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Eurasian Journal of Analytical Chemistry
ISSN: 1306-3057 OPEN ACCESS 2018 13 (6): 178-185
Received 14 September 2018 ▪ Revised 23 October 2018 ▪ Accepted 24 November 2018
Abstract: The fifth generation of mobile communication technology growth by next few
years, the traffic volume of mobile communication that rapidly increase because of the
enormous opportunities and applications significant benefit from Internet of Things (IoT)
across almost all Industries. Smart cities are considered amongst the top applications of
IoT which highly depend on Information Communication Technologies (ICT). Furthermore,
frequency spectrum available for mobile communication is limited, thus, the increasing
number of users will negatively affect frequency spectrum availability and accessibility.
That encouraged utilising cognitive radio communication techniques, in order to overcome
the deficiency in frequency spectrum, which drive to introduce a new approach to utilizing
the available frequency spectrum in communication and sharing this spectrum between
various users. Low-cost communication evaluation system is proposed to utilise idle
frequencies owned by licensed users and automatically make it available to other users
without distressing licensed user’s communication. Two aspects of cognitive radio are
investigated; users’ classification divided between primary and secondary users and
availability of idle frequency spectrums of licensed users. The effect of noise and
attenuation is analysed and compared using MATLAB simulation.
Keywords: Cognitive Radio (CR), Internet of Things (IoT), Primary Users (PUs), Secondary
users (SUs), and Signalto-Noise Ratio (SNR).
INTRODUCTION
By year 2020, the fifth generation of mobile communication (5G) is expected to be implemented
commercially, and the number of users will increase, therefore the demand of wireless communication
from all sectors will increase resulting in more IoT opportunities and applications[1];video on demand to
be smart for cities, grids, and health treatment are amongst applications highly sought after, and
availability of wireless communication bandwidth will face suffer because of these demands[2]. Smart
cities’ applications require enormous volume of data, Big Data [3]. Even though wireless communication
is very convenient channel of communication, frequency bandwidth which considered limited in
comparison to the increasing number of users. The upgrade of existing mobile communication
technologies, 3G and 4G,forward to 5G is imminent due to the high transfer speed which reaches up to 1G,
were increasing number of remote services and mobile devices [4].
The main idea behind cognitive radio is to utilise the frequency spectrum in an effective manner by
taking advantage of channel conditions, codebooks, and message transformation to share the spectrum
between different users. The mentioned features have empowered researchers to tackle the spectrum
scarcity problem, where users are classified into Primary Users (PUs), Licensed users authorised to use
the frequency spectrum, and Secondary Users (SUs) who use the spectrum when it is idle.(Yau et al.,
2009).
Faisal Y. Alzyoud*, Isra University. Email: faisal.alzyoud@iu.edu.jo
Wa’elJum’ah Al_Zyadat, Isra University.
Fadi Hamed, Isra University.
Fayez Shrouf, Isra University.
A Proposed Hybrid Approach Combined Qos
with CR System in Smart City
Faisal Y. Alzyoud*, Wa’elJum’ah Al_Zyadat, Fadi Hamed, Fayez Shrouf
179 Faisal Y. Alzyoud et.al
Fig.1: Utilization of White Spaces by Secondary Users
Spectrum Detection Techniques
In CR networks, creating a “friendly” environment for coexistence between the PUs and the SUs is
significant. Spectrum sensing is imperative to prevent interference between users.
Several techniques are commonly used in signal processing, these techniques are listed below:
1. Energy detection: the signal is detected when comparing the output energy level in relation to a
threshold level. Us are unable to differentiate between primary users signals and noise.[5].
2. Filters Matching: demodulation of a PUs signal is required to perform coherent detection in less
time, however this technique needs a special receiver for every PU[6].
3. Cyclostationary based on feature detector, this method able to exploit the inherent periodicity in
the received signal to detect primary signals since most signals vary with time periodically, it can
exploit the cyclostationarity features of the received signals and it can differentiate noise from
PUs signals as well. The issue suffers from longer processing time and higher computational
complexity [5].
