BLAST: Blockchain-based LLM-powered Agentic Spectrum Trading

TL;DR

BLAST introduces an autonomous, privacy-preserving spectrum trading framework combining LLM agents with blockchain, optimizing auction efficiency and market fairness in dynamic radio frequency management.

Contribution

It presents a novel integration of LLM-powered agents with blockchain for secure, autonomous spectrum trading, and evaluates multiple auction mechanisms for optimal social welfare.

Findings

Vickrey auction maximizes social welfare, capturing 71% of theoretical surplus.

LLM agents outperform heuristic models in market competition and welfare.

System preserves privacy by isolating sensitive bids while using cryptographic hashes.

Abstract

The management of radio frequency spectrum is undergoing a paradigm shift from static, centralized command-and-control models to dynamic, market-driven approaches. However, the realization of Dynamic Spectrum Management has been hindered by the lack of an automated, trustworthy, and intelligent coordination infrastructure that can operate without a central authority while preserving participant privacy. In this paper, we introduce BLAST (Blockchain-based LLM-powered Agentic Spectrum Trading), a comprehensive framework that integrates Large Language Model (LLM) Agents with a permissioned blockchain infrastructure to create a fully autonomous, private, and secure spectrum trading ecosystem. We propose a novel agent architecture that implements the Cognitive Radio cycle through a sequential decision pipeline (perceive, plan, act) enabling agents to reason strategically about economic value and market dynamics. We evaluate the framework through three distinct market mechanisms: Direct Sale, First-Price Sealed-Bid, and Second-Price (Vickrey) Sealed-Bid auctions. Experimental results demonstrate that the Second-Price (Vickrey) auction is the optimal choice for maximizing social welfare and allocative efficiency, capturing up to 71% of the theoretical surplus by incentivizing truthful bidding. We also compare the proposed model against a baseline non-LLM heuristic agentic model and show that utilizing LLM agents yields significant improvements in market competition, reduced wealth and asset concentration, and increased system welfare. Furthermore, we validate the system's privacy preservation, confirming that sensitive bid values remain isolated in private data collections while only cryptographic hashes are committed to the public ledger.