Deploying Neural-Symbolic Hybrid Models for Adaptive Spectrum Management in 6G-Ready Networks - Kumar Sheelam, Goutham (Journal of Neonatal Surgery, 2025)
The spectrum scarcity exacerbated by the increasing demand for connectivity from sectors such as the Internet of Things and Industry 4.0 has caused a shift in policy and regulation decisions towards a more flexible use of the spectrum. An example of this is the identification of tools such as Dynamic Spectrum Access which allow better outlets for unlicensed bands and the design of technology options capable of supporting it. This is the spirit of Cognitive Radio Technology but, to be practical, cultural issues must be addressed first; its application to commercial wireless networks is still a question mark. In wireless communication, Artificial Intelligence is now seen as a game changer, particularly in the way signals are defined and processed as well as in network management. The convergence of these two areas is seen by many as a natural path into the future, opening up new challenges and new business opportunities. Dynamic Spectrum Systems for Wireless Communication networks are at the core of this set of problems, Dynamic Access Spectrum is the new objective for designers and operators; the fulfillment of this objective allows the implementation of a new business in which Wireless Communication is ancillary to the production and commercialization of information. Cognitive Wireless Networks, built upon More than Moore paradigms (natural air interface processing, dynamic all-in-one network management), are seen as the exploitable fruit of the convergence of Artificial Intelligence with the current state-of-the-art in Radio Communication Theory applied to Wireless Networks. We explore these ideas, utilizing the current research activity and the lessons to be learned as a roadmap to the future..