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- W2108019530 abstract "Clustering is a promising interference mitigation scheme in dynamic TDD systems. However, most previous works just took large-scale path loss or coupling loss as criteria of the clustering schemes, thus the throughput performance would be limited by the varying traffic requirements among different small cells within one cluster. In this paper, a novel dynamic cluster-based Interference Mitigation and Traffic Adaptation (IMTA) scheme is proposed and evaluated with dense deployment of small cells (SCs). Firstly, a new clustering criterion named Differentiating Metric (DM) is defined. Based on the defined DM value, a DM matrix is formed and further presented by a clustering graph. In the clustering graph, the dynamic clustering strategy is mapped to a MAX K-CUT problem, which is addressed in polynomial time by a proposed heuristic clustering algorithm. Furthermore, the system level simulation results demonstrate a promising improvement on uplink traffic throughput (UTP) in our proposed scheme compared with traditional clustering schemes." @default.
- W2108019530 created "2016-06-24" @default.
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- W2108019530 date "2014-05-01" @default.
- W2108019530 modified "2023-09-23" @default.
- W2108019530 title "Max K-CUT Based Clustering for Interference Mitigation and Traffic Adaptation in TDD Systems" @default.
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- W2108019530 doi "https://doi.org/10.1109/vtcspring.2014.7022980" @default.
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