4. Cooperative detection method senses the number of different radios within a cognitive radio
network in a cooperative cognitive radio spectrum sensing system [7].
5. Waveform based detection method is usually utilized in wireless systems to assist
synchronization or for other purposes, it is used waveform-based sensing which requires short
measurement time [8].
Smart Cities
The rapid growth in cities population and the remarkable growth of digital devices usage such as
sensors, actuators, smart phones and smart appliances fulfill the objectives of IoT applications for both
population and organizations.. This growth increase the tendency to use the available yet limited
bandwidth. Furthermore, growth is a perfect use-case for IoT to perform various activities in many
sectors such as transportation sector, cars, parking, merchandise, waste management, street lighting, and
building management. IoT uses wide area network and the internet to manage the various heterogeneous
objects in smart cities; existing objects can be linked to the internet to communicate with each other [9].
Nowadays, many capital cities such as New York, Singapore, Tokyo, Seou, Shanghai l, Amsterdam, and
Dubai have supported smart projects to improve the services for their inhabitants and raise the quality of
service provided for them. Smart cities should serve the following tracks: transportation system,
healthcare system, weather monitoring systems and supporting people via internet in every place to
accessing the database of airports, railways [10]. Transferring to smart cities request to collect a huge
volume of data on central network nodes or servers, and this will increase the use of available bandwidth
which should be utilized effectively.
Cognitive Radio Applications
In this section, we discuss three existing applications in different scopes including to multimedia,
communication and wireless networking.
180 Eurasian Journal of Analytical Chemistry
1. The most promisingapplication for CR systems are multimedia applications in mobile downloads
(i.e. download of music/video files, cooperative games) which needed acceptable Quality of
Services (QoS) requirements [10].
2. Emergency communications applications all these applications need a moderate data rate and
localized coverage (i.e. disasters video transmission, fire video transmission from firemen’s’
helmets to the emergency control room).
3. Broadband wireless networking applications,these applications need high data rates, but where
users may be satisfied with localized “hotspot” services (i.e. using nomadic laptops). Multimedia
wireless networking applications: these applications need high data rates transmission (i.e.
audio/video distribution within homes). As these applications will increase the inhabitants’
satisfaction to smart city platform.
Full CRis likely to emerge in 2030 [10-12], this is bound to happen as conditions are fully flexible, and
software defined radio technologies are on the rise, these intelligent systems are able to exploit in CR
system. The challenges on hand revolve around applying CR techniques to create suitable cognitive
implemented as device, and to apply artificial intelligence to carry on the making decisions for utilize the
spectrum dynamically. There are devices that already have some elements of CR such as WLANs and
military follower jammers; however these devices are not mature enough for cognitive radio applications.
RELATED WORK COGNITIVE RADIO NETWORKS EXITING QOS
The number of mobile users has significantly increased parallel with mobile services. Therefore,
researchers in mobile communications focus on improving the QoS in bandwidth aspect scarce resource.
Furthermore, these aspects are considered as major issue for cognitive radio networks. Quality of Service
for secondary users is difficult to achieve as SUs use the idle channel which is not occupied with PUs.[13]
proposed an analysis model to obtain QoS for cognitive radio networks by taking blocking probability ,
completed traffic and termination probability of SUs . Meanwhile, it was proposed resources reservation
techniques for SUs . Multipath Activity Based Routing Protocol for Cognitive Radio Network (MACNRP)
which proposed in [14], a protocol to utilize channel availability and to create multiple node-
disjoint routes between the source and destination nodes. Multi-channel approach can increase the
network resources and increase the QoS, as well as distributed channel allocation algorithm to utilize
multi-channel due to approach the network performance parameters are enhanced, these parameters
include network throughput, end to end delay. Their algorithm refer to contention graph and adopts
contention factor to evaluate conflicts in a channel [15]. Multichip Multi-Channel Distributed QoS
Scheduling MAC scheme (MMDQS-MAC) was proposed to enhance the performance of cognitive radio in
Wireless Sensor Networks(WSN) by selecting the best channel for an individual wireless sensor node and
supporting dynamic channel assignment mechanism to decrease the probability of collision, interferences
and improves the overall network performance of WSNs[16]. QoS differential scheduling CR-based on
smart grid communications networks is studied by Rong Yu and others [17], they proposed schedule the
flow using a scheduler which is responsible for managing the spectrum resources and arranging the data
transmissions of smart grid users (SGUs). They proposed to assign different priorities according to their
roles and their current situations in the smart grid. Based on the QoS-aware priority policy, the scheduler
adjusts the channels allocation to minimize the transmission delay of SGUs.
