Strategic Bidding in 6G Spectrum Auctions with Large Language Models

TL;DR

This paper explores how Large Language Models can serve as strategic bidders in repeated 6G spectrum auctions, demonstrating their ability to adapt and sustain participation under various conditions.

Contribution

It is the first systematic evaluation of LLM-based bidding agents in repeated spectrum auctions, revealing their potential to approximate adaptive equilibria beyond static mechanisms.

Findings

LLM bidders recover near-equilibrium outcomes under VCG assumptions.

LLMs sustain longer participation and higher utilities when static assumptions break.

This work offers new insights into AI-driven strategic interactions in future 6G markets.

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.