iteration Range of Search Space {0,1} {0,1} Weightings Complex Random Complex Random Probability of Crossover Probability of Diversity Elite Sample (Nelite) Beta (B) Alpha (a) Integer (q) Source: [29]. 5G Wireless Communication System in Healthcare Informatics optimized P Initial P optimized P f Initial P. FIGURE 8.4 Optimized Pd.corresponding to its lowest Pf via the GA_MPCAlgorithm [29]. optimized Pd Initial Pd Optimized Pf Initial P, FIGURE 8.5 Optimized Pd.corresponding to its lowest Pf via the CE Algorithm [29]. Optimized Cognitive Radio Networks Optimized Pd by way of a GA-MPC optimization technique can be seen in Figure 8.4. It has been proven that the GA-MPC was able to optimize this complex problem while considering the lowest level of Pf as clearly shown in the green curve within the same figure. As seen in the same figure, the optimized pd is improved compared with its Benchmark Pd. Similarly, for Pf which is minimized compared to its corresponding standard p On the other hand, CE is applied to optimize pd with respect to the constraint Pf (namely, Pf lower than a certain target). The performance plotted in Figure 8.5 is good, where the optimized Pd shows improvements compared with the standard initial Pd. 8.7 CONCLUSION In this chapter, we have studied the task of spectrum sensing in the CRN. We considered the ED method of spectrum sensing. For this task, we first derived the probability of detection and the probability of false alarm in closed form using the approach of indefinite quadratic forms. Then, we developed two heuristic methods, GA-MPC and CE, to solve a constrained optimization problem that maximizes the probability of detection while constraining the probability of false alarm to less than a certain acceptable level. The results show that the CE algorithm has better perform- ance than the GA-MPC algorithm. REFERENCES 1. "Spectrum policy task force report," Federal Communication Commission, Washington, DC, USA, Tech. Rep. 02-155, Nov. 2002. 2. Akyildiz, I. F., Lee, W-Y., Vuran, M. C., and 