TY  - JOUR
T1  - Intelligent Throughput-Based Sleep Control Algorithm for the 5G Dense Heterogeneous Cellular Networks
AU - Mathonsi, Topside E. AU - M. Tshilongamulenzhe, Tshimangadzo 
JO  - Asian Journal of Information Technology
VL  - 20
IS  - 4
SP  - 121
EP  - 129
PY  - 2021
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2021.121.129
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2021.121.129
KW  - Heterogeneous cellular networks (HetNets)
KW  -Fifth generation (5G)
KW  -Radio access network (RANs)
KW  -Latency performance
KW  -Throughput QoS requirements
KW  -Intelligent Throughput-based Sleep Control (ITSC) algorithm
AB  - In the recent past, many mobile/telecom
operators have seen a continuously growing demand for
ubiquitous high-speed wireless access and an
unprecedented increase in connected wireless devices. As
a result, we have seen explosive growth in traffic volumes
and a wide range of QoS requirements. The fifthgeneration
(5G) heterogeneous cellular networks
(HetNets) have been developed by different mobile
operators to achieve the growing mass data capacity and
to reconnoiter the energy efficiency guaranteed trade-off
between throughput QoS requirements and latency
performance. However, existing energy efficiency
algorithms do not satisfy the throughput QoS
requirements such as reduced latency and packet loss,
longer battery lifetime, reliability and high data rates with
regards to the three components of energy consumption of
the 5G radio access network (RANs) that dominate the
overall mobile communication networks. In addition,
real-time traffic types such as voice and video require
high computational load at the terminal side which have
an undesirable impact on energy/battery lifetime which
further affects the throughput QoS performance such as
reduced packet loss, longer battery lifetime, reliability and
high data rates. As a result, this study proposed an
Intelligent Throughput-based Sleep Control (ITSC)
algorithm for throughput QoS and energy efficiency
enhancement in 5G dense HetNets. In the proposed ITSC
algorithm, a Deep Neural Network (DNN) was used to
determine the cell capacity ratio for the Small Base
Stations (SBSs). Hence, the SBSs cell capacity ratio was
employed as decision criteria to put the SBSs into a sleep
state. Furthermore, transferable payoff coalitional
game theory was used in order to ensure real-time
applications have a higher priority over non-real time
applications. Numerous Network Simulator 2 (NS-2)
results confirmed that the proposed ITSC algorithm reduced packet loss and produced better QoS. Moreover,
the ITSC algorithm provided a longer battery lifetime, reliability and high data rates for real-time traffic.
The network throughput was improved as a result.
ER  -