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QoS-Aware Cooperative and Opportunistic Scheduling Exploiting Multiuser Diversity for Rate-Adaptive Ad Hoc Networks

机译:速率自适应Ad Hoc网络利用QoS感知的协作和机会调度开发多用户多样性

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摘要

The recent research works in wireless networks prompt the opportunistic transmission that exploits channel fluctuations to improve the overall system performance. In wireless ad hoc networks, nodes may have packets destined to multiple neighboring nodes. We consider an opportunistic scheduling that takes advantage of a time-varying channel among different receivers to improve system performance. Maximizing the overall throughput and satisfying the QoS requirements for the transmission flows are two important objectives that need to be considered. In literature, many opportunistic scheduling policies for ad hoc networks have been proposed, in which each transmitter independently schedules the transmission. However, due to cochannel interference, the decisions of neighboring transmitters are highly correlated. Moreover, to achieve the QoS requirements, the nodes have to be cooperative to share the common wireless channel. In this paper, we formulate the opportunistic scheduling problem, taking the interaction among the neighboring transmitters into account. We present an optimal scheduling policy which maximizes the overall network performance while satisfying the QoS requirements of the individual flows. We also propose a distributed cooperative and opportunistic scheduling algorithm that modifies the IEEE 802.11 protocol to implement the optimal scheduling policy. Simulation results indicate that our implementation achieves higher network throughput and provides better QoS support than the existing solutions.
机译:最近在无线网络中的研究工作提示了机会传输,它利用信道波动来改善整体系统性能。在无线自组织网络中,节点可能具有发往多个相邻节点的数据包。我们考虑机会调度,它利用不同接收器之间的时变信道来提高系统性能。使总吞吐量最大化并满足传输流的QoS要求是需要考虑的两个重要目标。在文献中,已经提出了许多用于ad hoc网络的机会调度策略,其中每个发射机独立地调度传输。但是,由于同频道干扰,相邻发射机的决策高度相关。此外,为了达到QoS要求,节点必须协作以共享公共无线信道。在本文中,我们考虑了相邻发射器之间的相互作用,提出了机会调度问题。我们提出了一种最佳的调度策略,该策略可以在满足各个流的QoS要求的同时最大化整体网络性能。我们还提出了一种分布式合作与机会调度算法,该算法修改了IEEE 802.11协议以实现最佳调度策略。仿真结果表明,与现有解决方案相比,我们的实现可实现更高的网络吞吐量并提供更好的QoS支持。

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