Investigation of TV White Space for Maximum Spectrum Utilization in a Cellular Network Using CRT

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 03 Issue: 05 | May-2016 www.irjet.net p-ISSN: 2395-0072
© 2016, IRJET | Impact Factor value: 4.45 | ISO 9001:2008 Certified Journal | Page 1517
Investigation of TV White Space for Maximum Spectrum Utilization in a
Cellular Network Using CRT
Iroh Chioma Ulunma1, Nosiri Onyebuchi Chikezie2, Dike Damian Obioma3 Ononiwu Gordon4
1Postgraduate student, Department of Electrical and Electronic Engineering, Federal University of Technology,
Owerri, Nigeria
2,3,4 Lecturer, Department of Electrical and Electronic Engineering Federal University of Technology, Owerri,
Nigeria
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Abstract— The shortage of spectrum resource
availability in wireless communication network due to the
rapid increase in the number of subscribers and multimedia
applications has given rise to the need for effective spectrum
utilization of the licensed spectrum. Cognitive Radio
Technology (CRT) was adopted for the system analysis due
to its dynamism in accommodating both licensed and
unlicensed users within a particular spectrum band. The
study framework comprised television station channels in
Owerri and its environs and Mobile Telecommunication
Networks (MTN) in Owerri- the capital of Imo State, Nigeria
as the license and unlicensed users respectively. An outdoor
twenty-four hour spectrum occupancy measurement was
carried out in the frequency bands of the licensed networks
using 240-960 MHz Radio Frequency Spectrum analyzer to
determine the spectral usage of the licensed user. A
threshold of -95 dB was used to determine the presence of
the licensed users. From the results obtained, it was
observed that 60.7% of the spectrum band covered was
unoccupied, 31.5% was not fully occupied while 7.9% was
fully occupied. Energy Detection spectrum approach was
implemented by the unlicensed users for easy determination
of the spectrum status and resource management. The
research therefore determines the status of Radio Frequency
(RF) Spectrum receivable in Owerri and its environs and
proffer measures deployable in harnessing the unused RF
resources using CRT.
Key Words: RF spectrum, TV channels, Cellular
networks, Cognitive Radio Technology, Primary User,
Secondary User
1. INTRODUCTION
The increase in the demand for spectrum used for mobile
telecommunication subscribers has necessitated the
reason to maximize the Radio Frequency (RF) spectrum.
This increase is as a result of varying multimedia
applications deployable in wireless communication
systems. To ensure fairness in addition to improved service
delivery and maintenance in wireless communication
systems, Radio Frequencies (RFs) are allocated and
licensed to the various national regulatory bodies to
corporate network operators by the international
regulatory agency to avoid encroachment into adjacent
operators’ bandwidth. A consequent effect of this allocation
is spectral shortage; such shortage is in part due to the
current allocation policies by the regulatory body which
allocates spectral bands for exclusive use of a single entity
within a geographical area.
Studies have shown that while there is shortage of
spectrum resources for future wireless technology due to
the rapid increase in the number of subscribers, some of
the already licensed spectrums are underutilized across
time and geographical regions [1]. It becomes pertinent to
balance the obvious situation and improve the wireless
spectrum utilization. Confronting this rapid increase and
its effects using enhanced mechanisms has become a thing
of interest for researchers to investigate. One of the
efficient techniques currently deployed involves the use of
cognitive Radio Technology (CRT).
CRT is an intelligent wireless communication system
which is aware of its RF environment, learns from it and
adapts its internal states to statistical variation in the
incoming RF stimuli. It actualizes this by making
corresponding changes in certain operating parameter in
real time [2]. By way of this concept, more devices can be
incorporated in a frequency band already in use.
The paper is designed to investigate the status of the TV
white space for onward spectrum maximization using
CRT. The license users are the Television (TV) stations
channels in Owerri and its environs while the unlicensed
user is the Mobile Telecommunication Network (MTN) in
Owerri metropolis in Imo State, Nigeria.
2. RELATED REVIEWED WORK
Author of [3] presented an insight into spectrum
occupancy in Nigeria. They conducted an indoor spectrum
occupancy measurement within the radio frequency range
of 700MHz to 2.5 GHz in Gwarinpa district Abuja Nigeria;
the measurement lasted for twelve hours and covered
between 9am and 9pm. In their work, a threshold of -
76dBm based on the noise floor level of the setup was
used to decide the presence of primary users. Their results
showed that large portion of the allocated spectrum was

underutilized and could be considered for deployment of
cognitive radio technology. Their investigations however
was specifically for indoor and the measurements were
conducted for twelve hours only, further analysis for the
outdoor investigations at different locations were not
carried out which is necessary to actualize the mean peak
and off peak performances.
