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Sule!Lamido!University!Journal!of!Science!&!Technology!Vol.!4!No.!1&2![July,!2022],!pp.!205-218!
https://doi.org/10.56471/slujst.v4i.268!
©!The!Author(s),!under!exclusive!license!to!SLUJST!2022! !205!
Special!Issue!on!Computing!&!Advances!in!Information!Technology!
Online!ISSN:!2736-0903!
Print!ISSN:!2736-089X!
www.slujst.com.ng!
Intelligent Process of Spectrum Handoff in Cognitive Radio Networks
Emmanuel Alozie1*, Nasir Faruk2, Abdulkarim. A. Oloyede1, Olugbenga. A. Sowande1, Agbotiname Lucky
Imoize3, Abubakar Abdulkarim4 and Salisu Garba5
1Department of Telecommunication Science, University of Ilorin, Nigeria.
2Dept of Physics, Sule Lamido University, Kafin Hausa, Nigeria. Directorate of ICT, SLU.
3Department of Electrical and Electronics Engineering, University of Lagos, Nigeria.
4Department of Electrical and Electronics Engineering, Ahmadu Bello University Zaria, Nigeria.
5Department of Computer Science, Sule Lamido University, Kafin Hausa, Nigeria.
*alozieemmanuel298@gmail.com
Abstract
Spectrum handoff is a crucial function of Cognitive Radio (CR) which is the change of operating frequency. The
main problem in spectrum handoff is the time taken in the searching, selection, and switching to a new available
channel which can cause a significant amount of delay during spectrum handoff. This research aims to minimize
the delay that occurs during spectrum handoff. A Proactive Fuzzy-Based Backup Channel Selection Scheme
(PFBBCSS) was proposed where the Secondary User (SU) gathers backup channels in advance before the return
of the Primary User (PU), then fuzzy logic would be used for the selection of the best channel out of the available
backup channels. The proposed scheme was simulated and evaluated using the MATLAB Simulation tool and the
result was compared with a Pure Proactive Spectrum Handoff Scheme. Results showed, in terms of throughput and
efficient time utilization under a varying number of licensed channels, that the proposed scheme performed better,
making it a good mechanism to be used for handoff decisions by the Secondary User (SU).
Keywords: Cognitive Radio Network, Spectrum Handoff, Fuzzy Logic, Handoff Delay
1. Introduction
The emerging needs of wireless data services such as the 5G technology (Salami et al., 2019; Popoola et al.,
2017), public safety communications (Faruk et al., 2018a), e-health, and virtual clinics (Faruk et al., 2017; 2020),
there is a need for a more enhanced capacity in the wireless systems. The static (conventional) spectrum allocation
to Primary or Licensed Users (PU) limited the overall utilization of the spectrum since the spectrum may not be
completely utilized by the PU all of the time and in all locations, thereby, pushing more pressure on the already
exhausted radio spectrum frequencies (Somawanshi et al., 2016; Oloyede et al., 2017; 2019; Oloyede & Faruk,
2018).
Several researches that conducted spectrum occupancy measurements and analyses, aimed at assessing the
utilization of the spectrum allocated to PU, findings show low occupancy rates, with average utilization of less than
10% (Faruk et al., 2014; 2016; 2018b; 2019; Ganiyu et al., 2019; Babalola et al., 2015a; 2015b). However, Cognitive
Radio (CR) was developed, which allows Secondary or Unlicensed Users to utilize or share the unused licensed
frequency allocated to the PU. The major purpose of developing cognitive radio is to enable wireless transmission
adaptation through Dynamic Spectrum Access (DSA) to maximize the usage and benefits associated with fixed
spectrum assignment (Westphall & Westphall, 2011). The application of CR has been demonstrated for television
bands (i.e., TV white spaces) (Adediran et al., 2014; Faruk et al., 2015).
