d was originally proposed to perform continuous opti- mization [26] and to estimate rare event probabilities [27]. This method is a Monte Carlo technique [26]. If the CE method is used for an optimization task, the opti- mization problem is first transformed to rare event estimation problem and then the CE method is employed using cross entropy as a similarity measure between the two Optimized Cognitive Radio Networks variables which can be the target output and the GA-MPC output. The CE method can be implemented via stochastic optimization as explained in [28]. 8.5.3 OVERALL IMPLEMENTATION OF THE PROPOSED SCHEME The GA-MPC and the proposed CE based scheme is presented in [29]. The overall implementation of the proposed scheme involves the following steps: Initialize random population for the GA-MPC. Save the best chromosome for future utilization. Apply tournament selection for the offspring generation. Apply a multi-parent crossover operation using the procedure provided in Section 8.5.1.1. Apply diversity operation for the GA-MPC. 6. Evaluate the objective function or the cost function. If the criterion is met, save the result, otherwise repeat steps (1-5). 8.6 RESULTS AND DISCUSSION The results provided in this section are obtained from the recently published patent [29]. We consider the average transmitted symbol energy of PU signal and CCI (E/E) equal to 1. Besides that, the channel fading coefficients holi and hH are generated randomly for the PU channel and each CCI channel. Also, the noise variance of the additive white Gaussian (AWG) is considered constant in the channels as (o's = 0.01). The experiment is done for a node that is experiencing several CCI nodes (CCI = 5) which transmit samples (N = 5) and receive the signals via five different antenna elements (Ay=5). The tuning parameters of the GA-MPC and the CE are selected after various trails and are provided in Table 8.2 [29]. TABLE 8.1 Parameter Settings for GA-MPC and CE Algorithms Parameters GA-MPC Parameters CE parameter Population size No. of 