Cloud-based Software-defined Acoustic for Cross-domain Communication System
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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。Abstract: Typical marine monitoring applications need to transmit the data obtained from underwater to the monitoring center located on land. 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 cloud-based software-defined acoustic (C-SDA) architecture is proposed for the cross-interface system. C-SDA moves the underwater acoustic receiver from the surface relay to the cloud (i.e., the monitoring center), and uses the radio communication bandwidth to exchange the computing resources of cloud to demodulate and decode the acoustic signal. In this case, the dual-stack protocol of the surface gateway is simplified to a single-stack, which avoids packet capsulation and re-capsulation. The C-SDA not only enables advanced communication signal processing, but also promotes rapid technology update and iteration. The field-test experimental results show that, the proposed C-SDA can be applied to a higher performance equalizer and achieve a significant improvement in bit error rate, compared with the existing embedded underwater acoustic receiver.
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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 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 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 MAP MMSE app-MMSE 复杂度 $ O\left( {{q^{M - 1}}} \right) $ $ O\left( {{M^2} + {N^2}} \right) $ $ O\left( {M + N} \right) $ 
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[1] Zhao Y, Huang J, Zhang P, et al. Direct air–water communication by using an optical-acoustic method[J]. Measurement, 2023, 223: 113824. [2] Qian J, Qu F, Su J, et al. Theoretical model and experiments of focused phased array for cross-medium communication in misaligned transmitter/receiver scenarios[J]. IEEE Journal of Oceanic Engineering, 2023, 48(4): 1348-1361. doi: 10.1109/JOE.2023.3263202 [3] Lin Q, Gao D, Jin B, et al. Enhancing the sensitivity of photoacoustic spectrum system for liquid detection by coupling with acoustic metasurfaces[J]. Applied Physics Letters, 2023, 122(24): 242201. doi: 10.1063/5.0153453 [4] Jiang W, Diamant R. Long-range underwater acoustic channel estimation[J]. IEEE Transactions on Wireless Communications, 2023, 22(9): 6267-6282. doi: 10.1109/TWC.2023.3241230 [5] Wang Y, Guan Q, Ji F, et al. Impact and analysis of space-time coupling on slotted MAC in UANs[J]. IEEE/ACM Transactions on Networking, 2023: 1–13. [6] Liang Y, Yu H, Ji F, et al. Multitask sparse bayesian channel estimation for turbo equalization in underwater acoustic communications[J]. IEEE Journal of Oceanic Engineering, 2023, 48(3): 946-962. doi: 10.1109/JOE.2022.3229902 [7] Cipriano A M, Şahin S, Poulliat C. Practical frequency-domain decision feedback equalization based on expectation propagation[J]. IEEE Communications Letters, 2023, 27(10): 2777-2781. doi: 10.1109/LCOMM.2023.3303676 [8] Zhu Z, Zhou Y, Wang R, et al. Internet of underwater things infrastructure: a shared underwater acoustic communication layer scheme for real-world underwater acoustic experiments[J]. IEEE Transactions on Aerospace and Electronic Systems, 2023, 59(5): 6991-7003. [9] Zhilin I V, Bushnaq O M, Masi G D, et al. A universal multimode (acoustic, magnetic induction, optical, rf) software defined modem architecture for underwater communication[J]. IEEE Transactions on Wireless Communications, 2023, 22(12): 9105-9116. doi: 10.1109/TWC.2023.3268418 [10] Dabora R, Abakasanga E. On the capacity of communication channels with memory and sampled additive cyclostationary gaussian noise[J]. IEEE Transactions on Information Theory, 2023, 69(10): 6137-6166. doi: 10.1109/TIT.2023.3281519 [11] Muranov K, Smida B, Devroye N. On blind channel estimation in full-duplex wireless relay systems[J]. IEEE Transactions on Wireless Communications, 2021, 20(7): 4685-4701. doi: 10.1109/TWC.2021.3061518 [12] Wang J T. Oversampling-based combining under isi channels[J]. IEEE Wireless Communications Letters, 2023, 12(3): 555-559. doi: 10.1109/LWC.2023.3234209 [13] Priya P, Viterbo E, Hong Y. Low complexity MRC detection for OTFS receiver with oversampling[J]. IEEE Transactions on Wireless Communications, 2024, 23(2): 1459-1473. doi: 10.1109/TWC.2023.3289610 [14] SCHLÜTER M, et al. Bounds on phase, frequency, and timing synchronization in fully digital receivers with 1-bit quantization and oversampling[J]. IEEE Transactions on Communications, 2020, 68(10): 6499-6513. doi: 10.1109/TCOMM.2020.3005738 [15] Tuchler M, Koetter R, Singer A C. Turbo equalization: principles and new results[J]. IEEE Transactions on Communications, 2002, 50(5): 754-767. doi: 10.1109/TCOMM.2002.1006557 [16] 黄灿群. 基于多通道接收和发射的水声通信机[D]. 广州: 华南理工大学, 2021. -
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