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Cognitive Radio Network Simulation

Cognitive Radio Network is a type of wireless network that senses spectrum for both (License and Unlicensed) types of users. In this model, sensed spectrum is reallocated to users (Primary and Secondary) dynamically for improving the utilization rate of the spectrum and also avoids the spectrum scarcity issues. The Cognitive Radio Network Simulation model consists of two types of characteristics as follows, 

  • Reconfigurability: This feature represents the network can dynamically be adjusted with any set of configuration and simulation settings during communication. Also, access technologies can be easily changeable in this feature. 
  • Ability in Cognitive: In this feature, spectrum sensed for identifying the frequency level i.e. Under-Utilized Spectrum. The CRN decides and determines that the band is busy or running state. This will be predicted for every time and location. 

Based on the above new features of CRN, it is essential to use the appropriate simulation tool. Further, simulation settings are different according to environmental conditions. It is easy to choose a simulator that is easy to allow for simulating any kind of behaviour. On this page, we discussed the CRN simulation tools as a great visualization for research scholars and students.

How to select the tool for CRN simulation? 

            In general, selecting appropriate tool for simulation is not a challenging issue, but that requires some essential details to know before selection that is highlighted below by follows, 

  • Simulation tool must be suited to simulate any kind of operations of CRN and it should be executed with all functionalities and behaviors of CRN, which are used to simulate the CRN activities for any environment. In simple, it must be running with the complete design and construction of CRN
  • Simulators must be open-source that used for historical technical development and supports all kinds of available libraries, header files, and packages, etc. This must be ensured before going to choose the simulation tool for CRN. 

Still, we are emergently researching various simulation tools for CRN projects with a specific set of libraries and modules. Specifically, cognitive radio network simulation is used for various research areas. The widest research areas by researchers and students are given below. And we are currently working on the research areas that too. 


  • Design Protocol Architecture for CRN 
  • Spectrum Sharing / Allocation 
  • Incumbent Detection in CRN 
  • Spectrum Sensing and Management 
  • Spectrum Usage Modeling and Measurements 
  • Interference Management and Analysis 
  • Geolocation based Service Delivery 

Now, we discuss the fundamental requirements of CRN simulators. How each simulation tool can be different than others and also important features of each simulator are highlighted below. Further, CRN projects must have experimented with the following requirements as follows, 

Fundamental requirements for CRN simulators 

  • Simple and easy for configuration and installation 
  • Contains adaptive modular structure 
  • Able to perform simulation versatile and also improves the utilization rate of spectrum for any environments. 
  • Modify the OSI model of the structure for the reconfiguration and modifications of the core feature of CRN. 
  • It must be preferred for good simulation visualization and summarization of results (coding and output results). 
  • Contain several modules for data synthesis and signal processing for spectrum management and analysis. 

CRN is the main field that consists of several sub-research areas. For instance, Physical Level Waveform Generation and Analysis are one of the challenging tasks in CRN for which spectrum sensing by optimum manner is the essential task. Similarly, when we discuss the complete architecture of CRN, which is a wireless communication protocol designed for large scale simulation purposes, 

Many simulation tools are available for network simulation and modeling and particularly CRN demands many for doing simulation for upcoming research ideas and concepts too. The complete package must be available for CRN simulation experiments. 

Cognitive Radio Simulator Classification 

CRN simulator is classified into three classes as follows: 

  • Signal processor 
  • CogWave 
  • Matlab
  • GNU Radio 
  • Traditional Non-agent based Network Simulator 
  • NetSim
  • NS2
  • NS3 Simulator
  • OMNET++
  • Riverbed / OPNET 
  • Agent-based Simulator 
  • AnyLogic
  • NetLogo
  • Repast

In the following, we discuss the description of CRN simulation tools. 

