Comments responding to an FCC notice of inquiry that seeks insight into how to obtain more sophisticated real-time knowledge of non-Federal spectrum usage have highlighted the importance and potential of AI and machine learning systems.
The Satellite Industry Association stated that, “sensing the physical surroundings together with AI will further enhance situational awareness. Sensing supports various innovative applications such as high precision positioning and localization of devices and objects, high resolution and real-time 3D-mapping for automated and safe driving/transport, digital twins, and industrial automation.”
SpectrumX recognized the “substantial opportunity to apply [AI] tools to spectrum data analysis and spectrum management,” while Lockheed Martin agreed that “artificial intelligence  and machine learning  offer promise in evaluating big datasets and providing . . . insights into spectrum use over time, spectral band, and geography.” The NCTA stated that “Chairwoman Rosenworcel’s optimism on the transformative capabilities of AI and ML tools is well-founded,” noting that “AI and ML can be powerful tools to mine unstructured data and digest it into a more user-friendly format.”
AI developer DeepSig touted the benefits of using AI to measure and monitor spectrum usage, stating that “[a]rtificial intelligence  and machine learning  can be used to develop more accurate and efficient spectrum sensing and monitoring solutions…. allow[ing] us to better understand how and where spectrum is being used and to identify trends and patterns, such as the emergence of new spectrum uses and the impact of spectrum interference on different types of users.” HII Mission Technologies, another AI developer, echoed that sentiment, stating that algorithms like machine learning models and artificial neural networks “can offer improved accuracy in modeling complex spectrum usage patterns and may complement or enhance” existing spectrum consumption models.
Other commenters sounded a cautionary note. The GPS Innovation Alliance noted that “[t]he uses of emerging but still brittle technologies such as artificial intelligence and machine learning to assess spectrum use can further exacerbate challenges created by privacy concerns.” AT&T stated, “the use of ML/AI should be undertaken cautiously—with implementation only in controlled environments where the responsible party has a direct relationship with users involved in the data evaluation….The FCC should not, however, expand use of ML and AI use beyond implementations controlled by a licensee that impact only their own use, which raises a host of serious concerns.” And SpaceX stated that “while machine learning and artificial intelligence could play a role in spectrum management at some point in the future, automation of the Commission’s regulatory functions should be the last step in improving spectrum management policies, not the first.”
The Commission will need to carefully consider the role of artificial intelligence and machine learning in its spectrum management frameworks in the future.