Large Language Models as Bidding Agents in Repeated HetNet Auction
#Large Language Models #bidding agents #repeated auctions #heterogeneous networks #strategic decision-making #telecommunications #resource allocation
📌 Key Takeaways
- LLMs can function as bidding agents in repeated heterogeneous network auctions.
- The study explores LLMs' strategic decision-making in dynamic auction environments.
- LLM-based agents demonstrate adaptability in competitive bidding scenarios.
- Research highlights potential applications of AI in telecommunications and network resource allocation.
📖 Full Retelling
arXiv:2603.04455v1 Announce Type: cross
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
🏷️ Themes
AI Applications, Auction Theory
📚 Related People & Topics
Large language model
Type of machine learning model
A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre-trained transformers (GPTs) that provide the c...
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Connections for Large language model:
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Artificial intelligence
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Reinforcement learning
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Educational technology
2 shared
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Benchmark
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OpenAI
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Original Source
--> Computer Science > Networking and Internet Architecture arXiv:2603.04455 [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 Abstract: This paper investigates the integration of large language models as reasoning agents in repeated spectrum auctions within heterogeneous networks . While auction-based mechanisms have been widely employed for efficient resource allocation, most prior works assume one-shot auctions, static behavior, and idealized conditions. In contrast to traditional formulations where base station 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 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. Comments: Accepted at WCNC 2026. Code available here: this https URL Subjects: Netwo...
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