Large Language Models as Bidding Agents in Repeated HetNet Auction
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, ...
| Authors | Ismail Lotfi, Ali Ghrayeb, Samson Lasaulce et al. |
|---|---|
| Primary category | cs.NI |
| Published | 2 March 2026 |
| Last updated at source | 2 March 2026 |
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