Multi-agent Learning for Resource Allocationn Dense Heterogeneous 5G Network
Multi-agent Learning for Resource Allocationn Dense Heterogeneous 5G Network
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DOI：
10.1109/ent.2015.11
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发表时间：
2015-11
期刊：
影响因子：
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通讯作者：
Evgeni Bikov;D. Botvich
中科院分区：
文献类型：
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作者：
Evgeni Bikov;D. Botvich
小基站的概念对有关蜂窝网络结构的许多长期存在的假设提出了挑战。在这种方法中，部署大量低功率设备以提高所选区域的空间频率复用。高效的资源管理和干扰协调方案成为异构网络成功应用的一项重要要求。在本文中，我们提出一种分布式多智能体策略，其中小基站在本地控制资源使用，从而使整个系统容量最大化。主要目标在于使每个基站都有能力自主做出决策，同时动态考虑周围基站的资源占用情况。我们研究了与非合作宏环境共存的区域，并提出一种机制，通过一个智能的安全转移阶段来提高学习效率。我们阐述了这种分布式学习策略在子带分配中的应用，并提出了几种在无通信情况下提高收敛速度的机制。在长期演进（LTE）设置的情况下对所提出方法的性能进行了评估，并与多种资源分配方案进行了比较。我们通过系统级仿真验证了该算法，并表明在存在非合作智能体的异构部署中，它在不影响系统效率的情况下，实现了系统性能的显著提高。
The concept of small cells challenges many timehonored assumptions about the structure of cellular networks. In this approach the large number of low-power devices is deployed to increase the spatial frequency reuse of the selected area. The efficient resource management and interference coordination schemes became an important requirement for the successful adoption of heterogeneous networks. In this paper we propose a distributed multi-agent strategy, where small cells locally control resource usage, such that the overall system capacity is maximized. The main goal resides in providing each cell with the ability to make its decision autonomously while dynamically taking into account the resource occupation of the surrounding cells. We study an area of coexistence with non-cooperative macroenvironment and propose a mechanism to increase the efficiency of learning with a smart safe-shift phase. We illustrate the application of this distributed learning strategy for the subband allocation and propose several mechanisms to improve the convergence speed in the absence of communication. The performances of the proposed method are evaluated in the case of Long Term Evolution (LTE) setup and compared to a number of resource allocation schemes. We validate the algorithm with system level simulations and show that it achieves considerable improvement in system performance for heterogeneous deployment with non-cooperating agents, without compromising the efficiency of the system.