AI RESEARCH
Strategic Bidding in 6G Spectrum Auctions with Large Language Models
arXiv CS.AI
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ArXi:2604.24156v1 Announce Type: cross 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.