Optimal Bidding in Repeated Wireless Spectrum Auctions with Budget Constraints
Mehrdad Khaledi, Alhussein Abouzeid

TL;DR
This paper investigates optimal bidding strategies for budget-constrained operators in repeated wireless spectrum auctions, proposing a reinforcement learning approach that outperforms truthful bidding in terms of utility.
Contribution
It introduces a dynamic auction game model with reinforcement learning for distributed bidding under budget constraints in wireless spectrum markets.
Findings
Learning-based bidding yields higher utility than truthful bidding.
The Markov game model effectively captures opponent behavior.
Distributed algorithm performs well with limited local information.
Abstract
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…
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