Large Language Models as Bidding Agents in Repeated HetNet Auction

TL;DR

This paper explores using large language models as reasoning agents in repeated spectrum auctions for HetNets, demonstrating their ability to improve resource allocation and strategic bidding over traditional methods.

Contribution

It introduces a distributed auction framework with LLM-based agents capable of strategic reasoning and adaptation in repeated HetNet resource allocation scenarios.

Findings

LLM-based agents outperform classical bidding policies in access frequency.

LLM agents achieve better budget efficiency.

Simulation shows potential for intelligent, decentralized wireless networks.

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 competition, and adapting their bidding strategy across episodes. Simulation results reveal that the LLM-empowered UE consistently achieves higher channel access frequency and improved budget efficiency compared to benchmarks. These findings highlight the potential of reasoning-enabled agents in future decentralized wireless networks markets and pave the way for lightweight, edge-deployable LLMs to support intelligent resource allocation in next-generation HetNets.