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Capacity analysis of inhomogeneous hybrid wireless networks using directional antennas

机译:使用定向天线的非均匀混合无线网络的容量分析

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摘要

Most of studies on network capacity are based on the assumption that all the nodes are uniformly distributed, which means that the networks are characterized by homogeneity. However, many realistic networks exhibit inhomogeneity due to natural and man-made reasons. In this work, the capacity of inhomogeneous hybrid networks with directional antennas for the first time is studied. By setting different node distribution probabilities, the whole network can be devided into dense cells and sparse cells. On this basis, an inhomogeneous hybrid network model is proposed. The network can exhibit significant inhomogeneity due to the coexistence of two types of cells. Then, we derive the network capacity and maximize the capacity under different channel allocation schemes. Finally, how the network parameters influence the network capacity is analyzed. It is found that if there are plenty of base stations, the per-node throughput can achieve constant order, and if the beamwidth of directional antenna is small enough, the network capacity can scale.
机译:对网络容量的大多数研究都基于所有节点均均匀分布的假设,这意味着网络具有同质性。但是,由于自然和人为原因,许多现实网络都显示出不均匀性。在这项工作中,首次研究了具有定向天线的非均匀混合网络的容量。通过设置不同的节点分布概率,可以将整个网络划分为密集的小区和稀疏的小区。在此基础上,提出了一种不均匀的混合网络模型。由于两种类型的细胞共存,网络可能会表现出明显的不均匀性。然后,我们得出网络容量并在不同的信道分配方案下最大化容量。最后,分析了网络参数如何影响网络容量。发现如果有大量基站,则每个节点的吞吐量可以达到恒定的阶数,并且如果定向天线的波束宽度足够小,则网络容量可以扩展。

著录项

  • 来源
    《中南大学学报(英文版)》 |2016年第3期|644-653|共10页
  • 作者单位

    School of Information Science and Engineering, National University of Defense Technology, Changsha 430074, China;

    School of Information Science and Engineering, National University of Defense Technology, Changsha 430074, China;

    School of Computer, Natl University of Defense Technology, Changsha 430074, China;

    School of Information Science and Engineering, National University of Defense Technology, Changsha 430074, China;

  • 收录信息 中国科学引文数据库(CSCD);
  • 原文格式 PDF
  • 正文语种 eng
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  • 入库时间 2022-08-18 01:06:36
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