Spectrum management includes having a reliable Medium Access Control (MAC) Protocol that regulates the
access to the spectrum by the SUs, thereby coordinating the access of SUs to the free spectrum (holes) and vacating
the spectrum on return of the PUs (Faruk et al., 2012; 2013). Spectrum mobility refers to the mechanism by which
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Sule!Lamido!University!Journal!of!Science!&!Technology!Vol.!4!No.!1&2![July,!2022],!pp.!205-218!
https://doi.org/10.56471/slujst.v4i.268!
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Cognitive Radio hops between multiple spectrum holes, while, preserving seamless communication requirements
throughout transitions to a better spectrum. Spectrum Mobility can be classified into two; Spectrum Handoff and
Connection Management. This research is, however, limited to Spectrum Handoff without considering the other
functionalities of CR. Spectrum Handoff is the transmission of data from one working channel to another void
channel or hole without interrupting the present connection or losing the data that is being conveyed.
When a Primary User (PU) returns to the channel, the Secondary User vacates the channel and accesses a new
channel to complete its unfinished transmission (Mardini et al., 2018). The main problem with spectrum handoff is
the time required to search, select and switch to available channel. This research aims to reduce the time it takes to
find a new available channel, thereby, reducing Spectrum Handoff delay. This is achieved by designing and
simulating a Fuzzy Logic algorithm for effective spectrum handoff in CRN. This paper is structured in the
following: the related works are presented in Section 2; the research methodology employed in the work is provided
in Section 3; the results are presented in Section 4, while, the paper is concluded in Section 5.
2. Related Work
This section presents a brief discussion of the existing literature on fuzzy logic in Cognitive Radio Networks as
various techniques have been proposed by different authors.
In Giupponi and Pérez-Neira, (2008), a Fuzzy-based technique was presented and implemented with two Fuzzy
Logic Controllers (FLCs) where FLC1 was used to estimate the distance between the Primary User and Secondary
User, and FLC2 was used to make Spectrum-Handoff decision. Results showed that the proposed scheme
outperformed a solution based on predefined thresholds when compared in terms of spectrum handoff rate and
interference temperature recorded at the PU receiver. A Fuzzy Power Control Scheme that will enable Secondary
Users to alter their transmit power within the tolerable interference limits and switch between frequency bands was
proposed by Kaur et al. (2009). Two Fuzzy Logic Controllers (FLCs) were utilized where FLC1 was used for power
control and FLC2 was used for Spectrum mobility. Results demonstrated that lowering the number of handovers is
possible when the Secondary User's transmit power is controlled within the interfering tolerance limits.
In Tabakovic et al. (2009), a Fuzzy Logic transmit-power control was proposed which allows Secondary Users
to achieve the desired transmission rate and quality while limiting interference to primary and other concurrent
secondary users. The proposed scheme was simulated and results showed that Fuzzy-Logic transmit power
controller keeps a constant Secondary User receiver SINR with ±1 dB deviation compared to the required SINR.
Fuzzy logic was used in Kaur et al. (2010) to analyze a decision-making process that allows secondary users to
make effective utilization of the spectrum frequency in terms of the secondary user's velocity, the spectrum to be
used by the secondary user, and the distance of the secondary user from the primary user. Results showed that if the
distance between licensed and unlicensed users is small and the velocity of the secondary user is high, then the
probability of making a decision increase.
In Lala et al. (2013), a novel spectrum handoff algorithm for cognitive radio networks based on a fuzzy logic
approach was presented which made use of two Fuzzy Logic Controllers (FLCs) where FLC1 was used to calculate
the optimal transmission power for Secondary User (SU) to minimize interference to Primary User (PU) and FLC2
was used to make handoff decisions based on knowledge of transmission power, data rate, and Holding Time (HT)
(i.e., average idle duration) of a channel. Results showed that the maximum value of SUPOWER is obtained when
RPOWER is at its minimum and TPOWER is at its maximum. Somawanshi et al. (2016) provided a comprehensive
explanation of Cognitive Radio, including the requirements and physical architecture of Cognitive Radio as shown
in fig 1;
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Sule!Lamido!University!Journal!of!Science!&!Technology!Vol.!4!No.!1&2![July,!2022],!pp.!205-218!
