On the development of a Cognitive Radio Network Simulator based on - PowerPoint PPT Presentation

4 th Workshop of COST Action IC0902 “Cognitive Radio and Networking for Cooperative Coexistence of Heterogeneous Wireless Networks” October 9–11, 2013 Rome, Italy 1 Contribution to Working Group 2 – “Definition of cooperation-based cognitive algorithms, that take advantage of information exchange at a local level” “On the development of a Cognitive Radio Network Simulator based on OMNeT++/MiXiM” Giuseppe Caso Prof. Luca De Nardis DIET Department Sapienza University of Rome Rome, Italy Email: {caso, lucadn}@newyork.ing.uniroma1.it Rome, October 9-11, 2013

Outline 2 1. OMNeT++: Objective Modular Network Testbed in C++; 2. MiXiM: the MiXed SiMulator; 3. From a “ simple device” to a Spectrum Sensing Cognitive Radio (SSCR); 4. Simulation Framework & Results; 5. Conclusions & Future Works. Outline Rome, October 9-11, 2013

OMNeT++ 1/2 3 — Objective Modular Network Testbed in C++ ( http://www.omnetpp.org/); — Open source tool for discrete event simulations; — The simulator can be used for: ¡ Traffic modeling of telecommunication networks; ¡ Protocol modeling; ¡ Modeling queuing networks; ¡ Modeling multiprocessors and other distributed hardware; ¡ Modeling of scenarios when several entities behave independently and the output of every entity must be analyzed. — OMNeT++ allows for detailed simulation of the reciprocal effect of independent communications. OMNeT++ Rome, October 9-11, 2013

OMNeT++ 2/2 4 OMNeT++ represents a “framework approach”: — ¡ Instead of containing explicit and hardwired support for computer networks or other areas, it provides an infrastructure for writing such simulations; ¡ Specific application areas are catered by various simulation models and frameworks, most of them open source; ¡ These models are developed completely independently of OMNeT++, and follow their own release cycles. Partial list of OMNeT++-based network simulators and simulation — frameworks: Mobility Framework: for mobile and wireless simulations; ¡ INET Framework: for wired and wireless TCP/IP based simulations; ¡ Castalia: for wireless sensor networks; ¡ MiXiM: for mobile and wireless simulations. ¡ OMNeT++ Rome, October 9-11, 2013

MiXiM Project 5 MiXiM is a merger of several OMNeT++ frameworks written to — support mobile and wireless simulations; The predecessors of MiXiM are: — ¡ ChSim by Universität Paderborn; ¡ Mac Simulator by Technische Universiteit Delft; ¡ Mobility Framework by Technische Universität Berlin, Telecommunication Networks Group; ¡ Positif Framework by Technische Universiteit Delft. — It provides detailed models of the wireless channel and connectivity, mobility and obstacles models, and many communication protocols, especially at the MAC level; — It provides a user-friendly graphical representation of wireless and mobile networks, supporting debugging and defining even complex scenarios. MiXiM: Introduction Rome, October 9-11, 2013

MiXiM: General Structure 6 “ World ” utility module provides global parameters of the environment • (size, 2D or 3D). “ Objects ” are used to model the environment; • They influence radio signals and the mobility of other objects. • “ ObjectManager ” decides which objects are interfering. • “ConnectionManager” module dynamically manages connections • between objects: signal quality based on interference and on mobility. “ Nodes ” modules simulate entities desiring to communicate; • Different kinds of nodes can be specified with different characteristics . • MiXiM: Introduction Rome, October 9-11, 2013

Node Modules 1/2 7 — Adjacent layers are connected by two pairs of OMNeT++ “ gates ”: ¡ 1 st pair: to exchange up and down data messages, including control messages between nodes; ¡ 2 nd pair: to exchange control messages between the layers. — The PHY layer is the place to take Figure (a). Example of a node care of the reception and • Submods according to the IP collision handling; model: • Application Layer ( appl ); — Physical and MAC layer design is • Network Layer ( netw ); usually tightly coupled and very • MAC Layer ( mac ); specific for different • Physical Layer ( phy ). communication techniques. • Physical + MAC layer = Network Interface Card (NIC) module (Figure (b)) . MiXiM: Node Module Rome, October 9-11, 2013

