## 1 Introduction  
The field of radio communication has grown up with the emergence of new wireless devices and applications. Therefore, the demand for radio spectrum has increased, which is expected to increase further in the coming years [1]. On the other hand, most of the existing useful radio spectrum has been already allocated; therefore, it is becoming hard to find free spectrum for the deployment of new services or enhancement of the existing ones [1,2]. Moreover, the conventional approach to spectrum management is very rigid in the sense that each operator is allowed to operate in a certain frequency band. Similarly, studies show that assigned frequencies are not occupied all the time, resulting in under-utilization of the available spectrum. In that sense, cognitive radio has become one of the most important solutions to the spectrum under-utilization problem. Cognitive radio (CR) is defined by Federal Communications Commission (FCC) as: \"_A radio or system that senses its operational electromagnetic environment and can dynamically and autonomously adjust its radio operating parameters to modify system operation, such as maximize throughput, mitigate interference, facilitate interoperability, access secondary markets_\" [1]. Hence, the main concept behind CR is to exploit under-utilized spectral resources by reusing unused spectrum in an opportunistic manner. In other words, both license (primary) and unlicensed (secondary) users share same frequency band in such a way that the primary users are allowed to use the free spectrum spaces left by the secondary users in an opportunistic manner [3, 4, 5]. In order to exploit under-utilized spectral resources by reusing unused spectrum in an opportunistic manner, reliable sensing of the primary users (PU) spectrum is certainly of paramount importance. Such spectrum sensing is performed by secondary users (SU), either following a single-sensor or a multisensor approach. In a CR system, it is often difficult for a single receiver (sensor) (_i.e._, an unlicensed or secondary user) to meet the sensing requirements for detecting a primary signal when large-scale fading and other propagation disturbances are present. In this context, cooperation of multiple secondary users becomes a must choice, as already shown in several previous contributions [3,4]. The present literature for spectrum sensing is still in its early stages of development. Many algorithms have been proposed, including, e.g., likelihood ratio test (LRT), energy detection, matched-filter (MF) detection, and cyclostationarity based detection [6]. All of these techniques have been proposed for an individual secondary user or in a collaborative network of secondary users [1,3,4]. However, for all such schemes it is usually assumed that there is full or partial knowledge about primary signal such as primary signal characteristics, the channel between primary user and secondary user, and/or the noise power level at the secondary user. The LRT is optimal but it requires exact knowledge and distributions of the source signal and noise [7]. The MF-based method requires that the receiver has perfect knowledge about the channel responses from the primary user. In such method accurate synchronization is also required in order to achieve good performance [8,9]. However, this may not be possible as in most of the spectrum sensing applications, the primary users do not cooperate with the secondary users [4,8]. The cyclostationary detection method also requires information about the cyclic frequencies of the primary users, which may not be realistic for many spectrum sensing applications [4]. Energy detection does not need any information of the primary signal, however, it requires perfect knowledge of the noise power [4,7]. In most of the applications, the noise powers are unknown and these should be estimated. The estimated noise power could be quite inaccurate due to noise uncertainty. Inaccurate estimation of the noise power results in high probability of false alarm.