In order to make device-to-device (D2D) content sharing give full play to its advantage of improving local area services, one of the important issues is to decide the channels that D2D pairs occupy. Most existing works study this issue in static environment, and ignore the guidance for D2D pairs to select the channel adaptively. In this paper, we investigate this issue in dynamic environment where D2D pairs’ activeness and wireless channel are dynamic. Specifically, we propose a pricing-based approach to guide D2D pairs to select different channels according to the spectrum resource states adaptively. Then, we formulate the pricing-based channel selection problem as an expected global price-to-performance ratio minimum problem. In order to solve it in a tractable manner, we make an approximately equivalent transformation to it. After that, we model the transformed problem as a stochastic game and prove it to be an exact potential game, which has at least one pure strategy Nash Equilibrium (NE) point. In order to reach the pure strategy NE points in dynamic environment, we design a channel selection learning algorithm based on stochastic learning automata, which only requires little information exchange. Simulation results show that our proposed algorithm outperforms other benchmark algorithms.

中文翻译：

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动态环境中D2D内容共享的基于定价的频道选择

为了使设备对设备（D2D）内容共享充分发挥其改善局域服务的优势，重要的问题之一是确定D2D对占用的信道。现有的大多数工作都是在静态环境中研究此问题的，而忽略了D2D对对自适应选择通道的指导。在本文中，我们将在D2D对的活动性和无线信道是动态的动态环境中研究此问题。具体来说，我们提出了一种基于定价的方法来指导D2D对根据频谱资源状态自适应地选择不同的信道。然后，我们将基于定价的渠道选择问题表述为预期的全球性价比最高问题。为了以一种易处理的方式解决它，我们对其进行了近似等效的变换。之后，我们将转换后的问题建模为一个随机博弈，并证明它是一个精确的潜在博弈，它至少具有一个纯策略纳什均衡（NE）点。为了在动态环境中达到纯策略NE点，我们设计了一种基于随机学习自动机的信道选择学习算法，该算法只需要很少的信息交换即可。仿真结果表明，本文提出的算法优于其他基准算法。