HYBRID QOS WITH CR PROPOSED APPROACH
This research focuses in combining QoS differentiated services with cognitive radio techniques to
utilize the idle bandwidth, and classify secondary users in term of priority. The objective of this proposed
technique allows devices to share the bandwidth efficiently without causing any disturbance to primary
users. Secondary users depend on type of service to determine the priority degree assigned to them, in
order to use the idle channel in data transmission. Since primary users will use the transmission channel
on demand, the secondary users have to wait until primary transmission is done and channel becomes
available to them, hence, Hybrid QoS with CR takes effect to determine which secondary user can utilise
the idle channel.
In case primary user decided to transmit data, the proposed technique will drop the secondary user
with least priority classification to allow primary user to utilize the channel without disturbing secondary
users with higher priorities.
Figure 2 illustrates how the hybrid QoS with CR works on channel users’ classification; primary users
are always assigned with highest priority, and secondary users are given lower priority.
181 Faisal Y. Alzyoud et.al
Fig.2: Proposed Hybrid QoS Approach with CR System
When primary users are assigned with highest priority, the Hybrid QoS with CR will check the services
of all secondary users, and decide which of one them has higher priority than the other using the
differentiated service filed as per Figure 3.
Fig.3: Proposed Cognitive Radio with Differentiated QoS
182 Eurasian Journal of Analytical Chemistry
In order to simulate the technique on MATLAB, we optioned to the cognitive radio configuration
shown in Figure 4, where five frequency bands are configured, and Amplitude Modulation is used to
carry the signal. The Setup then estimates the Power Spectral Density (PSD) of every band in order to
assign the secondary user to the available bandwidth according to its priority degree, PSD is defined
as a positive real function of a frequency variable associated with a stationary stochastic process, or a
deterministic function of time, which has dimensions of power per hertz (Hz), or energy per hertz.
Noise is then added to mimic real environment. [12]
Initialize Five
Frequency Bands Amplitude
Modulation
Add Modulated
Signal to Create
Carrier
Estimate Power
Spectral Density
Classify
Secondary
Users(SUs)
Adding Noise
(AWGN)
Adding
Modulation
Output
Diagram
Output
Diagram
Fig.4: Block Diagram for Cognitive Radio with Differentiated QoS
DISCUSSION AND ANALYSIS OF SIMULATION RESULTS
Based on simulation setup model for cognitive radio starting from initialize 5 carrier frequencies Fc1
= 1000, Fc2 = 2000, Fc3 = 3000, Fc4=4000 & Fc5 = 5000 as and every user’s base band data signal is
modulated over the spectrum density. Power spectrum density is estimated, and the output is presented
using Periodogram. The results can be represented to show the allocated free space see Figure 5, after
estimating the free spaces SUs are classified by differentiating them according to their importance and if
the Pus take the channel from any of the SUs then the empty slot will be given to the highest priority SUs
and so on. Noise is added using Additive white Gaussian noise (AWGN) model, which is basic noise model
that occur in nature with constant spectral distribution. The noise is added to mimic real situation. AWGN
channel is a good model for many wireless deep space communication links [18]. Attenuation is added to
carrier signal to attenuate the designed system.
