An Enhanced Algorithm for Load-based Handover Decision-making in 5G Wireless Networks.

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 06 | Jun 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 858
An Enhanced Algorithm for Load-based Handover Decision-making in
5G Wireless Networks.
Lama Douba1, Ahmad Mahmoud Ahmad2, Ahmad Saker Ahmad3
1PhD Student, Dept. of System and Computer Networks Engineering, Tishreen University, Latakia, Syria.
2Assistant Professor, Dept. of System and Computer Networks Engineering, Tishreen University, Latakia, Syria.
3Professor, Dept. of System and Computer Networks Engineering, Tishreen University, Latakia, Syria.
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Abstract - The handover process is one of the most
important aspects of mobility management in 5G networks, it
ensures a user's network connection continuity while moving
from one location to another. It is also the cornerstone of
achieving mobility load balancing under the name of the
offloading process. The decision-making phase is one of the
handover stages that need improvement to choose the
appropriate station and accurately determine the handover
parameters to select the proper time to execute the handover.
In this paper, we proposed a Load-based Handover Decision-
making algorithm based on fuzzy logic, considering the cell's
load before implementing the handover decision to determine
whether the candidate cell is suitable forthehandoverprocess
to avoid problems resulting from handover toaheavilyloaded
cell. The results showed that the proposed algorithm provided
an improvement in terms of the standard deviation of theload
in the network, average throughput, packet loss rate, and
handover failure rate.
Key Words: 5G Networks, Handover, Handover Control
Parameters, Cell Load, Fuzzy Controller.
1.INTRODUCTION
Fifth-generation networks include many technologies
that offer features for users. Still, at the same time, they may
cause new problems, which refer to the deterioration of the
service provided by the network due to a failure in a
particular stage,suchaswhensettingspecific parameters[1].
Despite the significant benefit of small cells, they cause
difficulties that lead to a deterioration in the quality of
service, such as interference, unnecessary and frequent
handovers, failed handovers, and Ping-Pong events[2].
Failure management [1] is one of the basic concepts in
cellular networks, and given that the handover process is
critical, especially in high-density networks that are
accompanied by high mobility speeds, it is necessary to
reduce failurecasesduringitsimplementationtomitigatethe
negative impact on the quality of service provided by these
networks. The heavily loaded target cells are one of the
reasons for the failure of handover operations since they are
either unable to serve a new user or limitedserviceprovided.
This will result in unsuccessful handoveroperationsandlead
to a deterioration in the quality ofservice in the network due
to the loss of packets resulting from the connection
interruption.
2. THE IMPORTANCE OF THE RESEARCH AND ITS
OBJECTIVES
The importance of this research lies in proposing a
solution that ensures the target station has sufficient
resources to serve a new userbefore executing the handover
decision, thereby ensuring that the user moves to a cell that
can serve them first and provide convenient service for their
requirements secondly.Thisultimatelyachievestheresearch
goal of improving service quality using the enhanced
algorithm.
The research addresses the study of handoverproblems
in 5G, shedding light on the concept of cellloadanditsimpact
on handover performance,and then proposes a modification
to the handover decision-making algorithm that ensures not
moving to a congested cell. We then follow with practical
implementation, involvingchoosing the most suitable toolto
simulate the required network, building the network,
specifying the parameters, executing the simulation, and
analyzing the results. Finally, the research concludes with
recommendations that define the future direction of the
research.
3. RELATED WORK
Several existing works were done in research to enrich
the field of improving the handover process in the 5G
wireless networks. In [3], anFL-based handoverschemewas
proposed for5GUDNs,theproposedschemecandynamically
adjust two handover parameters, HOM and TTT, byusingthe
SINR and horizontal moving speed of UE as inputs to a Fuzzy
logic controller.
While the authors of [4] investigated various MRO
(Mobility Robustness Optimization) algorithms for the 5G
network at different mobility speeds and system setting
scenarios to address Mobility Management (MM) issues
during user mobility between cells to ensure a smooth
connection.

