How to Enable Python LTE simulation – “Through this article, we analyze the features of python other than as a programming language and its correlating features of simulation with the LTE network. At the end of your understanding of this article, we will unlock the possible research areas and the innovative research topics in the relevant field!!”

Python LTE Software is typically an open source framework for performance assessment and improving the development of the configuration of LTE networks. It shortly referred as PyLTE. It permits in describing the geographic position of cells, organizing the eNodeB like as the dimensional and power distribution of the clients.

Advantages of Python Simulation

  • Assessing reuse of various frequencies
  • Detecting the eNodeB’s optimal Txpower
  • Estimating network throughput of users
  • Evaluating the average network signal level (SINR)

How to Enable Python LTE Simulation of the above-mentioned advantages can also be the chances to implement any project. Other than the given advantages, there are numerous real-time advantages for the python. Besides, we display you the importance of the Python LTE modules.

Purpose of Python LTE modules

PyLTESim: this type of module is used to simulate LTE cellular network. The features of this module are

  • Script sample production large scale simulation
  • Evaluating units in large number
  • Scripts for numerous data visualization
  • Transmission frame for LTE OFDMA
  • WINNER channel model with directional antenna gain, mobility, fast fading
  • Distinct data generation, plotting for large scale simulation and collection
  • Config files configuration
  • Identical mobile station supply
  • Hexagonal base station circulation

Notable classes in PyLTE

  • BS(NetworkDevice): to generate base station with the help of PyLTESim package
  • UE(NetworkDevice): to produce User Equipment nodes with the help of PyLTESim package

Integration tools for Python LTE simulation

  • recommended: virtualenv
  • pyipopt/ipopt
  • matplotlib
  • scipy
  • numpy
  • python 2.7.3 with ssl

The exceeding packages are the essential requirements need to be installed before running the python simulation in any network. In addition to the required integration tools, we provide you the programming languages below.

Python LTE Simulation Top 6 tools
Python Simulation of Wireless Communication Systems

Programming languages

As the simulator has the python base, the supporting programming language for the PyLTESim is Python

Supporting OS

To perform the Python LTE simulation the system must have

  • Intel Core i5-9400F (Processor)
  • 8 GB (RAM)
  • 64-bit Operating System (System Type)

And the OS are

  • ubuntu-14.04
  • windows-7 64 bit

The operating system and its appropriate versions are more important while conducting a simulation process by using the system as a ground for the performance. In addition to the OS, we provide you the versions of PyLTESim tools

Versions of PyLTESim tools
  • Python-3  and Python-3.6
  • Python-2.7.0

Protocols for LTE simulation

  • Key Agreement protocol and novel group-based Authentication
    • It is used to get rid of computational Overhead and problem of heterogeneous devices. Basically it is for IoT enabled with LTE/LTE-A network.
  • Enhanced EAP Authentication Protocol
    • It is used to resolve the issues integrated access validation in the specified WLAN terminals in a scenario of converged networking. Its main objective focuses on the power private network

The above protocols are the significant protocols of the PyLTESim. For your kind information, we provided you our sample protocols. Apart from that we are implementing numerous protocols for effective results. Here are the subjects used in Python simulation.

Remarkable subjects used in Simulation

  • OFDM wireless Communication: It is generally a multicarrier system that can be implement in DAB, DVB-T, WiMAX and LTE and in other wireless transmission systems
  • Secure Communication: The following steps are the process on how secure communication are occurring in the PyLTESim
    • User requesting the service
    • Validating the user request
    • Responding validation to UE
    • Sending UE user authentication Response
    • Networking service starts

In addition to the above subjects PyLTESim, we are going to have a look on the result analytics of the PyLTESim, its difference on the results in various scenarios and the classification of our Python LTE Simulation important result analytic and its metrics

Experimental analysis of Python LTE simulator

  • Spectral Efficiency: it computes the effectiveness of spectrum by dividing the transmission data rates with the channel bandwidth in terms of bps/Hz
  • False Positive: it calculates the chances of input, which incorrectly rejected classes. It is measured in the terms of Ratio.
  • Antenna Efficiency: it computes the antenna efficiency as it is the ratio of the power radiation (Prad). The finest antenna efficiency result will be given by the tuning paths of the LTE frequency.

The above are our finest scenario evaluation of the PyLTESim parameters. Their function is to analyze the network efficiency and its performance. Here we present you the subject wise modules in the PyLTESim.

Important subject wise modules in Python simulation

Mobile data analytics and modeling

This module is one of our expensive modules, which is able to

  • Activate Machine Learning techniques
  • Forecast the QoS
  • Offering the same service even minimized
  • Consume less resource

The above module predicts the LTE QoS Parameters as observed by the users in accordance to the network history measurement. The users can select the PRB/Mb as a main QoS indicator. This is used as our experimental and we will afford you even for your research purpose. Here’s the main syntax for the PyLTESim

