s: data-based fusion, hard decision-based fusion, and soft decision- based fusion [12]. In [13], it was shown that CSS parameters can be optimized by employing a likelihood ratio test (LRT). In [14], different simpler CSS techniques are presented. Among these, the simplest method is the one that utilizes an OR operation [15]. Works in [16, 17] are based on the k-out-of-N fusion rule where the k value can be optimized. In ED based SS under Rayleigh fading, various combining techniques are investigated which include equal gain combining, selection combining, and switch and stay combining [18]. For hard fusion in CSS, there are several methods such as AND [19], OR [19], M-out-of-N [20], and optimal linear combination [21]. In [22], the weights of linear cooperation are optimized to enhance the overall CSS performance. In all the ED based SS techniques, the received signal energy is compared with a threshold value to decide the existence/absence of PU such that the probability of detection is maximized while the probability of a false alarm is maintained under some particular acceptable level. To do so, the precise choice of threshold value is also a challenge in the ED based SS methods [23]. For this purpose, there are various techniques to obtain a precise threshold value. One of the techniques is based on a constraint on the probability of a false alarm [23]. 8.3 SYSTEM MODEL: NON-COOPERATIVE SPECTRUM SENSING IN THE CRN Consider a non-cooperative spectrum sensing scenario where the local decision is made via processing of the PU signal by the energy detection method with respect to hypotheses H and H1 as shown in Figure 8.3. It consists of a number of co-channels, C. Each SU is equipped with an MX1 antenna array. Hypotheses H shows the scenario where the ith node decides that the primary user is not available (absent) and is given by: (8.1) Hypotheses H1 shows the scenario when the ith node decides that the primary user is available (present) and is given by: 5G Wireless Communication System in Healthcare Informat