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This paper focuses on the performance analysis and comparison of hard decision (HD) and soft decision (SD) based approaches for cooperative spectrum sensing in the presence of reporting channel errors. For cooperative sensing (CS) in cognitive radio networks, a distributed detection approach with displaced sensors and a fusion center (FC) is employed. For HD based CS, each secondary user (SU) sends a one-bit hard local decision to the FC. For SD based CS, each SU sends a quantized version of a local decision statistic such as the log-likelihood ratio or any suitable sufficient statistic. The decision statistics are sent through channels that may cause errors. The effects of channel errors are incorporated in the analysis through the bit error probability (BEP). For HD based CS, the counting rule or the -out-of-rule is used at the FC. For SD based CS, the optimal fusion rule in the presence of reporting channel errors is derived and its distribution is established. A comparison of the two schemes is conducted to show thatthere is a performance gain in using SD based CS even in the presence of reporting channel errors. In addition, a BEP wall is shown to exist for CS such that if the BEP is above a certain value, then irrespective of the received signal strength corresponding to the primary user, the constraints on false alarm probability and detection probability cannot be met. It is shown that the performance of HD based CS is very sensitive to the BEP wall phenomenon while the SD based CS is more robust in that sense.

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Cognitive radios sense the radio spectrum in order coding scheme, training or pilot signals, guard periods, and to find unused frequency bands and use them in an agile manner. the power level or correlation properties of the signal, just Transmission by the primary user must be detected reliably even to mention a few. These properties may be used to design a in the low signal-to-noise ratio (SNR) regime and in the face of shadowing and fading. Communication signals are typically deteto at worseinavy low SNRer an d hasl cyclostationary, and have many periodic statistical properties complexity and consequently low power consumpton. These related to the symbol rate, the coding and modulation schemes are very desirable properties especially for cognitive radios as well as the guard periods, for example. These properties in mobile applications. In the absence of any knowledge of can be exploited in designing a detector, and for distinguishing the signal, one may have to resort to classical techniques between the primary and secondary users' signals. In this paper, such as energy detection [1]. An energy detector may need to a generalized likelihood ratio test (GLRT) for detecting the suct a over lon [1]. ofene o detect the to presence of cyclostationarity using multiple cyclic frequencies is collect data over a long period of time to detect the primary proposed. Distributed decision making is employed by combining users reliably. Moreover, controlling the false alarm rates in the quantized local test statistics from many secondary users. mobile applications is difficult because the statistics of the User cooperation allows for mitigating the effects of shadowing signals, noise and interference may be time-varying. Another and provides a larger footprint for the cognitive radio system. . . 'Simulation examples demonstrate the resulting performance snficant drawback iS that energy detecton has no capability gains in the low SNR regime and the benefits of cooperative to distinguish among different types of transmissions or to detection. dichotomize between primary and secondary users of the

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Abstract-Cognitive radios sense the radio spectrum in order to find underutilized spectrum and then exploit it in an agile manner. Spectrum sensing has to be performed reliably in challenging propagation environments characterized by shadowing and fading effects as well as heavy-tailed noise distributions. In this paper a robust computationally efficient nonparametric cyclic correlation estimator based on the multivariate (spatial) sign function is proposed. Nonparametric statistics provide additional robustness against heavy-tailed noise and when the noise statistics are not fully known. Asymptotic distribution of the spatial sign cyclic correlation estimator under the null hypothesis is established. Tests using constraint on false alarm rate are derived based on the estimated spatial sign cyclic correlation for singleuser and collaborative spectrum sensing by multiple secondary users. Theoretical justification for detecting cyclostationary signals using the spatial sign cyclic correlation is provided. A sequential detection scheme for reducing the average detection time is proposed. Simulation experiments and theoretical results comparing the proposed method with cyclostationary spectrum sensing methods employing the conventional cyclic correlation estimator are presented. Simulations demonstrate the reliable and highly robust performance of the proposed nonparametric spectrum sensing method in both Gaussian and non-Gaussian noise environments.

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