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Figure 1: Physical architecture of the cognitive radio (a) Cognitive radio transceiver and (b) wideband RF/analog
front-end architecture. (Source: Somawanshi et al. 2016)
Spectrum awareness was also presented as a crucial element of a Cognitive Radio, which is the radio's ability to
acquire, measure, sense, learn, and be aware of the radio's operational environment to recognize spectrum space
opportunities and use them efficiently for adaptive transmission. The study was limited to presenting motivation
for advancements in opportunistic spectrum access, an overview of cognitive radio systems, and important technical
and scientific concerns in cognitive radio; thus, no experiment or simulation was performed. A Pure Proactive
Spectrum technique was proposed in Metti et al. (2014) where the Secondary User (SU) uses both Proactive
Spectrum Sensing and Proactive Handoff Action to anticipate the Primary User's arrival and evacuate the channel
before it arrives.
The proposed scheme was simulated using the Fuzzy Logic approach to make accurate decisions and the
Artificial Neural Network (ANN) technology to predict channel availability. Results showed that if both the BER
of SU and the BER of PU are medium, an SU Power Decrement is required but no Handoff (HO) is required. In
Bayrakdar and Calhan (2015), a Fuzzy logic-based spectrum handoff decision mechanism was proposed which
takes into account different data traffics such as audio and video traffic. Three inputs parameter was used; data rate,
channel usage, and priority with one output Handoff probability (probHandoff). Results showed that for audio
traffics, handoff probability increases with an increase in data rate, but for the effect of other input parameters,
handoff probability remains the same even if the data rate increases in some values while for video traffics, handoff
probability decreases as the data rate increases.
In Wang et al. (2015), a fuzzy-based dynamic channel allocation scheme was proposed in which the received
signal strength was used to determine the channel access priority of secondary users (SU). Four input parameters
were utilized; Spectrum Utilization Efficiency, Mobility, Distance, and Signal Strength to provide a Priority Factor
as an output. Results showed that the proposed outperformed Kaniezhil's scheme and the Random Channel
Assignment scheme in terms of total secondary network throughput and the Signal-to-Noise (SNR). In Alhammadi
et al. (2016), a Fuzzy-based negotiation was proposed which made use of Two Fuzzy Logic Controllers (FLCs)
where FLC1 was used to estimate the probability of successful negotiation between PU and SU, and FLC2 was
used to estimate the probability of successful negotiation for pricing and duration between PU and SU. Results
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Sule!Lamido!University!Journal!of!Science!&!Technology!Vol.!4!No.!1&2![July,!2022],!pp.!205-218!
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showed the percentage of successful negotiation, percentage of returned negotiation, probability of successful
negotiation with three different Price Rates of Primary User, and Handoff delay of different rates of Primary Users.
In Salgado et al. (2016), a Fuzzy algorithm for the spectrum decision function was proposed specifically for the
selection of a backup channel in spectral mobility based on multiple criteria, using data obtained from the Wi-Fi
frequency band. The proposed scheme was then compared with an Advanced Hierarchical Process (AHP) and
results obtained by experimental data from the Wi-Fi frequency band – 2.4 GHz to 2.5 GHz showed that the Fuzzy
Algorithm outperformed AHP by having a low rate of channel change when compared with using three different
metrics.
A Fuzzy-based support system that deals with channel selection and channel switching to improve the overall
throughput of Cognitive Radio Networks was proposed by Ali et al. (2019) which made use of two Fuzzy Logic
Controllers (FLCs). FLC1 was designed to estimate the power at which the SU can transmit its data without
interfering with the Primary User and other Secondary Users' transmissions while maintaining a certain QoS for its
transmissions and FLC2 was used to select the best channel from a list of available channels, allowing the SU to
use a channel for an extended period while gradually decreasing the channel switching rate. Results showed that
the proposed scheme outperformed a conventional scheme in terms of the number of handoffs, Throughput, and
Time consumed for channel selection under conditions such as varying transmission times, varying number of SUs,
and a varying number of licensed channels.
