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arXiv 1303.6541cs.NI
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随机网络编码的动态无线资源管理:功率控制和CSMA退避控制

Dynamic Radio Resource Management for Random Network Coding: Power Control and CSMA Backoff Control

Kai Su, Dan Zhang, Narayan B. Mandayam

摘要:

无线网络中的资源分配通常发生在PHY/MAC层,而随机网络编码(RNC)是网络层策略。一个有趣的问题是如何调整资源分配机制以提高RNC性能。通过一个微分方程的框架模型RNC吞吐量的低层参数,我们提出了一种基于梯度的方法,可以动态分配MAC和PHY层资源的目标是最大限度地提高最小网络编码吞吐量之间的所有目的地节点在RNC组播。我们将这种一般方法与两个资源分配问题:(i)功率控制,以提高网络编码吞吐量,和(ii)CSMA平均退避延迟控制,以提高网络编码吞吐量。针对功率控制和CSMA退避控制,设计了集中式算法和在线算法。我们的评估,包括数值求解微分方程的集中式算法和事件驱动的模拟的在线算法,表明这种基于梯度的动态资源分配产生显着的吞吐量改善RNC中的目的节点。此外,我们的数值结果表明,网络编码感知功率控制可以重新获得无线传输的广播优势,以提高吞吐量。

英文摘要:

Resource allocation in wireless networks typically occurs at PHY/MAC layers, while random network coding (RNC) is a network layer strategy. An interesting question is how resource allocation mechanisms can be tuned to improve RNC performance. By means of a differential equation framework which models RNC throughput in terms of lower layer parameters, we propose a gradient based approach that can dynamically allocate MAC and PHY layer resources with the goal of maximizing the minimum network coding throughput among all the destination nodes in a RNC multicast. We exemplify this general approach with two resource allocation problems: (i) power control to improve network coding throughput, and (ii) CSMA mean backoff delay control to improve network coding throughput. We design both centralized algorithms and online algorithms for power control and CSMA backoff control. Our evaluations, including numerically solving the differential equations in the centralized algorithm and an event-driven simulation for the online algorithm, show that such gradient based dynamic resource allocation yields significant throughput improvement of the destination nodes in RNC. Further, our numerical results reveal that network coding aware power control can regain the broadcast advantage of wireless transmissions to improve the throughput.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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