 of Things Journal 7, no. 10: 10200-10232, 2020. 34. Chanchaichujit, J., Tan, A., Meng, F., and Eaimkhong, S. "An introduction to healthcare 4.0." In Healthcare 4.0, pp. 1-15. Palgrave Pivot, Singapore, 2019. 35. Konstantinou, D., Rommel, S., Morales, A., Raddo, T. R., Johannsen, U., and Monroy, I. T. "An ehealth-care driven perspective on 5G networks and infrastructure." Interactive Mobile Communication, Technologies and Learning, Michael E. Auer and Thrasyvoulos Tsiatsos (Eds.), pp. 1076-1088. Springer, Cham, 2019. Optimized Cognitive Radio Networks for 5G Technology Mohammed Ali Ahmed Alrefaei, Ubaid M. Al-Saggaf, Muhammad Moinuddin, and Asmaa U. Al-Saggaf CONTENTS 8.1 Introduction Related Literature Review System Model: Non-Cooperative Spectrum Sensing in the CRN Derivation of the Probability of Detection and the Probability of False Alarm Proposed Heuristic Methods for Spectrum Sensing Optimization 8.5.1 Genetic Algorithm with Multi Parent Crossover 8.5.1.1 GA-MPC Crossover Operation 8.5.2 Cross Entropy Method 8.5.3 Overall Implementation of the Proposed Scheme Results and Discussion Conclusion References INTRODUCTION The rapid growth of wireless technologies with the limited frequency spectrum demands an intelligent utilization of electromagnetic spectrum resources [1]. The study in [2] shows that in the existing communication systems, only a small percentage of the spectrum is fully utilized. The rest of the spectrum is either mostly unutilized or underutilized as demonstrated in Figure 8.1. More precisely, the fixed spectrum allocation policy is not capable of accommo- dating the unutilized portion of the spectrum, and this is the main reason behind the development of the CRN. The CRN enhances the efficient utilization of the spectrum allocated to any region by reusing the vacant frequency slots. This vacant frequency slot is termed "spectrum hole" as shown in Figure 8.2. Spectrum hole is originally assigned to an absent licensed user, labeled the pri- mary user (PU). The task of the CRN is to detect such