1. Introduction
With the massive devices, traffic and the diversity of mobile applications introduced in the
mobile heterogeneous networks, the big data era for mobile internet is approaching[1][2].
There is the need to efficiently manage all the resources involved in the Mobile Internet.
Massive densification of Radio Access Network (RAN) infrastructure known as Ultra Dense
Network (UDN) is considered a promising solution to meet the rapidly increasing demand for
radio access networks. UDN is proposed to further improve throughput per user and to meet
the capacity requirement in peak hour per square meters [3]. The escalating data traffic volume
and the dramatic expansion of the network infrastructure will inevitably trigger an increased
energy consumption in future wireless networks. This will directly increase the greenhouse
gas emissions and mandate an ever increasing attention to the protection of the environment.
Consequently, both industry and academia are engaged in working towards enhancing the
network energy efficiency.
Maximizing the network energy efficiency may be supported by maximizing the amount of
throughput, while minimizing the total energy consumption. As far as the problem formulation
is concerned, maximizing the network energy efficiency can be expressed as minimizing the
total energy consumption while satisfying the associated traffic demands. For example, in the
valley of traffic, huge energy consumption by ultra-dense radio access network has become an
economic and environmental concern to network operators. However, redundancy of radio
resource was not exploited when user association relationship can be rechanged in ultra-dense
network. Hence, the network energy efficiency is crucially dependent on the user association
decisions [4]. In solving problems with the energy consumption of ultra-dense radio access
network, several studies in recent time suggested the scheme, which is known as Multiple
Base Station Scheduling(MBSS) [5][6][7][8]. MBSS leads to the substantial energy efficiency
gains in the LTE-compliant wireless network deployments. However, the main problem with
MBSS is its computational complexity[9]. With the increasing dimension of ultra-dense nodes,
the computational complexity will affect the effective practical implementation of the energy
saving algorithm. Moreover, the mobility of user, and the traffic dynamics compound the
computational problems associated with MBSS. An approach, flow Scheduling, is therefore
necessary not only to reduce the energy consumption but to take into account user mobility
and traffic dynamics within ultra-dense networks.
From the perspective of radio resource management, it is proposed in this paper, how
efficient energy consumption adapts to the density of traffic. This is achieved through the
proper scheduling of the flows over BS in dense small cell deployment. We proposed an
energy manager, which is a part of soft-RAN architecture. The architecture includes a
switch-on/off control algorithm by scheduling flows to reduce the number of active BSs based
on traffic dynamics, which considers the quality of real-time services.
The organization of the rest of this paper is as follows. Section II presents related works
and how differently our approach is. Section III presents the proposed soft-RAN architecture
necessary for the reduction in energy consumption. Section IV deals with the energy
management scheme whiles section V presents the performance evaluation of the proposed
scheme. The summary of the contributions of this paper and conclusion are presented in
section VI.