On the other hand [5] presented the common types of
Machine Learning and the techniques used from each typeto
optimize the HCPs of the MRO functions. Moreover, high-
mobility-aware and network topologies are presented in the
MRO function forfurther system enhancements. Besides,the
survey further highlights several potential problems for
upcoming research and provides futuredirectionstoaddress
the issues of next-generation wireless networks.
In the paper[6] the effect of different HCP settings on 5G
network performance was verified by analyzing fixed HCP
settings in various scenarios to explain the need for applying
more advanced technology in 5G networks. The proposed
algorithm in this paper was used to assess the HCP settings.
These optimization values were estimated according to the
speed of UE’s and loads of cells.
4. Background
4.1. Handover problems (handover failure):
The increaseindeploymentdensityofsmallbasestations
(SBS) is a solution to obtain high data traffic mobility in 5G
wireless networks. However, it causes an increase in ping-
pong events, accompanied by increased delay and handover
failure. Ping-pong events occur when the serving cell hands
over control of the mobile device to the target cell. Then, the
target cell hands back control to the previous serving cell
within alimited timeafter the last handover [7],causinghigh
signaling due to the messages exchanged between network
elements resulting from unnecessary handover operations.
Therefore, reducing frequent and unnecessary handover
operations saves network resources and improves overall
system performance [8].
Mobility failure occurs when the user equipment fails to
establish a radioconnectioninthesourcecellorcompletethe
handover process in the target cell. There are different
reasons for handover failure, either due to a problem in
synchronizing the necessarysignaling messages to complete
the handover process between the source and target base
stations or due to the loss of one of these signaling messages
caused by a problem in the radio link. Moreover, attempting
to move the phone to an already congested cell [2] can also
result in call failure due to resource limitations, making the
handover operation unsuccessful. If the call is completed
later, this process will reduce the service quality provided to
the existing users and the new user.
The timing of the handover process is critical. Therefore,
the main challenge of Mobility Robustness Optimization
(MRO) functions, which is the automatic adjustment of
handover control parameters, is to avoid failure cases as
much as possible. For example, reducing the values of TTT
and HOM can cause an early and unnecessary handover, as
there are types of errors that occur due to the user moving at
different speeds, such as the Too Early Handover problem,
which occurs when the signal strength of the target station is
still weak [9], The early handover occurs due to the low
values of the handover control parameters (HCP), which are
then adjusted to high values due to poor connection, this
causes failureto connect quickly when the handover process
begins. As a result, the device will return to the previously
served station, resulting in additional handover operations.
In addition to the Too Late HO problem, at high speeds,
very late handover occurs due to the user gnsssap through
several cells within a short period. In this case, adjusting low
values for the handover control parameters is essential to
reduce the failure rate of the radio link [10].
4.2. Radio resources in the cell:
Where:
 Is,j(τ) isa binary indicator that takes the value of 1 if user
j is served by cell s.
 Ns,j (τ) is the number of reserved physical resource
blocks (PRBs) by cell s for user j during the period t.
 PRB refers to the total number of available resource
blocks in cells, where all cells have the same maximum
limit.
Therefore, the total number assigned by a cell at any given
moment cannot exceed the value of .
When the RBUR value reaches 1, the cell resources are
exhausted, and any new user enteringthecellwilleitherhave
their connection dropped or will be served at a lower data
transmission rate than needed.
5. Proposed Algorithm: Load-based Handover
Decision-making algorithm (L-HD):
This study proposes a modification to a previously
introduced algorithm [13], called (RHOT-FLC) , designed for
handoverdecision-makingandcontrolparameterassignment
based on fuzzylogic. The basicalgorithmwasobservedtonot
consider the load value of thebasestationsbeforemakingthe
handover decision, which could result in user issues that
affect service quality.
(1)
The smallest unit of radio resources is the
physical resource block (PRB), which consists of 12
subcarrier frequency slots. The cell load is measured by
the Resource Block Utilization Ratio (RBUR) [11], defined as
the ratio of the reserved physical resource blocks (PRBs) by
the cell s to the total number of available blocks in the same
cell. The RBUR ratio directly determines the use of the
resource block by the number of devices that can be
served at a specific data transmission rate and with
specific latency constraints. Equation (1) [12] expresses
the average RBUR in the cell.

