The great developments in the field of wireless communications that has been accelerated by the commercial need for better services has led to the application of wireless systems in many fields of life. The effect of wireless technology is much widened, like for safety applications, home automation, smart grid control, medical wearable, embedded wireless devices, entertainment systems etc.
Direct and indirect surveillance of spectrum treatment has acknowledged the sequential and spatial accessibility of spectrum inside allocated frequency bands. This implies that spectrum deficiency is becoming a main problem. Therefore, spectrum deficiency problem arises here in terms of that most of the licensed primary users are not using their spectrum due to any reason of either geographical variation or temporal basis, and lot of allotted spectrum is unused and under-utilized. Moreover, along with spectrum deficiency problem, other issues are also linked like interference caused by secondary unlicensed users to primary licensed users while sharing the work load among each others, and problem of vacating the spectrum band in less fewer time frames after primary users detection.
Cognitive networks assure to tackle these spectrum deficiency and other associated problems by accommodating secondary (unlicensed) users, in the spectrum region which is under-utilized. Spectrum Sensing is the prime motivation for cognitive radio and ensures that secondary (unlicensed) users do not propose unbearable levels of interference to primary (licensed) users.
Cooperative Spectrum Sensing methodologies are still an open window of research. This work is related to cope up the problem of spectrum deficiency and associated problems, by developing an approach for establishment of grouping/clustering between secondary users in a cooperative spectral environment. This approach ensures that members within a group are highly correlated. As a result, the workload on each sensing node within a group is reduced. The effectiveness of this approach depends upon the accuracy of fused decision related to the presence or absence of primary (licensed) user at a particular band (50MHZ to 100MHZ). This approach also depends on the factor that time taken in sensing the primary (licensed) users should be less enough so that decision in vacating the band by the Cognitive Radio secondary users could be taken in fewer time frames. This latter metric is known to be ‘agility’, which eventually comes with the outcome of minimum interference to primary users via their early recognition.