The Implementation of Zoning for Winner Determination in Combinatorial Spectrum Auction

Ayi Purbasari, Arief Zulianto
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Abstract

Computational complexity is a problem faced in the frequency spectrum auction and is called the Winner Determination Problem. The complexity increases when the object auction increases and partitioned into groups of objects based on some pre-defined zone. With the zoning scheme, there are three alternative determination of winners, the bidders get the whole object of auction at the entire zone (also called all or nothing scheme), auction bidders get the whole object in some specific zones (also called partially zone scheme), bidders are assigned some object in some zones certain (partially block zone). In this paper the issue is viewed as a matter of Knapsack auctions, both Knapsack 0/1 and Bounded Knapsack. The issue is then simulated for the three alternatives to obtain the best solution based on the consideration of the highest revenue. We uses 14 data sets for simulation, with the technique used is the Brute Force and Dynamic Programming which has been implemented in the Java programming language. For the same data sets, the simulation results establish that the partially zone is producing the highest revenue, followed by partually block zone scheme, then the all or nothing scheme
组合频谱拍卖中分区确定中标者的实现
计算复杂性是频谱拍卖中面临的一个问题,被称为赢家确定问题。随着拍卖对象数量的增加和根据预定义的区域划分对象组,复杂性也随之增加。在分区方案中,有三种选择确定中标者,竞标者在整个区域获得整个拍卖对象(也称为全有或全无方案),竞标者在某些特定区域获得整个对象(也称为部分区域方案),竞标者在某些特定区域分配某些对象(部分封锁区域)。在本文中,这个问题被看作是背包拍卖的问题,包括背包0/1和有界背包。然后对三个备选方案进行问题模拟,以获得基于最高收入考虑的最佳解决方案。我们使用14个数据集进行模拟,使用的技术是在Java编程语言中实现的蛮力和动态规划。对于相同的数据集,仿真结果表明,部分区域产生的收益最高,其次是部分阻塞区域方案,然后是全或无方案
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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