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.

翻译：摘要：射频频谱管理正经历从静态集中式指挥控制模式向动态市场化方法的范式转变。然而，动态频谱管理的实现一直受限于缺乏能够无需中央权威运行、同时保护参与者隐私的自动化、可信且智能协调基础设施。本文提出BLAST（基于区块链的大语言模型驱动智能频谱交易）综合框架，该框架将大语言模型智能体与许可区块链基础设施相结合，构建完全自主、私密且安全的频谱交易生态系统。我们提出一种新颖的智能体架构，通过顺序决策流水线（感知-规划-行动）实现认知无线电周期，使智能体能够对经济价值和市场动态进行战略推理。我们通过三种不同市场机制评估该框架：直接销售、第一价格密封投标和第二价格（维克瑞）密封投标拍卖。实验结果表明，第二价格（维克瑞）拍卖是通过激励真实投标实现社会福利和配置效率最大化的最优选择，可捕获高达71%的理论剩余。我们还将所提模型与基线非大语言模型启发式智能体模型对比，证明采用大语言模型智能体能在市场竞争、降低财富与资产集中度、提升系统福利方面带来显著改进。此外，我们验证了系统的隐私保护能力，确认敏感投标值仅隔离存储在私有数据集合中，而只有加密哈希值被提交至公共账本。