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Dynamic TDD Interference Mitigation Using Graph Theory Based Cell Clustering in 5G Ultra-Dense Network

机译:5G超密网络中的基于图论基于Clast Cell聚类的动态TDD干扰减缓

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Dynamic time-division duplex (TDD) is considered a promising solution to handle the unbalanced bursty and quick varied traffic in 5G ultra dense networks. However, dynamic TDD also bring out additional cross-link interference that may degrade the system performance. Since the network scenario of 5G becomes denser compared to LTE, the distance between user equipment (UEs) is smaller, UE-UE cross-link interference becomes more important and should be mitigated together with BS-BS cross-link interference. In this paper, we use cell clustering to deal with the cross-link interference. We find a better threshold for clustering and proposed a novel cell clustering algorithm based on graph theory. Simulation results show that the novel algorithm offers significant gain in UE SINR and UE rate compared with dynamic TDD without clustering and dynamic TDD with traditional clustering.
机译:动态时分双工(TDD)被认为是在5G超密集网络中处理不平衡突发和快速变化的流量的有希望的解决方案。但是,动态TDD还会产生可能降低系统性能的额外交叉链路干扰。由于与LTE相比,5G的网络场景变得更浓,因此用户设备(UE)之间的距离较小,因此UE-UE交联干扰变得更重要,并且应该与BS-BS交联干扰一起减轻。在本文中,我们使用单元格聚类来处理交叉链路干扰。我们发现群集更好的阈值,并提出了一种基于图论的小区聚类算法。仿真结果表明,与具有传统聚类的动态TDD相比,新型算法在UE SINR和UE速率中提供了显着的增益。

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