MTC Data Aggregation for 5G Network Slicing
Yiqian Xu
1
, Gang Feng
*1
, Senior Member, IEEE, Liang Liang
2,1
, Shuang Qin
1
, Member IEEE, Zhi Chen
1
1
National Key Laboratory of Science and Technology on Communications,
University of Electronic Science and Technology of China, Chengdu, 611731, China
2
College of Communication Engineering, Chongqing University, Chongqing, 400044, China
Email: fenggang@uestc.edu.cn
Abstract—Recently network slicing has been identified as a
promising network architectural technology for the next
generation mobile cellular networks (5G) to address the
challenges stemming from a wide range of applications.
Especially, for machine type communication (MTC) application,
it is widely recognized that traditional cellular network
architecture is not adequate to meet the requirements in terms of
massive connectivity and low latency. For exploiting network
slicing, data aggregation (DA) can be adopted to effectively
address the massive connectivity and latency requirement. In this
paper, we propose an efficient network slicing data aggregation
(NSDA) scheme for MTC applications. Different from
conventional DA scheme where data aggregation is performed
based on device locations, we perform DA according to latency
requirement for MTC devices (MTCDs), with aim to exploit the
benefits of network slicing and thus improve network access
capacity and decrease access latency. We formulate the DA
problem as a 0-1 Linear Programming and propose an efficient
two-step algorithm to aggregate the MTC data for accessing a
specific network slice. We examine the performance of our
proposed NSDA in typical MTC scenarios via simulations.
Numerical results reveal that NSDA significantly outperforms
traditional MTC access schemes (without network slicing) in
terms of network capacity, access congestion degree, latency, etc.
Keywords—5G; MTC; Network Slicing; Data Aggregation;
Access Capacity; Latency
I.
I
NTRODUCTION
Recent years, the advent of Machine Type Communication
(MTC), one of the essential scenarios of next generation
mobile cellular networks (5G), has inspired a wide range of
emerging applications that have brought unprecedented
changes to people life, such as industrial automation,
telemedicine and smart home. MTC system refers to automated
systems involving devices that automatically collect data
(temperature, humidity, speed, position, heartbeat, etc.),
exchange information, and control automatically, without, or
with only limited, human intervention [1]. It is expected that
there will be more than 50 billion connected devices via MTC
system by 2020 [2]. Most MTC is uplink (UL) dominated, and
the size of data is extremely small with only a few bits or even
only one bit to denote YES or NO. Due to the data
characteristics of MTC, massive small data transmissions may
bring explosive congestion over the current cellular networks.
This work was supported by the National Science Foundation of China
under Grant number 61631004 and 61601067, and the Fundamental Research
Funds for the Central Universities under Grant number ZYGX2015Z005.
It is widely recognized that traditional cellular network
architecture is not adequate to meet the requirements in terms
of massive connectivity and low latency, and thus network
slicing has been identified as a promising 5G network
architectural technology. Network slicing can be understood as
cutting a physical network into a number of end-to-end virtual
network (including network devices, access network,
transmission network and core network), each of which is
logically independent. Each virtual network has different
functional characteristics, for fulfling different service
requirements. Currently, network slices vary in granularity
based on different use cases and business models [5]. There are
independent slices among access domain, IP bearer domain,
and data center domain, which support a complete business
scene or business model via splicing and sharing of slicing [5].
In the access domain, MTC radio access network (RAN)
should support efficient management mechanisms and
efficiently set up new slices to efficiently operate unified
business/services [6]. As illustration in [7], a system of vertical
and horizontal network slicing is established, with the aim to
improve the flexibility of the network.
For exploiting network slicing, data aggregation (DA) can
be adopted to effectively address the massive connectivity and
latency requirement for MTC application. DA is defined as the
process of aggregating the data from multiple MTCDs to
eliminate redundant transmissions and provide fused
information to the base station. In an area, the data generated
from neighboring MTCDs could be redundant and highly
correlated. Therefore, it is necessary to aggregate the collected
MTC data in a period of time according to service correlation,
and then send the aggregation directly to the base station [8].
Recently, there has been some research work on DA, with
focus on energy consumption of MTC applications. The
authors of [9] propose a priority-based data aggregation
scheme at the M2M gateway that effectively maintains a good
trade-off between the power consumption and delay
requirement. The authors of [10] propose an energy-efficient
data aggregation scheme for a hierarchical MTC network,
whose results show that the uplink coverage characteristics
dominate the trend of the energy consumption for the proposed
transmission modes. The work in [11] propose a Two-Tier
Aggregation for Multi-target Applications (TTAMA) solution
that performs two aggregation tiers, which aggregates the data,
reducing the overhead and the data redundancy. The solution
could effectively prolong the network lifetime and reduce the
energy consumption. Authors in [12] propose the concept of
“data-centric” clustering to exploit the correlation of data to be
gathered by a large number of machines to saving the MTCD
energy.
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