Base Station Switching and Resource Allocation for 5G Heterogenous
Networks
K. THAMIZHMARAN
Department of Electronics and Communication Engineering,
Government College of Engineering,
Bodinayakkanur, Tamilnadu, 625582,
INDIA
Abstract: - Enhanced 4th Generation Wireless Network (4G) is called 5th Generation Wireless Network (5G) as
it helps in increasing the data rate, capacity and therefore energy efficiency and spectral efficiency of 5G
network, in 5G massive MIMO, multiple numbers of antennas are used to transmit the signal with same time-
frequency to maximise the number of users, who can communicate with less number of channels. Energy
conception is the most dangerous issue in all the generations of wireless networks such as traditional first-
generation to fifth-generation because of interference, eco signals, fading, packet loss, wastage of bandwidth,
remaining energy and security like malicious attacks, blocking whole attacks and wormhole attacks. This
efficient research work focused energy-efficient resource allocation scheme based on the shortest job first
scheduling algorithm in wireless network (SJF) for the downlink orthogonal frequency division multiple
accesses (OFDMA) heterogeneous networks (HetNets) developed. To maximize the spectrum allocation
efficiency for the fifth generation (5G) mobile networks, frequency reuse-1 is employed. Thus, advanced
inter-cell interference coordination techniques are required to mitigate the inter-cell interference for 5G
HetNets. In this paper, the energy-efficient optimization problem based on coordinated scheduling is
formulated, which is a shortest path problem and link breakage is intractable to solve directly. The above
proposed model was analysed using different parameters energy, bandwidth, Quality of Service (QoS) and
interference.
Key-Words: - 5G Network, Energy Efficiency, Base Station, Resource Allocation, HetNets, SJF algorithm.
Received: July 6, 2022. Revised: August 29, 2023. Accepted: September 28, 2023. Published: November 28, 2023.
1 Introduction
Mobile traffic has been increasing day by day due
to the wide increase in the number of users and
different uses. Since the conventional network is
unable to fulfil that ever-increasing demand 5G
will serve as a boon for it. As the technology is
growing, the demand for it also continues to grow
and traffic, interference and low data rates are also
observed in the 5G networks, [1]. To meet the
Quality of service (QoS) and Quality of
Experience (QoE) requirements of the users, some
technologies must be incorporated in 5G for its
effective service; to meet the demands of the end
user in a polished way. One approach to tackle
these challenges could be to use a heterogeneous
network concept where traditional Macro Base
Stations (MBS) are deployed to provide the main
coverage of the network to the users for the
coverage of large areas and low-powered small
base stations are present for the coverage of
smaller areas as shows Figure 1 block diagram of
wireless communication system.
Fig. 1: Block Diagram of Wireless Communication
System
Different tier systems will be present here
containing small and large cells so it will provide
increased bandwidth, reduced latencies and higher
data rates which will overall increase the QoE and
QoS experience of the user. Energy efficiency is
one of the major concerns as far as 5G wireless
networks are concerned. A high energy
requirement increases the cost of operators and
also contributes towards the emission of harmful
greenhouse gases, [2], [3]. In addition to energy
efficiency, there is a need to increase the spectrum
efficiency of the network. Spectrum is very crucial
and limited and hence utilizing it effectively is of
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utmost importance. Due to increased traffic, the
spectrum gets overloaded and interference occurs,
call drops, and low latencies, low signal-to-noise
ratio are obtained will overall lead to deteriorated
QoS and QoE as shown in Figure 2 block diagram
of the 5G system controls plane for wireless
communication.
Fig. 2: 5G System Control Plane for Wireless
Communication
Spectrum slicing is one of the promising ways
for the better and more effective utilization of
spectrum for 5G wireless technology, [4]. In the
mm-Wave network many technologies such as
distributed Antenna systems (DAS), mobile
femtocells enabled networks, small cell networks
etc. are deployed for better signal-to-interference
noise ratio (SINR), reduced latencies and
decreased propagation loss. However deploying a
large number of small cells or microcells for the
coverage and better processing connecting macro
cells causes energy overload. All the cells need
energy and hence energy consumption of the BS
will drastically increase, [5], [6]. However, these
benefits come at a price the massive BS
deployment significantly increases the total energy
consumption of wireless systems. For a typical
LTE microcell with a cell size of 100 m and
bandwidth of 5 MHz, the power consumption is
ranges from 25 watts to 40 watts depending on the
traffic load. To achieve the coverage of a 1500 m
macrocell, more than 200 microcells need to be
deployed the aggregated power of microcells can
be more than 900 watts, which is comparable to a
typical LTE macrocell BS with 1500 m coverage,
[7], [8]. The increased energy consumption not
only increases the cost of wireless operators, but
also generates more greenhouse gas emissions.
