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首页> 外文期刊>IEEE Transactions on Vehicular Technology >Joint Encoding and Grouping Multiple Node Pairs for Physical-Layer Network Coding With Low-Complexity Algorithm
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Joint Encoding and Grouping Multiple Node Pairs for Physical-Layer Network Coding With Low-Complexity Algorithm

机译:物理层网络编码的低复杂度算法对多个节点对的联合编码和分组

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摘要

How to put fifth-generation (5G) into the commercial service by 2020 is still challenging. Since physical-layer network coding (PNC) does not bring unnecessary redundancy and offers satisfactory end-to-end services, it is promising to provide network services in 5G networks. Existing works on PNC mainly focus on the case that two packets are simultaneously sent by a node pair for information exchange, and the transmitted signals are superposed and encoded at a relay. To further exploit the potential performance improvement of PNC, this study aims at encoding multiple packets through jointly processing multiple superposed signals sent by diverse node pairs. Particularly, we formulate the problem of joint-encoding method design as a binary integer programming model and propose a low-complexity solution based on the idea of filling a Latin rectangle. We find that network throughput enhancement does not accompany the increasing number of coded node pairs, and some node pairs cannot be encoded together since they fail to decode the resulting encoded packet. Therefore, the decodability-based node pair grouping scheme is further investigated. Simulation results in various scenarios show that the proposed low-complexity scheme performs neck to neck to the optimum one with high computational complexity.
机译:如何在2020年之前将第五代(5G)投入商业服务仍是一个挑战。由于物理层网络编码(PNC)不会带来不必要的冗余并提供令人满意的端到端服务,因此有望在5G网络中提供网络服务。关于PNC的现有工作主要集中在节点对同时发送两个分组以进行信息交换,并且所发送的信号在中继器处被叠加和编码的情况。为了进一步利用PNC的潜在性能改进,本研究旨在通过联合处理由不同节点对发送的多个叠加信号来对多个数据包进行编码。特别是,我们将联合编码方法的设计问题表达为二进制整数规划模型,并基于填充拉丁矩形的思想提出了一种低复杂度的解决方案。我们发现,网络吞吐量的提高并不伴随着编码节点对数量的增加,并且某些节点对无法一起编码,因为它们无法解码所得的编码数据包。因此,将进一步研究基于可解码性的节点对分组方案。在各种情况下的仿真结果表明,所提出的低复杂度方案具有很高的计算复杂度,并不能达到最佳方案。

著录项

  • 来源
    《IEEE Transactions on Vehicular Technology》 |2017年第10期|9275-9286|共12页
  • 作者单位

    Key Laboratory of Medical Image Computing and the School of Computer Science and Engineering, Northeastern University, Shenyang, China;

    School of Software, Dalian University of Technology, Dalian, China;

    Key Laboratory of Medical Image Computing and the School of Computer Science and Engineering, Northeastern University, Shenyang, China;

    Key Laboratory of Medical Image Computing and the School of Computer Science and Engineering, Northeastern University, Shenyang, China;

    School of Electrical and Information Engineering, University of Sydney, Sydney, NSW, Australia;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Relays; Encoding; Artificial neural networks; Scheduling; Network coding; Scheduling algorithms; Wireless communication;

    机译:继电器;编码;人工神经网络;调度;网络编码;调度算法;无线通信;

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