Optimal spectrum sensing for cognitive radio network utilizing software defined radio platform

The static spectrum allocation policy in Malaysia and the rapid growth of wireless communication services have led to spectrum scarcity problem. Consequently, the Quality of Service (QoS) for new wireless services might be compromised as most of the radio bands are already assigned to licensed users. But, the spectrum occupancy’s measurement shows that the allocated spectrum is underutilized. Therefore, in this project, Opportunistic Spectrum Access (OSA) scheme is used to overcome the spectrum scarcity problem. The concept of OSA in cognitive radio technology is used to exploit the spectrum by permitting the secondary user to temporally use the licensed spectrum band when it is free. Hence, spectrum sensing is very important for the secondary user to avoid harmful interference to other wireless services. This project specifically will develop an optimal spectrum sensing mechanism using Particle Swarm Optimization (PSO) algorithm on Software Defined Radio (SDR) using platform called Universal Software Radio Peripheral (USRP). The data has been analysed to validate the performance of the spectrum sensing mechanism referring to the Probability of Detection (Pd) and Probability of False Alarm (Pf). The result shows that the optimal throughput is 93% for Pd 90%, SNR of 1.5dB and Pf 5% which is an improvement of 14% compared with non-optimal method

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

Energy-efficient spectrum sensing approaches for cognitive radio systems

Designing an energy efficient cooperative spectrum sensing for cognitive radio network is our main research objective in this dissertation. Two different approaches are employed to achieve the goal, clustering and minimizing the number of participating cognitive radio users in the cooperative process. First, using clustering technique, a multilevel hierarchical cluster-based structure spectrum sensing algorithm has been proposed to tackle the balance between cooperation gain and cost by combining two different fusion rules and exploiting the tree structure of the cluster. The algorithm considerably minimizes the reporting overhead while satisfying the detection requirements. Second, based on reducing the number of participating cognitive radio users, primary user protection is considered to develop an energy efficient algorithm for cluster-based cooperative spectrum sensing system. An iterative algorithm with low complexity has been proposed to design energy efficient spectrum sensing for cluster-based cooperative systems. Simulation results show that the proposed algorithm can significantly minimize the number of contributing of cognitive radio users in the collaboration process and can compromise the performance gain and the incurred overhead. Moreover, a variable sensing window size is also considered to propose three novel strategies for energy efficient centralized cooperative spectrum sensing system using the three hard decision fusion rules. The results show that strategies remarkably increase the energy efficiency of the cooperative system; furthermore, it is shown optimality of k out of N rule over other two hard decision fusion rules. Finally, joint optimization of transmission power and sensing time for a single cognitive radio is considered. An iterative algorithm with low computational requirements has been proposed to jointly optimize power and sensing time to maximize the energy efficiency metric. Computer results have shown that the proposed algorithm outperforms those existing works in the literature

Energy-Efficient Spectrum Sensing and Transmission for Cognitive Radio System

We study energy-efficient spectrum sensing and transmission for Cognitive Radio (CR) which jointly determines its sensing and transmission durations. Our results quantify the impact of different power consumption components (i.e., sensing, transmission, and idling) on SU's optimal sensing and transmission durations. Our results also show that with a limited power capacity, SU has to strike a balance in energy consumption between sensing and transmission via appropriate idling