认知小蜂窝自组织优化的博弈论视角
A Game Theoretic Perspective on Self-organizing Optimization for Cognitive Small Cells
摘要：
在这篇文章中，我们研究了认知小细胞（CSC）的自组织优化，它具有感知环境，从历史信息中学习，做出智能决策，并调整其操作参数的能力。通过探索的内在特征，一些基本的挑战，自组织优化的CSC提出和讨论。具体而言，CSC的密集和随机部署在可扩展性和自适应性方面带来了一些新的挑战;此外，不确定、动态和不完全的信息约束也在收敛性和鲁棒性方面带来了一些新的挑战。为了更好地为用户提供服务，提高资源利用率，提出并讨论了自组织优化的四个要求。遵循博弈论模型中的决策与自组织优化中的决策完全一致的吸引人的事实，即，分布式和自治的特点，建立了一个自组织优化的博弈论解决方案框架，并提出了一些有特色的博弈模型。具体地说，它们的基本模型，一些例子进行了讨论，并给出了未来的研究方向。
英文摘要：
In this article, we investigate self-organizing optimization for cognitive small cells (CSCs), which have the ability to sense the environment, learn from historical information, make intelligent decisions, and adjust their operational parameters. By exploring the inherent features, some fundamental challenges for self-organizing optimization in CSCs are presented and discussed. Specifically, the dense and random deployment of CSCs brings about some new challenges in terms of scalability and adaptation; furthermore, the uncertain, dynamic and incomplete information constraints also impose some new challenges in terms of convergence and robustness. For providing better service to the users and improving the resource utilization, four requirements for self-organizing optimization in CSCs are presented and discussed. Following the attractive fact that the decisions in game-theoretic models are exactly coincident with those in self-organizing optimization, i.e., distributed and autonomous, we establish a framework of game-theoretic solutions for self-organizing optimization in CSCs, and propose some featured game models. Specifically, their basic models are presented, some examples are discussed and future research directions are given.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