According to [4], in their study discussed Non-Cooperative
sensing network for spectrum sensing by secondary users.
Each of the SUs transmitters carried out the task of
deciding whether or not the PU transmitter is available on
the scanned channel. In their study, the detection
formulation was achieved using hypothesis testing where
the null hypothesis is absence of the PU transmitter and
the alternate implies its presence. The common lapses
associated with this network were the nonflexible
requirements by each SUs for reception, degradation of
received signal strength and much time required for
sensing to be completed by all the SUs before transmission
starts.
The author of [5] developed a Software Defined Radio Test
bed for research in dynamic spectrum access. The SDR test
bed was developed based on Universal Radio Software
Peripheral (URSP) with a focus on Spectrum Probing; a
seemly overlooked component of spectrum sensing
according to the author. The work expanded theoretical
and simulation analysis for comparing different spectrum
probing. Different spectrum probing methods were
implemented for an independent and cooperative network
of SU. The results showed that in independent sensing,
periodic probing achieved smallest delay while in
cooperative probing, randomization reduces probing
delay.
3. RESEARCH METHODOLOGY
A. Measurement Environments: A spectral occupancy
measurement within the frequency range of 240MHz-
960MHz with a span of 50 MHz apart was carried out for
24 hours using an RF Explorer Spectrum Analyzer at the
following locations: Hall of Mercy in Federal University of
Technology, Owerri (FUTO), Obinze, World Bank Housing
Estate and Douglas road, to determine the spectral usage
of the various Television stations within Owerri and its
environs. These locations were selected relative to the
population of residents there.
B. Measurement Equipment and Procedure: Scanning
through the RF bands of TV channel was made possible by
the use of the following equipment.
(1) RF Explorer Spectrum Analyzer: An RF spectrum
analyzer of range 240-960MHz was used to scan the RF
environment of the various measurement locations. It
displayed full frequency spectrum in-band including
carrier and modulated shape, activities of spread spectrum
existing are also displayed. It equally showed the
bandwidth to monitor collision and frequency deviation
from tone.
(2) Universal Serial Bus (USB) Cable: This was used to
connect the RF Spectrum analyzer to the laptop. It is
relevant to establish the interface between the spectrum
analyzer and the laptop which were among the devices
used for recording/saving of the measurement.
(3) Laptop: An HP 630 model laptop was connected to the
RF spectrum analyzer, this was used to record and capture
the videos and pictures of the measurements taken and for
their broader and quality display. To be able to execute the
functions, an RF Explorer windows software and Camtasia
Studio 8 were installed in the laptop.
(4) Power Generator: A 10 Amperes 250Volts power
generator was used to ensure steady supply of voltage to
the laptop and RF spectrum analyzer
Figure (1) presents the block diagram of the measurement
set-up.
Fig-1: the block diagram of the measurement set-up
C. Measurement Configuration: This constitutes the
software applications and the procedures used.
(1) Spectrum Analyzer Mode: The RF explorer switches
automatically to spectrum mode upon start. It consists of
two axes; the horizontal axis which denotes the frequency
in MHz while the vertical axis represents the actual
received power in decibel miliwatt (dBm). Once enabled;
there is an automatic display of small marker with a look
of an inverted triangle shape; mounted upon that marker
are two values one on top the other indicating the peak
amplitude found in the current frequency span, the upper
value indicates the measured frequency in MHz while the
lower value indicates the amplitude of power in dBm.
Figure 2 shows the diagram of the standard mode of
spectrum analyzer main screen.
Fig-2: the diagram of the standard mode of spectrum
analyzer main screen
(2). RF Explorer Windows Client: This is the software
used to ensure compatibility between the RF Explorer and
the Personal Computer (PC) window used. It was installed
in the PC and automatically gets connected to the RF
Explorer once connected together with a USB cable. RF
explorer window client software turns data collected from
RF spectrum analyzer into highly graphical charts and
displays, given opportunity to visualize the RF
Spectrum
Analyzer
USB Cord PC inter
phase
Power
generator set

environment, monitor RF signals, troubleshoot RF issues,
and detect sources of RF interference in the measurements.
Figure 3 shows the RF explorer PC inter phase outlook.
Fig-3: The RF explorer PC inter phase outlook
(3) Camtasia Record Studio: This is a software installed
in the PC to ensure proper documentation and saving of
the measurements’ output.
(4) Threshold Decision: A close observation of
measurements taken at various locations indicated that
channel activity was significant at values greater than and
equal to -90dBm of received signal strength displayed by
the RF spectrum analyzer.
