...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Mobile Computing, IEEE Transactions on >Multicast Capacity Scaling Laws for Multihop Cognitive Networks
【24h】

Multicast Capacity Scaling Laws for Multihop Cognitive Networks

机译：多跳认知网络的组播容量缩放定律

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this paper, we study multicast capacity for cognitive networks. We consider the cognitive network model consisting of two overlapping ad hoc networks, called the primary ad hoc network (PaN) and secondary ad hoc network (SaN), respectively. PaN and SaN operate on the same space and spectrum. For PaN (or SaN, respectively), we assume that primary (or secondary, respectively) nodes are placed according to a Poisson point process of intensity n (or m, respectively) over a unit square region. We randomly choose n_s (or m_s, respectively) nodes as the sources of multicast sessions in PaN (or SaN, respectively), and for each primary source v^p (or secondary source v^s, respectively), we pick uniformly at random n_d primary nodes (or m_d secondary nodes, respectively) as the destinations of v^p (or v^s, respectively). Above all, we assume that PaN can adopt the optimal protocol in terms of the throughput. Our main work is to design the multicast strategy for SaN by which the optimal throughput can be achieved, without any negative impact on the throughput for PaN in order sense. Depending on n_d and n, we choose the optimal one for PaN from two strategies called percolation strategy and connectivity strategy, respectively. Subsequently, we design the corresponding throughput-optimal strategy for SaN. We derive the regimes in terms of n, n_d, m, and m_d in which the upper bounds on multicast capacities for PaN and SaN can be achieved simultaneously. Unicast and broadcast capacities for the cognitive network can be derived by our results as the special cases by letting n_d=1 (or m_d=1) and n_d=n-1 (or m_d=m-1), respectively, which enhances the generality of this work.
机译：在本文中，我们研究了认知网络的组播能力。我们考虑由两个重叠的自组织网络组成的认知网络模型，分别称为主要自组织网络（PaN）和次要自组织网络（SaN）。 PaN和SaN在相同的空间和频谱上运行。对于PaN（或分别为SaN），我们假设主要（或次要）节点根据单位面积上强度为n（或m）的泊松点过程放置。我们随机选择n_s个节点（或分别为m_s个）作为PaN（或分别为SaN）中的多播会话的来源，并且对于每个主要来源v ^ p（或分别为次要来源v ^ s），我们随机选择n_d个主要节点（或m_d个辅助节点）分别作为v ^ p（或v ^ s）的目的地。最重要的是，我们假设PaN可以在吞吐量方面采用最佳协议。我们的主要工作是设计SaN的多播策略，通过该策略可以实现最佳吞吐量，而在顺序上对PaN的吞吐量没有任何负面影响。根据n_d和n，我们分别从称为渗流策略和连通性策略的两种策略中选择针对PaN的最优方法。随后，我们为SaN设计了相应的吞吐量优化策略。我们根据n，n_d，m和m_d导出了可以同时实现PaN和SaN的多播容量上限的机制。通过将n_d = 1（或m_d = 1）和n_d = n-1（或m_d = m-1）分别设置为特殊情况，我们的结果可以得出认知网络的单播和广播容量，这增强了通用性这项工作。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号