Cognitive Radio Cognitive Network Simulator (NS3 based)
Table of Contents:
Cognitive Radio Cognitive Network (CRCN) is a promising technology that has been studied in the research area for a long period. With CRCN, the problem of spectrum scarcity and spectrum underutilization might be effectively solved . This is attributed to the Dynamic Spectrum Access/Allocation (DSA) mechanism  of CRCN, which allows the CRCN subscribers to opportunistically occupy the spectrum resources that assigned by the FCC to the licensed networks (primary users) avoiding interfering to them. The coexistence of CRCN with primary users necessitates the CRCN nodes to be capable of being aware of their surrounding environments (e.g., the availability of a specified channel band), which is called spectrum sensing . The spectrum sensing is facilitated periodically (e.g., specified in IEEE802.22 standard ). Once the channel bands are detected to be unoccupied by the primary user, the CRCN may request to access into these channel bands. Otherwise, the CRCN immediately vacant the channel bands that occupied by the primary user and move to the other available bands. The CRCN coexists with not only the primary users, but also other CRCNs since multiple CRCNs may work simultaneously over the same available spectrum bands . This results in even more complicated design of CRCNs since both interference to the primary users and CRCN’s network performance should be optimized simultaneously .
Security is another significant topic considered in the design of CRCNs [7, 8]. Due to the cognitive features of CRCN (e.g. spectrum sensing and coexistence with primary users etc.), specific attacks for the CRCNs have been proposed in the literature, such as Primary User Emulation Attack (PUEA) [9, 10], Spectrum Sensing Data Falsification (SSDF) [11, 12], Jamming on Common Control Channel [13, 14] and so on. Without the effective defense approaches to these attacks, the CRCN performance will degrade severely and it may lead to the low spectrum utilization efficiency. Furthermore, the primary network might be interfered by the CRCN due to the fake spectrum sensing reports caused by the adversaries. A number of approaches have been proposed to defending against those attacks. However, considering the deployment of CRCN in reality, novel security challenges will be found which necessitates more effective and efficient solutions.
As cognitive radio research is emerging, more and more researchers are looking forward to a simulator that is suitable for cognitive radio. However, there is no existing simulator that is suitable for the demand of cognitive radio simulations. Many researchers implemented their algorithms for cognitive radios on existing network simulator such as NS-2 , OPNET , QUALNET . However, since these simulators are created for the ordinary wireless network, researchers cannot easily implement their cognitive radio algorithms over those simulators. Hence, there is a demand to extend existing simulators to support cognitive radio simulators.
We have developed a CRCN simulator with NS-2 (http://faculty.uml.edu/Tricia_Chigan/Research/CRCN_Simulator.htm), and this version has been widely utilized in the academia field. We are attempting to upgrade our CRCN simulator with NS-3  according to the following reasons:
1) Security is one of the important issues in the research of CRCN, we will provide the attack model interface in this updated version for users to evaluate the effects of their proposed attack models. Some of existing attack models will be given as example to show how to embed the attacks into the CRCN simulator.
2) The coexistence issue of CRCN has been drawing more attention of researchers. This propels us to provide the coexisting module interface to the users to evaluate their coexisting mechanism.
3) Recently, the development of CRCN has been paid more attention in the industrial field wherein a number of realistic constraints are considered. In order to make the network simulation more realistic, we attempt to develop the CRCN simulator with a tool which can be easily integrated into the hardware which can be considered as a network node. Eventually, a network testbed will be built with the network nodes implemented by the hardware integrated with our CRCN simulator.
NS-3  inherits many advantages from NS-2, for example: 1) NS-3 is open source software, thus any contributions to the NS-3 are accessible by the people around the world; 2) NS-3 provides many radio models such as 802.11, 802.16, 802.15.3, 802.15.4. Users can make use of these radio models for cognitive radio network simulations; and 3) NS-3 has incorporated with different topology and traffic generators, which enable users to create different simulation scenarios etc.
Compared with NS-2, NS-3 has following extra advantages [19, 20]:
1) A simulation script can be written as a C++ program, which is not possible in NS2.
2) With modern hardware capabilities, compilation time was not an issue like for NS2, NS3 can be developed with C++ entirely.
3) Ns-3 enables the testbed-based researcher to experiment with novel protocol stacks and emit/consume network packets over real device drivers or VLANs. The internal representation of packets is network-byte order to facilitate serialization.
4) NS3 performs better than NS2 in terms of memory management.
5) The aggregation system prevents unnecessary parameters from being stored, and packets don't contain unused reserved header space.
This cognitive radio cognitive network (CRCN) simulator is a software based network simulator for network-level simulations. It is based on open-source NS-3 (network simulator 3). CRCN simulator will be able to support performance evaluations for the proposed dynamic spectrum resource allocation, power control algorithms, coexistence mechanisms and the adaptive Cognitive Radio (CR) networking protocols such as the CR MAC protocols. The effects of attack models can also be evaluated using this CRCN simulator. This simulator uses NS-3 to generate realistic traffic and topology patterns. For each node in this simulator, a reconfigurable multi-radio multi-channel PHY layer is available by customizing the spectrum parameters such as transmission power etc.
Figure 1. Architecture of CRCN Simulator