The simulated system was built using MATLAB to enable the basics of a cognitive radio systems using
dynamic spectrum access at run time. Our approach was built by taking decisions by sensing the basis of
power spectral density of the channel which can be used cognitively to find out the available gaps those
can be assigned to new incoming users to improve the overall channel’s throughput.
Power Spread Speed Spectrum with Two Primary User’s and Three Empty Slots
First, the simulation is done by checking the available bandwidth by assigning two primary users and
leaving three empty slots to check the available gaps in the spectrum, the gathered results are depicted in
Figure 5. The results show that the power / frequency ratio is above zero level just for frequency one and
two respectively. The empty slot power is lower than zero showing that the channel is empty and can be
used.
183 Faisal Y. Alzyoud et.al
Fig.5: Power Spectrum Density Curve: Three Slots remaining in the frequency Spectrum
Effective Use of Spread Spectrum with SUs QoS Enabled
To optimize the capacity of the SUs, the system will automatically classify secondary users by enabling
the Type of Service (ToS) field and assign priorities to the SUs. The SUs will automatically assign to the
empty slots according to their priority so the system will be fully utilized as shown in Figure 6, which
shows that all the power frequency are above zeroes.
Fig.6: Fully Utilized Spectrum with SUs QoS Enabled
Noise Effect on Spread Spectrum Density
The noise can be reduced using spread spectrum[17], but unfortunately it has an inverse effect on
Power Spectrum Density as it is shown in Figure 7. Different Signal to noise ratio (SNR) is used starting
from 10, 20, 40 and 60 percentage to investigate their effect on power spectrum density. AWGN channel
model is used. The worst SNR effect on Power Spectrum Density is at SNR equal to 40.
0123456
Frequency (Khz)
-35
-30
-25
-20
-15
-10
-5
0
5
Power/frequency (dB/Hz)
Power spectral density curve: three slot remaining in the frequency spectrum
01 2 3456
Frequency (Khz)
-30
-25
-20
-15
-10
-5
0
5
Power/frequency (dB/Hz)
Effective Use of Spectrum With SUs QoS enabled
184 Eurasian Journal of Analytical Chemistry
Attenuation Effect on Spread Spectrum Density
Signal transmission is affect by different challenges before arriving at the receiver such as weather
attenuation losses [18], in this paper the effect of attenuation according to variety percentages of
attenuation starting from 10, 20, 40 and 60 respectively as shown in Figure 8. It is noted that Power
Spread Spectrum Density decreases with the increases of attenuation.
Fig.7: Power Spectrum Density with Different Added Noises
Fig.8: Power Spectrum Density with Different Percentages of Attenuations
CONCLUSION AND FUTURE WORK
Cognitive radio technology is promising technologies, which allow users to utilise the scarcity of
spectrum frequency; it can be used in a variety of important future applications as described earlier. By
the internet of things, many smart applications become applicable and can facilitate many important
aspects for human life such as smart healthcare, smart homes, smart energy, and smart cities. In this
paper, cognitive radio technology has been merged with QoS differentiated approach to provide suitable
platform of smart cities communications. Cognitive radio has been simulated using MATLAB, primary
users and secondary users are classified depending on priority and by enabling differentiated QoS
approaches to SUs. The results show that the spectrum was fully utilized, and the effect of noise and
attenuation are studied to show their effects on CRs system.
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
KHz
0
0.5
1
1.5
2
2.5
3
3.5
4
dB/Hz
Power Spectrum Density with Different noises
SNR=10
SNR=20
SNR=40
SNR=60
0 0.2 0.4 0.6 0.8 1
KHz
0
0.5
1
1.5
2
2.5
3
dB/Hz
Power Spectrum Density with Different Percentage Attenuation
10% Attenuation
20% Attenuation
40% Attenuation
60% Attenuation
185 Faisal Y. Alzyoud et.al
Future work includes setting a new plan that apply cognitive radio on a real environment and
compare different sensing techniques. A suggested mathematical model would be proposed for
computational variables of Power Spectrum Density.
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