Cooperative Multicast based on Moving Window Network Coding in Wireless Networks

Cooperative Multicast based on Moving Window Network Coding in Wireless Networks
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无线网络中基于移动窗口网络编码的协作组播

DOI:
10.1016/j.adhoc.2014.10.011
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发表时间:
2015
期刊:
Ad Hoc Networks (Elsevier)Journal
影响因子:
--
通讯作者:
Aiping Huang
Aiping Huang
中科院分区:
其他
文献类型:
--
作者:
Fei Wu;Cunqing Hua;Hangguan Shan;Aiping Huang

文献摘要

相似文献

协作组播是解决无线网络单跳广播瓶颈问题的有效方案。通过结合随机线性网络编码技术,现有方案可以显著降低重传开销。然而,由于采用批量译码方案,接收端可能会产生较大的译码时延和复杂度。此外,在较大的网络中,对显式反馈的依赖会导致可扩展性问题。基于一种新的移动窗口网络编码技术,提出了一种协作组播协议MWNCast。我们建立了分析模型,并证明了该方案的三个性质。首先,在没有显式反馈的情况下,随着窗口大小的增加,接收方的分组恢复丢失率几乎呈指数下降。其次,接收机的平均译码时延相对于其业务量强度ρ的上界为O_1(1-LTE)_2。第三,在给定目标吞吐量的情况下,MWNCast的译码复杂度随着窗口大小W的增加呈O(W)变化。仿真结果表明,MWNCast在吞吐量和译码时延之间取得了较好的折衷,同时在没有显式反馈的情况下将分组恢复丢失率和译码复杂度保持在很低的水平。
Cooperative multicast is an effective solution to address the bottleneck problem of single-hop broadcast in wireless networks. By incorporating with the random linear network coding technique, the existing schemes can reduce the retransmission overhead significantly. However, the receivers may incur large decoding delay and complexity due to the batch decoding scheme. In addition, the dependency on the explicit feedback leads to scalability problem in larger networks. In this paper, a cooperative multicast protocol named MWNCast is proposed based on a novel moving window network coding technique. We develop analytical models and show three properties of the proposed scheme. Firstly, without explicit feedback, the packet recovery loss probability of the receivers drops almost exponentially with the increase of window size. Secondly, the average decoding delay of a receiver is upper bounded by O 1 (1-ρ) 2 asymptomatically with respect to its traffic intensity ρ. Thirdly, given the target throughput, the decoding complexity of MWNCast scales as O (W) as the window size W increases. Simulation results show that MWNCast outperforms the existing schemes by achieving better tradeoff between the throughput and decoding delay, meanwhile keeping the packet recovery loss probability and decoding complexity at a very low level without explicit feedback.