Implementing Cognitive Radio Network Simulation Projects

Cognitive Radio Network Simulation Tools 

  • CogNS: This is an NS-2 based network simulator that is used to process the OSI layers (lower to upper layers) that is it analyses the impact of PHY, MAC, and Transport and Network layers. And analyses the performance over each layer such as packet loss rate, delay, and throughput. These metrics are QoS metrics that are essential to analyze for the cognitive radio networks areas. CRCN or CRAHN based cognitive frameworks can be supported in this tool by OTCL or C++ programming languages. 
  • NS3 (Cognitive Radio): This tool is often called CRE-NS3, which is a free simulation tool written using Python and C++ languages. The specific module i.e. CRE-NS3 consists of a specific set of functions, blocks, packages, and header files for cognitive radio network simulation. For a large-scale environment, it can be suited since it consists of an NS3 Wi-Fi module for adjusting the performance such as MAC and PHY by the primary user and secondary user activities. Able to handle toe different security attacks such as Jamming Attacks. 
  • NetSim: It is one of the most popularly used simulation tools for cognitive radio networks. It is written in C language and IEEE 802.22 framework and supported in Windows OS. For any CRN methodology, the performance of the network is analyzed for various performance metrics and measures the number of transmitted packets, control packets, and error packets for effectively handling the packet transmission from the source to the destination. 
  • OMNET++:  In the CRN framework, Crsimulator and other frameworks can be used for modeling the CRN architecture and simulation. It can be easily implemented with Physical layer and Mac layer protocols for monitoring spectrum sensing and handover features to operate with any emerging integrated technologies like WiFi, 5G Network, and so on. 

Cognitive Radio Simulation Configuration 

   The system configuration must be specified for cognitive radio network simulation which represents the various types of input parameters, settings, and values for cognitive radio network research concepts. The list of network simulation parameters for modeling the cognitive radio simulation is as follows. 

  • CRN Topology Parameters 
    • Network Topology Type 
    • Each Layer Parameters 
    • Mobility Model 
    • Nodes Number 
    • Nodes Spatial Distribution 
    • Network Area Size 
  • Physical Layer Parameters 
    • Bandwidth Each Channel 
    • Radio Transceivers Number 
    • Wireless Channels Number 
    • Operating Time 
    • Sensing Time 
    • Propagation Model 
    • Handoff Time 
  • Network, Application, and Transport Layer Parameters 
    • Routing Protocol Type 
    • Transport Protocol
    • Traffic Type 
    • Simulation Type 
    • TCP Connections Number 
    • Queue Management 

To handle the network OSI layers issues, simulation must be dynamically adjusted and according to the dynamic arrival of nodes and network behaviors, the cognitive radio simulation must be implemented. When considering these issues, Reliability and Real-time features are crucial to QoS improvement. We studied multiple techniques for accurate simulation of CRN ideas and also to the other wireless networks. In particular, low delay, and packet drop rate is essential for real-time applications which require CRN. According to the demand for real-time applications, we studied the most reputed benchmark papers for identifying the significant impact and new performance measures of the QoS in Cognitive Radio Network Simulation. From such experience, we listed here the most important metrics as follows, 

  • TCP Delay 
  • Packet Drop Probability 
  • TCP throughput 
  • Space Complexity 
  • Time Complexity 
  • Jitter 
  • Transmission Delay 
  • Throughput 
  • Spectrum Efficiency 
  • Packet Size and Arrival Rate 
  • Arrival and Departure Rate of Primary Users 
  • False Alarm Probability Rate of Primary Users 

Apart from the above-mentioned parameters, simulators, research areas, etc., we are experts in handling all types of areas in CRN, and our cognitive radio network simulation results are unique and detailed. Let’s check out the future research areas in CRN. 

Future Research Areas in CRN 

  • Determine the Spectrum Aware Transport Layer Protocol for CRN
  • Transport Layer Optimization using Specific Feature of CRNs 
  • Identify the TCP performance measures while finding the optimum size of packets

To conclude that, contact us for any kind of support in CRN i.e. Projects, Research Proposal Writing, Implementation / Coding, Research Paper Writing, and Publication, Thesis Writing, and Viva-Voice Examination. You can also contact us for any other types of wireless networks and as per your research area, concepts, algorithms, and methods. We suggest you the implementation of cognitive radio network simulation using network tools. Our support is broad and you can get help by any communication line.