Strategic Bidding in 6G Spectrum Auctions with Large Language Models

2026-04-27

AI Summary

This study addresses the challenge of efficiently and fairly allocating spectrum resources under budget constraints in 6G vehicular networks. It pioneers the integration of large language models (LLMs) as intelligent agents into repeated spectrum auctions based on the Vickrey–Clarke–Groves (VCG) mechanism. Leveraging prompt-driven reasoning and reinforcement learning–inspired historical feedback, the LLM dynamically optimizes its long-term bidding strategy. Experimental results demonstrate that, when theoretical VCG conditions are satisfied, the LLM’s behavior converges to the VCG equilibrium. More importantly, under practical constraints such as static budgets, the LLM adaptively adjusts its strategy, significantly prolonging participation duration and enhancing utility. This approach transcends the limitations of conventional static mechanism design and exhibits remarkable adaptability in strategic interactions.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Efficient and fair spectrum allocation is a central challenge in 6G networks, where massive connectivity and heterogeneous services continuously compete for limited radio resources. We investigate the use of Large Language Models (LLMs) as bidding agents in repeated 6G spectrum auctions with budget constraints in vehicular networks. Each user equipment (UE) acts as a rational player optimizing its long-term utility through repeated interactions. Using the Vickrey-Clarke-Groves (VCG) mechanism as a benchmark for incentive-compatible, dominant-strategy truthfulness, we compare LLM-guided bidding against truthful and heuristic strategies. Unlike heuristics, LLMs leverage historical outcomes and prompt-based reasoning to adapt their bidding behavior dynamically. Results show that when the theoretical assumptions guaranteeing truthfulness hold, LLM bidders recover near-equilibrium outcomes consistent with VCG predictions. However, when these assumptions break -- such as under static budget constraints -- LLMs sustain longer participation and achieve higher utilities, revealing their ability to approximate adaptive equilibria beyond static mechanism design. This work provides the first systematic evaluation of LLM bidders in repeated spectrum auctions, offering new insights into how AI-driven agents can interact strategically and reshape market dynamics in future 6G networks.

Problem

• spectrum allocation
• 6G networks
• repeated auctions
• budget constraints
• strategic bidding

Innovation

• Large Language Models
• Spectrum Auctions
• Strategic Bidding
• Adaptive Equilibrium
• 6G Networks