Wireless virtualization has become a key concept in future cellular networks which can provide multiple virtualized wireless networks for different mobile virtual network operators (MVNOs) over the same physical infrastructure. Resource allocation is a main challenging issue in wireless virtualization for which auction approaches have been widely used. However, for most existing auction-based allocation schemes, the objective is to maximize the social welfare (i.e., the sum of all valuations of winning bidders) due to its simplicity. While in reality, MVNOs are more interested in maximizing their own revenues (i.e., received payments from auction winners). However, the revenue-optimal auction problem is much more complex since the payment price is unknown before calculation. In this paper, we aim to design a revenue-optimal auction mechanism for resource allocation in wireless virtualization. Considering the complexity, deep learning techniques are applied. Specifically, we construct a multi-layer feed-forward neural network based on the analysis of optimal auction design. The neural network adopts users’ bids as the input and the allocation rule and conditional payment rule for the users as the output. The proposed auction mechanism possesses several desirable properties, e.g., individual rationality, incentive compatibility and budget constraint. Finally, simulation results demonstrate the effectiveness of the proposed scheme. Comparing with second-price auction and optimization-based schemes, the proposed scheme can increase the revenue by 10 and 30 percent on average, for single MVNO and multi-MVNO cases, respectively.

中文翻译：

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无线虚拟化资源分配的收益最优拍卖：一种深度学习方法

无线虚拟化已成为未来蜂窝网络中的一个关键概念，它可以在同一物理基础设施上为不同的移动虚拟网络运营商 (MVNO) 提供多个虚拟化无线网络。资源分配是无线虚拟化中的一个主要挑战问题，拍卖方法已被广泛使用。然而，对于大多数现有的基于拍卖的分配方案，由于其简单性，其目标是最大化社会福利（即，中标者的所有估值之和）。而在现实中，MVNO 更感兴趣的是最大化他们自己的收入（即从拍卖赢家那里收到的付款）。然而，收入最优拍卖问题要复杂得多，因为在计算之前支付价格是未知的。在本文中，我们的目标是为无线虚拟化中的资源分配设计一种收入最优的拍卖机制。考虑到复杂性，应用了深度学习技术。具体来说，我们构建了一个基于最优拍卖设计分析的多层前馈神经网络。神经网络以用户的出价为输入，以用户的分配规则和有条件的支付规则为输出。所提出的拍卖机制具有几个理想的属性，例如个体理性、激励相容性和预算约束。最后，仿真结果证明了所提方案的有效性。与基于二次价格拍卖和优化的方案相比，该方案在单个 MVNO 和多个 MVNO 的情况下平均可以分别增加 10% 和 30% 的收入。