利用学习、博弈论和优化作为宏细胞共存中自组织的仿生方法

A. Imran, M. Bennis, L. Giupponi
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引用次数: 18

摘要

在本文中,我们提出了在宏蜂窝和飞蜂窝网络共存的异构场景中使用几种仿生自组织(SO)方法。这些方法主要分为间接仿生和直接仿生。在间接仿生学下,我们讨论了1)学习理论和2)博弈论的新兴范式,探讨它们在异构网络中实现SO解决方案的潜力。通过数值结果,我们展示了这些间接仿生方法在宏观空间共存场景下设计SO的优缺点。此外,我们通过利用自然SO系统与基于室外固定中继(OFR)的异构网络系统模型之间的一对一映射,展示了直接仿生方法在设计SO中的使用。数值结果表明,该解析解通过对宏基站(BS)天线倾斜进行分布式自组织调整,提高了基于OFR的飞基站的无线回程容量。
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Use of learning, game theory and optimization as biomimetic approaches for Self-Organization in macro-femtocell coexistence
In this paper, we present the use of several Biomimetic approaches for Self Organization (SO) in heterogeneous scenarios where macrocell and femtocell networks coexist. Mainly these approaches are categorized in indirect biomimetics and direct biomimetics. Under indirect biomimetics we discuss 1) emerging paradigms in learning theory and 2) game theory for their potential to enable SO solutions in heterogeneous networks. By means of numerical results we demonstrate the pros and cons of these indirect biomimetic approaches for designing SO in macro-femto coexistence scenarios. Furthermore, we demonstrate the use of direct biomimetic approaches for designing SO by exploiting one to one mapping between a natural SO system and our system model for heterogeneous networks based on Outdoor Fixed Relays (OFR). Numerical results show that the proposed analytical solution can enhance wireless backhaul capacity of the OFR based femtocells by adapting the macro base station (BS) antenna tilts in a distributed and self organizing manner.
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