In the review of existing literature briefly discussed above, it can be seen that fuzzy logic has been proven to
be a useful technique used in Cognitive Radio Network although, most research work focused on using the fuzzy
logic algorithm for either spectrum handoff-decision or to regulate the transmit power of the Cognitive Radio (CR)
while sharing the same channel as the Primary User in order not to cause interference and complete its transmission.
Other research work focused on the comparison between Fuzzy Logic Algorithm and some other conventional
schemes in which fuzzy logic outperforms in each case. But no research has strictly focused on minimizing the
delay during spectrum handoff hence the need for further studies to investigate how the time taken to search, select
and switch to a new available channel can be minimized to increase the overall throughput of the network.
3. Research Methodology
This research utilized MATLAB Fuzzy Logic Toolbox 2021a for simulation and analysis of Fuzzy logic for the
selection of a backup channel for spectral mobility in Cognitive Radio Network (CRN) as well as investigated how
the Secondary User (SU) used fuzzy logic to select a better backup channel during handoff in cognitive radio
network to minimize the spectrum handoff delay thereby increasing the overall throughput in terms of Channel
Transmission Range (ChTR) and Channel Rank (ChRank) which was the two inputs parameters utilized with one
output of Select Channel (SelCH). This research would follow the procedure as shown in fig 2;
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Sule!Lamido!University!Journal!of!Science!&!Technology!Vol.!4!No.!1&2![July,!2022],!pp.!205-218!
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Figure 2: Flow Chart showing the research process followed
4. Proposed Fuzzy-Based Channel Selection Scheme
In this section, a brief discussion on the significance and working of Fuzzy Logic as well as the selected
parameters that were used to simulate the fuzzy logic controller is presented and the workings of the proposed fuzzy
logic controller to minimize the spectrum handoff delay and to improve the throughput of the system while selecting
the best available channel in the cognitive radio network.
4.1 Significance and Workings of Fuzzy Logic
Fuzzy Logic is a mathematical technique that is best suited for decision-making especially when all the input
values are not precise and qualitatively ambiguous. It is a multivalued logic that allows the definition of intermediate
values between standard evaluations such as yes & no, true & false, hot & cold, high & low, etc. A fuzzy logic
controller (FLC) can be used to implement decision-making processes and it consists of four modules as shown in
figure 3;
Figure 3: The Flowchart of a Fuzzy Logic Controller (FLC)
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1. Fuzzification module: this module does the conversion from a numerical value that is the crisp inputs or real
inputs into fuzzy inputs based on the membership functions that are stored in the fuzzy rule base.
2. Fuzzy Rule base: this module contains the IF-THEN conditions also known as rules as well as the membership
functions that regulate the decision-making in the fuzzy logic system.
3. Fuzzy Inference Engine: This module is for determining the ideal rules for a particular input. The fuzzy output
is then generated by applying these rules to the input data.
4. Defuzzification module: This module converts the fuzzy outputs from the inference engine into crisp outputs.
The defuzzification method adopted for this research is the Centroid method, also known as the Center of
Gravity (CoG) method, and can be represented mathematically as for two inputs x1 and x2;
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function and
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th rules fuzzy sets
4.2 Selection of Parameters
This paper proposed a Proactive Fuzzy-Based Backup Channel Selection Scheme (PFBCSS) where a
Secondary User maintains a list of available channels in advance before the arrival of the Primary User (PU) so that
it can handoff to an idle channel quickly to avoid having interference with the Primary User and to complete its
transmission. To choose the best channel out of the pool of available channels already gathered, the Secondary User
(SU) then uses the Fuzzy Logic based on the input parameters to select the best channel. This scheme utilized one
Fuzzy Logic Controller (FLC) which is used for making the selection decision based on the two selected input
parameters:
1. Channel Rank (ChRank): This is a channel indexing technique that allows SUs to choose the best and most stable
channel from a pool of idle channels. This parameter returns the availability of Primary User (PU) activity-
aware channels. ChRank-indexed channels are more stable, with fewer collisions and less interference from
PUs. That is, the selection of channels based on ChRank provides the lowest possible channel switching rate
and also the ability to perform quick switching decisions. The Channel Ranking algorithm only returns the ranks
of available channels at any given time without considering busy or unavailable at that time. Channel Rank can
be represented mathematically as;
>?@:AB ( ) CDC
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JCDC
(2)
Where; TFT denotes the Total Free Time, TUT denotes the Total Utilization Time that is the Total busy time
measured over the channel at a particular time, and TNPA denotes the Total Number of Primary User (PU)
Arrivals detected over the channel within a given time
2. Channel Transmission Range (ChTR): This is the maximum distance between two communicating nodes. A
transmission can be considered to be a success only when the receiving node is out of the interference range of
the corresponding sending node and within the transmission range. Different channels available to the
Secondary User (SU) have different transmission ranges – heterogeneity of available channels. Lower
transmission range channels have higher frequency bands as frequency is inversely proportional to wavelength
which can be sometimes regarded as distance/range. Channel Transmission Range can greatly reduce channel
switching because when the transmission range between two communicating pairs of Secondary Users nodes
is enough to span the distance between them, channel switching will be reduced.
