plot of signal power.
Figure 8. ROC comparison for the differrent SDs and input power of -96
dBm. Sun SPOT and TI eZ430 RF2500 peerform 100 samples averaging in
dBm domain, while USRP2 performs 100 samples
s
averaging in mW domain
Figure 7. Sun SPOT ROC curves for five different inpuut power levels, and
sample averaging of 100 RSSI values in dBm
m domain
Fig. 8 shows that the optimal detection
d
performances are
offered by the TI eZ430 RF2500 based
b
SD when using lower
bandwidth than the other two SDs.. However, the USRP2 did
not use the highest possible gain seetting in order to allow the
comparison and the integration. If the USRP2 used the highest
gain setting, it would provide the beest detection performances,
however in such case the mutual comparisons
c
with the other
SDs would not be possible.
Fig. 7 depicts that signals of -92 dBm
m and above are
reliably detected with the Sun SPOT. The figure also proves
that the Sun SPOT, due to hardware imperfecction, has a noise
uncertainty problem resulting in inconsisstent ROCs for
different input signal powers (the ROCs haave slopes in the
increase of the probability of detection). Thee Sun SPOT has
low noise samples variability and therefore thhe averaging case
would not offer a significant gain as in the preevious SD case.
IV.
D. Comparison of the SDs
This subsection focuses on the performancce comparison of
the elaborated sensing devices and fosterrs a benchmark
comparison and mutual calibration among thhe different SDs.
Table 1 presents the hardware and sensing reelated parameters
of the previously elaborated sensing devices.
Тhis section overviews the perfformances of the integrated
testbed platform. The platform comprises all previously
analyzed SDs, an SQL database forr sensed data storage in the
SDS, implementation of the modiffied IDW [10] interpolation
method in the RPC for the REM con
nstruction and a rich GUI as
an RDU (Fig. 1). One of the biggestt advantages of IDW [10] is
the ability to interpolate scattered data. The interpolation is
based on utilizing weighted coefficients. That are dependent on
oints (i.e. higher distance
the distance between the data po
between the points implies lower inffluence).
TABLE I. PERFORMANCE OF THE SENSING DEEVICES
Performance
metric
USRP2
Frequency bands
daughterboard
dependent
2.4– 2.485 GHz
Resolution
bandwidths
195 kHz – 25
MHz
58 kHz – 812.5 KHzz
Frequency steps
Frequency
switching delays
Sampling type
various
25kHz – 405 kHz
<200 μs
<809 μs
1 ms
IQ
>40 ns
(decimation
dependent)
-164 dBm/Hz
(for RFX2400)
RSSI
RSSI
<310 μs (bandwidthh
dependent)
128 μs
-83dBm (812 kHz)
-104dBm (203 kHz))
-91 dBm (2 MHz)
On-board
processing
capabilities
50 MHz 32bit
RISC CPU
C
16 MHz 16bit RISC
CPU
180 MHz 32bit
ARM920T
On-board memory
1 MB SRAM
1K RAM/32K ROM
M
512K RAM/4M
Flash
Sampling period
Sensitivity
TI eZ430 RF2500
CTION RESULTS
REM CONSTRUC
Sun SPOT
2.253 to 2.740
GHz
2 MHz (IEEE
802.15.4
compliant)
1 MHz
A. Integrated REM testbed platform
m
The integrated REM testbed platform
p
is located in an
indoor location in FEEIT’s premises (also presented at IEEE
DySPAN 2011 [4]). The locattion represents a single
laboratory space without obstacle walls and dimensions of
10x4 meters. A simplified represen
ntation of the laboratory is
depicted on Fig. 9. The capabiliities of the heterogeneous
spectrum sensing platform are dem
monstrated by its ability to
construct dynamic REM and provide
p
various statistical
characterization of the mapped enviironment.
811
proper adjustment of the SDs’ sensing threshold, duty cycle
calculation, estimation of spatio/temporal primary user activity
in specific bands, etc.
V.
Figure 9. The scenario location (red, green and blue circles represent SDs)
CONCLUSION
The ability to detect vacant spectrum opportunities allows
cognitive radio users to opportunistically reuse available
spectrum and increase the overall spectrum efficiency. The
combination of spectrum sensing with centralized database
based storage fosters the development of novel solutions for
accurate and up-to-date radio environment mapping.
This paper introduces a novel architecture for reliable
REM construction utilizing heterogeneous spectrum sensing
devices. It gives extensive details on the performance and
integration of several market available devices into a single
heterogeneous testbed platform. Additionally, the paper shows
results for the integrated architecture performance in terms of
its ability to dynamically construct a REM and provide
additional statistics from the measured radio field.
Future work will include integration of other possible
sensing devices in the heterogeneous platform, testing of
different toolboxes in the RPC (e.g. source localization, other
interpolation methods etc.), connecting the REM architecture
with already developed policy management architecture [5] etc.
The scenario of interest comprises six measurement devices
distributed in a random manner throughout the room. Their
locations are a priori known to the SDS. All devices sense a
single WLAN channel in the 2.4 GHz ISM band. A signal
source that transmits a signal with constant amplitude is placed
at a random position in the room. The source is turned OFF at
the beginning of the scenario. After turning the source ON, its
presence should be noticed by the SDs and displayed on the
REM.
B. REM visualization
Using the FP interface, the GUI sends periodical requests
for REM data to the RPC. Each device sends measured data
using the SS interfaces. The RPC entity uses the IDW
interpolation technique to calculate real-time REM [4]. Fig. 10
depicts a view of the GUI at time instant after the signal
generator is turned ON. The figure clearly shows presence of a
signal source at the right-hand side of the REM (the area
depicted with yellow and red color). The clear green color on
the left-hand side of the REM shows that the outputs of the
different measurement devices have similar values, due to the
calibration process. This implies that the integration and
calibration process of the devices is performed accurately
resulting in consistent spectrum measurements from all SDs.
ACKNOWLEDGMENT
This work was funded by the EC project FARAMIR (FP7248351) [11] and inspired by the EC project ACROPOLIS
(FP7-257626) [12]. The authors would like to thank everyone
involved. The authors also extend their gratitude to the COST
IC0902 action [13].
REFERENCES
[1]
[2]
[3]
Figure 10. A view of the GUI depicting a detected signal source
[4]
[5]
[6]
[7]
Figure 11. A snapshot of the GUI presenting CDF and duty cycle of two SDs
[8]
C. Statistical analysis of spectrum occupancy
The integrated testbed platform is suitable for derivation of
additional spectrum occupancy statistical characteristics. Fig.
11 depicts the CDF of the received power at the USRP2
located in the upper left corner and the duty cycle over time of
the TI RF2500 for detection threshold of -60 dBm.
This demo can be extended to analyze additional statistics
such as the average received signal power, the spatial
correlation of the measurement points, the PDF function of the
received power at every SD etc. This statistics can be used for
[9]
[10]
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[12]
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