Another measure considered for choosing threshold for
this study was the International Telecommunication Union
(ITU) recommendation [6]. It suggests that the threshold
for deciding the availability of signal in a scanned
environment should be 10dBm above the ambient noise.
The ambient noise for this research work based on the
measurements carried out is -110 dBm.
D. The Primary Networks: The primary networks
considered for this study were the TV stations in Owerri
Imo state and those receivable within its environs as
shown in Table 1.
Table-1: TV stations receivable within Owerri and its
environs
State Station Channel No Centre
Frequency (MHz)
Abia Broadcasting Corporation
of Abia State (BCA)
47 679.45
Abia Nigeria Television
Authority (NTA) Umuahia
21 471.25
Anambra NTA Onitsha 35 583.25
Anambra Anambra State
Broadcasting Corporation
(ASBC)
27 519.25
Anambra Minaj Broadcasting
International (MBI)
41 631.10
Anambra Silver Bird 30 543.25
Imo NTA Owerri 12 224.25
Imo Orient Television 59 775.25
Rivers NTA Portharcourt 10 210.25
Rivers Rivers State Television
(RSTV)
22 479.25
Rivers Silver Bird Television 23 487.25
Rivers Africa Independent
Television (AIT)
21 471.25
E. Secondary Network: MTN Cellular network is the
secondary networks. It was considered relative to its
greater number of subscribers in Owerri environs when
compared to other cellular networks.
F. Spectrum Sensing Technique: Spectrum sensing is the
most important task that must be carried out in order to
have an establishment of Cognitive Radio Network; this is
because it gives the awareness of availability of white
space. Actualizing greater output from this task is best
done usually at the idle period of most TV stations which
are 12:00 am to 6:00 am. Sensing could be done using
several techniques like Cyclostationary and Matched Filter
but were not deployed because of some limitations they
pose such as computational complexity and large power
consumption due to various receiver algorithms that
needed to be executed for detection.
Energy Detection technique as illustrated in figure 4 was
implemented in the study. It only depends on the power of
its received signal to decide the presence or absence of the
primary users and does not require a prior knowledge of
the primary signal as other techniques such as Matched
filter and Cyclostationary would require. It is also
applicable if the power of random Guassian noise can be
known. It is easily implementable in time and frequency
domain with low computational complexity when
compared with other reviewed techniques [7]. Decision is
taken by comparing the output of the energy detector with
a pre-defined threshold that depends on the noise floor.
Figure 4 presents the block set-up deployed while carrying
out energy detection spectrum sensing in a TV stations
radio frequency spectrum.
Fig-4: The block set-up of Energy Detection Technique.
Flowchart for implementation of the Energy Detection
Technique is shown figure 7.
Band Pass
Filter
A/D
Converter
Squaring
Device
Integrator
Fig-5: The flowchart implementation of Energy Detection
Technique
Stop
Is Y ≤ λ ?
Start
Signal strength detection
(Y)
RF environment scanning.
Analysis of Y
Transmission by SUs
Yes
No
Compare Y with λ

Decentralized Coordinated Cooperative network topology
consisting of six cognitive radios (CRs) was deployed in
this study as illustrated in figure 5(a)
.
Spectrum sensing was carried out by each of the co-
existence CRs which enabled acquaintance with the RF
environment and aided observations; each of the users
analyzed its observations and exchanged that among
themselves directly, therefore no controller or fusion
centre was required for such. From the updates obtained
by each of the SUs, decisions on whether or not the
availability of the PUs were taken, if the spectrum is
deemed free of PUs, transmission by SUs would be
initiated and continued until completed otherwise
spectrum mobility.
The reason for collaboration was to reduce the large
sensitivity requirement for individual cognitive users
assuming there was no cooperation among them. This
topology is a typical example of Star topology network
where on the event of damage of any link, the whole
system would be unaffected. Also it was preferred because
of the improved performance it has in the presence of
fading, shadowing and noise uncertainty. Algorithm used
for the network communication is illustrated below. The
flow chart for implementation of the Distributed Co-
ordinated network are shown in figures 5 (b)
 Observation by each CRs.
 Analysis of observation by each CRs.
 Exchange update.
 Decision would be taken. if the PUs are absent, then
share the spectrum and transmit.
 Else, spectrum mobility by CRs
 Else Else, transmission continues.
 Transmission completed by the CRs
 Stop.
Fig-5(a): Distributed Coordinated Topology
RF environment
User 5
User 2
User 6
User 3
User 4
User 1
Start
Have CRs
completed
transmission?
Transmission by SUs continues
Spectrum Sharing
RF Sensing by each SUs
RF Environment
Have PUs
arrive?