4.3 Proposed Fuzzy Logic Controller
This paper utilized one Fuzzy Logic Controller (FLC) for the selection of the best channel out of the available
backup channels, allowing the Secondary User (SU) to quickly choose a more reliable channel with minimal
interference from the Primary User (PU) or other Secondary Users.
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Sule!Lamido!University!Journal!of!Science!&!Technology!Vol.!4!No.!1&2![July,!2022],!pp.!205-218!
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Figure 4: The flowchart of the Fuzzy Logic Controller (FLC)
Before the selection of a new channel, the Secondary User first considers the two input parameters ChRank and ChTR
which will then be passed into the Fuzzification module to get the membership functions shown in figure 5. Then
the Fuzzy Inference Engine applies the rules, presented in table 1, on each input parameter and then forwards them
to the Defuzzification Module to get the crisp output where the Secondary User (SU) will make the appropriate
selection of the best available backup channel.
(a) (b)
Figure 5: Membership functions for FLC (a) Channel Rank (ChRank) and (b) Channel Transmission Range (ChTR)
A total of 9 rules were utilized for the Fuzzy Logic Controller as presented in Table 1. The total number of rules
was utilized because only two input parameters were used.
Table 1: Inference Rules for the Fuzzy Logic Controller
Rule Number
ChRank
ChTR
SelCH
1
Low
Short
×
2
Low
Average
×
3
Low
Long
✓
4
Medium
Short
✓
5
Medium
Average
✓
6
Medium
Long
✓
7
High
Short
×
8
High
Average
✓
9
High
Long
✓
Where Symbol;
× denotes No
✓ denotes Yes
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Sule!Lamido!University!Journal!of!Science!&!Technology!Vol.!4!No.!1&2![July,!2022],!pp.!205-218!
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5. Simulation of the Proposed Scheme
For the successful simulation, this research assumed that there are only 10 available backup channels and
random values were assumed for both Channel Rank and Channel Transmission Range which will form the crisp
inputs.
Table 2: Simulation Parameters
Parameters
Values
Simulation time
10-20 seconds
Total available channels
10 channels
Channel Rank
0 – 10
Channel Transmission Range
100 – 1000 meters
SU Waiting time
5 seconds
Total time required to search and select a
channel
5 seconds
Packet Size
1024 bytes
Number of Secondary Users
20
To simulate the proposed model, a MATLAB code was written where the already designed Fuzzy Logic model was
integrated into the code to select the best channel from the list of ten available channels. The code is divided into
four main sections;
1. Setting the number of Available Channels: here the number of available channels was set to 10 and other
variables such as some arrays required in the program were defined as well.
2. Taking input data: upon running the MATLAB code, three inputs are requested, namely; the Channel number
used to identify each channel, the value of the Channel Rank of that particular channel, and finally, the value
of the Channel Transmission Range of that particular channel. These values will then be fuzzified and mapped
to their respective linguistic variable such as the those defined earlier – Low, Medium, or High for Channel
Rank as well as Short, Average, or Long for Channel Transmission Range.
