[2603.04455] Large Language Models as Bidding Agents in Repeated HetNet Auction

Computer Science > Networking and Internet Architecture arXiv:2603.04455 (cs) [Submitted on 2 Mar 2026] Title:Large Language Models as Bidding Agents in Repeated HetNet Auction Authors:Ismail Lotfi, Ali Ghrayeb, Samson Lasaulce, Merouane Debbah View a PDF of the paper titled Large Language Models as Bidding Agents in Repeated HetNet Auction, by Ismail Lotfi and 2 other authors View PDF HTML (experimental) Abstract:This paper investigates the integration of large language models (LLMs) as reasoning agents in repeated spectrum auctions within heterogeneous networks (HetNets). While auction-based mechanisms have been widely employed for efficient resource allocation, most prior works assume one-shot auctions, static bidder behavior, and idealized conditions. In contrast to traditional formulations where base station (BS) association and power allocation are centrally optimized, we propose a distributed auction-based framework in which each BS independently conducts its own multi-channel auction, and user equipments (UEs) strategically decide both their association and bid values. Within this setting, UEs operate under budget constraints and repeated interactions, transforming resource allocation into a long-term economic decision rather than a one-shot optimization problem. The proposed framework enables the evaluation of diverse bidding behaviors -from classical myopic and greedy policies to LLM-based agents capable of reasoning over historical outcomes, anticipating competi...