Node Modules 2/2 8 — “ Mobility ” module: responsible for the movements of an entity. Different mobility models can be implemented in MiXiM; — “ Battery ” module: used for energy related issues; — “ Arp ” module handles the Address Resolution Protocol (ARP); — “ Utility ” module: derived from the Mobility Framework “ Blackboard ” module. Two main tasks: ¡ It provides a general interface for collecting statistical data of a simulation that has minimal impact on the performance; ¡ It maintains parameters that need to be accessed by more than one module within a node (e.g., the position of a node, calculated and updated by the mobility module, but also needed by the physical layer). MiXiM: Node Module Rome, October 9-11, 2013

From a MiXiM-based to a Cognitive Radio Scenario 9 Primary User vs. Secondary and Cognitive Users case of study: 1. Introduction of a particular Host simulating the Primary User; 2. Introduction of the Secondary Network. MiXiM-based Cognitive Radio Network Rome, October 9-11, 2013

From a MiXiM device to a Cognitive Radio 10 Introduction of peculiar cognitive functionalities in the OSI stack of a generic device: 1. (Cooperative) Spectrum Sensing; 2. Clustering; 3. Multi-Channel possibility; 4. … MiXiM-based Cognitive Radio Node Rome, October 9-11, 2013

CRN Simulator: Implementation and Use 11 The simulator was already used in network organization algorithms design and performance — evaluation, since it is possible to define different scenarios by adjusting key parameters as PUs behavior , cognitive network features (number of SUs, Energy Detector-based Spectrum Sensing modes, mobility models), operating frequencies (also DVB-T), medium access modalities , propagation channel models , network types and topologies , and so on. Comparative analysis and the so-far obtained results regarding, in particular, — Spectrum sensing performance (both local and centralized cooperative with different hard ¡ decision fusion rules); The impact of mobility and spatio-temporal correlation; ¡ The introduction of cluster-based solutions for the network organization and nodes ¡ selection; The achievable data throughput of the cognitive network in particular contention ¡ scenarios. Simulation Settings, Results and Discussions Rome, October 9-11, 2013

Simulation Settings: an example 12 DVB-T Transmitter (PU): — Located in the top left corner of a square area of 10 × 10 km 2 ; v Tx Power = 200 kW, within a DVB-T 8 MHz channel in the UHF band. v A set of SUs forming a CRN: — Located at the lower right area of the playground, within a 700 × 700 m 2 area, centered v on the position of the FC; The SUs are equipped with a data interface used to sense the PU channel and eventually v transmit data packets, and a control interface working on a common dedicated channel; Tx Power = 110 mW. v When mobility is assumed: — the SUs are allowed to move within the working area using a Gauss-Markov mobility v model with an average speed v = [5 10 15 20] m/s. Simulation Run Settings: — 1h of simulated time, during which each collaborating SU takes a local decision exploiting v a sensing phase of T = 50µs and then transmits its decision to the FC during the subsequent exchange phase of 1s . Finally, a global decision is taken by the FC each 5s. Simulation Settings, Results and Discussions Rome, October 9-11, 2013

Simulation (OMNeT++/MiXiM) Results: Validity of the proposed approximation for Majority Rule 13 Achievable Q d (CFAR mode) as a function of the requested target Q fa and for — different values of γ , in a CSS with Majority rule scenario [2] . 1. A good match between the analytical and the simulation results is obtained, confirming the reliability of the ongoing OMNeT++ based CRN simulator and, in particular, the validity of the proposed approximations. 2. A perfect knowledge about the number of collaborating SUs (N = 25) and the average SNR ( γ = [0 : 5 : 30]dB) is assumed. 3. As expected, for low γ values, the CRN should not set too stringent targets as this actually leads to worse global cooperative performance. For high γ values the performance greatly increase. Simulation Settings, Results and Discussions Rome, October 9-11, 2013

Simulation (OMNeT++/MiXiM) Results: Nodes selection framework 14 Achievable Q d for CSS with Majority fusion rule , as a function of the CFAR target Q fa — and the number of SUs, for schemes without and with nodes selection [1] . Simulation Settings, Results and Discussions Rome, October 9-11, 2013

Simulation (OMNeT++/MiXiM) Results: Study of CRNs Data Throughput 15 Achievable Data Throughput for different CRNs with non-cluster/cluster-based • CSS with Majority fusion rule [3]. SUs Data Throughput – Static Case 9 8 7 6 5 4 3 2 1 0 Non-Clustered MOBIC Model SENSIC Model Model Offered Traffic [pkt/s] 3.54 6.53 8.17 Throughput [pkt/s] 3.3 5.53 7.6 Simulation Settings, Results and Discussions Rome, October 9-11, 2013