3. Opening FIS created using Fuzzy Logic Toolbox: in the section, the Fuzzy Logic Model that has been created
using the Fuzzy Logic Toolbox is then called using the function readfis(). The fuzzy logic then applies the rules
on the input parameters based on the linguistic variable and then outputs the defuzzified value (crisp output).
4. Selecting the best channel: this is the last section and where the best channel is selected based on the highest
defuzzified value (crisp output) obtained from the previous section. The result of the code is then outputted
stating the best channel out of the available backup channels.
5.1 Simulation Results
In this section both the result obtained from the fuzzy logic toolbox simulation and the MATLAB code
simulation is presented.
The 3D graph shown in figure 6 presents the inputs–output relationship for the Fuzzy Inference System (FIS) that
describes the output decision based on the inference rules and the input parameters – ChRank and ChTR.
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Figure 6: 3D-Graph showing the input-output relationship for the Fuzzy Logic System
The results of the MATLAB code written to simulate Fuzzy Logic for the selection of the best available backup
channel are shown below;
Figure 7: Setting the total number of available backup channels
Figure 8: Entering the assumed values of the input parameters
The above dialog box shown in figure 8 will run 10 times for all 10 available backup channels to correctly accept
the values of the input parameters for each channel. After all values for the input parameters for each channel has
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been entered, the code was written to output all inputted values and their corresponding linguistic variable as shown
in figure 9;
Figure 9: Image showing the output of the values inputted
Then, values are transposed into rows and columns for each of the 10 available backup channels after which the
values are defuzzified based on the rules defined for the Fuzzy Logic Controller (FLC), and the channel with the
highest defuzzified value is chosen to be the best channel as shown in figure 10.
Figure 10: Image showing the defuzzified crisp values and the selected channel
The best channel selected is Channel 3 which has an assumed Channel Rank of 7 and a Channel Transmission
Range of 650m which based on the membership functions are seen to be High and Long respectively. It was chosen
to be the best channel because it has the highest defuzzified value of all the available backup channels.
6. Performance Evaluation of the Proposed Model
The proposed scheme was evaluated against a conventional scheme proposed in [26], which is a pure proactive
spectrum handoff where the Secondary User (SU) chooses a channel randomly out of the available channels.
However, in the proposed scheme, the Fuzzy Logic decision model is used to choose the best channel out of the
available channels rather than selecting randomly. The performance is evaluated in terms of the average throughput
and the total time used for channel selection under a varying number of channels.
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(3)
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Figure 11: Throughput against Transmission time
Figure 12: Total time used for channel selection under a varying number of channels
6.1 Discussion
The overall throughput and the total time required for channel selection for the proposed model was measured
for 10 licensed channels for a total of 15 seconds and the results are compared with a pure proactive scheme as
shown in Figs 11 and 12. Findings from the Figures revealed that the proposed scheme performed better than the
conventional scheme in terms of data packet transmission success and efficient time utilization under a varying
number of licensed channels. This is because the proposed scheme employs a fuzzy logic algorithm to select the
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best channel based on the channel rank (ChRank) and the channel transmission range (ChTR). The channel rank
provides more stable channels, with fewer collisions and less interference from PUs, and the channel transmission
range provides enough communication range to span the communicating SU node pair leading to quick handoff
decision-making. Hence, the lesser time spent to search and select a new channel will lead to a faster spectrum
handoff which eventually increases the overall throughput of the system.
7. Conclusion
Spectrum Handoff is one of the functions of Cognitive Radio where a Secondary User can change its operating
channel to continue its transmission and not cause interference with the Primary User. However, during spectrum
handoff, the time taken to search, select, and switch is considerably much causing a spectrum handoff delay which
in turn reduces the overall performance of the network. This research has proposed a Proactive Fuzzy-Based Backup
Channel Selection scheme to minimize spectrum handoff delay which when compared, using MATLAB simulation
tool, with a Pure Proactive Spectrum Handoff performed better by a throughput of 90bits/sec and in terms of
efficient time utilization under a varying number of licensed channels. Further research should employ the use of
more than two input parameters as well as more performance metrics for evaluation.
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