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基于云端软件定义的水声跨域通信系统

姚可星 官权升 谢佳轩 赵昊 王焱

姚可星, 官权升, 谢佳轩, 等. 基于云端软件定义的水声跨域通信系统[J]. 水下无人系统学报, 2024, 32(4): 1-7 doi: 10.11993/j.issn.2096-3920.2024-0075
引用本文: 姚可星, 官权升, 谢佳轩, 等. 基于云端软件定义的水声跨域通信系统[J]. 水下无人系统学报, 2024, 32(4): 1-7 doi: 10.11993/j.issn.2096-3920.2024-0075
YAO Kexing, GUAN Quansheng, XIE Jiaxuan, ZHAO Hao, WANG Yan. Cloud-based Software-defined Acoustic for Cross-domain Communication System[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2024-0075
Citation: YAO Kexing, GUAN Quansheng, XIE Jiaxuan, ZHAO Hao, WANG Yan. Cloud-based Software-defined Acoustic for Cross-domain Communication System[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2024-0075

基于云端软件定义的水声跨域通信系统

doi: 10.11993/j.issn.2096-3920.2024-0075
基金项目: 国家自然科学基金(U23A20281), 广东省科技计划项目资助(2023A0505050097).
详细信息
    作者简介:

    姚可星(1995-), 男, 博士生, 主要研究方向为水声通信、声电协同等

  • 中图分类号: TB567

Cloud-based Software-defined Acoustic for Cross-domain Communication System

  • 摘要: 典型海洋监测类应用需要将从水下获取的数据回传至陆地监测中心, 由于水声通信性能的制约, 跨水-空界面的数据传输性能难以进一步提升。本文提出了一种基于云端软件定义水声(cloud-based software-defined acoustic, C-SDA)跨域通信系统架构。C-SDA将水声接收机从水面中继节点移动到云端(即监测中心), 并利用无线电通信带宽交换云端计算资源进行水声信号解调和解码。中继网关的水声-无线电双栈网络协议被简化为单栈结构, 避免了数据包的封装和再封装, 节省了硬件成本。C-SDA不仅能实现先进的通信信号处理, 而且能促进水声通信技术的更新和迭代。现场测试实验结果表明, 与自研的嵌入式水声接收机相比, 本文提出的C-SDA能够适用更高性能的均衡器, 并获得显著的误码率改善。

     

  • 图  1  C-SDA 的系统模型

    Figure  1.  System model of C-SDA

    图  2  C-SDA系统框架设计

    Figure  2.  Architecture design of the C-SDA system

    图  3  C-SDA方案与传统方案对比

    Figure  3.  Comparison between the C-SDA protocol architecture and traditional protocol architecture

    图  4  整体实验场景

    Figure  4.  Scenario of the overall experiment

    图  5  不同复杂度Turbo均衡算法的性能比较

    Figure  5.  Performance comparison of Turbo equalization algorithms with different complexities

    图  6  嵌入式水声通信机

    Figure  6.  Embedded underwater acoustic communication machine

    图  7  嵌入式系统与PC系统相同算法的处理效率比较

    Figure  7.  Comparison of processing efficiency of the same algorithm between embedded system and PC system

    图  8  云接收机的BER随着中继采样率的增大而减小

    Figure  8.  The BER of the cloud receiver decreases as the sampling rate in the surface relay increases

    表  1  均衡器的算法复杂度

    Table  1.   Algorithm complexity of equalizer

    均衡算法MAPMMSEapp-MMSE
    复杂度$ O\left( {{q^{M - 1}}} \right) $$ O\left( {{M^2} + {N^2}} \right) $$ O\left( {M + N} \right) $
    下载: 导出CSV
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出版历程
  • 收稿日期:  2024-05-01
  • 修回日期:  2024-06-03
  • 录用日期:  2024-06-13
  • 网络出版日期:  2024-06-24

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