Also, the algorithm does not consider the case of a failed
connection establishment with the target station. Therefore,
it is necessary to add a condition to test the load value of the
cell before deciding while also ensuring the possibility of
connecting to an alternative station in case of a failed
connection with the target station.
The following steps represent the stagesofimplementingthe
proposed algorithm (L-HD) , as shown in Figure 1:
1. Both the Radio Signal Quality (RSRQ), Radio Signal
Power (RSRP), User Equipment (UE) velocity, and Cell
Load are considered inputs to this algorithm.
2. The RSRP value for neighboring cells is extracted from
measurement reports, sorted,andcomparedtotheRSRP
value of the serving cell.
3. The neighboring cell values are arranged according to
the Cell Load value, which is calculated based on
Equation (1) and takes values ranging from [0,1], where
a higher value indicates a heavier load on the cell.
4. The condition for triggering the handover process is
tested based on the RSRPs < RSRPt , where if it is met, the
handover process is not executedsincethemobilephone
has not approached the cell boundaries, and the signal
strength received from the serving station is still higher
than that received from the target station. On the other
hand, if the condition is not met, then there is a need to
trigger the handover process, and since the A3 event is
used in this algorithm, the fuzzy controller is reliedupon
to obtain the TTT & HOM values.
5. RSRQ, RSRP, and UE velocityareconsideredinputstothe
fuzzy controller, which are divided into levels indicated
in Table (1), where the values of these parameters are
transformed into fuzzy sets, and the degree for each rule
is calculated, resulting in obtaining the controlleroutput
which is both HOM & TTT values.
6. If the condition of equation (2) is not met the handover
process is not executed, and the current cell remains the
serving cell. However, if the condition is met, the Cell
Load value of the candidate cell is tested, and if it is
capable of serving a new user (Meaning the value of
BRUR did not exceed 0.9), the handover decision is
executed. Otherwise,thehandoverprocessisexecutedat
the second-best base station.
Fig -1: flowchart of the proposed algorithm
(2)

Table (1) shows the number of levels of input and
output[13]:
Table -1: input and output levels
Input Degree Range
UE Velocity
Slow 0 to 30 km/h
Moderate 25 to 70 km/h
High 65 to 135 km/h
Very high 130 to 160 km/h
RSRP
Weak -160 to -95 dBm
Medium -100 to -73 dBm
strong -80 to -20 dBm
RSRQ
Poor -60 to -18 dB
Good -22 to -12 dB
Very good -14 to -6 dB
Excellent -10 to +20 dB
output
TTT
v. small 0 to 220 ms
Small 210 to 380 ms
Average 370 to 520 ms
Large 510 to 640 ms
HOM
v. small 0 to 0.3 dB
Small 0.2 to 0.5 dB
Average 0.4 to 0.8 dB
Large 7.0
to 1 dB
6 . Performance Evaluation
6.1. Simulation Setup:
It was decided to implement the practical section using
MATLAB 2022a, relying on the 5g toolbox [14] [15] [16],
which provides functions compatible with the established
standards forfifth-generationwirelessnetworks.Inaddition,
it includes reference examples for modeling, simulation, and
verification of communication systems.
The network was built in a standalone mode based on
previous studies showing better handover performance in
standalone architecture [17]. In addition, the Fuzzy Logic
Toolbox [18] was relied upon as it provides functions and
applications for analyzing, designing, and simulating fuzzy
logic systems. The fuzzy logic controller was designed as
shown in Figure 2,
Fig -2: fuzzy logic controller
and the parameters shown in Table (2) were adopted, which
were chosen based on [13] and according to standards.