Remarkable syntax for PyLTESim

class UE(NetworkDevice):


    def __init__(self):

        self.ID = 0

        self.connectedToBS = 0

        self.inside = True

    def distanceToBS(self, BS):

        return math.sqrt((self.x-BS.x)**2+(self.y-BS.y)**2)

    def isSeenFromBS(self, BS):

        if BS.omnidirectionalAntenna == True:

            return True

        #returns true if angle allow signal receive, else False

        a_y = BS.y-self.y

        distance_bs_ue = self.distanceToBS(BS)

        if distance_bs_ue == 0 or BS.turnedOn == False:

            return FalseMessage Automation and Protocol Simulation in python

        ue_angle_rad = math.acos(a_y/distance_bs_ue)

        ue_angle = math.degrees(ue_angle_rad)

        if self.x <= BS.x:

            ue_angle = 360 – ue_angle

        if BS.angle > ue_angle:

            alpha_diff = BS.angle – ue_angle


            alpha_diff = ue_angle – BS.angle

        if alpha_diff <= 60 or alpha_diff >= 300:

            return True


            return False

    def connectToNearestBS(self, BS_vector):

        closestDistance = -1

        foundBS = -1

        for bs in BS_vector:

            if self.isSeenFromBS(bs):

                currentDistance = self.distanceToBS(bs)

                if currentDistance < closestDistance or foundBS == -1:

                    closestDistance = currentDistance

                    foundBS = bs.ID

        self.connectedToBS = foundBS

    def connectToTheBestBS(self, BS_vector, obstacleVector = None):

        theBestSINR = -1000

        foundBS = -1

        for bs in BS_vector:

            if self.isSeenFromBS(bs):

                self.connectedToBS = bs.ID

                currentSINR = self.calculateSINR(BS_vector, obstacleVector)

                if theBestSINR < currentSINR or foundBS == -1:

                    theBestSINR = currentSINR

                    foundBS = bs.ID

        self.connectedToBS = foundBS

Syntaxes may vary according to the application over various network simulators. For instance we provided you the syntax to configure the UE Nodes Configuration. Following the syntax, we provide you the applications of PyLTESim.

Applications in Python LTE simulation

  • Applications based on Ultra-Wideband: It is an application of radio technology uses optimized energy level and altered bandwidth for each ranges. It is typically known for non-cooperative radar imaging application.
  • LTE OFDMA transmission frame applications: in this process, the bit stream transfers the bits to the serial to parallel gateway for the mapping process and through IDFT and parallel to serial, the bits were added CP and transmitting to the common channel. In the receiving process, the channel regulates the bits to remove CP and the injected bits estimating the channel in the equalizer. This process then demapped and sent to the receiving side by modulating the bits.
  • Hexagonal base station distribution applications: The interior UE users of the application are involved in the voice over packetizing on the 4G LTE (VoLTE) and the exterior users of this application are afforded with the high speed data access of 4G LTE and python is applied in this algorithm.

These three are the major applications and functions for the Python LTE simulation. In addition to the applications, we provide you our finest algorithms in PyLTESim as listed below. Algorithms are chief as the protocols and applications in the simulation network.

Important algorithms in PyLTESim

  • Proportional fairness: used in the network of multicell downlink LTE-LAA. Fairness expansion by assuming limited channel state information (CSI) interchange. It is applied by python (Version 2.7) and TensorFlow library (Version 1.3.0 GPU).
  • Parallel iterated Matching: it is able to detect the ict-free cells for effective transmission by creating xed scheduling from the existing ows through the switch.
  • iSLIP: it is a typical scheduling algorithm, which has compatibility with hardware. It is able to succeed 100% throughput in handling the uniform traffic and it is able to modify itself for uneven traffic too.

The above algorithms are our sample ones for the research purpose. For practical purpose, we have many more algorithms. If you are interested, we are even willing to implement to your project. In addition to algorithms, let’s take a look on the areas in PYLTESim

Major areas of PyLTESim

   Here we provide you the automation features of the protocol simulation & message automation

  • Remote accessibility and evaluation controls multiple nodes
  • Running the test bed setups automatically (because the scheduler preloads the test defining) and the configurations run automatically in a specified time.
  • Automation through clients like C++, Java, Python, TCL
  • Finishing multiple calls serially or casually to deal with the incoming or outgoing calls.

The above are the attractive automation features of the PyLTESim areas in the protocol simulation and message automation. And there are identified as the project ideas in PyLTESim  as per the suggestion of our engineering team. Let’s see the major process involved in the Python LTE Simulation in different layers as listed below.

Important LTE Qos Parameters

Major process in PyLTESim

In order to transform the data packets in the layers, it uses the Random access performance, Random access process. It occurs in the three layers of the system like,

  • RRC layer
  • MAC Layer     
  • Physical layer

These are the main layers of the system, where the major process of the Python LTE simulation takes place. Here are the major steps in simulation.

Major steps in Python simulation

  • The process used in the UE to initiate the data transfer process. It happens in all the three layer of the network and in the first layer, where the physical layer produces the MAC layer to initiate MAC control over the data with the help of MAC PDUs and the MAC sends RLC PDUs to create RLC layer to induce the RLC control and PDCP control at PDCP layer respectively

The above major steps take place in all important layers of the system and this is how an effective LTE simulation in python takes place. Here we provide you the list of important LTE protocols as following,

Routing in Python LTE Network

  • Tree Adaptive Routing (STAR): it is used to measure the overhead network load on a small routing with importance. It has a nature of link-state and table-driven characteristics.
  • Multimedia support in mobile wireless networks (MMWN): This type of network has a clustering hierarchy. There is a location manager of each cluster and they had two types of mobile nodes,
    • Switches
    • Endpoints

There are many type of routing protocols available for the python LTE simulation. These are the samples. In addition to the routing protocols, let’s take a look in the main objective i.e. our suggestion on project titles as displayed below.

Innovative projects on LTE simulation

  • We can help you to perform projects on sensed packet transmission process on the basis of relay with the help of PyLTE
  • We can help you to perform projects on cluster packet transmission process with the help of Python simulation

To cater the needs of the users, the speed of networking is on the rise. So we cannot say the networking may outdated from the trend. What mainly entertain and benefits us is internet. And the world has started to function on the basis of technology. We are around you to provide the project service in addition to the assignment and homework help. How to Enable Python LTE Simulation are extending out path of service from introducing you to the various areas of networking and network security. Above all, clutch our hands to have a wonderful subject and project experience!!