NS-2 based simulation framework for cognitive radio sensor networks

In this paper, we propose a simulation model for cognitive radio sensor networks (CRSNs) which is an attempt to combine the useful properties of wireless sensor networks and cognitive radio networks. The existing simulation models for cognitive radios cannot be extended for this purpose as they do not consider the strict energy constraint in wireless sensor networks. Our proposed model considers the limited energy available for wireless sensor nodes that constrain the spectrum sensing process—an unavoidable operation in cognitive radios. Our model has been thoroughly tested by performing experiments in different scenarios of CRSNs. The results generated by the model have been found accurate which can be considered for realization of CRSNs

D5.2 - Evaluation of Selected Measurement-based Techniques

Deliverable D5.2 del projecte FARAMIRPreprin

Discrete time analysis of cognitive radio networks with imperfect sensing and saturated source of secondary users, Computer Communications

Sensing is one of the most challenging issues in cognitive radio networks. Selection of sensing parameters raises several tradeoffs between spectral efficiency, energy efficiency and interference caused to primary users (PUs). In this paper we provide representative mathematical models that can be used to analyze sensing strategies under a wide range of conditions. The activity of PUs in a licensed channel is modeled as a sequence of busy and idle periods, which is represented as an alternating Markov phase renewal process. The representation of the secondary users (SUs) behavior is also largely general: the duration of transmissions, sensing periods and the intervals between consecutive sensing periods are modeled by phase type distributions, which constitute a very versatile class of distributions. Expressions for several key performance measures in cognitive radio networks are obtained from the analysis of the model. Most notably, we derive the distribution of the length of an effective white space; the distributions of the waiting times until the SU transmits a given amount of data, through several transmission epochs uninterruptedly; and the goodput when an interrupted SU transmission has to be restarted from the beginning due to the presence of a PU. (C) 2015 Elsevier B.V. All rights reserved.The research of A. S. Alfa was partially supported by the NSERC (Natural Sciences and Engineering Research Council) of Canada under Grant G00315156. Most of the contribution of V. Pla was done while visiting the University of Manitoba. This visit was supported by the Ministerio de Educacion of Spain under Grant PR2011-0055, and by the UPV through the Programa de Apoyo a la Investigacion y Desarrollo (PAID-00-12). The research of the authors from the Universitat Politecnica de Valencia was partially supported by the Ministry of Economy and Competitiveness of Spain under Grant TIN2013-47272-C2-1-R.Alfa, AS.; Pla, V.; Martínez Bauset, J.; Casares Giner, V. (2016). Discrete time analysis of cognitive radio networks with imperfect sensing and saturated source of secondary users, Computer Communications. Computer Communications. 79:53-65. https://doi.org/10.1016/j.comcom.2015.11.012S53657

Contributions to Improve Cognitive Strategies with Respect to Wireless Coexistence

Cognitive radio (CR) can identify temporarily available opportunities in a shared radio environment to improve spectral efficiency and coexistence behavior of radio systems. It operates as a secondary user (SU) and accommodates itself in detected opportunities with an intention to avoid harmful collisions with coexisting primary user (PU) systems. Such opportunistic operation of a CR system requires efficient situational awareness and reliable decision making for radio resource allocation. Situational awareness includes sensing the environment followed by a hypothesis testing for detection of available opportunities in the coexisting environment. This process is often known as spectral hole detection. Situational knowledge can be further enriched by forecasting the primary activities in the radio environment using predictive modeling based approaches. Improved knowledge about the coexisting environment essentially means better decision making for secondary resource allocation. This dissertation identifies limitations of existing predictive modeling and spectral hole detection based resource allocation strategies and suggest improvements. Firstly, accurate and efficient estimation of statistical parameters of the radio environment is identified as a fundamental challenge to realize predictive modeling based cognitive approaches. Lots of useful training data which are essential to learn the system parameters are not available either because of environmental effects such as noise, interference and fading or because of limited system resources particularly sensor bandwidth. While handling environmental effects to improve signal reception in radio systems has already gained much attention, this dissertation addresses the problem of data losses caused by limited sensor bandwidth as it is totally ignored so far and presents bandwidth independent parameter estimation methods. Where, bandwidth independent means achieving the same level of estimation accuracy for any sensor bandwidth. Secondly, this dissertation argues that the existing hole detection strategies are dumb because they provide very little information about the coexisting environment. Decision making for resource allocation based on this dumb hole detection approach cannot optimally exploit the opportunities available in the coexisting environment. As a solution, an intelligent hole detection scheme is proposed which suggests classifying the primary systems and using the documented knowledge of identified radio technologies to fully understand their coexistence behavior. Finally, this dissertation presents a neuro-fuzzy signal classifier (NFSC) that uses bandwidth, operating frequency, pulse shape, hopping behavior and time behavior of signals as distinct features in order to xii identify the PU signals in coexisting environments. This classifier provides the foundation for bandwidth independent parameter estimation and intelligent hole detection. MATLAB/Simulink based simulations are used to support the arguments throughout in this dissertation. A proof-of-concept demonstrator using microcontroller and hardware defined radio (HDR) based transceiver is also presented at the end.</p