Table -2: Simulation parameters settings
Parameters values
# of gNB 50
Cell radius 150 m
Tx Power of UE 23 dBm
Tx Power of gNB 46 dBm
Number of Measured UE 10 UEs
Maximum Number of UE
per Cell
10
Channel bandwidth 400 MHZ
Path loss mode Log-normal path loss model
(path loss exponent=3.0)
Fading Model Friis spectrum propagation
loss model
Mobility model Constant velocity mobility
model
Carrier frequency 25 GHz
UE velocity Up to 160 km/h
TTT range Adaptive: 0 ms … 640 ms
HOM range Adaptive : 0db … 1 dB
Simulation time 30 s
6.2. Result analysis:
The performance of the proposed algorithm (L-HD) was
evaluated by comparingitwiththe(RHOT-FLC)algorithmfor
three different values of user equipment velocity in terms of
several metrics, including quality of service and handover
performance. Because improving the handover performance
contributes to improving the network's overall performance
[19].
6.2.1. Standard deviation of load:
The standard deviation of the load indicates the load
balance level in the network cells and iscalculated as follows
in equation (3):
Where
 PBURNet(t): the averageload in the networkconsistingof
S cells during a period t .
 S: the number of active cells in the network.
 RBURs(t): the average load incell s at time t. The value of
σ ranges from 7 to 1, and the closer the value is to zero,
the more balanced the network is.
Chart 1 shows the performanceevaluationofthealgorithmin
terms of the standard deviation of the load. The proposed
(3)

algorithm (L-HD) performed better than the (RHOT-FLC)
algorithm since it ensures that the target cell's average load
does not exceed 0.9 or it transfers the user tothesecond-best
station, thus contributing to load distribution between cells
and reduces the difference between them.
Chart -1: standard deviation of load
6.2.2 Average throughput :
The comparison of the two algorithms in terms of
average throughput is shown in Chart 2, for three different
user equipment velocities (10, 60, and 100 km/h). The
proposed algorithm (L-HD) gave a higher throughput than
the baseline algorithm since it guaranteestheuser'stransfer
to a cell capable of serving itwith sufficientresources.Incase
of failure to transfer to the candidate cell, there is an
alternative celltocompletethehandoverprocess,resultingin
a reduced probabilityofconnectioninterruptionbetweenthe
user equipment and the cell. Moreover,sinceitcontributesto
load balancing between cells, it increases the utilization of
resources, resulting in higher throughput.
Chart -2: average Throughput
6.2.3 Packet Loss Rate (PLR) :
Chart 3 shows the evaluation of the proposed algorithm (L-
HD) in terms of packet loss rate, which showedimprovement
over the baseline algorithm (RHOT-FLC). The improvement
ensures a reduction in the probability of connection
interruption resulting from the inability of the target cell to
serve the user.
Chart -3: Packet Loss Rate
6.2.4 Handover failure :
Chart 4 shows the evaluation of the proposed algorithm in
terms of the failure rate of handover processes. The
improvement in (L-HD) contributed to a decrease in the
handover failure rate due to a reduction in the unsuccessful
handover process. The improvement isevidentatlowspeeds
since high speeds cause an increase in the handover rate per
second, which increases the likelihood of handover process
failure.
Chart -4: Handover failure
7. CONCLUSIONS
This research focuses on improvingapreviouslyproposed
algorithm for making handover decisions and selecting
control parameters for handover. Theimprovementincludes
a test that is applied before making the handover decision, in
which it is verified whether the candidate cell can serve a
new user. If it is unable to do so, the handover operation is
executed to the second-best base station. This improvement

reduces the probability of establishing a failed connection
with a congestedcellandimprovesnetworkbalancesincethe
phone involved in the handoveroperationwillbetransferred
to the second-best cell, thus distributing the burden among
cells. The results have shownthat theproposedalgorithm(L-
HD) improves the standard deviation of the load in the
network, average throughput, packet lossrate,andhandover
failure rate. Based on the results obtained, the focus will be
on improving the solution to increase the network load
balancing ratio with the shortest possible latency.
ACKNOWLEDGEMENT
The authors wish to acknowledge the Faculty ofInformation
Engineering at Tishreen University for their support of this
research.
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