# Optimal Bidding in Repeated Wireless Spectrum Auctions with Budget Constraints

arXiv: https://arxiv.org/abs/1608.07357 · HTML: https://mlsanity.com/paper/1608.07357/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2016.

## Built on by (most cited first, 4 of 4)

- [Multi-Agent Distributed Reinforcement Learning for Making Decentralized Offloading Decisions](https://mlsanity.com/paper/2204.02267/) (2022)
- [Learning to Bid Long-Term: Multi-Agent Reinforcement Learning with Long-Term and Sparse Reward in Repeated Auction Games](https://mlsanity.com/paper/2204.02268/) (2022)
- [Multi-Agent Reinforcement Learning for Long-Term Network Resource Allocation through Auction: a V2X Application](https://mlsanity.com/paper/2208.04237/) (2022)
- [Large Language Models as Bidding Agents in Repeated HetNet Auction](https://mlsanity.com/paper/2603.04455/) (2026)

## Abstract (arXiv)

Small operators who take part in secondary wireless spectrum markets typically have strict budget limits. In this paper, we study the bidding problem of a budget constrained operator in repeated secondary spectrum auctions. In existing truthful auctions, truthful bidding is the optimal strategy of a bidder. However, budget limits impact bidding behaviors and make bidding decisions complicated, since bidders may behave differently to avoid running out of money. We formulate the problem as a dynamic auction game between operators, where knowledge of other operators is limited due to the distributed nature of wireless networks/markets. We first present a Markov Decision Process (MDP) formulation of the problem and characterize the optimal bidding strategy of an operator, provided that opponents' bids are i.i.d. Next, we generalize the formulation to a Markov game that, in conjunction with model-free reinforcement learning approaches, enables an operator to make inferences about its opponents based on local observations. Finally, we present a fully distributed learning-based bidding algorithm which relies only on local information. Our numerical results show that our proposed learning-based bidding results in a better utility than truthful bidding.