2 research outputs found
Incentive Mechanisms for Hierarchical Spectrum Markets
In this paper, we study spectrum allocation mechanisms in hierarchical
multi-layer markets which are expected to proliferate in the near future based
on the current spectrum policy reform proposals. We consider a setting where a
state agency sells spectrum channels to Primary Operators (POs) who
subsequently resell them to Secondary Operators (SOs) through auctions. We show
that these hierarchical markets do not result in a socially efficient spectrum
allocation which is aimed by the agency, due to lack of coordination among the
entities in different layers and the inherently selfish revenue-maximizing
strategy of POs. In order to reconcile these opposing objectives, we propose an
incentive mechanism which aligns the strategy and the actions of the POs with
the objective of the agency, and thus leads to system performance improvement
in terms of social welfare. This pricing-based scheme constitutes a method for
hierarchical market regulation. A basic component of the proposed incentive
mechanism is a novel auction scheme which enables POs to allocate their
spectrum by balancing their derived revenue and the welfare of the SOs.Comment: 9 page
A prior-free revenue maximizing auction for secondary spectrum access
Abstract—Dynamic spectrum allocation has proven promising for mitigating the spectrum scarcity problem. In this model, primary users lease chunks of under-utilized spectrum to secondary users, on a short-term basis. Primary users may need financial motivations to share spectrum, since they assume costs in obtaining spectrum licenses. Auctions are a natural revenue generating mechanism to apply. Recent design on spectrum auctions make the strong assumption that the primary user knows the probability distribution of user valuations. We study revenue-maximizing spectrum auctions in the more realistic priorfree setting, when information on user valuations is unavailable. A two-phase auction framework is constructed. In phase one, we design a strategyproof mechanism that computes a subset of users with an interference-free spectrum allocation, such that the potential revenue in the second phase is maximized. A tailored payment scheme ensures truthful bidding at this stage. The selected users then participate in phase two, where we design a randomized competitive auction and prove its strategyproofness through the argument of bid independence. Our solution applies iterative bidder partitioning on judiciously selected bidder subsets. Employing probabilistic techniques, we prove that our auction generates a revenue that is at least 1 3 of the optimal revenue, improving the best known ratio of 1 4 proven for similar settings. I