Thus, energy saving has become an important
design objective of wireless systems in recent
years. Meanwhile, energy saving needs to be
achieved without sacrificing the quality of service
(QoS) of users. As the 5G system is expected to
provide 1000x data rates, energy efficiency (EE),
typically measured by bits/Joule, also needs to be
increased by 1000 times if the total energy
consumption remains at its original level, [9]. BS
ON-OFF switching (also known as BS sleep
control) has been considered an efficient approach
for both energy saving and EE improvement
Figure 3 block diagram of resource allocation
graph for wireless communication.
Fig. 3: Resource Allocation Graph for Wireless
Communication
2 Background Work
BS ON-OFF switching (also known as BS sleep
control) has been considered an efficient approach
for both energy saving and EE improvement. As
the traffic pattern fluctuates over both time and
space, under-utilized BSs can be dynamically
turned off to save energy, [10]. In 2009, China
Mobile began to apply BS sleep control and the
estimated reduction of energy consumption is 36
million kWh per year. Due to such great potential,
considerable efforts have been devoted to the
design of BS ON-OFF switching strategies in
different network scenarios. However, as the 5G
system is an integration of different techniques
with highly heterogeneous network architecture,
[11], [12], the design of BS ON-OFF switching
faces special challenges in 5G systems, which can
be summarized as follows. On the other hand,
processing and explosively increased amount of
mobile data traffic in 5G systems will also bring
ever-increasing energy consumption and carbon
footprint to the mobile communication industry. In
particular, the whole information and
communication technology (ICT) industry has
been estimated to contribute to about 2% of global
CO2 emissions, to which the mobile
communication industry contributes 15-20%, [13].
With increasing awareness of the potential harmful
impact on the environment and the depletion of
non-renewable energy sources, establishing
greener mobile communication networks has
become an economic issue and a big challenge for
sustainable development, [14], [15]. In particular,
100 times energy efficiency improvement has been
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proposed as another technical challenge in the
design of 5G systems, [16].
Specifically, according to some surveys on
energy consumption, [17], [18],, 80% of energy
consumption in mobile communication networks is
due to the operation of BSs. Further, based on the
results from laboratory tests done by China Mobile
Communications Corporation, a BS consumes
100% energy in the state with the maximum traffic
load and about 50%-60% energy in the state with
zero traffic load, while the energy consumption of
a BS can be reduced to 40% if it is switched off
(i.e., in the sleeping state). Therefore, an effective
way to achieve energy saving in mobile
communication networks is to dynamically switch
off BSs, especially for scenarios with low traffic
load where fewer BSs can meet the traffic needs of
all user equipment (UEs), [19].
A traditional BS consists of baseband unit
(BBU) for a baseband signal processing and a
remote radio head (RRH) for
transmitting/receiving radio signals, [20]. When a
traditional BS is switched off, BBU and RRH of
this BS would be switched off together. In
contrast, in cloud radio access networks (CRANs)
which would be investigated and pursued in 5G
systems, BBUs of several traditional BSs are
centralized in a single location and the
corresponding BBU resources are sliced via
virtualization technologies, while RRHs are left at
cell sites. With this kind of system architecture, the
switch-off operation for RRHs and virtual BBUs
could be done separately, through combination
with flexible resource allocation on virtual BBUs.