Is spectrum
available?
Transmission by CRs
CRs spectrum
Mobility
Analysis by each SU
Communicate update to
all SUs
Stop
Yes
Yes
Yes
No
No
No
Fig-5 (b): the flowchart implementation of Distributed
Coordinated network
Transmission
by SUs
Compa
rator: is
ỳ > λ ?
Band Pass
Filter
ADC IntegratorSquaring
Device
D/A
Controller
Adjustment
S
U
4
S
U
3
S
U
2
S
U
1
Fig-6: Proposed Experimental Test bed

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SRO
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G. System Parameters
(1) Antenna Two discone antennas embedded into one
structure that cooperate well with cell phones were
deployed. One was omini- directional and used for
scanning the required RF band while the other was more
directional and reconfigurable and used for tuning to the
required frequency used by the secondary users.
(2) Band Pass Filter (BPF): The BPF considered was a
second order Butterworth filter; it was such that is
sensitive and selective to the desired frequency among
other frequencies available in the frequency spectrum.
(3) Analogue to Digital Converter (ADC): To ensure
compatibility with components of system used before and
after controller, ADC and Digital to Analogue Converter
(DAC) were deployed to ensure that time varying signals
from the band pass filter were converted to discrete form
and versa without altering the essential contents of the
signal. It was also necessary to achieve improved signal to
noise performance.
(4) Squaring Device: For effective performance of the
integrator circuit, the input to the integrator must be a
square wave; otherwise it does the work of low pass filter.
For this conversion, a 555timer integrated circuit
configured as Schmitt trigger was deployed to perform the
task.
(5) Integrator: An integrator used was to ensure that
continuous varying input signal from band pass filter was
summed up into an output signal represented in time
integral of the input, hence determines the observational
interval. Mathematically represented as y=⨜x dt where x
and y are the input and output of the system.
(6) Comparator: This was used to compare the output
from the integrator with the predefined threshold value of
(λ = -95 dBm). The value was considered relative to the
study and determined whether or not the transmission by
SU would be successful else a feedback error message
would be send back to the local oscillator through the
controller.
(7) Controller: This is an adaptive adjuster used for the
system. It works based on the algorithm developed
relative to the system desired output.
Algorithm for Controller Adjustment using Fuzzy rules
Process Variable (input to the controller): Power of the
received signal (ỳ).
Control variable (output from the controller): Rescan
or Transmission
Operator’s Control rules: IF ỳ <λ, THEN Transmission.
: IF ỳ >λ, THEN Rescan.
System: Energy Detection for one input, one output and
two rules
Values for control rules: IF ỳ <λ [0 0.5]= Transmission.
: IF ỳ <λ [0.5 1.0]= Rescan.
4. ANALYSIS OF SPECTRAL OCCUPANCY
For the frequency range captured during measurement, a
measure of spectrum occupancy for each frequency
channel in each location is done in order to ascertain
percentage occupancy of each channel for the period
measurements. Duty cycle which is described as the
percentage of time a frequency band or channel is
occupied over a given period of time. It is utilized to
indicate how often signals are perceived during a sample
period of scanning. It is given as
Occupied period is dependent on measurement, total
measurement period =24 hours. Another estimate
necessary for consideration is the Spectrum Resource
Occupancy (SRO), it is given as
Total number of channels measured = 89, Number of
unoccupied channels = 54, Number of channels not fully
occupied = 28, Number of channels fully occupied = 7.
Therefore, number of occupied channels (both fully and
non-fully occupied channels inclusive) equals to 7+28 =
35. Hence
While the percentage of unoccupied resource equals
Figures 7(a), (b), (c) and (d) present the occupancy
distribution of the measurement.
Fig-7(a): The bar chart representation for the frequency
range of 270-742 MHz versus the duty cycle (%).
Fig-7(b): The bar chart representation for the frequency
range of 270-742 MHz versus the duty cycle (%)

5 RESULTS DISCUSSION
About 89 channels were captured in measurements. From
the results of figures 7(a), 7(b), 7(c) and 7(d), it can be
observed that 7.8 % of the spectrum was fully occupied,
31.5% was partially occupied while 60.7% which is of
greatest portion of the channels measured was not
occupied at all. For an instance, within ranges of 240-264
MHz, 486-542 MHz and 782-838 MHz etc, the bands were
idle. While within the ranges of 280-312 MHz, 606-622
MHz and 774-782 MHz etc the bands were not fully
occupied. Also within the ranges of 424-432 MHz, 550-558
MHz and 942-950 MHz etc, the bands were fully occupied.
