Cross-Domain Communication System with Cloud-Based Software-Defined Acoustic
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摘要: 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。针对该问题, 文中提出了一种基于云端软件定义水声(C-SDA)的跨域通信系统架构。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, C-SDA能够适用更高性能的均衡器, 使误码率得到显著改善。Abstract: Due to the restriction of acoustic communication performance, the performance of data transmission across the water-air interface is difficult to further improve. In this paper, a cross-domain communication system architecture with cloud-based software-defined acoustic(C-SDA) was proposed. C-SDA moved the underwater acoustic receiver from the surface relay to the cloud (i.e., the monitoring center) and used the radio communication bandwidth to exchange the computing resources of the cloud to demodulate and decode the acoustic signal. In this case, the acoustic-radio dual-stack protocol of the relay gateway was simplified to a single-stack structure, which avoided packet capsulation and re-capsulation and saved hardware costs. The C-SDA not only enabled advanced communication signal processing but also promoted rapid updates and iteration of acoustic communication technology. The field test results show that C-SDA can be applied to equalizers with higher performance and achieve a significant improvement in bit error rate compared with the self-developed embedded underwater acoustic receiver.
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Key words:
- cross-domain communication /
- acoustic signal /
- software-defined
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图 3 C-SDA方案与传统方案跨域通信协议体系对比
Figure 3. Comparison of cross-domain communication protocol architecture between C-SDA scheme and traditional scheme
图 5 不同复杂度下Turbo均衡算法性能比较
Figure 5. Performance comparison of Turbo equalization algorithms with different complexities
图 7 嵌入式系统与PC系统相同算法处理效率对比
Figure 7. Comparison of processing efficiency of the same algorithm between embedded system and PC system
图 8 云端接收机BER随中继采样率变化曲线
Figure 8. Curves of BER of the cloud receiver with relay sampling rate
表 1 不同均衡器算法复杂度
Table 1. Algorithm complexity of different equalizers
均衡算法 复杂度 MAP $ O\left( {{q^{M - 1}}} \right) $ MMSE $ O\left( {{M^2} + {N^2}} \right) $ app-MMSE $ O\left( {M + N} \right) $ 
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