The energy consumption on the base station (BS)
accounts for more than 50% of the total energy
consumption of the cellular network. Due to the
space-time characteristics of the traffic, the BS
cannot allocate resources reasonably, which results
in wasted energy consumption and low energy
efficiency (EE), [21]. Base station ON-OFF
switching in 5G wireless networks: approaches and
challenges to achieve the expected 1000x data
rates under the exponential growth of traffic
demand, a large number of base stations (BS) or
access points (AP) will be deployed in the fifth
generation (5G) wireless systems, to support high
data rate services and to provide seamless
coverage. Although such BSs are expected to be
small-scale with lower power, the aggregated
energy consumption of all BSs would be
remarkable, resulting in increased environmental
and economic concerns, [22], [23]. However, in
5G systems with new physical layer techniques
and highly heterogeneous network architecture,
new challenges arise in the design of BS ON-OFF
switching strategies. In this article, we begin with a
discussion on the inherent technical challenges of
BS ON-OFF switching. We then provide a
comprehensive review of recent advances in
switching mechanisms in different application
scenarios. Spectrum Slicing is arising as an
important notion for 5G wireless networks as it
helps in increasing the data rate, capacity and
therefore energy efficiency and spectral efficiency
of 5G networks. In this paper, traffic modelling is
done based on user density and demand. The
system model for spectrum slicing is analyzed
based on traffic density pattern analysis so that
utilization of spectrum is based on the probability
of active users in different zones i.e. urban,
suburban and rural areas which has the objective of
increasing spectral efficiency. Moreover, the
Hidden Markov Model is used for training and
preserving of Base station such that probabilistic
spectrum allocation to different user densities can
be achieved which aims to use the spectrum
efficiently.
3 Existing Work
Till now, we have been scheduling the processes
according to their arrival time (in FCFS
scheduling). However, the SJF scheduling
algorithm, schedules the processes according to
their burst time. In SJF scheduling, the process
with the lowest burst time, among the list of
available processes in the ready queue, is going to
be scheduled next. However, it is very difficult to
predict the burst time needed for a process hence
this algorithm is very difficult to implement in the
system. The advantages of SJF are the maximum
throughput and Minimum average waiting and
turnaround time. The disadvantage of SJF is they
may suffer from the problem of starvation. And
also it is not implementable because the exact
Burst time for a process can't be known in
advance. There are different techniques available
by which, the CPU burst time of the process can be
determined. We will discuss them later in detail.
Since-, No Process arrives at time 0 hence;
there will be an empty slot in the Gantt
chart from time 0 to 1 (the time at which the first
process arrives). According to the algorithm, the
OS schedules the process which is having the
lowest burst time among the available processes in
the ready queue. Till now, we have only one
process in the ready queue hence the scheduler will
schedule this to the processor no matter what is its
burst time. This will be executed for 8 units of
time.
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Table 1. In the following example, there are five jobs named P1, P2, P3, P4 and P5. Their arrival time and
burst time are given in the table below.
PID
Arrival Time
Burst Time
Turn Around Time
Waiting Time
1
1
7
7
0
2
3
3
10
7
3
6
2
4
2
4
7
10
24
14
5
9
8
12
4
Till then we have three more processes arrived
in the ready queue hence the scheduler will choose
the process with the lowest burst time. Among the
processes given in Table 1, P3 will be executed
next since it is having the lowest burst time among
all the available processes. So that's how the
procedure will go in the shortest job first
(SJF) scheduling algorithm.
A routing protocol is also known as a routing
policy. Most Internet Protocol (IP) networks use
the following routing protocols:
Routing Information Protocol (RIP) and
Interior Gateway Routing Protocol
(IGRP): These provide interior gateway
routing through path or distance vector
protocols.
Open Shortest Path First (OSPF): This
provides interior gateway routing through
link-state routing protocols.
Border Gateway Protocol (BGP) v4: This
provides public Internet routing protocol through
exterior gateway routing.
Fig. 4: SJF Ready Queues
Shortest Job First (SJF) always chooses the
shortest job available as shown Figure 4. Here, we
use a sort list to order the processes to the shortest.
When adding a new process/ task, we need to
figure out where in the list to insert.
Step 1: Sort all the processes according to their
arrival time.
Step 2: Select the process with minimum arrival
time as well as minimum burst time.
Step 3: After completion of the process, select
from the ready queue the process which has the
minimum burst time.
