用大模型当竞标代理，让基站和用户在重复拍卖中智能分配频谱资源。
Large Language Models as Bidding Agents in Repeated HetNet Auction
- 用户以大模型为代理，在多轮拍卖中自主决策接入与出价策略。
- 大模型代理比传统方法提升频谱获取频率与预算使用效率。
- 适合研究智能无线资源分配与去中心化网络市场的研究人员。
本文研究将大语言模型（LLMs）作为推理代理，应用于异构网络（HetNets）中的重复频谱拍卖。尽管拍卖机制广泛用于高效资源分配，但以往工作大多假设一次性拍卖、静态投标行为和理想条件。与集中优化基站关联和功率分配的传统方法不同，本文提出一种分布式拍卖框架：每个基站独立开展多通道拍卖，用户设备（UEs）则战略性地决定关联和出价。在预算约束和多轮交互下，资源分配转化为长期经济决策问题。该框架可评估多种投标行为——从传统的短视贪婪策略到具备历史推演、竞争预判和跨轮次策略适应能力的LLM代理。仿真结果表明，采用大模型的用户设备在频道接入频率和预算效率上持续优于基准方案。这些发现揭示了具备推理能力的智能体在未来去中心化无线网络市场中的潜力，并为轻量化边缘部署的LLM支持下一代HetNets智能资源分配铺平道路。
原文摘要 · Abstract (English)
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.
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