From the results obtained, it was observed that CRT is well
suited for adoption and implementation in the study
environments considering the greater portion of the idle
state.
Another observation made from the measurement results
at various locations was that even though the results
obtained at those locations are related, each location has a
unique dB loss relative to its environment. For example,
measurement results at Concord junction location showed
that noise level decreased from -100 and -110dB and the
signal level increased from the usual level of -90 to -80dB.
This was because that particular environment was
apparently noise free and very close to Orient
transmission antenna which enhanced reception when
compared with other locations.
6. CONCLUSION
A spectrum occupancy measurement within the range of
240 -290 MHz was carried out to determine the spectral
status of the licensed bands of various TV stations in
Owerri capital city and its environments in Imo State,
Nigeria. A threshold value of λ = -95 dBm was used in
deciding the presence of primary user above noise level
and below which the spectrum band was deemed free and
can be utilized by the secondary users. The measurement
results showed that 60.7 % of the spectrum was
unoccupied, 31.5% was not fully occupied while 7.9% was
fully occupied indicating lots of spectrum utilization
opportunity. The empirical characterization of the
environment showed that it is an excellent environment
for deployment of CRT for optimal RF utilization for MTN
network.
REFERENCES
[1] J. Mitola, “Cognitive Radio: An integrated Agent
Architecture for Software Defined Radio” PhD
Dissertation, Royal Institute of Technology KTH
Stockholm Sweden, 2000.
[2] S. Haykin, “Cognitive Radio: Brain-empowered
Wireless Communication” IEEE Journal on selected
areas in communication. vol. 25 pp 201-200, 2005.
[3] B. Najashi, F. Werijiang and C. Kadiri “An in-sight into
spectrum occupancy in Nigeria” IJCSI International
Journal of Computer Science Issues, Vol. 10, Issue 1, No
1, ISSN : 1694-0814, 2013.
[4] R. Tandra and A. Sahai “SNR Walls for Signal
Detection”. IEEE Journal of Selected Topics in Signal
Processing, Vol. 2, No. 1. pp: 4 – 17, 2008.
[5] A. David, “A software defined radio testbed for
research in dynamic spectrum access” A Thesis
Submitted to the department of Electrical Engineering,
Purdue University, Fort Wayne, Indiana, 2012.
[6] www.itu.int./ITU-T/recommendation
[7] H. Arslan, “Cognitive Radio Software Defined and
Adaptive Wireless Systems”. Springer, University of
South Florida Tampa FL USA, 2007.
BIOGRAPHIES
Iroh, Chioma Ulunma received her B.Eng. in
2009 from department of Electrical
/Electronic Engineering, Michael Okpara
University of Agriculture Umudike, Nigeria.
She is currently pursuing her Master Degree
programme from the department of
Electrical/Electronic Engineering, Federal
University of Technology Owerri Nigeria. Her
research interest includes wireless
communication, Cognitive Radio Technology
and next generation networks.

2nd
Author
Photo
Nosiri Onyebuchi Chikezie is a lecturer 1 in the
department of Electrical and Electronic
Engineering, Federal University of Technology,
Owerri, Nigeria. He is a research scholar in
current trends in wireless and data
communication systems. He obtained his PhD
in Communication Engineering and has
published several articles in wireless
communication engineering. He is an IEEE
member, a COREN registered engineer and a
member, Nigerian Society of Engineers.
Damian Obioma Dike is an Associate Professor at
Federal University of Technology Owerri Nigeria.
He obtained his PhD in Electrical Engineering at
Tennessee Technological University, USA 2008.
He has worked in various international
Engineering firms such as Cummins Inc (Power
Generation), USA as a senior project Engineer, as
a coordinating consultant engineer to JENCEL
Consults limited etc. His research interest
includes integration of modeling programming
and simulation tools for power systems,
distribution system modeling and protection,
power system studies and automation, converter
modeling FACTS and system integration.
Dr. Gordon Ononiwu - Received his B.Eng, in
Electrical and Electronic Engineering from the
Federal University of Technology (FUTO),
Owerri, his M.Sc in Computer Systems
Engineering from Halmstad University in
Sweden, and his PhD in Computer Engineering
from FUTO. He is a senior Lecturer at the
department of Electrical and Electronic
Engineering of the Federal University of
Technology, Owerri. He is a fellow of the Swedish
Institute (SI), and also an International Faculty
Fellow (2015/2016) at the Massachusetts
Institute of Technology (MIT). He was a guest
scholar at the Centre for Research on Embedded
Systems (CERES) in Sweden (2009 - 2011). He is
a member of Nigerian Society of Engineers and is
registered by COREN. His research interest is in
the field of embedded systems engineering.

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