System level simulation of LTE Networks (also known as SLS) is typically a set of techniques applied in the system engineering field for the obvious purpose of simulation to obtain the system network performance by its analyzing features.
“Through this article, we are proving you the possibilities of implementing the system level simulation of LTE networks and attempting to enrich your research perspectives based on LTE’s performance metrics and its simulation tools!!”
The action of simulation takes over the computer by relating its overall performance with a large cyber-physical system that has been constructed by physical entities that are controlled by certain computing elements. On the whole, we are implementing the system-level simulation in the field of LTE networks.
In addition to the overview of the system level simulation of LTE networks, let’s see the major features of the system-level simulation as listed below.
It is because the SLS need not have entire details about every part of its implementing system. This type of feature helps us to apply the simulation process on the desired segments of the system network that too at the initial process.
The remarkable approach of the system level simulation is that it reduces the number of design cycles. And, the automated model generation, powerful simulation frameworks, and systematic approach in behavioral modeling are the obstacles in using the modeling and simulation effectively.
The above-mentioned types are the major SLS categories. In the current scenario, the mobile networks are frequently moving to the LTE network by neglecting the existing GSM and 3G services. Another attractive feature of the LTE is, it is able to support the 5G network Research too. The LTE is a typical IP-based system, which has been designed upon the reconstructed OFDMA and the physical layer. Let’s have a look at the architecture of the LTE network.
Usually, the LTE RAN copes with the following aspects,
Apart from the above features, the technical objectives of LTE are the latency of the transmission time is less than 5ms in a model scenario with an uplink capacity of 50mbps in 20 MHz bandwidth and downlink capacity of 100 Mbps at the same bandwidth. The user throughput of a usual LTE network will be 6HSDPA and it is able to release 6 (improved uplink) at a given time. The following are the applications of the LTE network.
“The LTE networking is a trending network available for all kinds of fixed and mobile networking, and the system level simulation of LTE networks it is a developing research area.”
LTE can be implemented in most of the real time applications to get
In addition to the above features, the LTE network provides high performance to obtain higher data rates, lower access latency, and strong resiliency in fading the multipath, improved spectral efficiency, and unified integration, comparing to other LTE wireless technologies. Along with the applications, we introduce our finest simulation tools for LTE.
The strategy elements in Link-measurement model are
In link performance model, the elements are
In this case, simulating the combination of user equipment and the eNodeBs is not a promising method of conducting the system level simulation of LTE Networks, because, this process needs a huge amount of computational power. Here are the methods used in LTE.
These are the wide range of methods used in the LTE network. However, the LTE network is of enormous scope, particularly in practical implementations and in all fixed and mobile networks. Here are the methods of Machine learning for implementation in the LTE network.
Machine learning has become a useful tool to solve complex problems in many fields. It is an important subject that gives machine intelligence and solves many problems and these techniques can be applied to different areas of LTE. Machine learning techniques can be applied to both two areas to improve the network performance and bring better user experience (UE) as LTE focuses on both performance requirements and efficiency requirements.
Moreover, many functions and implementations in LTE networks require the assistance of intelligent technologies. The performance requirement of LTE particularizes the peak data rate, mobility, latency, and so on. To satisfy these requirements, LTE developed many techniques which include RRM, CSI, channel prediction, traffic requirement prediction, and so on. The machine-learning algorithm which could bring network efficiency and intelligence is introduced to optimize the performance of the LTE system.
The above-qualified attributes of machine learning correlate with the equivalent and beneficial qualities of the System Level Simulation of LTE network. The following are the applications and their appropriate tools of machine learning.
The above is the machine learning tools for implementing the system level simulation of LTE Networks and the qualities of the machine learning to match the equivalent qualities of the LTE network. Here we provide you the performance analysis of the LTE network on various parameters as listed below.
The recent encounter facing by the Mobile Network Operators is to afford broadband services in an extraordinary performance. The 4G/LTE has been established to satisfy the user’s need by providing high-speed data transmission. To provide high-quality services and to attain better resource usage, the Key Performance Indicators should observe and improve the network performance. The key performance indicators are consistent by the Third Generation Partnership Project (3GPP) and generally classified into
The following performance metrics denotes both the LTE system performance targets of both 1 and 2
The uses of system level simulation of LTE networks are on the rise in the current technological world and the features of LTE networks are similar to be applicable over numerous real-time scenarios. It enables superfast data transmission and high-quality audio and video streaming. It is our unique feature to create a signature path in all-new research domains because we are having updated technical teams on the rise. So we are notifying you not to miss the opportunity to get our project service.