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.

System-level simulation is mainly characterized by

• The probability of generating the simulation process even the system is specified partially.

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 SLS requires certain amount of details according to the complexities of large cyber physical systems, in order to adapt the applied simulation in the networks like industry, aircraft and power generating plants.

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.

What are the different types of system simulation?

• System Dynamics Simulation Solutions: it is different from agent-based modeling and discrete event modeling as it doesn’t specify any system details. As it is an abstract simulation model, it cannot provide the machinery and labor data for manufacturing utilities.
• Discrete Event Simulation: it is used in monitoring particularized events that will be useful for the business process. 
• Agent-Based Modeling and Simulation: this type of system-level simulation analyses the impact created either by the environment or agent on the system. Here, the term ‘agent’ can denote any practical individuals like people or the equipment. And this simulation involves the ‘agent behavior’, a set of regulations to let those agents know how to behave with the system.
• Risk analysis simulation/ Monte Carlo: this simulation has the advantage to detect possible threats at the initial stage and it suggests when to take action against threats. This simulation was designed on the mathematical model by Monte Carlo, who implemented the experimental data of the real input and output system. So that it could identify indecisions and possible risks in transmissions. 

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. 

LTE Network Architecture

Usually, the LTE RAN copes with the following aspects,

• MIMO and TX diversity utilities
• Agreed QoS among transmission and the destination
• Decreases the variable delay and jitter
• Separates the traffic handling
• Decreasing the single point of failure
• Reducing the new interface amount
• Packet bearer support
• Flexible spectrum (up to 20 MHz carrier)
• Concurred with GSM and WCDMA

Objectives of LTE network Architecture

• Minimized cost and system maintenance
• Less redundant feature
• Reduced complexities
• Easy structure

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.”

Applications of LTE

    LTE can be implemented in most of the real time applications to get

• Downloading high quality of data
• Extraordinary video streaming of high quality, television service broadcasting and true on-demand television
• High quality of audio streaming and E-Newspapers and other paid information
• Instant content uploading in social media and incredible speed of browsing
• High quality mobile e-mail, video messaging and other P2F messaging services
• High quality video conferencing and VoIP

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.

Simulation Tools for LTE

• 5GPy
  • Language: python
  • Type: system/ Link level
  • Features: used for different statistical analysis in NFV and Fog computing and it is TWDM-PON simulator in the level of link and PHY
• MyiFogSim
  • Language: JAVA
  • Type: system level
  • Features: VM migrations among cloudlets by supporting mobility
• EdgeCloudSim
  • Language: JAVA
  • Type: system level
  • Features: it is used to calculate network delay in XML device configuration and also in queue model with single server. It is also having the features of CloudSim in Cloud computing
• Vienna 5G
  • Language: MATLAB
  • Type: system level
  • Features: it is used to perform macroscopic propagation analysis and propagation model simulation in heterogeneous network interfaces and networks
• GTEC 5G
  • Language: MATLAB
  • Type: link level
  • Features: it serves as a link layer simulator for both OFDM and FBMC
• mmWave
  • Language: NS-3
  • Type: link level
  • Features: it is a 5G level system simulation that permits the layers of both PHY and MAC in a system
• C-RAN Sim.
  • Language: MATLAB
  • Type: system/link level
  • Features: it is a typical simulator in modeling the atmosphere for realistic channel by approving the TU-Vienna simulator. Also it serves to centralize the user scheduling in global per antenna carrier aggregation and edge-user joint transmission.

The strategy elements in Link-measurement model are

• Power allocation strategy
• Resource scheduling strategy
• Interference structure
• Network layout

In link performance model, the elements are

• Link adaption strategy
• Throughput, error rates, error distribution
• Precoding
• Micro, macro scale fading

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.

Methods Used for LTE

• Accessing techniques
  • For DL, the accessing technique is OFDMA
  • For UL, the accessing technique is SC-FDMA
• Duplexing Methods
  • Through FDD method, we can get real-time reach in UL and DL, but in TDD method, the UL and DL could not touch the instantaneous peak traffic.

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 for LTE

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.

How does machine learning correlate with LTE?

• Discovering relationship of machine learning correlates with the discovering knowledge of LTE networks
• The prediction quality on estimating the variables of machine learning correlates with the accurate network improves the network node behaviors
• The data clustering quality of machine learning correlates with the various kinds of processes generated for intended purposes
• The critical issue resolving nature of machine learning correlates with the dynamic environment managements of LTE networks

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.

• Mobility terminal energy efficiency
  • Machine learning tools: SVR, neural Network
  • Description: predicts the availability of controlling information
• eNodeB energy efficiency
  • Machine learning tools: online Q-learning
  • Description: Automatically switch on/off eNodeB
• Cognitive radio spectrum efficiency
  • Machine learning tools: ANN, RNN and Q-learning
  • Description: learning algorithms and deciding the CR metrics
• Traffic load prediction
  • Machine learning tools: KM, kNN
  • Description: predicts mobile data usage and traffic
• Channel quality prediction
  • Machine learning tools: regression tree, Random forest, neural network
  • Description: predicts channel connectivity and quality
• Interference Resource Management
  • Machine learning tools: Q-Learning
  • Description: reducing interference by altering the power control parameters
• Channel state information resource management
  • Machine learning tools: Q-learning, SVM, neural network
  • Description: improving the CQI report way and also predicts CQI
• Handover resource management
  • Machine learning tools: SOM, Q-learning, neutral networking
  • Description: determines whether to handover by learning optimal handover parameters
• Access control management
  • Machine learning tools: feed forwarding neural network, Q-Learning
  • Description: reduce collision and determines to access eNodeB
• Radio Resource Management
  • Machine learning tools: RNN, Q-Learning
  • Description: reduce interference by altering radio parameters

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.

Performance analysis of LTE

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

• Mobility
• Accessibility
• Integrity
• Retainability
• Availability.

The following performance metrics denotes both the LTE system performance targets of both 1 and 2

• Improved MBMS: Transmits nearly 16 multimedia channels in single carrier
• Mobility range: Packet transmission at 15kmph based on the user activity and takes 120kmph over the accepted behavior and delivers low data rate and throughput at 350kmph speed.
• Range covered: data transmission covers up to 5 km in high throughput
• Spectrum efficiency: the LTE network usually denotes the high spectrum efficiency in terms of cell/MHz/bits.
• User throughput: the average throughput taken by the LTE from the users is 95% and the below average user throughput will be 5%

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.