|Yanjiao Chen||State Key Lab of Software Engineering, Wuhan University, P.R. China|
|Long Lin||Wuhan University, P.R. China|
|Guiyan Cao||Wuhan University, P.R. China|
|Zhenzhong Chen||Wuhan University, P.R. China|
|Baochun Li||University of Toronto, Canada|
The use of a combinatorial auction is believed to be an effective way to distribute spectrum to buyers who have diversified valuations for different spectrum combinations. However, the allocation of spectrum with combinatorial auctions mainly aims at optimizing over certain utility functions, e.g., social welfare, but ignores individual preferences of buyers and sellers, who have incentives to deviate from globally optimal allocation results to improve their own utility. In this paper, we explore the possibility of designing a new stable matching algorithm for com-binatorial spectrum allocations. Starkly different from existing efforts on spectrum matching mechanism design, our proposed combinatorial spectrum matching framework not only allows buyers to express preferences towards spectrum combinations (rather than individual channels), but also computes the payment that should be transferred from buyers to sellers. Payment determination , while essential in spectrum exchange, has never been addressed in existing spectrum matching frameworks. We design a novel algorithm to achieve a stable combinatorial spectrum matching and to compute the corresponding payment profiles. We conducted an extensive array of experiments to compare the performance of stable combinatorial spectrum matching with spectrum auctions. It is shown that the combinatorial spectrum matching sacrifices little allocation efficiency in terms of social welfare and spectrum utilization, but achieves a much higher individual buyer utility, which will incentivize buyers to participate and comply with the allocation results.