. As the number of devices as well as the demand for network capacity has increased, the expansion of licensed band services to the unlicensed band has been proposed. LTE-U provides a view of how to use the unlicensed band to improve the licensed users' performance. Finally, symbiotic schemes such as AmBC help researchers deal with the massive growth of IoT devices that normally have power and resource limitations, providing a new paradigm for spectrum sharing. Although these research studies share some overlapping features such as sensing and access control, they each have their distinctive focuses. 5G has a very broad technical scope and needs to address a variety of device communication problems. Therefore, investigating SS issues under different frameworks can provide very instrumental insights for future communication development. With the advancement of computing technologies such as GPU and algorithm develop- ment, ML has garnered tremendous interest and recently demonstrated astonishing poten- tial for tackling large-scale, highly dynamic, very complicated problems that traditional techniques cannot easily handle. ML algorithms have gained advantages in processing, classification, decision-making, recognition, and other problems [17]. SS frameworks naturally share common features with ML, making the combination of ML with SS networks very appealing. For all the frameworks mentioned above, users/ coordinators in the SS network need to observe the spectrum resource usage and make corresponding decisions in accordance with the three main conditions for intelligence, Figure 10.1 Spectrum sharing paradigm. LTE-U/LTE- LSA/CBRS Database/ Opportunistic Coexist/LBT/ Sensing/Exclusive Symbiotic spectrum access Duty cycle on zone 10 Secure Spectrum Sharing with Machine Learning: An Overview i.e. perception, learning, and reasoning [17]. In ML, the intelligent agent first senses the surrounding environment and internal states through perception to obtain information. It further transforms that information into knowle