Decentralized and Agentic Spectrum Management in Cognitive Wireless Networks

Abstract

Dynamic spectrum management and sharing have been the subject of extensive research and development for many years. The ever-increasing demand for wireless spectrum from an exponentially growing number of devices and applications has led to a spectrum scarcity problem that remains unsolved. In addition, the rigid and prolonged nature of the regulatory processes of manually allocating spectrum has led to large swaths of spectrum bands being underutilized and inaccessible to new applications. Dynamic spectrum sharing can alleviate these problems by enabling new applications and devices to opportunistically access unused spectrum. Multiple spectrum sharing frameworks have been proposed by regulatory bodies where access to the shared spectrum is controlled and managed by a centralized third-party controller. However, these centralized spectrum sharing frameworks fail to provide truly dynamic and scalable spectrum sharing as they lack mechanisms for spectrum trading and do not provide incentives for primary users to participate in such models. In addition, existing decentralized spectrum management approaches rely on numerical optimization models that lack autonomous decision making capabilities, and are semantically blind and unable to interpret the unstructured regulatory policies and requirements. The need for a fully dynamic, and autonomous, spectrum sharing framework that satisfies the regulatory requirements and provides built-in economic incentives still exists.

In this thesis, we propose and implement a fully decentralized spectrum management and sharing framework that resolves the issues inherent in the centralized model and closes the semantic gap through autonomous cognitive agents. We implement a comprehensive decentralized model that converges blockchain technology, federated learning, and Large Language Model (LLM) agents to automate and optimize dynamic spectrum sharing, sensing, and access in a single framework. The implemented model eliminates the reliance on centralized brokers through a two-tier Hyperledger Fabric blockchain network that guarantees trust, transparency, and immutable audit trails for spectrum sharing while eliminating single points of failure. In addition, the model facilitates cooperative decentralized spectrum sensing via federated model training on the blockchain achieving 92% detection accuracy. Finally, we implement BLAST (Blockchain LLM Agentic Spectrum Trading), which eliminates static decision-making and requirements analysis through autonomous cognitive agents. We demonstrate that LLM-driven agents employing game-theoretic reasoning within second-price sealed-bid auctions maximize social welfare and spectrum allocation efficiency and significantly outperform traditional heuristic strategies and state-of-the-art non-LLM decentralized models. This research establishes a concrete architectural blueprint for 6G and beyond, where decentralized intelligence, economic incentives, and regulatory compliance coexist within a unified, autonomous execution framework.