A Prior-Free Spectrum Auction for Approximate Revenue Maximization

作者：Ying, Chen; Huang, Hao^*; Gopinathan, Ajay; Li, Zongpeng

来源：Computer Journal, 2017, 60(6): 898-910.

DOI：10.1093/comjnl/bxx022

• secondary spectrum market
• prior-free auctions
• truthful bidding
• revenue maximization

Dynamic spectrum allocation has been proven as a promising solution to the spectrum scarcity problem. Auctions represent a natural allocation mechanism that generates a monetary remuneration for primary users. We study approximate revenue-maximizing spectrum auctions in a priorfree setting, when information on user valuations on channels is unavailable. A two-phase auction framework is presented. In Phase 1, a strategyproof mechanism computes a subset of users with an interference-free spectrum allocation, such that the potential revenue to be gained in the second phase is maximized. A carefully tailored payment scheme ensures truthful bidding at this stage. The selected users advance into Phase 2, where eventual auction winners are computed through a recursive random partitioning and revenue extraction procedure. While no strategyproof auction can achieve absolute optimal revenue in the prior-free setting, our random partition auction is both truthful in expectation and achieves the best known ratio 1/3 of the optimal revenue.

• 出版日期2017-6
• 单位武汉大学; Google Inc, Mountain View, CA