Step 4: Repeat thee above processes until all
processes have finished their execution.
4 Problem Identification
To identify and counter network attacks it is
common to employ a combination of multiple
systems to prevent attacks from happening or to
detect and stop ongoing attacks if they cannot be
prevented initially. These systems are usually
comprised of an intrusion prevention system such
as a firewall as the first layer of security with
intrusion detection systems representing the
second layer. Consequently, an efficient routing
approach may generate route failures. This paper
aims to provide an unbreakable route for secured
transmission by proposing the SJF scheduling-
based resource allocation algorithm for
overcoming the QoS parameters as delay factor,
throughput, and energy conservation.
5 Proposed Work
In this section, we discussed the shortest job first
scheduling algorithm for resource allocation in 5G
heterogeneous networks for wireless
communication, in which the process having the
smallest execution time is chosen for the next
execution. It significantly reduces the average
waiting time for other processes awaiting
execution. We propose an algorithm which can
minimize the delay. It can improve process
throughput by making sure that shorter jobs are
executed first. The scheduling algorithm said that
if turnaround time, waiting time, and burst time for
each process can be reduced. This will help to
increase the speed of packet delivery. Shortest Job
First (SJF) scheduler with resource allocation is
used to process the non-real-time data packets that
are present at the same level of priority. SJF used
by taking requests with the short-term time task
will be prioritised first later it admits the long-term
process by CPU request which reduces the time for
execution if time is required is minimal helping in
energy consumption also reduced to low.
Calculating the average waiting time of a process
is done for time allotment for each process which
helps lower energy consumption. From the
proposed system there must be an improvement in
QoS for a secured transmission without the
misbehavior of the malicious node with the
scheduling algorithm as shown in Figure 5
resource allocation using SJF.
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Fig. 5: Resource Allocation using SJF
Following are the basic algorithm steps used in
SJF-based resource allocation:
STEP 1: Start the resource allocation from source
to destination.
STEP 2: Generate the information with dummy
data.
STEP 3: The route request and route replay will
take place.
STEP 4: If acknowledgement comes at the
particular time no malicious node is obtained to
start the original data transmission.
STEP 5: If the acknowledgement does not come,
the data is not dispatched to the destination due to
a malicious node.
STEP 6: Now, we use the SJF-RA protocol to
choose the best packet from the flow.
STEP 7: If the packet is best then directly dispatch
at the destination.
STEP 8: SJF is used to send packets, which we
assign weight to every flow of the network.
STEP 9: To identify and detect the malicious node
to attenuate the node recover the data and improve
QOS using SJF-RA.
STEP 10: To improve the quality and send the
original data for transmission.
STEP 11: At last, dispatch the data to the
destination.
STEP 12: End the transmission.
Flowchart (Figure 5) for Resource Allocation
using SJF
To facilitate the comparison of the simulation
results with the other research work, the default
setting in (NS 2.34) is adopted. The maximum
number of hops allowed in this configuration
setting is four. Both the physical layer and the
802.11 MAC layer are included in the non-wired
extension of (NS 2.34), where the total bits
transmitted are calculated using only the
application layer. Simulation parameters are listed
below in Table 2.
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Table 2. Simulation Parameters
Parameters
Values
Simulation area
800 m * 800 m
Number of nodes
90
The average speed of
nodes
0-25 m/sec
Mobility model
Random waypoint
Number of packets sent
40
Transmission range
250 m
Initial energy/node
100 joules
Antenna model
Omni directional
Simulation time
500 sec
Max. no. of malicious
nodes
12
6 Result and Discussion
The results have been obtained by using the NS-
simulator and results show that our proposed
algorithm performed better SJF-based resource
allocation than the traditional resource allocation
model in terms of throughput, packet delivery
ratio, packet drop rate, remaining energy and
interference. Energy efficiency can be described as
the ratio between the total number of packets
received at the destination node and the total
energy spent by the network to deliver these
packets. Thus the drops in energy, packet delivery
ratio throughput and quality have been improved
by using the shortest job first in a scheduling-based
resource allocation algorithm. Evident Table 2
shows our proposed SJF-RA model performs
improved packet delivery ratio, throughput and
remaining energy than SJF and reduces delay and
packet loss than SJF with 800 m * 800 m of
topology size, when several nodes 90, our adaptive
partial method achieves minimum output than
other two methods. Figure 6 shows the achieved
simulation results on the scheduling-based model
better than others. The proposed algorithm
increases no of connections and this system is
capable of decreasing failed unbreakable routes
between the source and the destination; it is
possible to save more energy.
