r area compared with the existing sampling benchmarks. Using VHF-band radio sensors and the ML technique, an outdoor location estimation scheme of a high-priority DSS system was proposed in [71]. The delay profiles measured in the very high frequency (VHF) band were employed to estimate location. The precision of the EZ could then be improved based on the estimated location of the PUs. By using the ARIB STD-T103 system operating in the VHF-band, they measured delay profiles in a mountainous environment in Japan with the Deep Neural Network (DNN). With the trained DNN, the location cluster of the high-priority terminal could be predicted with- out GPS by simply measuring the delay profile of the PUs. It was shown that their method could significantly improve the total correct localization rate by up to 80.0%. 10.2.2.2 Incumbent Detection According to the FCC, the IUs in the CBRS band include authorized federal users such as U.S. Navy shipborne SPN-43 air traffic control radar operating in the 3550-3700 MHz band, Fixed Satellite Service (space-to-Earth) earth stations operating in the 3600-3650 MHz band, and for a finite period, grandfathered wireless broadband licensees operating in the 3650-3700 MHz band. Due to security restrictions, the SAS cannot access the information of those IUs. To alleviate harmful interference from PAL and GAA users, the Environ- mental Sensing Capability (ESC) enabled by sensor networks could detect transmissions from the Department of Defense radar systems and transmitted that information to the SAS. The SAS could then assign the spectrum resources to users with different priorities dynamically. However, the single sensor detection lacked precision due to its geolocation, while distributed multiple sensor networks led to a high information exchange overhead. Moreover, the extreme operational characteristics of incumbent military wireless applica- tions could overwhelm the existing spectrum sensing methods. Several studies have sought to address these issues. 10.2 ML-Based Methodologies