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首页> 外文期刊>Journal of Computers >Fair Gain Based Dynamic Channel Allocation for Cognitive Radios in Wireless Mesh Networks
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Fair Gain Based Dynamic Channel Allocation for Cognitive Radios in Wireless Mesh Networks

机译：无线网状网络中的认知收音机的公平增益基于动态信道分配

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—Wireless mesh networks have the potential to deliver Internet broadband access, wireless local area network coverage and network connectivity at low costs. The capacity of a wireless mesh network is improved by equipping mesh nodes with multi-radios tuned to non-overlapping channels. By letting these nodes utilize the available channels opportunistically, we increase the utilization of the available bandwidths in the channel space. The essential problem is how to allocate the channels to these multi-radio nodes, especially when they are heterogeneous with diverse transmission types and bandwidths. Most of current work has been based on the objective to achieve maximal total bandwidths. In this paper, we propose a new bipartite-graph based model and design channel allocation algorithms that maximize the minimal channel gain to achieve relative fairness. Our model maps heterogeneous network environment to a weighted graph. We then use augmenting path to update channel allocation status and use canonical form to compare the new status with previous status to achieve better fairness. Evaluations demonstrate that our algorithms improve fairness compared with related algorithms.
机译：- 以低成本，Wireless Mesh网络具有可提供互联网宽带访问，无线局域网覆盖和网络连接。通过向非无线电调整到非重叠信道来改进无线网状网络的容量。通过让这些节点充分利用可用的频道，我们增加了频道空间中可用带宽的利用率。基本问题是如何将通道分配给这些多无线电节点，尤其是当它们具有不同传输类型和带宽的异质时。目前的大部分工作都是基于目标，实现最大的总带宽。在本文中，我们提出了一种新的基于二分拉图的模型和设计信道分配算法，最大化最小的信道增益来实现相对公平性。我们的模型将异构网络环境映射到加权图。然后，我们使用增强路径来更新通道分配状态并使用规范形式将新状态与以前状态进行比较以实现更好的公平性。评估表明，与相关算法相比，我们的算法改善了公平性。

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