Table 3. Simulation results of different parameters
RP/NN
15
30
45
60
75
90
packet delivery ratio
RA
0.89
0.82
0.75
0.68
0.61
0.54
SJF-RA
0.97
0.89
0.81
0.77
0.69
0.61
Throughput
RA
0.24
0.29
0.34
0.39
0.44
0.49
SJF-RA
0.36
0.49
0.61
0.64
0.67
0.71
remaining energy
RA
0.90
0.85
0.80
0.75
0.70
0.63
SJF-RA
0.98
0.95
0.93
0.89
0.87
0.84
Delay
RA
0.12
0.19
0.26
0.33
0.42
0.49
SJF-RA
0.05
0.11
0.15
0.23
0.31
0.37
packet loss
RA
0.12
0.18
0.24
0.28
0.33
0.38
SJF-RA
0.05
0.07
0.14
0.19
0.21
0.23
Energy-delay trade-off under different sleep modes with heterogeneous traffic requirements
Standby only
0.33
0.36
0.40
0.44
0.45
0.47
Deep sleep only
0.27
0.31
0.36
0.38
0.39
0.41
Adaptive partial
0.23
0.27
0.32
0.34
0.35
0.37
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packet delivery ratio
Throughput
Delay
Packet loss
Energy consumption
Different modes of traffic
Fig. 6: Responses of various parameters
Table 4. Energy-delay trade-off under different sleep modes with heterogeneous traffic
RP/NN
Stand by only
Deep sleep only
Adaptive partial
Base station
0.45
0.33
0.25
Urban
0.27
0.18
0.14
Sub-urban
0.23
0.15
0.12
Rural
0.15
013
0.10
Fig. 7: Energy-delay trade-off with different sleep modes
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Simulation results of energy-delay trade-off under
different sleep modes with heterogeneous traffic
proposed shortest job first resource allocation
compared to the normal shortest job first
performance as shown in figure 7 and Table 3
shows that three different sleep modes with four
different areas. If any of the intermediate nodes is
found to be busy or link failure, then the traffic
condition of the proposed algorithm can find an
alternate shortest route from the previous node
itself this avoids more overhead which reduces the
delay.
From all the above figures and Table 1, Table
2, Table 3, Table 4, it is clear that our proposed
new design shortest job first scheduling based
resource allocation and existing shortest job first
scheduling schemes, show the packet deliver ratio,
throughput and remaining energy increase and
delay and packet loss decrease with the increase in
the number of nodes from.
7 Conclusion
Finally, in this section’s crisp discussion of the
overall outcomes of this efficient research
manuscript, it is clear that the proposed model has
always been a major threat to the security in
MANETs during the transmission drop (or) attack
the packet, if wireless communication is done, in
this research, a proposed scheduling algorithm
named SJF-RA is proposed. The simulation results
propose an algorithm as compared with the
existing algorithm in different parameters with
varying numbers of nodes through the network
simulation 2. This developed model’s ability to
detect misbehaviour nodes improves the average
packet delivery ratio by 6.7%, average throughput
by 12.9% and increases the average remaining
energy by 21% than existing method also solves
the weakness of the existing method, to reduce the
average packet drop by 7.4%, average delay 8%
compared to the existing method. There is a plan
to investigate the following issues in the future
however, the same concept can be applied in
satellite to reduce end-to-end delay in the route and
reduce packet loss, possibilities of adopting secure
quality-oriented techniques to further improve the
network performance of quality.
Acknowledgment:
I would like to thank the above researchers and
respected expected reviewers who give their
valuable review comments with suggestions for
updating to improve quality of this research paper.
Also, I would like to thank authorities of the
estimated intuitions Annamalai University,
Tamilnadu, India and Government College of
Engineering, Bodinayakkanur, Tamilnadu, India.
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