Spectrum Sensing Methodologies for Cognitive Radio Systems: A Review <s> V. COOPERATIVE SPECTRUM SENSING <s> There are new system implementation challenges involved in the design of cognitive radios, which have both the ability to sense the spectral environment and the flexibility to adapt transmission parameters to maximize system capacity while coexisting with legacy wireless networks. The critical design problem is the need to process multigigahertz wide bandwidth and reliably detect presence of primary users. This places severe requirements on sensitivity, linearity and dynamic range of the circuitry in the RF front-end. To improve radio sensitivity of the sensing function through processing gain we investigated three digital signal processing techniques: matched filtering, energy detection and cyclostationary feature detection. Our analysis shows that cyclostationary feature detection has advantages due to its ability to differentiate modulated signals, interference and noise in low signal to noise ratios. In addition, to further improve the sensing reliability, the advantage of a MAC protocol that exploits cooperation among many cognitive users is investigated. <s> BIB001 </s> Spectrum Sensing Methodologies for Cognitive Radio Systems: A Review <s> V. COOPERATIVE SPECTRUM SENSING <s> The spectrum sensing problem has gained new aspects with cognitive radio and opportunistic spectrum access concepts. It is one of the most challenging issues in cognitive radio systems. In this paper, a survey of spectrum sensing methodologies for cognitive radio is presented. Various aspects of spectrum sensing problem are studied from a cognitive radio perspective and multi-dimensional spectrum sensing concept is introduced. Challenges associated with spectrum sensing are given and enabling spectrum sensing methods are reviewed. The paper explains the cooperative sensing concept and its various forms. External sensing algorithms and other alternative sensing methods are discussed. Furthermore, statistical modeling of network traffic and utilization of these models for prediction of primary user behavior is studied. Finally, sensing features of some current wireless standards are given. <s> BIB002 </s> Spectrum Sensing Methodologies for Cognitive Radio Systems: A Review <s> V. COOPERATIVE SPECTRUM SENSING <s> This paper proposes a distributed average consensus algorithm in order to solve the cooperative spectrum sensing task without a cognitive base station. The proposed consensus algorithm converges to the optimal decision statistic in the limit. Since in practice the iteration number has to be finite, we derive high probability bounds on the iteration number at which all the CRs are at most e away from the optimal decision statistic. Moreover, we compute the performance characteristics of the proposed in-network cooperative spectrum sensing at a given iteration. <s> BIB003 </s> Spectrum Sensing Methodologies for Cognitive Radio Systems: A Review <s> V. COOPERATIVE SPECTRUM SENSING <s> Spectrum sensing is a key function of cognitive radio to prevent the harmful interference with licensed users and identify the available spectrum for improving the spectrum's utilization. However, detection performance in practice is often compromised with multipath fading, shadowing and receiver uncertainty issues. To mitigate the impact of these issues, cooperative spectrum sensing has been shown to be an effective method to improve the detection performance by exploiting spatial diversity. While cooperative gain such as improved detection performance and relaxed sensitivity requirement can be obtained, cooperative sensing can incur cooperation overhead. The overhead refers to any extra sensing time, delay, energy, and operations devoted to cooperative sensing and any performance degradation caused by cooperative sensing. In this paper, the state-of-the-art survey of cooperative sensing is provided to address the issues of cooperation method, cooperative gain, and cooperation overhead. Specifically, the cooperation method is analyzed by the fundamental components called the elements of cooperative sensing, including cooperation models, sensing techniques, hypothesis testing, data fusion, control channel and reporting, user selection, and knowledge base. Moreover, the impacting factors of achievable cooperative gain and incurred cooperation overhead are presented. The factors under consideration include sensing time and delay, channel impairments, energy efficiency, cooperation efficiency, mobility, security, and wideband sensing issues. The open research challenges related to each issue in cooperative sensing are also discussed. <s> BIB004
This scheme was proposed as a solution to the problem of noise uncertainty, fading and shadowing. This scheme decreases the probabilities of false detection and false alarm. Cooperative sensing can also be used to solve the problem of hidden primary user problem and can also reduce sensing time BIB002 . The major idea of cooperative sensing is that it increases the sensing performance by exploiting the spatial diversity in the observation of spatially located cognitive radio users BIB004 . By cooperating, cognitive radio users can share their sensing information for making a combined decision more accurate than individual decision BIB001 . The process of sensing starts with local sensing; this is when spectrum sensing is performed individually at each cognitive radio. The local sensing can be formulated as hypothesis problem as follows BIB003 . Where ( ) is the received signal at the cognitive radio user, ( ) is the transmitted primary signal, ( ) is the zero mean additive white Gaussian noise (AWGN), 0 and , 1 R denote the hypothesis of the absence and presence of signals respectively. The detection performance probability and the probability of false alarm are defined as: Where Υ is the decision statistics and is λ the decision threshold. Cooperative spectrum sensing implementation can be categorized into three, they include: Centralized, Distributed and Relay assisted.