BLAST: Blockchain-based LLM-powered Agentic Spectrum Trading

2026-04-13

AI Summary

Existing spectrum trading mechanisms struggle to simultaneously achieve decentralization, privacy preservation, and efficient resource allocation. This work proposes the first autonomous spectrum trading platform integrating large language model (LLM) agents with permissioned blockchain, establishing a “perceive–plan–act” decision pipeline to enable strategic market participation. The authors innovatively design a privacy-preserving decentralized auction mechanism that stores only bid hashes on-chain and comparatively evaluate three market designs: direct sale, first-price sealed-bid, and second-price sealed-bid (Vickrey) auctions. Experimental results demonstrate that the Vickrey auction attains 71% of the theoretical social surplus, significantly enhancing market fairness and system welfare while effectively mitigating wealth concentration.

Technology Category

• Game Theory and Economic Paradigms: Auctions and Market-Based Systems
• Multiagent Systems: Mechanism Design
• Machine Learning: Privacy

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Security and Privacy: Privacy-enhancing technologies
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

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.

Problem

• Dynamic Spectrum Management
• Decentralized Coordination
• Privacy Preservation
• Spectrum Trading
• Trustworthy Infrastructure

Innovation

• LLM Agents
• Blockchain
• Dynamic Spectrum Trading
• Vickrey Auction
• Privacy-Preserving